# 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/5t/c5txhhxqwgxdtwmgseoxqhkhd47b4q5u6chxi7shnrpkkyfwlubq.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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, 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_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_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    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 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2j/c2jp3ao3qc7s2piwrwrsa4nfhuwr2y6p5vmtnr3avjozont334gm.py
# Topologically Sorted Source Nodes: [scores, max_1], Original ATen: [aten.div, aten.max]
# Source node to ATen node mapping:
#   max_1 => max_1
#   scores => div
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_11, 1.0), kwargs = {})
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.default](args = (%div,), kwargs = {})
triton_per_fused_div_max_1 = async_compile.triton('triton_per_fused_div_max_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.persistent_reduction(
    size_hints=[1, 1024],
    reduction_hint=ReductionHint.INNER,
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_max_1', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_div_max_1(in_ptr0, out_ptr0, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 1024
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sh/cshxyr7ubygdgkvu2jcbl36m76i56v5sozjjpqddp4enliefpxwc.py
# Topologically Sorted Source Nodes: [scores, sub, scores_1], Original ATen: [aten.div, aten.sub, aten._softmax]
# Source node to ATen node mapping:
#   scores => div
#   scores_1 => amax, exp, sub_1, sum_1
#   sub => sub
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_11, 1.0), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %max_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%sub, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_div_sub_2 = async_compile.triton('triton_poi_fused__softmax_div_sub_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: '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_div_sub_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_div_sub_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp5 = tmp2 - tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = tmp7 - tmp4
    tmp9 = triton_helpers.maximum(tmp5, tmp8)
    tmp11 = tmp10 * tmp1
    tmp12 = tmp11 - tmp4
    tmp13 = triton_helpers.maximum(tmp9, tmp12)
    tmp15 = tmp14 * tmp1
    tmp16 = tmp15 - tmp4
    tmp17 = triton_helpers.maximum(tmp13, tmp16)
    tmp18 = tmp5 - tmp17
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp8 - tmp17
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp12 - tmp17
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp16 - tmp17
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tl.store(out_ptr0 + (x0), tmp17, xmask)
    tl.store(out_ptr1 + (x0), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ab/cabhniu4tyer6exl4prbgpjbgmqgwyhnmgevuz4airuaxunzvkui.py
# Topologically Sorted Source Nodes: [scores, sub, scores_1, isnan, scores_2], Original ATen: [aten.div, aten.sub, aten._softmax, aten.isnan, aten.masked_fill]
# Source node to ATen node mapping:
#   isnan => isnan
#   scores => div
#   scores_1 => amax, div_1, exp, sub_1, sum_1
#   scores_2 => full_default, where
#   sub => sub
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_11, 1.0), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %max_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%sub, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%div_1,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), 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 = (%isnan, %full_default, %div_1), kwargs = {})
triton_poi_fused__softmax_div_isnan_masked_fill_sub_3 = async_compile.triton('triton_poi_fused__softmax_div_isnan_masked_fill_sub_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=[1024], 
    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_div_isnan_masked_fill_sub_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_div_isnan_masked_fill_sub_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp5 = tmp2 - tmp4
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tmp11 = libdevice.isnan(tmp10).to(tl.int1)
    tmp12 = 0.0
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tl.store(out_ptr0 + (x2), tmp13, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, primals_3, buf3, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_3
        buf4 = reinterpret_tensor(buf0, (4, 4, 4, 1, 4), (64, 16, 4, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(buf1, primals_5, buf4, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_5
        buf5 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf4, (64, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [scores, max_1], Original ATen: [aten.div, aten.max]
        triton_per_fused_div_max_1.run(buf5, buf6, 1, 1024, grid=grid(1), stream=stream0)
        buf7 = reinterpret_tensor(buf1, (4, 4, 4, 4, 1), (64, 16, 4, 1, 256), 0); del buf1  # reuse
        buf8 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256), torch.float32)
        # Topologically Sorted Source Nodes: [scores, sub, scores_1], Original ATen: [aten.div, aten.sub, aten._softmax]
        triton_poi_fused__softmax_div_sub_2.run(buf5, buf6, buf7, buf8, 256, grid=grid(256), stream=stream0)
        buf9 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [scores, sub, scores_1, isnan, scores_2], Original ATen: [aten.div, aten.sub, aten._softmax, aten.isnan, aten.masked_fill]
        triton_poi_fused__softmax_div_isnan_masked_fill_sub_3.run(buf5, buf6, buf7, buf8, buf9, 1024, grid=grid(1024), stream=stream0)
        del buf7
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(buf2, primals_8, buf10, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_8
        buf11 = reinterpret_tensor(buf2, (64, 4, 1), (4, 1, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf9, (64, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf10, (64, 4, 1), (4, 1, 0), 0), out=buf11)
    return (reinterpret_tensor(buf11, (4, 4, 4, 4), (64, 16, 1, 4), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), buf5, buf6, reinterpret_tensor(buf9, (64, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf10, (64, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (64, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf4, (64, 4, 1), (4, 1, 4), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 4, 4), (64, 16, 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])
    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 numpy as np
import torch.nn as nn
import torch.distributions


class MultiHeadAttention(nn.Module):

    def __init__(self, d_model, n_heads, kq_same=False, bias=True):
        super().__init__()
        """
        It has projection layer for getting keys, queries and values. Followed by attention.
        """
        self.d_model = d_model
        self.h = n_heads
        self.d_k = self.d_model // self.h
        self.kq_same = kq_same
        if not kq_same:
            self.q_linear = nn.Linear(d_model, d_model, bias=bias)
        self.k_linear = nn.Linear(d_model, d_model, bias=bias)
        self.v_linear = nn.Linear(d_model, d_model, bias=bias)

