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 math as tl_math
import math
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
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_relu_threshold_backward_0(in_out_ptr0, 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
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_out_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, 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), (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, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf0
        buf10 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(64)](buf2,
            primals_3, buf10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        buf3 = reinterpret_tensor(buf1, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf1
        buf9 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(64)](buf3,
            primals_5, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf4
        triton_poi_fused__softmax_2[grid(64)](buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 
            0), primals_1, out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf7
        triton_poi_fused_add_3[grid(64)](buf8, primals_1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0
        ), primals_1, buf6, reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0), buf9, buf10


class SelfAttentionLayer(nn.Module):

    def __init__(self, elem_size, embd_size):
        super(SelfAttentionLayer, self).__init__()
        self.embd_size = embd_size
        self.query_lin = nn.Linear(elem_size, embd_size)
        self.key_lin = nn.Linear(elem_size, embd_size)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        if len(x.shape) == 3:
            x = x.unsqueeze(1)
        _N, _num_patches, _seq_size, elem_size = x.shape
        Q = F.relu(self.query_lin(x))
        K = F.relu(self.key_lin(x))
        attention_mat = torch.matmul(Q, K.permute(0, 1, 3, 2)) / math.sqrt(
            elem_size)
        attention_mat = F.softmax(attention_mat, dim=-1)
        new_values = torch.matmul(attention_mat, x)
        out = new_values
        out = x + out
        return out, attention_mat


class PatchedSelfAttentionLayerNew(nn.Module):

    def __init__(self, elem_size, embd_size, window_size, use_V=False):
        super(PatchedSelfAttentionLayerNew, self).__init__()
        self.sa_layer = SelfAttentionLayer(elem_size, embd_size)
        self.window_size = window_size
        self.embd_size = embd_size

    def forward(self, input_0):
        primals_2 = self.sa_layer.query_lin.weight
        primals_3 = self.sa_layer.query_lin.bias
        primals_4 = self.sa_layer.key_lin.weight
        primals_5 = self.sa_layer.key_lin.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]


Measured latency_ms: 0.095596