# 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/fg/cfg742icmosiwp5ugziye26din5ueqx3v7ntptkkpyackudldrxs.py
# Topologically Sorted Source Nodes: [eq], Original ATen: [aten.eq]
# Source node to ATen node mapping:
#   eq => eq
# Graph fragment:
#   %eq : [num_users=2] = call_function[target=torch.ops.aten.eq.Scalar](args = (%primals_10, 0), kwargs = {})
triton_poi_fused_eq_0 = async_compile.triton('triton_poi_fused_eq_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_eq_0', '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_eq_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/dq/cdq47qo4ts2g3xlphk7jfetedy4flk56wockrd2oftlqur23w5s5.py
# Topologically Sorted Source Nodes: [energy_1, energy_2], Original ATen: [aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   energy_1 => full_default, where
#   energy_2 => exp, sum_1
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1.0000000200408773e+20), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %view_9), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 0.5), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_masked_fill_1 = async_compile.triton('triton_poi_fused__softmax_masked_fill_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.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_masked_fill_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_masked_fill_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + ((4*x2) + (16*y3)), xmask & ymask, eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (y0 + (16*y1)), ymask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + (4*x2) + (16*y3)), xmask & ymask, eviction_policy='evict_last').to(tl.int1)
    tmp9 = tl.load(in_ptr2 + (4 + y0 + (16*y1)), ymask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + (4*x2) + (16*y3)), xmask & ymask, eviction_policy='evict_last').to(tl.int1)
    tmp15 = tl.load(in_ptr2 + (8 + y0 + (16*y1)), ymask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (4*x2) + (16*y3)), xmask & ymask, eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr2 + (12 + y0 + (16*y1)), ymask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = -1.0000000200408773e+20
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp10 = tmp1 * tmp9
    tmp11 = tl.where(tmp8, tmp4, tmp10)
    tmp12 = tmp11 * tmp6
    tmp13 = triton_helpers.maximum(tmp7, tmp12)
    tmp16 = tmp1 * tmp15
    tmp17 = tl.where(tmp14, tmp4, tmp16)
    tmp18 = tmp17 * tmp6
    tmp19 = triton_helpers.maximum(tmp13, tmp18)
    tmp22 = tmp1 * tmp21
    tmp23 = tl.where(tmp20, tmp4, tmp22)
    tmp24 = tmp23 * tmp6
    tmp25 = triton_helpers.maximum(tmp19, tmp24)
    tmp26 = tmp7 - tmp25
    tmp27 = 2.0
    tmp28 = tmp26 * tmp27
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp12 - tmp25
    tmp31 = tmp30 * tmp27
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp29 + tmp32
    tmp34 = tmp18 - tmp25
    tmp35 = tmp34 * tmp27
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp33 + tmp36
    tmp38 = tmp24 - tmp25
    tmp39 = tmp38 * tmp27
    tmp40 = tl_math.exp(tmp39)
    tmp41 = tmp37 + tmp40
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp25, xmask & ymask)
    tl.store(out_ptr1 + (x2 + (4*y3)), tmp41, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zx/czxkw2dfowl52i5ol7rtewbkdja272fzxfs3ljnrg5rjb2jehxdz.py
# Topologically Sorted Source Nodes: [energy_1, energy_2], Original ATen: [aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   energy_1 => full_default, where
#   energy_2 => div_1, exp
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1.0000000200408773e+20), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %view_9), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, 1), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 0.5), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_masked_fill_2 = async_compile.triton('triton_poi_fused__softmax_masked_fill_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: '*i1', 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_masked_fill_2', '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__softmax_masked_fill_2(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
    x4 = xindex
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x0 = xindex % 4
    x5 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x4), xmask).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (x2 + (4*x1) + (16*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x2 + (4*x0) + (16*x3)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + (x5), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + (x5), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = -1.0000000200408773e+20
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = 2.0
    tmp11 = tmp9 * tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tmp12 / tmp13
    tl.store(out_ptr0 + (x4), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/gt/cgtqpaqfwouqxpwri7ygertlpra3qn2kcexi7wu53bb7eglybfrs.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   out => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_8,), 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=[16, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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, 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')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, YBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp3, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/cv/ccva5wkkqbsuazdvak5m7p7hmc25v5vunf5dixpzgwz7tfno5e3i.py
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_2 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_15, %view_14], -1), kwargs = {})
triton_poi_fused_cat_4 = async_compile.triton('triton_poi_fused_cat_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=[128], 
    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_cat_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x3 = (xindex // 8)
    x1 = (xindex // 8) % 4
    x2 = (xindex // 32)
    x4 = xindex
    tmp0 = x0
    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*x3) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x1 + (4*((-4) + x0)) + (16*x2)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x4), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_3, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_4, (1, 1), (1, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (1, 1), (1, 1))
    assert_size_stride(primals_7, (1, ), (1, ))
    assert_size_stride(primals_8, (1, 1), (1, 1))
    assert_size_stride(primals_9, (1, ), (1, ))
    assert_size_stride(primals_10, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_11, (4, 8), (8, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [query_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 1), (1, 1), 0), primals_4, alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [key_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_2, (64, 1), (1, 1), 0), primals_6, alpha=1, beta=1, out=buf3)
        del primals_6
        del primals_7
        buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 1), (1, 1), 0), primals_8, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [eq], Original ATen: [aten.eq]
        stream0 = get_raw_stream(0)
        triton_poi_fused_eq_0.run(primals_10, buf5, 256, grid=grid(256), stream=stream0)
        del primals_10
        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: [energy_1, energy_2], Original ATen: [aten.masked_fill, aten._softmax]
        triton_poi_fused__softmax_masked_fill_1.run(buf5, buf1, buf3, buf6, buf7, 16, 4, grid=grid(16, 4), stream=stream0)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [energy_1, energy_2], Original ATen: [aten.masked_fill, aten._softmax]
        triton_poi_fused__softmax_masked_fill_2.run(buf5, buf1, buf3, buf6, buf7, buf8, 256, grid=grid(256), stream=stream0)
        del buf6
        buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf4, primals_9, buf9, 16, 4, grid=grid(16, 4), stream=stream0)
        del primals_9
        buf10 = reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [out], 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, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_4.run(buf1, buf10, buf11, 128, grid=grid(128), stream=stream0)
        buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf11, (16, 8), (8, 1), 0), reinterpret_tensor(primals_11, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf12)
        del primals_12
    return (reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0), buf8, reinterpret_tensor(primals_1, (64, 1), (1, 1), 0), buf1, reinterpret_tensor(primals_2, (64, 1), (1, 1), 0), buf3, reinterpret_tensor(primals_3, (64, 1), (1, 1), 0), buf5, buf8, reinterpret_tensor(buf11, (16, 8), (8, 1), 0), primals_11, reinterpret_tensor(buf9, (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((4, 4, 4, 1), (16, 4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 1), (16, 4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 1), (16, 4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12])
    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 torch
import torch.nn as nn


