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_zeros_0(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
    tmp0 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(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 % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused__unsafe_view_clone_2(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 + (4 * x1 + 16 * (y0 // 4) + y0 % 4), xmask &
        ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, 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')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_zeros_0[grid(64)](buf0, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(64)](primals_1, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf2 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0,
            (16, 4), (1, 16), 0), reinterpret_tensor(buf1, (16, 4), (1, 16),
            0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), beta=0)
        del buf0
        del primals_2
        buf3 = buf2
        del buf2
        buf4 = reinterpret_tensor(buf1, (16, 4), (4, 1), 0)
        del buf1
        triton_poi_fused__unsafe_view_clone_2[grid(16, 4)](buf3, buf4, 16, 
            4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        extern_kernels.mm(buf4, primals_3, out=buf5)
        del primals_3
        buf6 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0)
        del buf5
        triton_poi_fused_add_3[grid(64)](buf6, primals_4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_4
    return buf6, reinterpret_tensor(buf4, (4, 16), (1, 4), 0)


class GraphConvNew(nn.Module):

    def __init__(self, input_dim, output_dim, add_self=False,
        normalize_embedding=False, dropout=0.0, bias=True):
        super(GraphConvNew, self).__init__()
        self.add_self = add_self
        self.dropout = dropout
        if dropout > 0.001:
            self.dropout_layer = nn.Dropout(p=dropout)
        self.normalize_embedding = normalize_embedding
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.weight = nn.Parameter(torch.FloatTensor(input_dim, output_dim))
        if bias:
            self.bias = nn.Parameter(torch.FloatTensor(output_dim))
        else:
            self.bias = None

    def forward(self, input_0, input_1):
        primals_2 = self.weight
        primals_4 = self.bias
        primals_1 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.072926