# 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_6/inductor_cache/xj/cxjhy4b2emwxu6foyq65uamx25utrr6yabi4u4clztnbed2dydcn.py
# Topologically Sorted Source Nodes: [att_logits_1, softmax], Original ATen: [aten.add, aten._softmax]
# Source node to ATen node mapping:
#   att_logits_1 => add
#   softmax => amax, exp, sub, sum_1
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_4, %squeeze), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_add_0 = async_compile.triton('triton_poi_fused__softmax_add_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=[64], 
    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__softmax_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 + tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tmp2 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp5 - tmp14
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp9 - tmp14
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp13 - tmp14
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tl.store(out_ptr0 + (x2), tmp14, xmask)
    tl.store(out_ptr1 + (x2), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/no/cnoe2ucpkq6j5eael53gjc3t3cs46utcvmwkmw73j6lruo6fspxe.py
# Topologically Sorted Source Nodes: [mul, utter_rep], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   utter_rep => sum_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %unsqueeze), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
triton_poi_fused_mul_sum_1 = async_compile.triton('triton_poi_fused_mul_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 20, '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_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex % 64
    x3 = (xindex // 64)
    x5 = (xindex // 4) % 16
    x2 = (xindex // 16) % 4
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x2 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x2 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (64 + x4), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (16 + x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (16 + x5), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (4 + x2 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr4 + (4 + x2 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (128 + x4), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (32 + x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr2 + (32 + x5), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + (8 + x2 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr4 + (8 + x2 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr0 + (192 + x4), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr1 + (48 + x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr2 + (48 + x5), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr3 + (12 + x2 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr4 + (12 + x2 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 * tmp8
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp18 = tmp16 / tmp17
    tmp19 = tmp10 * tmp18
    tmp20 = tmp9 + tmp19
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 - tmp25
    tmp27 = tl_math.exp(tmp26)
    tmp29 = tmp27 / tmp28
    tmp30 = tmp21 * tmp29
    tmp31 = tmp20 + tmp30
    tmp35 = tmp33 + tmp34
    tmp37 = tmp35 - tmp36
    tmp38 = tl_math.exp(tmp37)
    tmp40 = tmp38 / tmp39
    tmp41 = tmp32 * tmp40
    tmp42 = tmp31 + tmp41
    tl.store(out_ptr0 + (x7), tmp42, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 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: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [att_logits_1, softmax], Original ATen: [aten.add, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_add_0.run(primals_4, buf1, buf2, buf3, 64, grid=grid(64), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, utter_rep], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_1.run(primals_1, primals_4, buf1, buf2, buf3, buf4, 256, grid=grid(256), stream=stream0)
        del buf2
        del buf3
    return (buf4, primals_1, primals_4, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 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 SelfAttentionPooling(nn.Module):
    """
    Implementation of SelfAttentionPooling 
    Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
    https://arxiv.org/pdf/2008.01077v1.pdf
    """

    def __init__(self, input_dim):
        super(SelfAttentionPooling, self).__init__()
        self.W = nn.Linear(input_dim, 1)
        self.softmax = nn.functional.softmax

    def forward(self, batch_rep, att_mask):
        """
        input:
        batch_rep : size (N, T, H), N: batch size, T: sequence length, H: Hidden dimension
        
        attention_weight:
        att_w : size (N, T, 1)
        
        return:
        utter_rep: size (N, H)
        """
        batch_rep.shape[1]
        att_logits = self.W(batch_rep).squeeze(-1)
        att_logits = att_mask + att_logits
        att_w = self.softmax(att_logits, dim=-1).unsqueeze(-1)
        utter_rep = torch.sum(batch_rep * att_w, dim=1)
        return utter_rep


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


def get_init_inputs():
    return [[], {'input_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 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__softmax_add_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 + tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tmp2 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp5 - tmp14
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp9 - tmp14
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp13 - tmp14
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tl.store(out_ptr0 + x2, tmp14, xmask)
    tl.store(out_ptr1 + x2, tmp25, xmask)


@triton.jit
def triton_poi_fused_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex % 64
    x3 = xindex // 64
    x5 = xindex // 4 % 16
    x2 = xindex // 16 % 4
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr4 + (x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr0 + (64 + x4), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (16 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr2 + (16 + x5), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (4 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr4 + (4 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (128 + x4), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (32 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr2 + (32 + x5), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + (8 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr4 + (8 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (192 + x4), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr1 + (48 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp34 = tl.load(in_ptr2 + (48 + x5), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr3 + (12 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp39 = tl.load(in_ptr4 + (12 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 * tmp8
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp18 = tmp16 / tmp17
    tmp19 = tmp10 * tmp18
    tmp20 = tmp9 + tmp19
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 - tmp25
    tmp27 = tl_math.exp(tmp26)
    tmp29 = tmp27 / tmp28
    tmp30 = tmp21 * tmp29
    tmp31 = tmp20 + tmp30
    tmp35 = tmp33 + tmp34
    tmp37 = tmp35 - tmp36
    tmp38 = tl_math.exp(tmp37)
    tmp40 = tmp38 / tmp39
    tmp41 = tmp32 * tmp40
    tmp42 = tmp31 + tmp41
    tl.store(out_ptr0 + x7, tmp42, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 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_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_add_0[grid(64)](primals_4, buf1, buf2,
            buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sum_1[grid(256)](primals_1, primals_4, buf1,
            buf2, buf3, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf2
        del buf3
    return buf4, primals_1, primals_4, buf1


class SelfAttentionPoolingNew(nn.Module):
    """
    Implementation of SelfAttentionPooling 
    Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
    https://arxiv.org/pdf/2008.01077v1.pdf
    """

    def __init__(self, input_dim):
        super(SelfAttentionPoolingNew, self).__init__()
        self.W = nn.Linear(input_dim, 1)
        self.softmax = nn.functional.softmax

    def forward(self, input_0, input_1):
        primals_2 = self.W.weight
        primals_3 = self.W.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
