# 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_8/inductor_cache/xu/cxuxmhz7cepjp4vjwppki2dqyg7eikcdiuff36rdkcryhwu6d2ab.py
# Topologically Sorted Source Nodes: [cached_kv], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   cached_kv => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%permute_2, %permute_3],), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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=[128], 
    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_stack_0', '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_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x3 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 + x0 + (12*x1) + (48*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + (8 + x0 + (12*x1) + (48*((-4) + x2))), tmp6 & xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/yp/cypnjebs6j7r2f6dhwegd3kxx7t6gutrxri4rbhiplk4ojfdcu4x.py
# Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[16, 4], tile_hint=TileHint.SQUARE,
    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_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/so/csofmscwslhuddhgtulfsbs555c5vsyfmaldv7a2zxknv6bef5m7.py
# Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_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=[16, 4], tile_hint=TileHint.SQUARE,
    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_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/4x/c4xr6stly5py5ohu6yhs3ywkmafmkw5jbqozn7c642uhpw673kgk.py
# Topologically Sorted Source Nodes: [attention_mask], Original ATen: [aten.tril]
# Source node to ATen node mapping:
#   attention_mask => le, sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %le : [num_users=2] = call_function[target=torch.ops.aten.le.Scalar](args = (%sub, 0), kwargs = {})
triton_poi_fused_tril_3 = async_compile.triton('triton_poi_fused_tril_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i1', 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_tril_3', '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_tril_3(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = x0 + ((-1)*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/lu/clu4l2aeduhdenj4upvwizyn6mlghsrrlfiiwzo5lvdweta7x4ba.py
# Topologically Sorted Source Nodes: [attn_1, mul, sub, mul_1, attn_2, attn_3], Original ATen: [aten.div, aten.mul, aten.rsub, aten.sub, aten._softmax]
# Source node to ATen node mapping:
#   attn_1 => div
#   attn_2 => sub_2
#   attn_3 => amax, exp, sub_3, sum_1
#   mul => mul
#   mul_1 => mul_1
#   sub => sub_1
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_8, 1.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %view_9), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %view_9), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, 10000.0), kwargs = {})
#   %sub_2 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%sub_2, [-1], True), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_div_mul_rsub_sub_4 = async_compile.triton('triton_poi_fused__softmax_div_mul_rsub_sub_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__softmax_div_mul_rsub_sub_4', '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__softmax_div_mul_rsub_sub_4(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = (-1)*x0
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp13 * tmp1
    tmp15 = 1 + ((-1)*x0)
    tmp16 = tmp15 <= tmp4
    tmp17 = tl.where(tmp16, tmp1, tmp6)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp1 - tmp17
    tmp20 = tmp19 * tmp10
    tmp21 = tmp18 - tmp20
    tmp22 = triton_helpers.maximum(tmp12, tmp21)
    tmp24 = tmp23 * tmp1
    tmp25 = 2 + ((-1)*x0)
    tmp26 = tmp25 <= tmp4
    tmp27 = tl.where(tmp26, tmp1, tmp6)
    tmp28 = tmp24 * tmp27
    tmp29 = tmp1 - tmp27
    tmp30 = tmp29 * tmp10
    tmp31 = tmp28 - tmp30
    tmp32 = triton_helpers.maximum(tmp22, tmp31)
    tmp34 = tmp33 * tmp1
    tmp35 = 3 + ((-1)*x0)
    tmp36 = tmp35 <= tmp4
    tmp37 = tl.where(tmp36, tmp1, tmp6)
    tmp38 = tmp34 * tmp37
    tmp39 = tmp1 - tmp37
    tmp40 = tmp39 * tmp10
    tmp41 = tmp38 - tmp40
    tmp42 = triton_helpers.maximum(tmp32, tmp41)
    tmp43 = tmp12 - tmp42
    tmp44 = tl_math.exp(tmp43)
    tmp45 = tmp21 - tmp42
    tmp46 = tl_math.exp(tmp45)
    tmp47 = tmp44 + tmp46
    tmp48 = tmp31 - tmp42
    tmp49 = tl_math.exp(tmp48)
    tmp50 = tmp47 + tmp49
    tmp51 = tmp41 - tmp42
    tmp52 = tl_math.exp(tmp51)
    tmp53 = tmp50 + tmp52
    tl.store(out_ptr0 + (x2), tmp42, xmask)
    tl.store(out_ptr1 + (x2), tmp53, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ka/ckaxjyfdlcx7l3jdsrqaswxcg2vplkey5zyddkjh66uo2yh4aen3.py
# Topologically Sorted Source Nodes: [attn_1, mul, sub, mul_1, attn_2, attn_3], Original ATen: [aten.div, aten.mul, aten.rsub, aten.sub, aten._softmax]
# Source node to ATen node mapping:
#   attn_1 => div
#   attn_2 => sub_2
#   attn_3 => div_1, exp, sub_3
#   mul => mul
#   mul_1 => mul_1
#   sub => sub_1
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_8, 1.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %view_9), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %view_9), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, 10000.0), kwargs = {})
