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
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__to_copy_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.int64)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_embedding_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * tmp4), xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (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,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.int64)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[grid(16)](primals_1, buf0, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_embedding_1[grid(64)](buf0, primals_2, buf1, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_avg_pool2d_2[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf2, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf3)
        del primals_4
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, buf3, reinterpret_tensor(primals_5,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_6
    return buf4, buf0, buf1, reinterpret_tensor(buf2, (4, 4), (4, 1), 0
        ), buf3, primals_5, primals_3


class WordAVGModelNew(nn.Module):

    def __init__(self, vocab_size, embedding_dim, output_dim, dropout=0.5):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.fc1 = nn.Linear(embedding_dim, int((embedding_dim + output_dim
            ) / 2))
        self.fc2 = nn.Linear(int((embedding_dim + output_dim) / 2), output_dim)
        self.dropout = nn.Dropout(dropout)
    """ seq: [batch_size, seq_size]"""

    def forward(self, input_0):
        primals_1 = self.embedding.weight
        primals_2 = self.fc1.weight
        primals_4 = self.fc1.bias
        primals_3 = self.fc2.weight
        primals_6 = self.fc2.bias
        primals_5 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.058987