# 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_4/inductor_cache/5w/c5w2ivcaay7hmnlw23thxogizmzq3npdt5ymyrwyfkyg43dvhddx.py
# Topologically Sorted Source Nodes: [attention_weights_1, attention_weights_2, attention_weights_3, sum_1, attention_weights_sum], Original ATen: [aten.add, aten.tanh, aten.exp, aten.sum]
# Source node to ATen node mapping:
#   attention_weights_1 => add
#   attention_weights_2 => tanh
#   attention_weights_3 => exp
#   attention_weights_sum => add_1
#   sum_1 => sum_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_2, %view_1), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), 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_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-07), kwargs = {})
triton_poi_fused_add_exp_sum_tanh_0 = async_compile.triton('triton_poi_fused_add_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_sum_tanh_0', '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_add_exp_sum_tanh_0(in_ptr0, in_ptr1, 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), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + (16*x1)), xmask)
    tmp5 = tl.load(in_ptr1 + (4 + x0 + (16*x1)), xmask)
    tmp10 = tl.load(in_ptr1 + (8 + x0 + (16*x1)), xmask)
    tmp15 = tl.load(in_ptr1 + (12 + x0 + (16*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp0 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp4 + tmp8
    tmp11 = tmp0 + tmp10
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp9 + tmp13
    tmp16 = tmp0 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp14 + tmp18
    tmp20 = 1e-07
    tmp21 = tmp19 + tmp20
    tl.store(out_ptr0 + (x2), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rh/crhzdoraggjsyh6e4ua42zcjdcuzxhhr2mfdfaw6mjfbaf2rxf2v.py
# Topologically Sorted Source Nodes: [attention_weights_1, attention_weights_2, attention_weights_3, sum_1, attention_weights_sum, attention_weights_4, mul, attended], Original ATen: [aten.add, aten.tanh, aten.exp, aten.sum, aten.div, aten.mul]
# Source node to ATen node mapping:
#   attended => sum_2
#   attention_weights_1 => add
#   attention_weights_2 => tanh
#   attention_weights_3 => exp
#   attention_weights_4 => div
#   attention_weights_sum => add_1
#   mul => mul
#   sum_1 => sum_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_2, %view_1), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), 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_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-07), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %add_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %primals_3), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
triton_poi_fused_add_div_exp_mul_sum_tanh_1 = async_compile.triton('triton_poi_fused_add_div_exp_mul_sum_tanh_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_exp_mul_sum_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, '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_exp_mul_sum_tanh_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = (xindex // 4)
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x3), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x4 + (64*x2)), xmask)
    tmp9 = tl.load(in_ptr1 + (4 + x1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (16 + x4 + (64*x2)), xmask)
    tmp17 = tl.load(in_ptr1 + (8 + x1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr3 + (32 + x4 + (64*x2)), xmask)
    tmp25 = tl.load(in_ptr1 + (12 + x1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr3 + (48 + x4 + (64*x2)), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp4 / tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp0 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp12 / tmp5
    tmp15 = tmp13 * tmp14
    tmp16 = tmp8 + tmp15
    tmp18 = tmp0 + tmp17
    tmp19 = libdevice.tanh(tmp18)
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp20 / tmp5
    tmp23 = tmp21 * tmp22
    tmp24 = tmp16 + tmp23
    tmp26 = tmp0 + tmp25
    tmp27 = libdevice.tanh(tmp26)
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp28 / tmp5
    tmp31 = tmp29 * tmp30
    tmp32 = tmp24 + tmp31
    tl.store(out_ptr0 + (x5), tmp32, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 70, 1), (70, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_weights], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf2 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [attention_weights_1, attention_weights_2, attention_weights_3, sum_1, attention_weights_sum], Original ATen: [aten.add, aten.tanh, aten.exp, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_exp_sum_tanh_0.run(primals_4, buf1, buf2, 16, grid=grid(16), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_weights_1, attention_weights_2, attention_weights_3, sum_1, attention_weights_sum, attention_weights_4, mul, attended], Original ATen: [aten.add, aten.tanh, aten.exp, aten.sum, aten.div, aten.mul]
        triton_poi_fused_add_div_exp_mul_sum_tanh_1.run(primals_4, buf1, buf2, primals_3, buf3, 64, grid=grid(64), stream=stream0)
        del buf2
    return (buf3, primals_3, buf1, reinterpret_tensor(primals_4, (1, 4, 1), (70, 1, 1), 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((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 70, 1), (70, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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, feature_dim, maxlen=70):
        super().__init__()
        self.attention_fc = nn.Linear(feature_dim, 1)
        self.bias = nn.Parameter(torch.zeros(1, maxlen, 1, requires_grad=True))

    def forward(self, rnn_output):
        """
        forward attention scores and attended vectors
        :param rnn_output: (#batch, #seq_len, #feature)
        :return: attended_outputs (#batch, #feature)
        """
        attention_weights = self.attention_fc(rnn_output)
        seq_len = rnn_output.size(1)
        attention_weights = self.bias[:, :seq_len, :] + attention_weights
        attention_weights = torch.tanh(attention_weights)
        attention_weights = torch.exp(attention_weights)
        attention_weights_sum = torch.sum(attention_weights, dim=1, keepdim
            =True) + 1e-07
        attention_weights = attention_weights / attention_weights_sum
        attended = torch.sum(attention_weights * rnn_output, dim=1)
        return attended


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


def get_init_inputs():
    return [[], {'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_poi_fused_add_exp_sum_tanh_0(in_ptr0, in_ptr1, 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, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask)
    tmp10 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp0 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp4 + tmp8
    tmp11 = tmp0 + tmp10
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp9 + tmp13
    tmp16 = tmp0 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp14 + tmp18
    tmp20 = 1e-07
    tmp21 = tmp19 + tmp20
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused_add_div_exp_mul_sum_tanh_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex // 4
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x4 + 64 * x2), xmask)
    tmp9 = tl.load(in_ptr1 + (4 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr3 + (16 + x4 + 64 * x2), xmask)
    tmp17 = tl.load(in_ptr1 + (8 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr3 + (32 + x4 + 64 * x2), xmask)
    tmp25 = tl.load(in_ptr1 + (12 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr3 + (48 + x4 + 64 * x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp4 / tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp0 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp12 / tmp5
    tmp15 = tmp13 * tmp14
    tmp16 = tmp8 + tmp15
    tmp18 = tmp0 + tmp17
    tmp19 = libdevice.tanh(tmp18)
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp20 / tmp5
    tmp23 = tmp21 * tmp22
    tmp24 = tmp16 + tmp23
    tmp26 = tmp0 + tmp25
    tmp27 = libdevice.tanh(tmp26)
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp28 / tmp5
    tmp31 = tmp29 * tmp30
    tmp32 = tmp24 + tmp31
    tl.store(out_ptr0 + x5, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 70, 1), (70, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf2 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_sum_tanh_0[grid(16)](primals_4, buf1, buf2,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_exp_mul_sum_tanh_1[grid(64)](primals_4,
            buf1, buf2, primals_3, buf3, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf2
    return buf3, primals_3, buf1, reinterpret_tensor(primals_4, (1, 4, 1),
        (70, 1, 1), 0)


class AttentionNew(nn.Module):

    def __init__(self, feature_dim, maxlen=70):
        super().__init__()
        self.attention_fc = nn.Linear(feature_dim, 1)
        self.bias = nn.Parameter(torch.zeros(1, maxlen, 1, requires_grad=True))

    def forward(self, input_0):
        primals_4 = self.bias
        primals_1 = self.attention_fc.weight
        primals_2 = self.attention_fc.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
