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

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


# kernel path: runs/run_shard_7/inductor_cache/vb/cvbxdocetk5tj67qrxlvwxnmwf3z2efb37af24dzpjl5kw5bkmq5.py
# Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul_1 => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_1, 1.0), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_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: '*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_mul_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_mul_0(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (1 + (3*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (1 + (3*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/q6/cq6z6xhbr2gyiw5id4lceemepctrgo25eo5gvvxlgbncy77qxm4p.py
# Topologically Sorted Source Nodes: [mul, softmax], Original ATen: [aten.mul, aten._softmax]
# Source node to ATen node mapping:
#   mul => mul
#   softmax => amax, clone, exp, sub, sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem, 1.0), kwargs = {})
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%view_3,), kwargs = {memory_format: torch.contiguous_format})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_mul_1 = async_compile.triton('triton_poi_fused__softmax_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 4
    x3 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (3*x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (3*x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x0 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (4 + x0 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (8 + x0 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + (12 + x0 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp4 * tmp7
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp4 * tmp10
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tmp4 * tmp13
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp16 = tmp6 - tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp8 - tmp15
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp15
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp14 - tmp15
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tl.store(out_ptr0 + (x4), tmp4, xmask)
    tl.store(out_ptr1 + (x4), tmp15, xmask)
    tl.store(out_ptr2 + (x4), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qh/cqhxytgzmsgejzq2rbt7fdr5j2twqslr3w2cz4yhih2lbgvx6yly.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, clone, div, exp, sub, sum_1
# Graph fragment:
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%view_3,), kwargs = {memory_format: torch.contiguous_format})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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__softmax_2', '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_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = (xindex // 4)
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x3 = (xindex // 64)
    x2 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + (4*x0) + (16*x3)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tl.store(out_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uq/cuqseg2vikrs3ttioriunrdhmp6cffp4xwpf2b6xdz7w3atftosj.py
# Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   einsum_1 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_6,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_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_clone_3', '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_clone_3(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 % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (2 + (3*x1) + (12*x0) + (48*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (2 + (3*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6t/c6t5a5ere3lqjiu7zh3uu4oxmpdoujdaqqmeunxqapgzo4m74uav.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   x_2 => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view_8,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_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=[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_4', '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_4(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_7/inductor_cache/sz/csz5twnkhe2zz3eaafwmgidmezpgvs3favuuva73uud6rs7ouhj6.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_2 => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_10, %primals_5), kwargs = {})
triton_poi_fused_add_5 = async_compile.triton('triton_poi_fused_add_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=[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_5', '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_5(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, (12, 4), (4, 1))
    assert_size_stride(primals_2, (12, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 12), (1, 4), 0), out=buf0)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(buf0, primals_2, buf2, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
        # Topologically Sorted Source Nodes: [mul, softmax], Original ATen: [aten.mul, aten._softmax]
        triton_poi_fused__softmax_mul_1.run(buf0, primals_2, buf2, buf1, buf3, buf4, 64, grid=grid(64), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf1, buf2, buf3, buf4, buf5, 256, grid=grid(256), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf0, primals_2, buf6, 64, grid=grid(64), stream=stream0)
        del buf0
        del primals_2
        buf7 = reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.clone]
        triton_poi_fused_clone_4.run(buf7, buf8, 16, 4, grid=grid(16, 4), stream=stream0)
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0); del buf9  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.add]
        triton_poi_fused_add_5.run(buf10, primals_5, 64, grid=grid(64), stream=stream0)
        del primals_5
    return (buf10, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4, 1, 1), (16, 1, 4, 1, 1), 0), reinterpret_tensor(buf2, (4, 4, 1, 4, 1), (16, 1, 1, 4, 1), 0), buf5, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), primals_4, reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 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((12, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    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
import torch as th


class QKVMultiheadAttention(nn.Module):

    def __init__(self, n_heads: 'int', n_ctx: 'int'):
        super().__init__()
        self.n_heads = n_heads
        self.n_ctx = n_ctx

