# 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/r6/cr6neze6yovkog6kjrk5k2db63h47ozkojywfys6karxe7dlumrz.py
# Topologically Sorted Source Nodes: [alphas], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   alphas => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), 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=[64], 
    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_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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nv/cnvwmrivk6laberpxowj4upjz5sug5kbkrhxjzvo2ifndn7n5rma.py
# Topologically Sorted Source Nodes: [alphas, ne, alphas_1], Original ATen: [aten._softmax, aten.ne, aten.masked_fill]
# Source node to ATen node mapping:
#   alphas => div, sum_1
#   alphas_1 => full_default, where
#   ne => ne
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %ne : [num_users=2] = call_function[target=torch.ops.aten.ne.Tensor](args = (%div, %div), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ne, %full_default, %div), kwargs = {})
triton_poi_fused__softmax_masked_fill_ne_1 = async_compile.triton('triton_poi_fused__softmax_masked_fill_ne_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: '*i1', 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__softmax_masked_fill_ne_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_masked_fill_ne_1(in_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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
    tmp9 = tmp8 != tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp9, tmp10, tmp8)
    tl.store(out_ptr1 + (x2), tmp9, xmask)
    tl.store(out_ptr2 + (x2), tmp11, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [scores], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), out=buf1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [alphas], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(buf1, buf2, 64, grid=grid(64), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alphas, ne, alphas_1], Original ATen: [aten._softmax, aten.ne, aten.masked_fill]
        triton_poi_fused__softmax_masked_fill_ne_1.run(buf2, buf4, buf5, 64, grid=grid(64), stream=stream0)
        buf6 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [alphas_1, bmm_1], Original ATen: [aten.masked_fill, aten.bmm]
        extern_kernels.bmm(buf5, primals_1, out=buf6)
        del buf5
    return (buf6, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), buf1, buf4, )


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), (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((4, 4, 4), (16, 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
from torch import nn
import torch.nn.functional as F


class Attention(nn.Module):
    """
    score_i = f(Q, K_i), i = 1, 2, ..., t
        dot:        f(Q, K_i) = Q.transpose · K_i
        scaled_dot: f(Q, K_i) = Q.transpose · K_i / √(key_dim)
        general:    f(Q, K_i) = Q.transpose · W · K_i
        concat:     f(Q, K_i) = V.transpose · tanh(W · [Q; K_i])
        perceptron: f(Q, K_i) = V.transpose · tanh(W · Q + U · K_i)

    alpha_i = softmax(score_i)

    context = Σ(alpha_i · V_i)

    Args:
        query_dim: Dimension of query vector (Q).
        key_dim: Dimension of key vectors (K_i, i = 1, 2, ..., t).
        method: dot/scaled_dot/general/concat/perceptron
    """

    def __init__(self, query_dim, key_dim, value_dim=0, method='general',
        dropout_rate=0.0):
        super(Attention, self).__init__()
        self.method = method
        self.dropout = nn.Dropout(dropout_rate)
        if self.method == 'dot' or self.method == 'scaled_dot':
            assert query_dim == key_dim, 'The query_dim must equals key_dim.'
            if value_dim == 0:
                value_dim = key_dim
            self.linear_q = nn.Linear(query_dim, query_dim)
            self.linear_k = nn.Linear(key_dim, key_dim)
            self.linear_v = nn.Linear(value_dim, value_dim)
        elif self.method == 'general':
            self.W = nn.Linear(query_dim, key_dim, bias=False)
        elif self.method == 'concat':
            self.W = nn.Linear(query_dim + key_dim, query_dim + key_dim,
                bias=False)
            self.V = nn.Linear(query_dim + key_dim, 1, bias=False)
        elif self.method == 'perceptron':
            self.W = nn.Linear(query_dim, query_dim, bias=False)
            self.U = nn.Linear(key_dim, query_dim, bias=False)
            self.V = nn.Linear(query_dim, 1, bias=False)
        else:
            raise ValueError(
                'The method must be one of the following: dot, scaled_dot, general, concat or perceptron.'
                )