class SelfAttention(nn.Module):

    def __init__(self, embed_dims, heads):
        super(SelfAttention, self).__init__()
        self.heads = heads
        self.embed_dims = embed_dims
        self.depth = embed_dims // heads
        self.query = nn.Linear(self.depth, self.depth)
        self.key = nn.Linear(self.depth, self.depth)
        self.value = nn.Linear(self.depth, self.depth)
        self.fc_out = nn.Linear(self.depth * self.heads * 2, self.embed_dims)

    def forward(self, query, key, value, mask, isDecoder=False):
        batch, q_len, k_len, v_len = query.shape[0], query.shape[1], key.shape[
            1], value.shape[1]
        query = query.reshape(batch, q_len, self.heads, self.depth)
        key = key.reshape(batch, k_len, self.heads, self.depth)
        value = value.reshape(batch, v_len, self.heads, self.depth)
        query = self.query(query)
        key = self.key(key)
        value = self.value(value)
        energy = torch.einsum('bqhd, bkhd -> bhqk', [query, key])
        if isDecoder:
            None
            None
            None
            None
        if mask is not None:
            if isDecoder:
                None
            energy = energy.masked_fill(mask == 0, float('-1e20'))
        if isDecoder:
            None
            None
        energy = torch.softmax(energy / (self.depth ** 1 / 2), dim=-1)
        out = torch.einsum('bhqv, bvhd -> bqhd', [energy, value])
        out = out.reshape(batch, q_len, self.heads * self.depth)
        query = query.reshape(batch, q_len, self.heads * self.depth)
        out = torch.cat([query, out], dim=-1)
        out = self.fc_out(out)
        return out, energy


