# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/gx/cgxa3ynix245ic4ukdndmwpojaikvex2djgvdniumettuh76k6xs.py
# Topologically Sorted Source Nodes: [renorm, w], Original ATen: [aten.renorm, aten.mul]
# Source node to ATen node mapping:
#   renorm => add, full_default, gt, mul, mul_1, pow_1, pow_2, reciprocal, sum_1, where
#   w => mul_2
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [0], True), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%pow_2, 1e-05), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_2, 1e-07), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1e-05), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %full_default), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %where), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, 100000.0), kwargs = {})
triton_poi_fused_mul_renorm_0 = async_compile.triton('triton_poi_fused_mul_renorm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_renorm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_renorm_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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-05
    tmp14 = tmp12 > tmp13
    tmp15 = 1e-07
    tmp16 = tmp12 + tmp15
    tmp17 = tl.full([1], 1, tl.int32)
    tmp18 = tmp17 / tmp16
    tmp19 = tmp18 * tmp13
    tmp20 = 1.0
    tmp21 = tl.where(tmp14, tmp19, tmp20)
    tmp22 = tmp0 * tmp21
    tmp23 = 100000.0
    tmp24 = tmp22 * tmp23
    tl.store(out_ptr0 + (x2), tmp24, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/q2/cq2ynu7pk3y34gqjnwjbvmojd33ymfglb7cwfysube6dasadwaya.py
# Topologically Sorted Source Nodes: [cos_theta, cos_theta_1], Original ATen: [aten.div, aten.clamp, aten.ge, aten.le, aten.logical_and]
# Source node to ATen node mapping:
#   cos_theta => div
#   cos_theta_1 => clamp_max, clamp_min
# Graph fragment:
#   %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm, %view), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%div, -1), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%div, -1), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%div, 1), kwargs = {})
#   %logical_and : [num_users=1] = call_function[target=torch.ops.aten.logical_and.default](args = (%ge, %le), kwargs = {})
triton_poi_fused_clamp_div_ge_le_logical_and_1 = async_compile.triton('triton_poi_fused_clamp_div_ge_le_logical_and_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_div_ge_le_logical_and_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_div_ge_le_logical_and_1(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = tmp0 / tmp12
    tmp14 = -1.0
    tmp15 = triton_helpers.maximum(tmp13, tmp14)
    tmp16 = 1.0
    tmp17 = triton_helpers.minimum(tmp15, tmp16)
    tmp18 = tmp13 >= tmp14
    tmp19 = tmp13 <= tmp16
    tmp20 = tmp18 & tmp19
    tl.store(out_ptr0 + (x2), tmp17, xmask)
    tl.store(out_ptr1 + (x2), tmp20, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [renorm, w], Original ATen: [aten.renorm, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_renorm_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [renorm, w, inner_wx], Original ATen: [aten.renorm, aten.mul, aten.mm]
        extern_kernels.mm(primals_2, buf0, out=buf1)
        buf2 = buf1; del buf1  # reuse
        buf3 = buf0; del buf0  # reuse
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [cos_theta, cos_theta_1], Original ATen: [aten.div, aten.clamp, aten.ge, aten.le, aten.logical_and]
        triton_poi_fused_clamp_div_ge_le_logical_and_1.run(buf2, primals_2, buf3, buf4, 16, grid=grid(16), stream=stream0)
        del buf2
    return (buf3, primals_1, primals_2, buf4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as torch_nn
from torch.nn import Parameter
import torch.utils


class P2SActivationLayer(torch_nn.Module):
    """ Output layer that produces cos	heta between activation vector x
    and class vector w_j
    in_dim:     dimension of input feature vectors
    output_dim: dimension of output feature vectors 
                (i.e., number of classes)
    
    
    Usage example:
      batchsize = 64
      input_dim = 10
      class_num = 5
      l_layer = P2SActivationLayer(input_dim, class_num)
      l_loss = P2SGradLoss()
      data = torch.rand(batchsize, input_dim, requires_grad=True)
      target = (torch.rand(batchsize) * class_num).clamp(0, class_num-1)
      target = target.to(torch.long)
      scores = l_layer(data)
      loss = l_loss(scores, target)
      loss.backward()
    """

    def __init__(self, in_dim, out_dim):
        super(P2SActivationLayer, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.weight = Parameter(torch.Tensor(in_dim, out_dim))
        self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-05).mul_(100000.0)
        return

    def forward(self, input_feat):
        """
        Compute P2sgrad activation
        
        input:
        ------
          input_feat: tensor (batchsize, input_dim)
        output:
        -------
          tensor (batchsize, output_dim)
          
        """
        w = self.weight.renorm(2, 1, 1e-05).mul(100000.0)
        x_modulus = input_feat.pow(2).sum(1).pow(0.5)
        w.pow(2).sum(0).pow(0.5)
        inner_wx = input_feat.mm(w)
        cos_theta = inner_wx / x_modulus.view(-1, 1)
        cos_theta = cos_theta.clamp(-1, 1)
        return cos_theta


def get_inputs():
    return [torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'in_dim': 4, 'out_dim': 4}]

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 torch_nn
from torch.nn import Parameter
import torch.utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_mul_renorm_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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-05
    tmp14 = tmp12 > tmp13
    tmp15 = 1e-07
    tmp16 = tmp12 + tmp15
    tmp17 = tl.full([1], 1, tl.int32)
    tmp18 = tmp17 / tmp16
    tmp19 = tmp18 * tmp13
    tmp20 = 1.0
    tmp21 = tl.where(tmp14, tmp19, tmp20)
    tmp22 = tmp0 * tmp21
    tmp23 = 100000.0
    tmp24 = tmp22 * tmp23
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused_clamp_div_ge_le_logical_and_1(in_out_ptr0, in_ptr0,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = tmp0 / tmp12
    tmp14 = -1.0
    tmp15 = triton_helpers.maximum(tmp13, tmp14)
    tmp16 = 1.0
    tmp17 = triton_helpers.minimum(tmp15, tmp16)
    tmp18 = tmp13 >= tmp14
    tmp19 = tmp13 <= tmp16
    tmp20 = tmp18 & tmp19
    tl.store(out_ptr0 + x2, tmp17, xmask)
    tl.store(out_ptr1 + x2, tmp20, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 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_mul_renorm_0[grid(16)](primals_1, buf0, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, buf0, out=buf1)
        buf2 = buf1
        del buf1
        buf3 = buf0
        del buf0
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_clamp_div_ge_le_logical_and_1[grid(16)](buf2,
            primals_2, buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf2
    return buf3, primals_1, primals_2, buf4


class P2SActivationLayerNew(torch_nn.Module):
    """ Output layer that produces cos	heta between activation vector x
    and class vector w_j
    in_dim:     dimension of input feature vectors
    output_dim: dimension of output feature vectors 
                (i.e., number of classes)
    
    
    Usage example:
      batchsize = 64
      input_dim = 10
      class_num = 5
      l_layer = P2SActivationLayer(input_dim, class_num)
      l_loss = P2SGradLoss()
      data = torch.rand(batchsize, input_dim, requires_grad=True)
      target = (torch.rand(batchsize) * class_num).clamp(0, class_num-1)
      target = target.to(torch.long)
      scores = l_layer(data)
      loss = l_loss(scores, target)
      loss.backward()
    """

    def __init__(self, in_dim, out_dim):
        super(P2SActivationLayerNew, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.weight = Parameter(torch.Tensor(in_dim, out_dim))
        self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-05).mul_(100000.0)
        return

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