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
import numpy as np
import torch.nn.functional as F
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_repeat_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
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    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_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_5(in_out_ptr0, in_ptr0, in_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 % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_6(in_ptr0, in_ptr1,
    in_ptr2, 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 % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp0 + tmp5
    tmp7 = 0.0
    tmp8 = tmp5 <= tmp7
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_repeat_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_1[grid(4, 16)](buf1, primals_4, buf4, 4, 16,
            XBLOCK=16, YBLOCK=4, num_warps=1, num_stages=1)
        del primals_4
        buf5 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf1
        triton_poi_fused_clone_1[grid(4, 16)](buf2, primals_6, buf5, 4, 16,
            XBLOCK=16, YBLOCK=4, num_warps=1, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = buf6
        del buf6
        triton_poi_fused__softmax_3[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf7
        buf9 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf2
        triton_poi_fused_clone_1[grid(4, 16)](buf3, primals_8, buf9, 4, 16,
            XBLOCK=16, YBLOCK=4, num_warps=1, num_stages=1)
        del primals_8
        buf10 = reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 1), 0)
        del buf3
        extern_kernels.bmm(buf8, reinterpret_tensor(buf9, (16, 4, 1), (4, 1,
            0), 0), out=buf10)
        buf11 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf10, buf11, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0)
        del buf10
        extern_kernels.mm(reinterpret_tensor(buf11, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf12)
        buf13 = reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0)
        del buf12
        triton_poi_fused_add_5[grid(64)](buf13, primals_10, buf0, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_10
        buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf14)
        buf15 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_6[grid(64)](buf13,
            buf14, primals_12, buf15, buf16, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf14
        del primals_12
    return buf15, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf8, reinterpret_tensor(buf11, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf13, (16, 4), (4, 1), 0
        ), buf16, primals_11, primals_9, reinterpret_tensor(buf9, (16, 1, 4
        ), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0), primals_3


def qkv_attention(queries, keys, values, presence=None):
    """
    Transformer-like self-attention.

    Args:
      queries: Tensor of shape [B, N, d_k].
      keys: Tensor of shape [B, M, d_k].
      values: : Tensor of shape [B, M, d_v].
      presence: None or tensor of shape [B, M].

    Returns:
      Tensor of shape [B, N, d_v]
    """
    d_k = queries.shape[-1]
    routing = torch.matmul(queries, keys.transpose(1, 2))
    if presence is not None:
        routing -= (1.0 - presence.unsqueeze(-2)) * 1e+32
    routing = F.softmax(routing / np.sqrt(d_k), -1)
    return torch.matmul(routing, values)


class MultiHeadQKVAttention(nn.Module):
    """Multi-head version of Transformer-like attention."""

    def __init__(self, d_k, d_v, n_heads):
        super().__init__()
        self.d_k = d_k
        self.d_v = d_v
        self.n_heads = n_heads
        d_k_p = int(math.ceil(d_k / n_heads)) * n_heads
        d_v_p = int(math.ceil(d_v / n_heads)) * n_heads
        self.q_projector = nn.Linear(d_k, d_k_p)
        self.k_projector = nn.Linear(d_k, d_k_p)
        self.v_projector = nn.Linear(d_v, d_v_p)
        self.o_projector = nn.Linear(d_v_p, d_v)

    def forward(self, queries, keys, values, presence=None):
        """
        Multi-head transformer-like self-attention.

        Args:
          queries: Tensor of shape [B, N, d_k].
          keys: Tensor of shape [B, M, d_k].
          values: : Tensor of shape [B, M, d_v].
          presence: None or tensor of shape [B, M].

        Returns:
          Tensor of shape [B, N, d_v]
        """
        assert queries.shape[2] == keys.shape[2]
        assert keys.shape[1] == values.shape[1]
        if presence is not None:
            assert values.shape[:2] == presence.shape
        B, N, _d_k = queries.shape
        M, _d_v = values.shape[1:]
        H = self.n_heads
        q_p = self.q_projector(queries)
        k_p = self.k_projector(keys)
        v_p = self.v_projector(values)
        del queries, keys, values
        q = q_p.view(B, N, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
            B, N, -1)
        k = k_p.view(B, M, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
            B, M, -1)
        v = v_p.view(B, M, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
            B, M, -1)
        if presence is not None:
            presence = presence.repeat(self.n_heads, 1)
        o = qkv_attention(q, k, v, presence)
        o = o.view(H, B, N, -1).permute(1, 2, 0, 3).contiguous().view(B, N, -1)
        return self.o_projector(o)


class MAB(nn.Module):

    def __init__(self, d, n_heads, layer_norm=False):
        super().__init__()
        self.layer_norm = layer_norm
        self.mqkv = MultiHeadQKVAttention(d_k=d, d_v=d, n_heads=n_heads)
        if layer_norm:
            self.ln0 = nn.LayerNorm(d)
            self.ln1 = nn.LayerNorm(d)
        self.fc = nn.Linear(d, d)

    def forward(self, queries, keys, presence=None):
        h = self.mqkv(queries, keys, keys, presence)
        h = h + queries
        if presence is not None:
            assert presence.shape[1] == queries.shape[1] == keys.shape[1]
            h = h * presence.unsqueeze(-1)
        if self.layer_norm:
            h = self.ln0(h)
        h = h + F.relu(self.fc(h))
        if self.layer_norm:
            h = self.ln1(h)
        return h


class PMANew(nn.Module):

    def __init__(self, d, n_heads, n_seeds, layer_norm=False):
        super().__init__()
        self.mab = MAB(d=d, n_heads=n_heads, layer_norm=layer_norm)
        self.S = nn.Parameter(torch.zeros(1, n_seeds, d), requires_grad=True)
        with torch.no_grad():
            nn.init.xavier_uniform_(self.S)

    def forward(self, input_0):
        primals_2 = self.S
        primals_3 = self.mab.mqkv.q_projector.weight
        primals_4 = self.mab.mqkv.q_projector.bias
        primals_5 = self.mab.mqkv.k_projector.weight
        primals_6 = self.mab.mqkv.k_projector.bias
        primals_7 = self.mab.mqkv.v_projector.weight
        primals_8 = self.mab.mqkv.v_projector.bias
        primals_9 = self.mab.mqkv.o_projector.weight
        primals_10 = self.mab.mqkv.o_projector.bias
        primals_11 = self.mab.fc.weight
        primals_12 = self.mab.fc.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12])
        return output[0]


Measured latency_ms: 0.150483