# 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/bs/cbsjjiba7tb74nqp4kgxnwhguhyb74na52swveq2ubkqgsjdda3o.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.linalg_vector_norm]
# Source node to ATen node mapping:
#   x => pow_1, sum_1
# 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, [1], True), kwargs = {})
triton_red_fused_linalg_vector_norm_0 = async_compile.triton('triton_red_fused_linalg_vector_norm_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.reduction(
    size_hints=[16384, 512],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_linalg_vector_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_red_fused_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 16384
    rnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 4096
    x1 = (xindex // 4096)
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (x0 + (4096*r2) + (2097152*x1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tl.store(out_ptr0 + (x3), tmp3, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2f/c2f3ux3klo77lszm3nvopysyqiori3pmw2rvdwykye3peprglsni.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   x => div
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %expand), kwargs = {})
triton_poi_fused_div_1 = async_compile.triton('triton_poi_fused_div_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=[2048, 4096], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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_div_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_div_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 2048
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y1 = (yindex // 512)
    y0 = yindex % 512
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + (4096*y1)), None, eviction_policy='evict_last')
    tmp2 = libdevice.sqrt(tmp1)
    tmp3 = 1e-12
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 / tmp4
    tl.store(out_ptr0 + (y0 + (512*x2) + (2097152*y1)), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zi/czipfmhzrmxtd7w2efbevwasjwg2kxu2mexohgjrxt6ftf2fzdpw.py
# Topologically Sorted Source Nodes: [normalize_1], Original ATen: [aten.linalg_vector_norm, aten.div]
# Source node to ATen node mapping:
#   normalize_1 => div_1, pow_3, pow_4, sum_2
# Graph fragment:
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [1], True), kwargs = {})
#   %pow_4 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_2, %expand_1), kwargs = {})
triton_per_fused_div_linalg_vector_norm_2 = async_compile.triton('triton_per_fused_div_linalg_vector_norm_2', '''
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.persistent_reduction(
    size_hints=[4, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_linalg_vector_norm_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_div_linalg_vector_norm_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, rnumel):
    xnumel = 4
    XBLOCK: tl.constexpr = 1
    rnumel = 512
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (512*x0)), None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = 1e-12
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp5, None)
    tl.store(out_ptr0 + (r1 + (512*x0)), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4p/c4pdvfoceuyvd32bhypazb7cirwblteciqlpkarnapamsshlu3hk.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 10.0), kwargs = {})
triton_poi_fused_mul_3 = async_compile.triton('triton_poi_fused_mul_3', '''
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, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16384*y1)), ymask, eviction_policy='evict_last')
    tmp1 = 10.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2 + (4096*y3)), tmp2, ymask)
''', 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, 512, 64, 64), (2097152, 4096, 64, 1))
    assert_size_stride(primals_2, (4, 512, 1, 1), (512, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 64, 64), (4096, 16384, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.linalg_vector_norm]
        stream0 = get_raw_stream(0)
        triton_red_fused_linalg_vector_norm_0.run(primals_1, buf0, 16384, 512, grid=grid(16384), stream=stream0)
        buf1 = empty_strided_cuda((4, 512, 64, 64), (2097152, 1, 32768, 512), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.div]
        triton_poi_fused_div_1.run(primals_1, buf0, buf1, 2048, 4096, grid=grid(2048, 4096), stream=stream0)
        del buf0
        del primals_1
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf3 = reinterpret_tensor(buf2, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf2  # reuse
        buf4 = empty_strided_cuda((4, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [normalize_1], Original ATen: [aten.linalg_vector_norm, aten.div]
        triton_per_fused_div_linalg_vector_norm_2.run(buf3, primals_2, buf4, 4, 512, grid=grid(4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf1, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf6 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        triton_poi_fused_mul_3.run(buf5, buf6, 16, 4096, grid=grid(16, 4096), stream=stream0)
        del buf5
    return (buf6, primals_2, buf1, buf3, 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, 512, 64, 64), (2097152, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 512, 1, 1), (512, 1, 1, 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
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
import torch.utils.data
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_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 4096
    x1 = xindex // 4096
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (x0 + 4096 * r2 + 2097152 * x1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp3, None)


@triton.jit
def triton_poi_fused_div_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y1 = yindex // 512
    y0 = yindex % 512
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4096 * y1), None, eviction_policy=
        'evict_last')
    tmp2 = libdevice.sqrt(tmp1)
    tmp3 = 1e-12
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 / tmp4
    tl.store(out_ptr0 + (y0 + 512 * x2 + 2097152 * y1), tmp5, None)


@triton.jit
def triton_per_fused_div_linalg_vector_norm_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 512 * x0), None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = 1e-12
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp5, None)
    tl.store(out_ptr0 + (r1 + 512 * x0), tmp8, None)


@triton.jit
def triton_poi_fused_mul_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16384 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = 10.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 512, 64, 64), (2097152, 4096, 64, 1))
    assert_size_stride(primals_2, (4, 512, 1, 1), (512, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 64, 64), (4096, 16384, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_red_fused_linalg_vector_norm_0[grid(16384)](primals_1, buf0,
            16384, 512, XBLOCK=64, RBLOCK=8, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 512, 64, 64), (2097152, 1, 32768, 512
            ), torch.float32)
        triton_poi_fused_div_1[grid(2048, 4096)](primals_1, buf0, buf1, 
            2048, 4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del buf0
        del primals_1
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf3 = reinterpret_tensor(buf2, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf2
        buf4 = empty_strided_cuda((4, 512, 1, 1), (512, 1, 512, 512), torch
            .float32)
        triton_per_fused_div_linalg_vector_norm_2[grid(4)](buf3, primals_2,
            buf4, 4, 512, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf1, buf4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf6 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_mul_3[grid(16, 4096)](buf5, buf6, 16, 4096, XBLOCK
            =64, YBLOCK=16, num_warps=4, num_stages=1)
        del buf5
    return buf6, primals_2, buf1, buf3, buf4


class CosineClassifierNew(nn.Module):

    def __init__(self, classes, channels=512):
        super().__init__()
        self.channels = channels
        self.cls = nn.Conv2d(channels, classes, 1, bias=False)
        self.scaler = 10.0

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