#   %sub_2 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_div_mul_rsub_sub_5 = async_compile.triton('triton_poi_fused__softmax_div_mul_rsub_sub_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=[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__softmax_div_mul_rsub_sub_5', 'mutated_arg_names': ['in_out_ptr0'], '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__softmax_div_mul_rsub_sub_5(in_out_ptr0, in_ptr0, in_ptr1, 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
    x1 = (xindex // 4) % 4
    x4 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp13 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = x0 + ((-1)*x1)
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp17 = tmp15 / tmp16
    tl.store(in_out_ptr0 + (x3), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vv/cvvhis67uzj3m3ebbd4sgghaemqhihabasphltk5wytqdd6fe74t.py
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   y => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_6 = async_compile.triton('triton_poi_fused_clone_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, 4], tile_hint=TileHint.SQUARE,
    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_poi_fused_clone_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/lw/clwfsjrjxeb2gmxy5p3lplvcrvrn37iuw4atjria32bxp2jajrtc.py
# Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear_1 => clone_4
# Graph fragment:
#   %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view_13,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_7 = async_compile.triton('triton_poi_fused_clone_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=[16, 4], tile_hint=TileHint.SQUARE,
    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_poi_fused_clone_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/kv/ckvhqbggfxjoaoj7mjis3tcd3uyabrjnjhqd674uo4e73ky2qtft.py
# Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   linear_1 => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_15, %primals_5), kwargs = {})
triton_poi_fused_add_8 = async_compile.triton('triton_poi_fused_add_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=[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_add_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (12, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 12), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((8, 4, 4, 1), (16, 1, 4, 16), torch.float32)
        # Topologically Sorted Source Nodes: [cached_kv], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(buf0, buf1, 128, grid=grid(128), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf0, buf2, 16, 4, grid=grid(16, 4), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf0, buf3, 16, 4, grid=grid(16, 4), stream=stream0)
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf3, (16, 1, 4), (4, 0, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [attention_mask], Original ATen: [aten.tril]
        triton_poi_fused_tril_3.run(buf5, 16, grid=grid(16), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [attn_1, mul, sub, mul_1, attn_2, attn_3], Original ATen: [aten.div, aten.mul, aten.rsub, aten.sub, aten._softmax]
        triton_poi_fused__softmax_div_mul_rsub_sub_4.run(buf4, buf6, buf7, 64, grid=grid(64), stream=stream0)
        buf8 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [attn_1, mul, sub, mul_1, attn_2, attn_3], Original ATen: [aten.div, aten.mul, aten.rsub, aten.sub, aten._softmax]
        triton_poi_fused__softmax_div_mul_rsub_sub_5.run(buf8, buf6, buf7, 256, grid=grid(256), stream=stream0)
        buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.clone]
        triton_poi_fused_clone_6.run(buf0, buf9, 16, 4, grid=grid(16, 4), stream=stream0)
        del buf0
        buf10 = reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf9, (16, 4, 1), (4, 1, 0), 0), out=buf10)
        buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_7.run(buf10, buf11, 16, 4, grid=grid(16, 4), stream=stream0)
        buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf11, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf12)
        buf13 = reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0); del buf12  # reuse
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.add]
        triton_poi_fused_add_8.run(buf13, primals_5, 64, grid=grid(64), stream=stream0)
        del primals_5
    return (buf13, reinterpret_tensor(buf1, (2, 4, 4, 4, 1), (64, 16, 1, 4, 4), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf5, buf8, reinterpret_tensor(buf11, (16, 4), (4, 1), 0), primals_4, reinterpret_tensor(buf9, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf2, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 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((12, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((12, ), (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)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    return print_performance(fn, times=times, repeat=repeat)


