# 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_1/inductor_cache/2m/c2mkmylxu3vdmqgiibpy6bwbw7aqe2olw2svraqjjcfitwc6dayw.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention => exp
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_2, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 2.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + (x2), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/57/c57am5fxjlxtumd4udah53kjn6yrsnl46yg37giczde4ocgcuypi.py
# Topologically Sorted Source Nodes: [attention, attention_1], Original ATen: [aten._softmax, aten.mean]
# Source node to ATen node mapping:
#   attention => div_1, sum_1
#   attention_1 => mean
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%div_1, [0]), kwargs = {})
triton_poi_fused__softmax_mean_1 = async_compile.triton('triton_poi_fused__softmax_mean_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_mean_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 20, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl.load(in_ptr0 + (1))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (2))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (3))
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp14 = tl.load(in_ptr0 + (4))
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp16 = tl.load(in_ptr0 + (5))
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK])
    tmp19 = tl.load(in_ptr0 + (6))
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp22 = tl.load(in_ptr0 + (7))
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp27 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp28 = tl.load(in_ptr0 + (8))
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK])
    tmp30 = tl.load(in_ptr0 + (9))
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK])
    tmp33 = tl.load(in_ptr0 + (10))
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
    tmp36 = tl.load(in_ptr0 + (11))
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK])
    tmp41 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp42 = tl.load(in_ptr0 + (12))
    tmp43 = tl.broadcast_to(tmp42, [XBLOCK])
    tmp44 = tl.load(in_ptr0 + (13))
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK])
    tmp47 = tl.load(in_ptr0 + (14))
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK])
    tmp50 = tl.load(in_ptr0 + (15))
    tmp51 = tl.broadcast_to(tmp50, [XBLOCK])
    tmp5 = tmp2 + tmp4
    tmp8 = tmp5 + tmp7
    tmp11 = tmp8 + tmp10
    tmp12 = tmp0 / tmp11
    tmp18 = tmp15 + tmp17
    tmp21 = tmp18 + tmp20
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 / tmp24
    tmp26 = tmp12 + tmp25
    tmp32 = tmp29 + tmp31
    tmp35 = tmp32 + tmp34
    tmp38 = tmp35 + tmp37
    tmp39 = tmp27 / tmp38
    tmp40 = tmp26 + tmp39
    tmp46 = tmp43 + tmp45
    tmp49 = tmp46 + tmp48
    tmp52 = tmp49 + tmp51
    tmp53 = tmp41 / tmp52
    tmp54 = tmp40 + tmp53
    tmp55 = 4.0
    tmp56 = tmp54 / tmp55
    tl.store(out_ptr0 + (x0), tmp56, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_2], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(buf2, buf3, 16, grid=grid(16), stream=stream0)
        buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [attention, attention_1], Original ATen: [aten._softmax, aten.mean]
        triton_poi_fused__softmax_mean_1.run(buf3, buf4, 4, grid=grid(4), stream=stream0)
        del buf3
        buf5 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf4, (1, 4), (4, 1), 0), primals_1, out=buf5)
        del buf4
    return (reinterpret_tensor(buf5, (4, ), (1, ), 0), buf0, buf2, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), buf1, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import *


class SelfAttentionLayer2(nn.Module):

    def __init__(self, dim, da):
        super(SelfAttentionLayer2, self).__init__()
        self.dim = dim
        self.Wq = nn.Parameter(torch.zeros(self.dim, self.dim))
        self.Wk = nn.Parameter(torch.zeros(self.dim, self.dim))
        nn.init.xavier_uniform_(self.Wq.data, gain=1.414)
        nn.init.xavier_uniform_(self.Wk.data, gain=1.414)

    def forward(self, h):
        h.shape[0]
        assert self.dim == h.shape[1]
        q = torch.matmul(h, self.Wq)
        k = torch.matmul(h, self.Wk)
        e = torch.matmul(q, k.t()) / math.sqrt(self.dim)
        attention = F.softmax(e, dim=1)
        attention = attention.mean(dim=0)
        x = torch.matmul(attention, h)
        return x


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


def get_init_inputs():
    return [[], {'dim': 4, 'da': 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
from torch.utils.data import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, xmask)


@triton.jit
def triton_poi_fused__softmax_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    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_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl.load(in_ptr0 + 1)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + 2)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + 3)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp14 = tl.load(in_ptr0 + 4)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp16 = tl.load(in_ptr0 + 5)
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK])
    tmp19 = tl.load(in_ptr0 + 6)
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp22 = tl.load(in_ptr0 + 7)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp27 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp28 = tl.load(in_ptr0 + 8)
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK])
    tmp30 = tl.load(in_ptr0 + 9)
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK])
    tmp33 = tl.load(in_ptr0 + 10)
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
    tmp36 = tl.load(in_ptr0 + 11)
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK])
    tmp41 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp42 = tl.load(in_ptr0 + 12)
    tmp43 = tl.broadcast_to(tmp42, [XBLOCK])
    tmp44 = tl.load(in_ptr0 + 13)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK])
    tmp47 = tl.load(in_ptr0 + 14)
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK])
    tmp50 = tl.load(in_ptr0 + 15)
    tmp51 = tl.broadcast_to(tmp50, [XBLOCK])
    tmp5 = tmp2 + tmp4
    tmp8 = tmp5 + tmp7
    tmp11 = tmp8 + tmp10
    tmp12 = tmp0 / tmp11
    tmp18 = tmp15 + tmp17
    tmp21 = tmp18 + tmp20
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 / tmp24
    tmp26 = tmp12 + tmp25
    tmp32 = tmp29 + tmp31
    tmp35 = tmp32 + tmp34
    tmp38 = tmp35 + tmp37
    tmp39 = tmp27 / tmp38
    tmp40 = tmp26 + tmp39
    tmp46 = tmp43 + tmp45
    tmp49 = tmp46 + tmp48
    tmp52 = tmp49 + tmp51
    tmp53 = tmp41 / tmp52
    tmp54 = tmp40 + tmp53
    tmp55 = 4.0
    tmp56 = tmp54 / tmp55
    tl.store(out_ptr0 + x0, tmp56, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0),
            out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused__softmax_mean_1[grid(4)](buf3, buf4, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del buf3
        buf5 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (1, 4), (4, 1), 0),
            primals_1, out=buf5)
        del buf4
    return reinterpret_tensor(buf5, (4,), (1,), 0
        ), buf0, buf2, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), buf1


class SelfAttentionLayer2New(nn.Module):

    def __init__(self, dim, da):
        super(SelfAttentionLayer2New, self).__init__()
        self.dim = dim
        self.Wq = nn.Parameter(torch.zeros(self.dim, self.dim))
        self.Wk = nn.Parameter(torch.zeros(self.dim, self.dim))
        nn.init.xavier_uniform_(self.Wq.data, gain=1.414)
        nn.init.xavier_uniform_(self.Wk.data, gain=1.414)

    def forward(self, input_0):
        primals_1 = self.Wq
        primals_2 = self.Wk
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
