# 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/yu/cyuwtcvlyje5oivweblg6md6zleesmplamupxz3a54qd5gw5p6wj.py
# Topologically Sorted Source Nodes: [w2, ger, clamp, cosine, scatter_, mul, sub, output], Original ATen: [aten.linalg_vector_norm, aten.mul, aten.clamp, aten.div, aten.scatter, aten.sub]
# Source node to ATen node mapping:
#   clamp => clamp_min
#   cosine => div
#   ger => mul
#   mul => mul_1
#   output => mul_2
#   scatter_ => scatter_upon_const_tensor
#   sub => sub
#   w2 => pow_3, pow_4, sum_2
# Graph fragment:
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [1]), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %pow_4), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul, 1e-08), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm, %clamp_min), kwargs = {})
#   %scatter_upon_const_tensor : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.scatter_upon_const_tensor](args = (), kwargs = {shape: [4, 4], background_val: 0, dtype: torch.float32, dim: 1, selector: %view_1, val: 1.0})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%scatter_upon_const_tensor, 0.2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 7.0), kwargs = {})
triton_poi_fused_clamp_div_linalg_vector_norm_mul_scatter_sub_0 = async_compile.triton('triton_poi_fused_clamp_div_linalg_vector_norm_mul_scatter_sub_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: '*fp32', 3: '*i64', 4: '*fp32', 5: '*fp32', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_div_linalg_vector_norm_mul_scatter_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, '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_linalg_vector_norm_mul_scatter_sub_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr3 + (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)
    tmp14 = tmp13 * tmp13
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp12 * tmp24
    tmp26 = 1e-08
    tmp27 = triton_helpers.maximum(tmp25, tmp26)
    tmp28 = tmp0 / tmp27
    tmp30 = x0
    tmp31 = tmp29 == tmp30
    tmp32 = 1.0
    tmp33 = 0.0
    tmp34 = tl.where(tmp31, tmp32, tmp33)
    tmp35 = 0.2
    tmp36 = tmp34 * tmp35
    tmp37 = tmp28 - tmp36
    tmp38 = 7.0
    tmp39 = tmp37 * tmp38
    tl.store(out_ptr0 + (x2), tmp28, xmask)
    tl.store(out_ptr1 + (x2), tmp39, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (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: [ip], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [w2, ger, clamp, cosine, scatter_, mul, sub, output], Original ATen: [aten.linalg_vector_norm, aten.mul, aten.clamp, aten.div, aten.scatter, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_div_linalg_vector_norm_mul_scatter_sub_0.run(buf0, primals_2, primals_1, primals_3, buf1, buf2, 16, grid=grid(16), stream=stream0)
        del primals_3
    return (buf2, buf1, primals_1, primals_2, buf0, )


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)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
from torch import nn


def cosine_sim(x1, x2, dim=1, eps=1e-08):
    ip = torch.mm(x1, x2.t())
    w1 = torch.norm(x1, 2, dim)
    w2 = torch.norm(x2, 2, dim)
    return ip / torch.ger(w1, w2).clamp(min=eps)


class LargeMarginCosLoss(nn.Module):
    """
    CosFace: Large Margin Cosine Loss for Deep Face Recognition. CVPR2018
    """

    def __init__(self, feature_size, class_num, s=7.0, m=0.2):
        super(LargeMarginCosLoss, self).__init__()
        self.feature_size = feature_size
        self.class_num = class_num
        self.s = s
        self.m = m
        self.weight = nn.Parameter(torch.randn(class_num, feature_size))
        nn.init.xavier_uniform_(self.weight)

    def forward(self, x, label):
        cosine = cosine_sim(x, self.weight)
        one_hot = torch.zeros_like(cosine)
        one_hot.scatter_(1, label.view(-1, 1), 1.0)
        output = self.s * (cosine - one_hot * self.m)
        return output, cosine


def get_inputs():
    return [torch.rand([4, 4]), torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'feature_size': 4, 'class_num': 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
from torch import 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_clamp_div_linalg_vector_norm_mul_scatter_sub_0(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr3 + 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)
    tmp14 = tmp13 * tmp13
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp12 * tmp24
    tmp26 = 1e-08
    tmp27 = triton_helpers.maximum(tmp25, tmp26)
    tmp28 = tmp0 / tmp27
    tmp30 = x0
    tmp31 = tmp29 == tmp30
    tmp32 = 1.0
    tmp33 = 0.0
    tmp34 = tl.where(tmp31, tmp32, tmp33)
    tmp35 = 0.2
    tmp36 = tmp34 * tmp35
    tmp37 = tmp28 - tmp36
    tmp38 = 7.0
    tmp39 = tmp37 * tmp38
    tl.store(out_ptr0 + x2, tmp28, xmask)
    tl.store(out_ptr1 + x2, tmp39, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_div_linalg_vector_norm_mul_scatter_sub_0[grid
            (16)](buf0, primals_2, primals_1, primals_3, buf1, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return buf2, buf1, primals_1, primals_2, buf0


def cosine_sim(x1, x2, dim=1, eps=1e-08):
    ip = torch.mm(x1, x2.t())
    w1 = torch.norm(x1, 2, dim)
    w2 = torch.norm(x2, 2, dim)
    return ip / torch.ger(w1, w2).clamp(min=eps)


class LargeMarginCosLossNew(nn.Module):
    """
    CosFace: Large Margin Cosine Loss for Deep Face Recognition. CVPR2018
    """

    def __init__(self, feature_size, class_num, s=7.0, m=0.2):
        super(LargeMarginCosLossNew, self).__init__()
        self.feature_size = feature_size
        self.class_num = class_num
        self.s = s
        self.m = m
        self.weight = nn.Parameter(torch.randn(class_num, feature_size))
        nn.init.xavier_uniform_(self.weight)

    def forward(self, input_0, input_1):
        primals_1 = self.weight
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]
