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
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_cat_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 + 4 * x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_1(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 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_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
    tmp0 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = 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,), (1,))
    assert_size_stride(primals_4, (4, 12), (12, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 4), (4, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        buf1 = reinterpret_tensor(buf6, (4, 4), (12, 1), 0)
        extern_kernels.addmm(primals_3, buf0, reinterpret_tensor(primals_2,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        buf2 = buf0
        del buf0
        triton_poi_fused_cat_1[grid(16)](primals_1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf6, (4, 4), (12, 1), 4)
        extern_kernels.addmm(primals_3, buf2, reinterpret_tensor(primals_2,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        buf4 = buf2
        del buf2
        triton_poi_fused_cat_2[grid(16)](primals_1, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf6, (4, 4), (12, 1), 8)
        extern_kernels.addmm(primals_3, buf4, reinterpret_tensor(primals_2,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_2
        del primals_3
        buf7 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        triton_poi_fused_relu_3[grid(48)](buf6, buf7, 48, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf1
        del buf3
        del buf5
        del buf6
        buf8 = buf4
        del buf4
        extern_kernels.mm(buf7, reinterpret_tensor(primals_4, (12, 4), (1, 
            12), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_4[grid(16)](buf9, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_6, (4, 4), (1, 4
            ), 0), out=buf10)
        buf11 = buf10
        del buf10
        triton_poi_fused_relu_4[grid(16)](buf11, primals_7, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_7
        buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf11, reinterpret_tensor(primals_8,
            (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf13)
        del primals_9
    return buf13, reinterpret_tensor(primals_1, (4, 4), (16, 4), 0
        ), reinterpret_tensor(primals_1, (4, 4), (16, 4), 1
        ), reinterpret_tensor(primals_1, (4, 4), (16, 4), 2
        ), buf7, buf9, buf11, primals_8, primals_6, primals_4


class MlpNetMNew(nn.Module):
    """Implements a simple fully connected mlp network."""

    def __init__(self, sa_dim, n_agents, hidden_size, agent_id=0,
        agent_shuffle='none'):
        super(MlpNetMNew, self).__init__()
        self.linear1 = nn.Linear(sa_dim, hidden_size)
        self.linear2 = nn.Linear(hidden_size * 3, hidden_size)
        self.linear3 = nn.Linear(hidden_size, hidden_size)
        self.V = nn.Linear(hidden_size, 1)
        self.V.weight.data.mul_(0.1)
        self.V.bias.data.mul_(0.1)
        self.n_agents = n_agents
        self.agent_id = agent_id
        self.agent_shuffle = agent_shuffle

    def forward(self, input_0):
        primals_2 = self.linear1.weight
        primals_3 = self.linear1.bias
        primals_4 = self.linear2.weight
        primals_5 = self.linear2.bias
        primals_6 = self.linear3.weight
        primals_7 = self.linear3.bias
        primals_8 = self.V.weight
        primals_9 = self.V.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])
        return output[0]


Measured latency_ms: 0.051004