    def head_split(self, x):
        new_x_shape = x.size()[:-1] + (self.h, self.d_k)
        return x.view(*new_x_shape).transpose(-2, -3)

    def forward(self, q, k, v, mask=None):
        origin_shape = q.size()
        if not self.kq_same:
            q = self.head_split(self.q_linear(q))
        else:
            q = self.head_split(self.k_linear(q))
        k = self.head_split(self.k_linear(k))
        v = self.head_split(self.v_linear(v))
        output = self.scaled_dot_product_attention(q, k, v, self.d_k, mask)
        output = output.transpose(-2, -3).reshape(origin_shape)
        return output

    @staticmethod
    def scaled_dot_product_attention(q, k, v, d_k, mask=None):
        """
        This is called by Multi-head attention object to find the values.
        """
        scores = torch.matmul(q, k.transpose(-2, -1)) / d_k ** 0.5
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -np.inf)
        scores = (scores - scores.max()).softmax(dim=-1)
        scores = scores.masked_fill(torch.isnan(scores), 0)
        output = torch.matmul(scores, v)
        return output


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


def get_init_inputs():
    return [[], {'d_model': 4, 'n_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 libdevice, math as tl_math
import numpy as np
import torch.nn as nn
import torch.distributions
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_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    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 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_per_fused_div_max_1(in_ptr0, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp5, None)


@triton.jit
def triton_poi_fused__softmax_div_sub_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, 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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp5 = tmp2 - tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = tmp7 - tmp4
    tmp9 = triton_helpers.maximum(tmp5, tmp8)
    tmp11 = tmp10 * tmp1
    tmp12 = tmp11 - tmp4
    tmp13 = triton_helpers.maximum(tmp9, tmp12)
    tmp15 = tmp14 * tmp1
    tmp16 = tmp15 - tmp4
    tmp17 = triton_helpers.maximum(tmp13, tmp16)
    tmp18 = tmp5 - tmp17
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp8 - tmp17
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp12 - tmp17
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp16 - tmp17
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tl.store(out_ptr0 + x0, tmp17, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused__softmax_div_isnan_masked_fill_sub_3(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp5 = tmp2 - tmp4
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tmp11 = libdevice.isnan(tmp10).to(tl.int1)
    tmp12 = 0.0
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](buf0, primals_3, buf3, 64, 4,
            XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_3
        buf4 = reinterpret_tensor(buf0, (4, 4, 4, 1, 4), (64, 16, 4, 4, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(64, 4)](buf1, primals_5, buf4, 64, 4,
            XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf4, (64, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_div_max_1[grid(1)](buf5, buf6, 1, 1024, num_warps=
            8, num_stages=1)
        buf7 = reinterpret_tensor(buf1, (4, 4, 4, 4, 1), (64, 16, 4, 1, 256), 0
            )
        del buf1
        buf8 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256),
            torch.float32)
        triton_poi_fused__softmax_div_sub_2[grid(256)](buf5, buf6, buf7,
            buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused__softmax_div_isnan_masked_fill_sub_3[grid(1024)](buf5,
            buf6, buf7, buf8, buf9, 1024, XBLOCK=128, num_warps=4, num_stages=1
            )
        del buf7
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf8
        triton_poi_fused_clone_0[grid(64, 4)](buf2, primals_8, buf10, 64, 4,
            XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf11 = reinterpret_tensor(buf2, (64, 4, 1), (4, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf9, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf10, (64, 4, 1), (4, 1, 0), 0), out=buf11)
    return reinterpret_tensor(buf11, (4, 4, 4, 4), (64, 16, 1, 4), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0
        ), buf5, buf6, reinterpret_tensor(buf9, (64, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf10, (64, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf3, (64, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf4, (64, 4, 1), (4, 1, 4), 0)


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, d_model, n_heads, kq_same=False, bias=True):
        super().__init__()
        """
        It has projection layer for getting keys, queries and values. Followed by attention.
        """
        self.d_model = d_model
        self.h = n_heads
        self.d_k = self.d_model // self.h
        self.kq_same = kq_same
        if not kq_same:
            self.q_linear = nn.Linear(d_model, d_model, bias=bias)
        self.k_linear = nn.Linear(d_model, d_model, bias=bias)
        self.v_linear = nn.Linear(d_model, d_model, bias=bias)

    def head_split(self, x):
        new_x_shape = x.size()[:-1] + (self.h, self.d_k)
        return x.view(*new_x_shape).transpose(-2, -3)

    @staticmethod
    def scaled_dot_product_attention(q, k, v, d_k, mask=None):
        """
        This is called by Multi-head attention object to find the values.
        """
        scores = torch.matmul(q, k.transpose(-2, -1)) / d_k ** 0.5
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -np.inf)
        scores = (scores - scores.max()).softmax(dim=-1)
        scores = scores.masked_fill(torch.isnan(scores), 0)
        output = torch.matmul(scores, v)
        return output

    def forward(self, input_0, input_1, input_2):
        primals_2 = self.q_linear.weight
        primals_3 = self.q_linear.bias
        primals_4 = self.k_linear.weight
        primals_5 = self.k_linear.bias
        primals_7 = self.v_linear.weight
        primals_8 = self.v_linear.bias
        primals_1 = input_0
        primals_6 = input_1
        primals_9 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