    def forward(self, qkv):
        bs, n_ctx, width = qkv.shape
        attn_ch = width // self.n_heads // 3
        scale = 1 / math.sqrt(math.sqrt(attn_ch))
        qkv = qkv.view(bs, n_ctx, self.n_heads, -1)
        q, k, v = th.split(qkv, attn_ch, dim=-1)
        weight = th.einsum('bthc,bshc->bhts', q * scale, k * scale)
        wdtype = weight.dtype
        weight = th.softmax(weight.float(), dim=-1).type(wdtype)
        return th.einsum('bhts,bshc->bthc', weight, v).reshape(bs, n_ctx, -1)


class MultiheadAttention(nn.Module):

    def __init__(self, n_ctx, width, heads):
        super().__init__()
        self.n_ctx = n_ctx
        self.width = width
        self.heads = heads
        self.c_qkv = nn.Linear(width, width * 3)
        self.c_proj = nn.Linear(width, width)
        self.attention = QKVMultiheadAttention(heads, n_ctx)

    def forward(self, x):
        x = self.c_qkv(x)
        x = self.attention(x)
        x = self.c_proj(x)
        return x


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


def get_init_inputs():
    return [[], {'n_ctx': 4, 'width': 4, 'heads': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import torch.nn as nn
import torch as th
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_mul_0(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (1 + 3 * x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (1 + 3 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 4
    x3 = xindex // 16
    tmp0 = tl.load(in_ptr0 + 3 * x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 3 * x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr2 + (4 + x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr2 + (8 + x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr2 + (12 + x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp4 * tmp7
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp4 * tmp10
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tmp4 * tmp13
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp16 = tmp6 - tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp8 - tmp15
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp15
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp14 - tmp15
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tl.store(out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr1 + x4, tmp15, xmask)
    tl.store(out_ptr2 + x4, tmp26, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + 4 * x0 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp7, xmask)


@triton.jit
def triton_poi_fused_clone_3(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 % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (2 + 3 * x1 + 12 * x0 + 48 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (2 + 3 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_4(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_5(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, (12, 4), (4, 1))
    assert_size_stride(primals_2, (12,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 12), (1, 4), 0), out=buf0)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(64)](buf0, primals_2, buf2, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
        triton_poi_fused__softmax_mul_1[grid(64)](buf0, primals_2, buf2,
            buf1, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf1, buf2, buf3, buf4, buf5,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0)
        del buf4
        triton_poi_fused_clone_3[grid(64)](buf0, primals_2, buf6, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del primals_2
        buf7 = reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf7, buf8, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
        del buf7
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0)
        del buf9
        triton_poi_fused_add_5[grid(64)](buf10, primals_5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
    return buf10, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (4, 4, 4, 1, 1), (16, 1, 4, 1, 1), 0
        ), reinterpret_tensor(buf2, (4, 4, 1, 4, 1), (16, 1, 1, 4, 1), 0
        ), buf5, reinterpret_tensor(buf8, (16, 4), (4, 1), 0
        ), primals_4, reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0)


class QKVMultiheadAttention(nn.Module):

    def __init__(self, n_heads: 'int', n_ctx: 'int'):
        super().__init__()
        self.n_heads = n_heads
        self.n_ctx = n_ctx

    def forward(self, qkv):
        bs, n_ctx, width = qkv.shape
        attn_ch = width // self.n_heads // 3
        scale = 1 / math.sqrt(math.sqrt(attn_ch))
        qkv = qkv.view(bs, n_ctx, self.n_heads, -1)
        q, k, v = th.split(qkv, attn_ch, dim=-1)
        weight = th.einsum('bthc,bshc->bhts', q * scale, k * scale)
        wdtype = weight.dtype
        weight = th.softmax(weight.float(), dim=-1).type(wdtype)
        return th.einsum('bhts,bshc->bthc', weight, v).reshape(bs, n_ctx, -1)


class MultiheadAttentionNew(nn.Module):

    def __init__(self, n_ctx, width, heads):
        super().__init__()
        self.n_ctx = n_ctx
        self.width = width
        self.heads = heads
        self.c_qkv = nn.Linear(width, width * 3)
        self.c_proj = nn.Linear(width, width)
        self.attention = QKVMultiheadAttention(heads, n_ctx)

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