    def forward(self, queries, keys, values=None, mask=None, top_k=None):
        """
        Args:
            queries: Batch of query vectors (Q). Tensor[batch_size, query_len, query_dim]
            keys: Batch of key vectors (K_i, i = 1, 2, ..., t). Tensor[batch_size, key_len, key_dim]
            values: Batch of value vectors (V_i, i = 1, 2, ..., t). Tensor[batch_size, value_len, value_dim]
            mask: Use none zero value as valid flag and 0 as pad flag. Tensor[batch_size, query_len, key_len]
            top_k: Select top K relative values. int(0, ∞)

        Return:
            Batch of context vector (C). Tensor[batch_size, query_len, value_dim]
        """
        if values is None:
            values = keys
        else:
            assert values.shape[-2] == keys.shape[-2
                ], 'value_len Must equals key_len.'
        if self.method == 'dot' or self.method == 'scaled_dot':
            queries = self.linear_q(queries)
            keys = self.linear_k(keys)
            values = self.linear_v(values)
        scores = self.score(queries, keys)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -float('inf'))
        alphas = F.softmax(scores, dim=-1)
        alphas = alphas.masked_fill(alphas != alphas, 0)
        if top_k is not None:
            _, indices = torch.topk(alphas, k=top_k, dim=-1, largest=True)
            self.get_device(alphas)
            topk_mask = torch.zeros(alphas.shape).scatter_(dim=-1, index=
                indices, src=torch.ones(indices.shape))
            alphas = alphas.masked_fill(topk_mask == 0, 0)
            alphas = F.softmax(alphas, dim=-1)
            alphas = alphas.masked_fill(alphas != alphas, 0)
        alphas = self.dropout(alphas)
        return torch.bmm(alphas, values)

    def score(self, queries, keys):
        """
        Args:
            queries: Tensor[batch_size, query_len, query_dim]
            keys: Tensor[batch_size, key_len, key_dim]

        Return:
            Batch of attention scores. Tensor[batch_size, query_len, key_len]
        """
        if self.method == 'dot' or self.method == 'scaled_dot':
            scores = torch.bmm(queries, keys.transpose(-1, -2))
            if self.method == 'scaled_dot':
                scores /= math.sqrt(keys.shape[-2])
            return scores
        elif self.method == 'general':
            return torch.bmm(self.W(queries), keys.transpose(-1, -2))
        elif self.method == 'concat':
            queries = queries.unsqueeze(2).expand(-1, -1, keys.shape[1], -1)
            keys = keys.unsqueeze(1).expand(-1, queries.shape[1], -1, -1)
            scores = torch.cat([queries, keys], dim=-1)
            scores = self.W(scores)
            scores = torch.tanh(scores)
            return self.V(scores).squeeze(3)
        elif self.method == 'perceptron':
            queries = queries.unsqueeze(2).expand(-1, -1, keys.shape[1], -1)
            keys = keys.unsqueeze(1).expand(-1, queries.shape[1], -1, -1)
            scores = self.W(queries) + self.U(keys)
            scores = torch.tanh(scores)
            return self.V(scores).squeeze(3)

    @staticmethod
    def get_device(t):
        try:
            device_id = t.get_device()
        except:
            return 'cpu'
        else:
            return 'cpu' if device_id < 0 else 'cuda'