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

import math
import torch
import torch.nn as nn


class Attention(nn.Module):

    def __init__(self, embedding_size, num_attention_heads,
        attention_dropout, residual_dropout):
        super(Attention, self).__init__()
        self.num_attention_heads = num_attention_heads
        self.size_per_head = embedding_size // num_attention_heads
        self.embedding_size = embedding_size
        self.query_key_value = nn.Linear(embedding_size, embedding_size * 3)
        self.attn_drop = nn.Dropout(attention_dropout)
        self.resid_drop = nn.Dropout(residual_dropout)
        self.dense = nn.Linear(embedding_size, embedding_size)

    def split_heads(self, x):
        x = x.reshape([-1, self.seq_len, self.num_attention_heads, self.
            size_per_head])
        return x.permute(0, 2, 1, 3)

    def forward(self, x, kv_cache=None):
        self.seq_len = x.shape[1]
        x = self.query_key_value(x)
        q, k, v = torch.split(x, split_size_or_sections=self.embedding_size,
            dim=2)
        q = self.split_heads(q)
        k = self.split_heads(k)
        v = self.split_heads(v)
        if kv_cache is not None:
            pk, pv = kv_cache[0], kv_cache[1]
            k = torch.cat([pk, k], dim=-2)
            v = torch.cat([pv, v], dim=-2)
        cached_kv = torch.stack([k, v])
        attn = torch.matmul(q, k.transpose(-1, -2))
        attn = attn / math.sqrt(self.size_per_head)
        attention_mask = torch.tril(torch.ones(self.seq_len, self.seq_len,
            dtype=torch.float32, device=x.device))
        attention_mask = attention_mask.reshape([1, 1, self.seq_len, self.
            seq_len])
        attn = attn * attention_mask - 10000.0 * (1.0 - attention_mask)
        attn = nn.Softmax(dim=-1)(attn)
        attn = self.attn_drop(attn)
        y = torch.matmul(attn, v)
        y = y.permute(0, 2, 1, 3)
        y = torch.reshape(y, [-1, self.seq_len, self.embedding_size])
        y = self.resid_drop(self.dense(y))
        return y, cached_kv


