# 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/4z/c4z6axnati677lar56tqq32osnwyc2nqdbtcugxierfswhu2xihu.py
# Topologically Sorted Source Nodes: [mean, sub, mul, std, add, truediv, norm], Original ATen: [aten.mean, aten.sub, aten.mul, aten.std, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   mul => mul
#   norm => add_1
#   std => sqrt, var
#   sub => sub
#   truediv => div
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_2, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %mean), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %sub), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_2, [-1]), kwargs = {correction: 1.0, keepdim: True})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-06), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_3), kwargs = {})
triton_poi_fused_add_div_mean_mul_std_sub_0 = async_compile.triton('triton_poi_fused_add_div_mean_mul_std_sub_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: '*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_add_div_mean_mul_std_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x4 = xindex
    x5 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask)
    tmp2 = tl.load(in_ptr1 + (4*x5), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + (4*x5)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (2 + (4*x5)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (3 + (4*x5)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (x3), xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp0 * tmp11
    tmp13 = tmp2 - tmp10
    tmp14 = tmp13 * tmp13
    tmp15 = tmp3 - tmp10
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = tmp5 - tmp10
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp21 = tmp7 - tmp10
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = 3.0
    tmp25 = tmp23 / tmp24
    tmp26 = libdevice.sqrt(tmp25)
    tmp27 = 1e-06
    tmp28 = tmp26 + tmp27
    tmp29 = tmp12 / tmp28
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + (x4), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zx/czxyj46vflw3xf3gn4axvzsnxwd7qjciys4yptnip4ydf5fkid5d.py
# Topologically Sorted Source Nodes: [wrapped_sqrt, scores_1], Original ATen: [aten.sqrt, aten._softmax]
# Source node to ATen node mapping:
#   scores_1 => exp
#   wrapped_sqrt => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 2.0), kwargs = {dtype: torch.float64, layout: torch.strided, device: cpu, pin_memory: False})
#   %scalar_tensor_default : [num_users=2] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (1,), kwargs = {dtype: torch.float32, device: cuda:0, pin_memory: False})
#   %ge_scalar : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%full_default, 0), kwargs = {})
#   %neg_default : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%scalar_tensor_default,), kwargs = {})
#   %where_self : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%ge_scalar, %scalar_tensor_default, %neg_default), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_8, %where_self), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_self, %full_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, %mul_tensor_1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
triton_poi_fused__softmax_sqrt_1 = async_compile.triton('triton_poi_fused__softmax_sqrt_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: '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__softmax_sqrt_1', '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_sqrt_1(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp8 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 2.0, tl.float64)
    tmp2 = tl.full([1], 0.0, tl.float64)
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp9 = tmp8 * tmp6
    tmp11 = tmp10 * tmp6
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tmp13 * tmp6
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = tmp16 * tmp6
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp19 = tmp7 - tmp18
    tmp20 = tmp6.to(tl.float64)
    tmp21 = tmp20 * tmp1
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 / tmp22
    tmp24 = tl_math.exp(tmp23)
    tl.store(out_ptr0 + (x2), tmp24, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/3f/c3fx6bzkalkw7u7askqdnz4rzlcoyqiec4r434sjc5x3axxgkrmr.py
# Topologically Sorted Source Nodes: [scores_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   scores_1 => div_2, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_2 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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__softmax_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_2(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ck/cckqpocvlv6hl7nn6zons2xult3w2niv265o4qj66g4363uu6q3f.py
# Topologically Sorted Source Nodes: [add_2], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add_2 => add_2
# Graph fragment:
#   %add_2 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %view_11), kwargs = {})
triton_poi_fused_add_3 = async_compile.triton('triton_poi_fused_add_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=[256], 
    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_3', '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_3(in_out_ptr0, in_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 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x0), 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, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (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, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, sub, mul, std, add, truediv, norm], Original ATen: [aten.mean, aten.sub, aten.mul, aten.std, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_mean_mul_std_sub_0.run(primals_1, primals_2, primals_3, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        del primals_3
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_9
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [wrapped_sqrt, scores_1], Original ATen: [aten.sqrt, aten._softmax]
        triton_poi_fused__softmax_sqrt_1.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [scores_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf5, buf6, 256, grid=grid(256), stream=stream0)
        buf7 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [add_2], Original ATen: [aten.add]
        triton_poi_fused_add_3.run(buf8, buf0, 256, grid=grid(256), stream=stream0)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean_1, sub_1, mul_1, std_1, add_3, truediv_2, norm_1], Original ATen: [aten.mean, aten.sub, aten.mul, aten.std, aten.add, aten.div]
        triton_poi_fused_add_div_mean_mul_std_sub_0.run(primals_10, buf8, primals_11, buf9, 256, grid=grid(256), stream=stream0)
        del primals_11
    return (buf9, primals_2, primals_10, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), buf6, buf8, reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), primals_8, primals_6, primals_4, )


