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_gt_0(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_where_1(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp1 + tmp2
    tmp4 = -8999999815811072.0
    tmp5 = tl.where(tmp0, tmp3, tmp4)
    tmp9 = tmp7 + tmp8
    tmp10 = tl.where(tmp6, tmp9, tmp4)
    tmp11 = triton_helpers.maximum(tmp5, tmp10)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.where(tmp12, tmp15, tmp4)
    tmp17 = triton_helpers.maximum(tmp11, tmp16)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.where(tmp18, tmp21, tmp4)
    tmp23 = triton_helpers.maximum(tmp17, tmp22)
    tmp24 = tmp5 - tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp10 - tmp23
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp16 - tmp23
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tmp32 = tmp22 - tmp23
    tmp33 = tl_math.exp(tmp32)
    tmp34 = tmp31 + tmp33
    tl.store(out_ptr0 + x0, tmp23, xmask)
    tl.store(out_ptr1 + x0, tmp34, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_where_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, 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).to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x2, xmask)
    tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = -8999999815811072.0
    tmp5 = tl.where(tmp0, tmp3, tmp4)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_elu_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf0, (16, 4, 4), (16, 1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_gt_0[grid(256)](primals_4, buf2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_mul_where_1[grid(64)](buf2, buf1,
            primals_3, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused__softmax_mul_where_2[grid(256)](buf5, buf2,
            primals_3, buf3, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del buf4
        del primals_3
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_elu_3[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf7, buf0, buf2, buf5, buf6, reinterpret_tensor(primals_2, (4, 
        64), (1, 4), 0)


def module_test_print(var_input, var_inmed, var_ouput):
    for var in (var_input, var_inmed, var_ouput):
        None
        for key, value in var.items():
            None
            None


class GraphAttentionLayerNew(nn.Module):

    def __init__(self, dim_input, dim_output, dropout=0.0,
        negative_slope_LeakyRelu=0.01, module_test=False):
        super(GraphAttentionLayerNew, self).__init__()
        self.dim_input = dim_input
        self.dim_output = dim_output
        self.dropout = dropout
        self.W = nn.Parameter(torch.empty(size=(dim_input, dim_output)))
        nn.init.xavier_uniform_(self.W.data, gain=nn.init.calculate_gain(
            'leaky_relu', negative_slope_LeakyRelu))
        self.leakyrelu = nn.LeakyReLU(negative_slope_LeakyRelu)
        self.module_test = module_test

    def _attention_score(self, x, y=None):
        return torch.matmul(x, x.transpose(-2, -1))

    def __repr__(self):
        return '{}({}->{})'.format(self.__class__.__name__, self.dim_input,
            self.dim_output)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.W
        primals_2 = input_0
        primals_3 = input_1
        primals_4 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.080825