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


def get_init_inputs():
    return [[], {'embedding_size': 4, 'num_attention_heads': 4,
        'attention_dropout': 0.5, 'residual_dropout': 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 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_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 12 * x1 + 48 * x2), tmp4 & xmask,
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (8 + x0 + 12 * x1 + 48 * (-4 + x2)), tmp6 &
        xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_tril_3(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0 + -1 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_div_mul_rsub_sub_4(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = -1 * x0
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp13 * tmp1
    tmp15 = 1 + -1 * x0
    tmp16 = tmp15 <= tmp4
    tmp17 = tl.where(tmp16, tmp1, tmp6)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp1 - tmp17
    tmp20 = tmp19 * tmp10
    tmp21 = tmp18 - tmp20
    tmp22 = triton_helpers.maximum(tmp12, tmp21)
    tmp24 = tmp23 * tmp1
    tmp25 = 2 + -1 * x0
    tmp26 = tmp25 <= tmp4
    tmp27 = tl.where(tmp26, tmp1, tmp6)
    tmp28 = tmp24 * tmp27
    tmp29 = tmp1 - tmp27
    tmp30 = tmp29 * tmp10
    tmp31 = tmp28 - tmp30
    tmp32 = triton_helpers.maximum(tmp22, tmp31)
    tmp34 = tmp33 * tmp1
    tmp35 = 3 + -1 * x0
    tmp36 = tmp35 <= tmp4
    tmp37 = tl.where(tmp36, tmp1, tmp6)
    tmp38 = tmp34 * tmp37
    tmp39 = tmp1 - tmp37
    tmp40 = tmp39 * tmp10
    tmp41 = tmp38 - tmp40
    tmp42 = triton_helpers.maximum(tmp32, tmp41)
    tmp43 = tmp12 - tmp42
    tmp44 = tl_math.exp(tmp43)
    tmp45 = tmp21 - tmp42
    tmp46 = tl_math.exp(tmp45)
    tmp47 = tmp44 + tmp46
    tmp48 = tmp31 - tmp42
    tmp49 = tl_math.exp(tmp48)
    tmp50 = tmp47 + tmp49
    tmp51 = tmp41 - tmp42
    tmp52 = tl_math.exp(tmp51)
    tmp53 = tmp50 + tmp52
    tl.store(out_ptr0 + x2, tmp42, xmask)
    tl.store(out_ptr1 + x2, tmp53, xmask)


@triton.jit
def triton_poi_fused__softmax_div_mul_rsub_sub_5(in_out_ptr0, in_ptr0,
    in_ptr1, 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
    x1 = xindex // 4 % 4
    x4 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp13 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = x0 + -1 * x1
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp17 = tmp15 / tmp16
    tl.store(in_out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (12,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 12), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((8, 4, 4, 1), (16, 1, 4, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(128)](buf0, buf1, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 4)](buf0, buf2, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 4)](buf0, buf3, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf3, (16, 1, 4), (4, 0, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_tril_3[grid(16)](buf5, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_div_mul_rsub_sub_4[grid(64)](buf4, buf6,
            buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf8 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        triton_poi_fused__softmax_div_mul_rsub_sub_5[grid(256)](buf8, buf6,
            buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf7
        triton_poi_fused_clone_6[grid(16, 4)](buf0, buf9, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        buf10 = reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 1), 0)
        del buf6
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf9, (16, 4, 1), (4, 1, 0), 0), out=buf10)
        buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_7[grid(16, 4)](buf10, buf11, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0)
        del buf10
        extern_kernels.mm(reinterpret_tensor(buf11, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf12)
        buf13 = reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0)
        del buf12
        triton_poi_fused_add_8[grid(64)](buf13, primals_5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
    return buf13, reinterpret_tensor(buf1, (2, 4, 4, 4, 1), (64, 16, 1, 4, 
        4), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf5, buf8, reinterpret_tensor(buf11, (16, 4), (4, 1), 0
        ), primals_4, reinterpret_tensor(buf9, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf2, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 4), 0)


class AttentionNew(nn.Module):

    def __init__(self, embedding_size, num_attention_heads,
        attention_dropout, residual_dropout):
        super(AttentionNew, self).__init__()
        self.num_attention_heads = num_attention_heads
        self.size_per_head = embedding_size // num_attention_heads
        self.embedding_size = embedding_size
        self.query_key_value = nn.Linear(embedding_size, embedding_size * 3)
        self.attn_drop = nn.Dropout(attention_dropout)
        self.resid_drop = nn.Dropout(residual_dropout)
        self.dense = nn.Linear(embedding_size, embedding_size)

    def split_heads(self, x):
        x = x.reshape([-1, self.seq_len, self.num_attention_heads, self.
            size_per_head])
        return x.permute(0, 2, 1, 3)

    def forward(self, input_0):
        primals_2 = self.query_key_value.weight
        primals_3 = self.query_key_value.bias
        primals_4 = self.dense.weight
        primals_5 = self.dense.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]
