# 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_7, 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/gi/cgigq3nmkiarfa2lsu2ewuoxhrciji4hp7kqxlj3643jtco4b7zf.py
# Topologically Sorted Source Nodes: [energy_1, attention, einsum_1], Original ATen: [aten.masked_fill, aten._softmax, aten.sum]
# Source node to ATen node mapping:
#   attention => exp, sum_1
#   einsum_1 => sum_3
#   energy_1 => full_default, where
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 9.999999960041972e-13), 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, [3], 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, 1.0), 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, [3], True), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%permute_6, [4], True), kwargs = {})
triton_poi_fused__softmax_masked_fill_sum_1 = async_compile.triton('triton_poi_fused__softmax_masked_fill_sum_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: '*fp32', 1: '*i1', 2: '*fp32', 3: '*fp32', 4: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_masked_fill_sum_1', 'mutated_arg_names': ['in_out_ptr0'], '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_sum_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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 = 9.999999960041972e-13
    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 = tmp26 * tmp6
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp12 - tmp25
    tmp30 = tmp29 * tmp6
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp28 + tmp31
    tmp33 = tmp18 - tmp25
    tmp34 = tmp33 * tmp6
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp32 + tmp35
    tmp37 = tmp24 - tmp25
    tmp38 = tmp37 * tmp6
    tmp39 = tl_math.exp(tmp38)
    tmp40 = tmp36 + tmp39
    tmp41 = tmp28 / tmp40
    tmp42 = tmp31 / tmp40
    tmp43 = tmp41 + tmp42
    tmp44 = tmp35 / tmp40
    tmp45 = tmp43 + tmp44
    tmp46 = tmp39 / tmp40
    tmp47 = tmp45 + tmp46
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x2 + (4*y3)), tmp47, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/hy/chyogtq555lqqjbhlbl5tofbrxemwefdvsj4arhko6vfdhq2uh2t.py
# Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   einsum_1 => sum_2
# Graph fragment:
#   %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%permute_5, [5], True), kwargs = {})
triton_poi_fused_sum_2 = async_compile.triton('triton_poi_fused_sum_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], 
    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_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sum_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/dc/cdcusklnql4qhayhtcv2kwbzsiewihis6rf4t2e4pelxbdbr3v6f.py
# Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   einsum_1 => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, %sum_3), kwargs = {})
triton_poi_fused_mul_3 = async_compile.triton('triton_poi_fused_mul_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_mul_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_mul_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
    y1 = (yindex // 4)
    y0 = yindex % 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask)
''', 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, 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), (1, 1))
    assert_size_stride(primals_6, (1, 1), (1, 1))
    assert_size_stride(primals_7, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [values_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 1), (1, 1), 0), primals_4, out=buf0)
        del primals_4
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [keys_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 1), (1, 1), 0), primals_5, out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [queries_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 1), (1, 1), 0), primals_6, out=buf2)
        del primals_6
        buf3 = 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_7, buf3, 256, grid=grid(256), stream=stream0)
        del primals_7
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 1, 1, 1), (16, 1, 4, 64, 64, 64), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [energy_1, attention, einsum_1], Original ATen: [aten.masked_fill, aten._softmax, aten.sum]
        triton_poi_fused__softmax_masked_fill_sum_1.run(buf7, buf3, buf2, buf1, 16, 4, grid=grid(16, 4), stream=stream0)
        buf6 = empty_strided_cuda((4, 1, 4, 1, 1, 1), (4, 4, 1, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.sum]
        triton_poi_fused_sum_2.run(buf0, buf6, 16, grid=grid(16), stream=stream0)
        buf8 = reinterpret_tensor(buf0, (4, 4, 4, 1, 1, 1), (16, 4, 1, 1, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.mul]
        triton_poi_fused_mul_3.run(buf6, 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.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf9)
        del primals_9
    return (reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_2, (64, 1), (1, 1), 0), reinterpret_tensor(primals_3, (64, 1), (1, 1), 0), buf1, reinterpret_tensor(primals_1, (64, 1), (1, 1), 0), buf2, buf3, buf6, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), primals_8, )