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), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (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, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((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)
    primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
    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.nn.functional as F


class Norm(nn.Module):

    def __init__(self, dim_seq, input_size, eps=1e-06):
        super().__init__()
        self.size = input_size
        self.seq = dim_seq
        self.alpha = nn.Parameter(torch.ones((self.size, self.seq)))
        self.bias = nn.Parameter(torch.zeros((self.size, self.seq)))
        self.eps = eps

    def forward(self, x):
        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim
            =-1, keepdim=True) + self.eps) + self.bias
        return norm


class Attention(nn.Module):

    def __init__(self, dim_seq, input_size, dropout=0.1):
        super().__init__()
        self.dim_seq = dim_seq
        self.dk = input_size
        self.q_linear = nn.Linear(dim_seq, dim_seq)
        self.k_linear = nn.Linear(dim_seq, dim_seq)
        self.v_linear = nn.Linear(dim_seq, dim_seq)
        self.norm_1 = Norm(dim_seq, input_size)
        self.norm_2 = Norm(dim_seq, input_size)
        self.dropout_1 = nn.Dropout(dropout)
        self.dropout_2 = nn.Dropout(dropout)

    def forward(self, s):
        s = self.norm_1(s).float()
        q = self.q_linear(s)
        k = self.k_linear(s)
        v = self.v_linear(s)
        scores = torch.matmul(q, k.transpose(-2, -1)) / np.sqrt(self.dk)
        scores = F.softmax(scores, dim=-1)
        scores = self.dropout_1(scores)
        output = torch.matmul(scores, v)
        s = self.norm_2(s + self.dropout_2(output))
        return s


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


def get_init_inputs():
    return [[], {'dim_seq': 4, 'input_size': 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 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_add_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x4 = xindex
    x5 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask)
    tmp2 = tl.load(in_ptr1 + 4 * x5, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * x5), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (2 + 4 * x5), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (3 + 4 * x5), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp0 * tmp11
    tmp13 = tmp2 - tmp10
    tmp14 = tmp13 * tmp13
    tmp15 = tmp3 - tmp10
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = tmp5 - tmp10
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp21 = tmp7 - tmp10
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = 3.0
    tmp25 = tmp23 / tmp24
    tmp26 = libdevice.sqrt(tmp25)
    tmp27 = 1e-06
    tmp28 = tmp26 + tmp27
    tmp29 = tmp12 / tmp28
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + x4, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_sqrt_1(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp8 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], 2.0, tl.float64)
    tmp2 = tl.full([1], 0.0, tl.float64)
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp9 = tmp8 * tmp6
    tmp11 = tmp10 * tmp6
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tmp13 * tmp6
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = tmp16 * tmp6
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp19 = tmp7 - tmp18
    tmp20 = tmp6.to(tl.float64)
    tmp21 = tmp20 * tmp1
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 / tmp22
    tmp24 = tl_math.exp(tmp23)
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_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 = tl.load(in_out_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (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, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_mean_mul_std_sub_0[grid(256)](primals_1,
            primals_2, primals_3, buf0, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_1
        del primals_3
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_9
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_sqrt_1[grid(256)](buf4, buf5, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf7
        triton_poi_fused_add_3[grid(256)](buf8, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_std_sub_0[grid(256)](primals_10,
            buf8, primals_11, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
    return buf9, primals_2, primals_10, reinterpret_tensor(buf0, (64, 4), (
        4, 1), 0), buf6, buf8, reinterpret_tensor(buf3, (16, 4, 4), (16, 1,
        4), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0
        ), primals_8, primals_6, primals_4


class Norm(nn.Module):

    def __init__(self, dim_seq, input_size, eps=1e-06):
        super().__init__()
        self.size = input_size
        self.seq = dim_seq
        self.alpha = nn.Parameter(torch.ones((self.size, self.seq)))
        self.bias = nn.Parameter(torch.zeros((self.size, self.seq)))
        self.eps = eps

    def forward(self, x):
        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim
            =-1, keepdim=True) + self.eps) + self.bias
        return norm


class AttentionNew(nn.Module):

    def __init__(self, dim_seq, input_size, dropout=0.1):
        super().__init__()
        self.dim_seq = dim_seq
        self.dk = input_size
        self.q_linear = nn.Linear(dim_seq, dim_seq)
        self.k_linear = nn.Linear(dim_seq, dim_seq)
        self.v_linear = nn.Linear(dim_seq, dim_seq)
        self.norm_1 = Norm(dim_seq, input_size)
        self.norm_2 = Norm(dim_seq, input_size)
        self.dropout_1 = nn.Dropout(dropout)
        self.dropout_2 = nn.Dropout(dropout)

    def forward(self, input_0):
        primals_1 = self.q_linear.weight
        primals_5 = self.q_linear.bias
        primals_3 = self.k_linear.weight
        primals_7 = self.k_linear.bias
        primals_4 = self.v_linear.weight
        primals_9 = self.v_linear.bias
        primals_6 = self.norm_1.alpha
        primals_8 = self.norm_1.bias
        primals_10 = self.norm_2.alpha
        primals_11 = self.norm_2.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
