# 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/4z/c4zk4xma2fr7mgb4js3gtlkdjjzo3d55c4vbaf24gqrlcohs5nti.py
# Topologically Sorted Source Nodes: [u_1, a, sum_1, add, a_1], Original ATen: [aten.tanh, aten.exp, aten.sum, aten.add, aten.div]
# Source node to ATen node mapping:
#   a => exp
#   a_1 => div
#   add => add
#   sum_1 => sum_1
#   u_1 => tanh
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%mm_1,), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%tanh,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-10), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %add), kwargs = {})
triton_per_fused_add_div_exp_sum_tanh_0 = async_compile.triton('triton_per_fused_add_div_exp_sum_tanh_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_exp_sum_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_add_div_exp_sum_tanh_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp6 = 1e-10
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp7, None)
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/5k/c5kbt564pgd5flt5efelgun7u2rnldlfhs6qagkr5sfloqvweovl.py
# Topologically Sorted Source Nodes: [sum_2], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   sum_2 => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mm_2, [1]), kwargs = {})
triton_poi_fused_sum_1 = async_compile.triton('triton_poi_fused_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_sum_1(in_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_out_ptr0 + (x0), xmask)
    tl.store(in_out_ptr0 + (x0), tmp0, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (1, 4), (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: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((1, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, reinterpret_tensor(buf0, (4, 64), (64, 1), 0), out=buf1)
        buf2 = empty_strided_cuda((1, 1), (1, 1), torch.float32)
        buf3 = buf2; del buf2  # reuse
        buf4 = empty_strided_cuda((1, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [u_1, a, sum_1, add, a_1], Original ATen: [aten.tanh, aten.exp, aten.sum, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_exp_sum_tanh_0.run(buf3, buf1, buf4, 1, 64, grid=grid(1), stream=stream0)
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weighted_input], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 64), (64, 1), 0), reinterpret_tensor(buf4, (64, 1), (1, 0), 0), out=buf5)
        del buf4
        buf6 = reinterpret_tensor(buf5, (4, ), (1, ), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [sum_2], Original ATen: [aten.sum]
        triton_poi_fused_sum_1.run(buf6, 4, grid=grid(4), stream=stream0)
    return (buf6, primals_1, buf1, buf3, reinterpret_tensor(primals_3, (4, 1), (1, 4), 0), reinterpret_tensor(buf0, (64, 4), (1, 64), 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((1, 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 torch
import torch.nn as nn


class Attention(nn.Module):

    def __init__(self, input_dim, feature_dim):
        super(Attention, self).__init__()
        self.feature_dim = feature_dim
        self.input_dim = input_dim
        weight = torch.zeros(self.feature_dim, self.feature_dim)
        nn.init.kaiming_uniform_(weight)
        self.weight = nn.Parameter(weight)
        w = torch.zeros(1, self.feature_dim)
        nn.init.kaiming_uniform_(w)
        self.w = nn.Parameter(w)

    def forward(self, input, context=None):
        u = torch.matmul(input.contiguous().view(-1, self.feature_dim),
            self.weight).view(-1, self.feature_dim)
        u = torch.tanh(torch.matmul(self.w, u.view(self.feature_dim, -1)))
        if context is not None:
            u = u * context
        a = torch.exp(u)
        a = a / (torch.sum(a, 1, keepdim=True) + 1e-10)
        weighted_input = torch.matmul(input.view(self.feature_dim, -1), a.
            view(-1, 1))
        return torch.sum(weighted_input, 1)


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'feature_dim': 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.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_per_fused_add_div_exp_sum_tanh_0(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp6 = 1e-10
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp7, None)
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp8, None)


@triton.jit
def triton_poi_fused_sum_1(in_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_out_ptr0 + x0, xmask)
    tl.store(in_out_ptr0 + x0, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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, (1, 4), (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),
            primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((1, 64), (64, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(buf0, (4, 64), (64,
            1), 0), out=buf1)
        buf2 = empty_strided_cuda((1, 1), (1, 1), torch.float32)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((1, 64), (64, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_exp_sum_tanh_0[grid(1)](buf3, buf1, buf4, 
            1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 64), (64, 1), 0
            ), reinterpret_tensor(buf4, (64, 1), (1, 0), 0), out=buf5)
        del buf4
        buf6 = reinterpret_tensor(buf5, (4,), (1,), 0)
        del buf5
        triton_poi_fused_sum_1[grid(4)](buf6, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
    return buf6, primals_1, buf1, buf3, reinterpret_tensor(primals_3, (4, 1
        ), (1, 4), 0), reinterpret_tensor(buf0, (64, 4), (1, 64), 0)


class AttentionNew(nn.Module):

    def __init__(self, input_dim, feature_dim):
        super(AttentionNew, self).__init__()
        self.feature_dim = feature_dim
        self.input_dim = input_dim
        weight = torch.zeros(self.feature_dim, self.feature_dim)
        nn.init.kaiming_uniform_(weight)
        self.weight = nn.Parameter(weight)
        w = torch.zeros(1, self.feature_dim)
        nn.init.kaiming_uniform_(w)
        self.w = nn.Parameter(w)

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