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), (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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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])
    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_size, heads):
        super(SelfAttention, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        assert self.head_dim * self.heads == self.embed_size, 'Embed size need to be div by heads'
        self.value = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.key = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.query = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(self.heads * self.head_dim, self.embed_size)

    def forward(self, values, keys, queries, mask):
        N = queries.shape[0]
        values_len, keys_len, queries_len = values.shape[1], keys.shape[1
            ], queries.shape[1]
        values = values.reshape(N, values_len, self.heads, self.head_dim)
        keys = keys.reshape(N, keys_len, self.heads, self.head_dim)
        queries = queries.reshape(N, queries_len, self.heads, self.head_dim)
        values = self.value(values)
        keys = self.key(keys)
        queries = self.query(queries)
        energy = torch.einsum('NQHD, NKHD -> NHQK', [queries, keys])
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float(1e-12))
        attention = torch.softmax(energy / self.head_dim ** 0.5, dim=3)
        out = torch.einsum('NVHD, NHQK->NQHD', [values, attention]).reshape(N,
            queries_len, self.heads * self.head_dim)
        out = self.fc_out(out)
        return out


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_size': 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_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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 = 9.999999960041972e-13
    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 = tmp26 * tmp6
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp12 - tmp25
    tmp30 = tmp29 * tmp6
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp28 + tmp31
    tmp33 = tmp18 - tmp25
    tmp34 = tmp33 * tmp6
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp32 + tmp35
    tmp37 = tmp24 - tmp25
    tmp38 = tmp37 * tmp6
    tmp39 = tl_math.exp(tmp38)
    tmp40 = tmp36 + tmp39
    tmp41 = tmp28 / tmp40
    tmp42 = tmp31 / tmp40
    tmp43 = tmp41 + tmp42
    tmp44 = tmp35 / tmp40
    tmp45 = tmp43 + tmp44
    tmp46 = tmp39 / tmp40
    tmp47 = tmp45 + tmp46
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x2 + 4 * y3), tmp47, xmask & ymask)


@triton.jit
def triton_poi_fused_sum_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_mul_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
    y1 = yindex // 4
    y0 = yindex % 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


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, 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), (1, 1))
    assert_size_stride(primals_6, (1, 1), (1, 1))
    assert_size_stride(primals_7, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 1), (1, 1), 0),
            primals_4, out=buf0)
        del primals_4
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 1), (1, 1), 0),
            primals_5, out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 1), (1, 1), 0),
            primals_6, out=buf2)
        del primals_6
        buf3 = 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_7, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_7
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 1, 1, 1), (16, 1, 4, 64, 
            64, 64), 0)
        del buf5
        triton_poi_fused__softmax_masked_fill_sum_1[grid(16, 4)](buf7, buf3,
            buf2, buf1, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 1, 4, 1, 1, 1), (4, 4, 1, 1, 1, 1),
            torch.float32)
        triton_poi_fused_sum_2[grid(16)](buf0, buf6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf8 = reinterpret_tensor(buf0, (4, 4, 4, 1, 1, 1), (16, 4, 1, 1, 1,
            1), 0)
        del buf0
        triton_poi_fused_mul_3[grid(16, 4)](buf6, buf7, buf8, 16, 4, XBLOCK
            =4, YBLOCK=8, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
        del buf7
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf8, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf9)
        del primals_9
    return reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_2, (64, 1), (1, 1), 0
        ), reinterpret_tensor(primals_3, (64, 1), (1, 1), 0
        ), buf1, reinterpret_tensor(primals_1, (64, 1), (1, 1), 0
        ), buf2, buf3, buf6, reinterpret_tensor(buf8, (16, 4), (4, 1), 0
        ), primals_8


class SelfAttentionNew(nn.Module):

    def __init__(self, embed_size, heads):
        super(SelfAttentionNew, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        assert self.head_dim * self.heads == self.embed_size, 'Embed size need to be div by heads'
        self.value = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.key = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.query = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(self.heads * self.head_dim, self.embed_size)

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