def get_inputs():
    return [torch.rand([4, 4, 4, 1]), torch.rand([4, 4, 4, 1]), torch.rand(
        [4, 4, 4, 1]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'embed_dims': 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 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_eq_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, 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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (4 * x2 + 16 * y3), xmask & ymask,
        eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x2 + 16 * y3), xmask & ymask,
        eviction_policy='evict_last').to(tl.int1)
    tmp9 = tl.load(in_ptr2 + (4 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + 4 * x2 + 16 * y3), xmask & ymask,
        eviction_policy='evict_last').to(tl.int1)
    tmp15 = tl.load(in_ptr2 + (8 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x2 + 16 * y3), xmask & ymask,
        eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr2 + (12 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = -1.0000000200408773e+20
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp10 = tmp1 * tmp9
    tmp11 = tl.where(tmp8, tmp4, tmp10)
    tmp12 = tmp11 * tmp6
    tmp13 = triton_helpers.maximum(tmp7, tmp12)
    tmp16 = tmp1 * tmp15
    tmp17 = tl.where(tmp14, tmp4, tmp16)
    tmp18 = tmp17 * tmp6
    tmp19 = triton_helpers.maximum(tmp13, tmp18)
    tmp22 = tmp1 * tmp21
    tmp23 = tl.where(tmp20, tmp4, tmp22)
    tmp24 = tmp23 * tmp6
    tmp25 = triton_helpers.maximum(tmp19, tmp24)
    tmp26 = tmp7 - tmp25
    tmp27 = 2.0
    tmp28 = tmp26 * tmp27
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp12 - tmp25
    tmp31 = tmp30 * tmp27
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp29 + tmp32
    tmp34 = tmp18 - tmp25
    tmp35 = tmp34 * tmp27
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp33 + tmp36
    tmp38 = tmp24 - tmp25
    tmp39 = tmp38 * tmp27
    tmp40 = tl_math.exp(tmp39)
    tmp41 = tmp37 + tmp40
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp25, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 4 * y3), tmp41, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_2(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
    x4 = xindex
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x0 = xindex % 4
    x5 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x4, xmask).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * x1 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x2 + 4 * x0 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x5, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + x5, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = -1.0000000200408773e+20
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = 2.0
    tmp11 = tmp9 * tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tmp12 / tmp13
    tl.store(out_ptr0 + x4, tmp14, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, 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')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, YBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp3, xmask & ymask)


@triton.jit
def triton_poi_fused_cat_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x3 = xindex // 8
    x1 = xindex // 8 % 4
    x2 = xindex // 32
    x4 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x3 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x1 + 4 * (-4 + x0) + 16 * x2), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x4, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_3, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_4, (1, 1), (1, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (1, 1), (1, 1))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (1, 1), (1, 1))
    assert_size_stride(primals_9, (1,), (1,))
    assert_size_stride(primals_10, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_11, (4, 8), (8, 1))
    assert_size_stride(primals_12, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 
            1), (1, 1), 0), primals_4, alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_2, (64, 
            1), (1, 1), 0), primals_6, alpha=1, beta=1, out=buf3)
        del primals_6
        del primals_7
        buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 1), (1, 1), 0),
            primals_8, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_0[grid(256)](primals_10, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_10
        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_masked_fill_1[grid(16, 4)](buf5, buf1,
            buf3, buf6, buf7, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1,
            num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_masked_fill_2[grid(256)](buf5, buf1, buf3,
            buf6, buf7, buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf6
        buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0)
        del buf7
        triton_poi_fused_clone_3[grid(16, 4)](buf4, primals_9, buf9, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_9
        buf10 = reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 1), 0)
        del buf4
        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, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_4[grid(128)](buf1, buf10, buf11, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0)
        del buf10
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf11, (16, 8),
            (8, 1), 0), reinterpret_tensor(primals_11, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf12)
        del primals_12
    return reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0
        ), buf8, reinterpret_tensor(primals_1, (64, 1), (1, 1), 0
        ), buf1, reinterpret_tensor(primals_2, (64, 1), (1, 1), 0
        ), buf3, reinterpret_tensor(primals_3, (64, 1), (1, 1), 0
        ), buf5, buf8, reinterpret_tensor(buf11, (16, 8), (8, 1), 0
        ), primals_11, reinterpret_tensor(buf9, (16, 1, 4), (4, 1, 1), 0)


class SelfAttentionNew(nn.Module):

    def __init__(self, embed_dims, heads):
        super(SelfAttentionNew, self).__init__()
        self.heads = heads
        self.embed_dims = embed_dims
        self.depth = embed_dims // heads
        self.query = nn.Linear(self.depth, self.depth)
        self.key = nn.Linear(self.depth, self.depth)
        self.value = nn.Linear(self.depth, self.depth)
        self.fc_out = nn.Linear(self.depth * self.heads * 2, self.embed_dims)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_4 = self.query.weight
        primals_5 = self.query.bias
        primals_6 = self.key.weight
        primals_7 = self.key.bias
        primals_8 = self.value.weight
        primals_9 = self.value.bias
        primals_11 = self.fc_out.weight
        primals_12 = self.fc_out.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        primals_10 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12])
        return output[0], output[1]
