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
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_red_fused_div_linalg_vector_norm_relu_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 64
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask & xmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tmp5 = libdevice.sqrt(tmp3)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp6 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp7 = 1e-12
        tmp8 = triton_helpers.maximum(tmp5, tmp7)
        tmp9 = tmp6 / tmp8
        tmp10 = tl.full([1, 1], 0, tl.int32)
        tmp11 = triton_helpers.maximum(tmp10, tmp9)
        tl.store(out_ptr0 + (r1 + 4096 * x0), tmp11, rmask & xmask)


@triton.jit
def triton_per_fused_div_linalg_vector_norm_tanh_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = 1e-12
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 / tmp8
    tmp10 = libdevice.tanh(tmp9)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4096, 4), (4, 1))
    assert_size_stride(primals_3, (4096,), (1,))
    assert_size_stride(primals_4, (4096, 4096), (4096, 1))
    assert_size_stride(primals_5, (4096,), (1,))
    assert_size_stride(primals_6, (16, 4096), (4096, 1))
    assert_size_stride(primals_7, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4096), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((64, 1), (1, 64), torch.float32)
        buf2 = reinterpret_tensor(buf1, (64, 1), (1, 1), 0)
        del buf1
        buf3 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        get_raw_stream(0)
        triton_red_fused_div_linalg_vector_norm_relu_0[grid(64)](buf2, buf0,
            buf3, 64, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        buf4 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf3, reinterpret_tensor(primals_4,
            (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf4)
        del primals_5
        buf5 = empty_strided_cuda((64, 1), (1, 64), torch.float32)
        buf6 = reinterpret_tensor(buf5, (64, 1), (1, 1), 0)
        del buf5
        buf7 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        triton_red_fused_div_linalg_vector_norm_relu_0[grid(64)](buf6, buf4,
            buf7, 64, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        buf8 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf7, reinterpret_tensor(primals_6,
            (4096, 16), (1, 4096), 0), alpha=1, beta=1, out=buf8)
        del primals_7
        buf9 = empty_strided_cuda((64, 1), (1, 64), torch.float32)
        buf10 = reinterpret_tensor(buf9, (64, 1), (1, 1), 0)
        del buf9
        buf11 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        triton_per_fused_div_linalg_vector_norm_tanh_1[grid(64)](buf10,
            buf8, buf11, 64, 16, XBLOCK=8, num_warps=2, num_stages=1)
    return (buf11, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf0,
        buf2, buf3, buf4, buf6, buf7, buf8, buf10, buf11, primals_6, primals_4)


def _get_fc_layer(in_cn, out_cn):
    x = nn.Linear(in_cn, out_cn)
    x.bias.data.zero_()
    nn.init.normal_(x.weight, 0.0, 0.001)
    return x


class PoseRegHeadNew(nn.Module):

    def __init__(self, dim_in, dim_out, num_units=4096):
        super(PoseRegHeadNew, self).__init__()
        self.dim_in = dim_in
        self.dim_out = dim_out * 4
        self.num_units = num_units
        self.poses_fc1 = _get_fc_layer(self.dim_in, num_units)
        self.poses_fc2 = _get_fc_layer(num_units, num_units)
        self.poses_fc3 = _get_fc_layer(num_units, self.dim_out)

    def forward(self, input_0):
        primals_2 = self.poses_fc1.weight
        primals_3 = self.poses_fc1.bias
        primals_4 = self.poses_fc2.weight
        primals_5 = self.poses_fc2.bias
        primals_6 = self.poses_fc3.weight
        primals_7 = self.poses_fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.168129