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]


Measured latency_ms: 0.0482