# 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_6/inductor_cache/nd/cnd5kwfcw2l5nrdr5r2ynnsjauf4za5upwzug5ecxyobm5ikwqmv.py
# Topologically Sorted Source Nodes: [attention_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention_1 => exp, sum_1
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_6, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [2], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 1.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_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: '*fp32', 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', '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_0(in_ptr0, in_ptr1, 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
    x3 = xindex
    x0 = xindex % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (32*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (8 + x0 + (32*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (16 + x0 + (32*x2)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (24 + x0 + (32*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = tmp10 * tmp3
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp0 * tmp13
    tmp15 = tmp14 * tmp3
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 - tmp16
    tmp18 = tmp17 * tmp3
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp7 - tmp16
    tmp21 = tmp20 * tmp3
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp19 + tmp22
    tmp24 = tmp11 - tmp16
    tmp25 = tmp24 * tmp3
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 + tmp26
    tmp28 = tmp15 - tmp16
    tmp29 = tmp28 * tmp3
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp27 + tmp30
    tl.store(out_ptr0 + (x3), tmp16, xmask)
    tl.store(out_ptr1 + (x3), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/rm/crmrye2ctlbugttcjxab3r7dotmfauenzqvryebukxh332d6i2np.py
# Topologically Sorted Source Nodes: [attention_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention_1 => div, exp
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_6, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [2], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 1.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_1,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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=[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_1', '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_1(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
    x0 = xindex % 4
    x4 = (xindex // 16)
    x1 = (xindex // 4) % 4
    x3 = (xindex // 64)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + (8*x1) + (32*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x0 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (x0 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(out_ptr0 + (x5), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7a/c7a5kf2lbc7npgaim3dwx342dxc6rsfexsi3o3hjbv7uivdat2sc.py
# Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   einsum_1 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_6,), 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, 16], 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, 3), 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 = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (64*y1)), xmask & ymask)
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/au/caupdm26txfqqf2244ylltcfavd62xlhjdbkkl4l4ovlqxg3w5ws.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_7,), 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.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_3', '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_3(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 + (8*x2) + (32*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_6/inductor_cache/6t/c6t5a5ere3lqjiu7zh3uu4oxmpdoujdaqqmeunxqapgzo4m74uav.py
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   out_1 => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view_11,), 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_6/inductor_cache/sz/csz5twnkhe2zz3eaafwmgidmezpgvs3favuuva73uud6rs7ouhj6.py
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   out_1 => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_13, %primals_7), 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, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (8, 4), (4, 1))
    assert_size_stride(primals_5, (8, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 8), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_1], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(buf0, buf1, buf2, buf3, 64, grid=grid(64), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf0, buf1, buf2, buf3, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf4, buf5, 16, 16, grid=grid(16, 16), stream=stream0)
        buf6 = reinterpret_tensor(buf3, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf1, buf6, 16, 4, grid=grid(16, 4), stream=stream0)
        buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0); del buf2  # 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: [out_1], 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: [out_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (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: [out_1], Original ATen: [aten.add]
        triton_poi_fused_add_5.run(buf10, primals_7, 64, grid=grid(64), stream=stream0)
        del primals_7
    return (buf10, buf4, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf0, reinterpret_tensor(buf1, (4, 1, 4, 4, 1), (32, 1, 8, 1, 1), 0), buf4, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), primals_6, reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0), 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((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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
from torch import nn


class MultiHeadAttention(nn.Module):

    def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.0):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim_self // num_heads
        self.scale = head_dim ** -0.5
        self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)
        self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)
        self.project = nn.Linear(dim_self, dim_self)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, y=None, mask=None):
        y = y if y is not None else x
        b, n, c = x.shape
        _, m, _d = y.shape
        queries = self.to_queries(x).reshape(b, n, self.num_heads, c //
            self.num_heads)
        keys_values = self.to_keys_values(y).reshape(b, m, 2, self.
            num_heads, c // self.num_heads)
        keys, values = keys_values[:, :, 0], keys_values[:, :, 1]
        attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale
        if mask is not None:
            if mask.dim() == 2:
                mask = mask.unsqueeze(1)
            attention = attention.masked_fill(mask.unsqueeze(3), float('-inf'))
        attention = attention.softmax(dim=2)
        out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b,
            n, c)
        out = self.project(out)
        return out, attention


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


def get_init_inputs():
    return [[], {'dim_self': 4, 'dim_ref': 4, 'num_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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, in_ptr1, 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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 32 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr1 + (8 + x0 + 32 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr1 + (16 + x0 + 32 * x2), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (24 + x0 + 32 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = tmp10 * tmp3
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp0 * tmp13
    tmp15 = tmp14 * tmp3
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 - tmp16
    tmp18 = tmp17 * tmp3
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp7 - tmp16
    tmp21 = tmp20 * tmp3
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp19 + tmp22
    tmp24 = tmp11 - tmp16
    tmp25 = tmp24 * tmp3
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 + tmp26
    tmp28 = tmp15 - tmp16
    tmp29 = tmp28 * tmp3
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp27 + tmp30
    tl.store(out_ptr0 + x3, tmp16, xmask)
    tl.store(out_ptr1 + x3, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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
    x0 = xindex % 4
    x4 = xindex // 16
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + (x0 + 8 * x1 + 32 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x0 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr3 + (x0 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(out_ptr0 + x5, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_3(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 + 8 * x2 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@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, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (8, 4), (4, 1))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 8), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf0, buf1, buf2, buf3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf0, buf1, buf2, buf3, buf4,
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_2[grid(16, 16)](buf4, buf5, 16, 16, XBLOCK=
            16, YBLOCK=16, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0)
        del buf3
        triton_poi_fused_clone_3[grid(16, 4)](buf1, buf6, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
        del buf2
        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_6, (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_7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_7
    return buf10, buf4, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf1, (4, 1, 4, 4, 1), (32, 1, 8, 1, 1), 0
        ), buf4, reinterpret_tensor(buf8, (16, 4), (4, 1), 0
        ), primals_6, reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0)


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.0):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim_self // num_heads
        self.scale = head_dim ** -0.5
        self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)
        self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)
        self.project = nn.Linear(dim_self, dim_self)
        self.dropout = nn.Dropout(dropout)

    def forward(self, input_0):
        primals_2 = self.to_queries.weight
        primals_3 = self.to_queries.bias
        primals_4 = self.to_keys_values.weight
        primals_5 = self.to_keys_values.bias
        primals_6 = self.project.weight
        primals_7 = self.project.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
