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 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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    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
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_tanh_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x2 = xindex // 8192 % 4
    x3 = xindex // 32768
    x4 = xindex % 8192
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 512 * x2), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x4 + 8192 * x3), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + x5, tmp3, None)


@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 512), (512, 1))
    assert_size_stride(primals_3, (512, 512), (512, 1))
    assert_size_stride(primals_4, (512, 4), (4, 1))
    assert_size_stride(primals_5, (1, 512), (512, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_3, (512, 
            512), (1, 512), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_1, buf1, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 512), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 16, 512), (32768, 8192, 512, 1),
            torch.float32)
        triton_poi_fused_add_tanh_1[grid(131072)](buf0, buf2, buf3, 131072,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del buf0
        del buf2
        buf4 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (256, 512), (512, 1), 0),
            reinterpret_tensor(primals_5, (512, 1), (1, 512), 0), out=buf4)
        buf7 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_per_fused__softmax_2[grid(16)](buf4, buf7, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf7, reinterpret_tensor(primals_1, (4, 16, 4),
            (64, 1, 16), 0), out=buf8)
    return buf8, primals_2, reinterpret_tensor(buf1, (64, 4), (4, 1), 0
        ), buf3, buf7, reinterpret_tensor(primals_1, (4, 4, 16), (64, 16, 1), 0
        ), primals_5, primals_3


def constant_init(module, val, bias=0):
    nn.init.constant_(module.weight, val)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def kaiming_init(module, a=0, is_rnn=False, mode='fan_in', nonlinearity=
    'leaky_relu', bias=0, distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        if is_rnn:
            for name, param in module.named_parameters():
                if 'bias' in name:
                    nn.init.constant_(param, bias)
                elif 'weight' in name:
                    nn.init.kaiming_uniform_(param, a=a, mode=mode,
                        nonlinearity=nonlinearity)
        else:
            nn.init.kaiming_uniform_(module.weight, a=a, mode=mode,
                nonlinearity=nonlinearity)
    elif is_rnn:
        for name, param in module.named_parameters():
            if 'bias' in name:
                nn.init.constant_(param, bias)
            elif 'weight' in name:
                nn.init.kaiming_normal_(param, a=a, mode=mode, nonlinearity
                    =nonlinearity)
    else:
        nn.init.kaiming_normal_(module.weight, a=a, mode=mode, nonlinearity
            =nonlinearity)
    if not is_rnn and hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def xavier_init(module, gain=1, bias=0, distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        nn.init.xavier_uniform_(module.weight, gain=gain)
    else:
        nn.init.xavier_normal_(module.weight, gain=gain)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def init_weights(modules):
    for m in modules:
        if isinstance(m, nn.Conv2d):
            kaiming_init(m)
        elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
            constant_init(m, 1)
        elif isinstance(m, nn.Linear):
            xavier_init(m)
        elif isinstance(m, (nn.LSTM, nn.LSTMCell)):
            kaiming_init(m, is_rnn=True)


class PVABlockNew(nn.Module):

    def __init__(self, num_steps, in_channels, embedding_channels=512,
        inner_channels=512):
        super(PVABlockNew, self).__init__()
        self.num_steps = num_steps
        self.in_channels = in_channels
        self.inner_channels = inner_channels
        self.embedding_channels = embedding_channels
        self.order_embeddings = nn.Parameter(torch.randn(self.num_steps,
            self.embedding_channels), requires_grad=True)
        self.v_linear = nn.Linear(self.in_channels, self.inner_channels,
            bias=False)
        self.o_linear = nn.Linear(self.embedding_channels, self.
            inner_channels, bias=False)
        self.e_linear = nn.Linear(self.inner_channels, 1, bias=False)
        init_weights(self.modules())

    def forward(self, input_0):
        primals_2 = self.order_embeddings
        primals_4 = self.v_linear.weight
        primals_3 = self.o_linear.weight
        primals_5 = self.e_linear.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.134571