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


def get_init_inputs():
    return [[], {'query_dim': 4, 'key_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 math
from torch import 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_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_ne_1(in_ptr0, out_ptr1, out_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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
    tmp9 = tmp8 != tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp9, tmp10, tmp8)
    tl.store(out_ptr1 + x2, tmp9, xmask)
    tl.store(out_ptr2 + x2, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0),
            out=buf1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_masked_fill_ne_1[grid(64)](buf2, buf4,
            buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf6 = buf2
        del buf2
        extern_kernels.bmm(buf5, primals_1, out=buf6)
        del buf5
    return buf6, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), buf1, buf4


class AttentionNew(nn.Module):
    """
    score_i = f(Q, K_i), i = 1, 2, ..., t
        dot:        f(Q, K_i) = Q.transpose · K_i
        scaled_dot: f(Q, K_i) = Q.transpose · K_i / √(key_dim)
        general:    f(Q, K_i) = Q.transpose · W · K_i
        concat:     f(Q, K_i) = V.transpose · tanh(W · [Q; K_i])
        perceptron: f(Q, K_i) = V.transpose · tanh(W · Q + U · K_i)

    alpha_i = softmax(score_i)

    context = Σ(alpha_i · V_i)

    Args:
        query_dim: Dimension of query vector (Q).
        key_dim: Dimension of key vectors (K_i, i = 1, 2, ..., t).
        method: dot/scaled_dot/general/concat/perceptron
    """

    def __init__(self, query_dim, key_dim, value_dim=0, method='general',
        dropout_rate=0.0):
        super(AttentionNew, self).__init__()
        self.method = method
        self.dropout = nn.Dropout(dropout_rate)
        if self.method == 'dot' or self.method == 'scaled_dot':
            assert query_dim == key_dim, 'The query_dim must equals key_dim.'
            if value_dim == 0:
                value_dim = key_dim
            self.linear_q = nn.Linear(query_dim, query_dim)
            self.linear_k = nn.Linear(key_dim, key_dim)
            self.linear_v = nn.Linear(value_dim, value_dim)
        elif self.method == 'general':
            self.W = nn.Linear(query_dim, key_dim, bias=False)
        elif self.method == 'concat':
            self.W = nn.Linear(query_dim + key_dim, query_dim + key_dim,
                bias=False)
            self.V = nn.Linear(query_dim + key_dim, 1, bias=False)
        elif self.method == 'perceptron':
            self.W = nn.Linear(query_dim, query_dim, bias=False)
            self.U = nn.Linear(key_dim, query_dim, bias=False)
            self.V = nn.Linear(query_dim, 1, bias=False)
        else:
            raise ValueError(
                'The method must be one of the following: dot, scaled_dot, general, concat or perceptron.'
                )

    def score(self, queries, keys):
        """
        Args:
            queries: Tensor[batch_size, query_len, query_dim]
            keys: Tensor[batch_size, key_len, key_dim]

        Return:
            Batch of attention scores. Tensor[batch_size, query_len, key_len]
        """
        if self.method == 'dot' or self.method == 'scaled_dot':
            scores = torch.bmm(queries, keys.transpose(-1, -2))
            if self.method == 'scaled_dot':
                scores /= math.sqrt(keys.shape[-2])
            return scores
        elif self.method == 'general':
            return torch.bmm(self.W(queries), keys.transpose(-1, -2))
        elif self.method == 'concat':
            queries = queries.unsqueeze(2).expand(-1, -1, keys.shape[1], -1)
            keys = keys.unsqueeze(1).expand(-1, queries.shape[1], -1, -1)
            scores = torch.cat([queries, keys], dim=-1)
            scores = self.W(scores)
            scores = torch.tanh(scores)
            return self.V(scores).squeeze(3)
        elif self.method == 'perceptron':
            queries = queries.unsqueeze(2).expand(-1, -1, keys.shape[1], -1)
            keys = keys.unsqueeze(1).expand(-1, queries.shape[1], -1, -1)
            scores = self.W(queries) + self.U(keys)
            scores = torch.tanh(scores)
            return self.V(scores).squeeze(3)

    @staticmethod
    def get_device(t):
        try:
            device_id = t.get_device()
        except:
            return 'cpu'
        else:
            return 'cpu' if device_id < 0 else 'cuda'

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