optim output code

`--> TORCH_LOGS="output_code" python optim_repro.py
[WARNING]:Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
[DEBUG]:Output code: 

# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long
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.codecache import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall

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
alloc_from_pool = torch.ops.inductor._alloc_from_pool
reinterpret_tensor = torch.ops.inductor._reinterpret_tensor
async_compile = AsyncCompile()


# kernel path: /tmp/torchinductor_mlazos/qw/cqwdta64dilqxthaf7r7oq6533jvl3wc6hr6slcetf5nno7l7mkg.py
# Source Nodes: [], Original ATen: []

triton_for_fused_0 = async_compile.triton('triton_', '''
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.foreach(
    num_warps=8,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32'}, '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), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_for_fused_0', 'backend_hash': '63937d058519033f995f0585a4aab6c8c8898fe6839dd14ce1536da9b902b160', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': 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_(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2):
    xpid = tl.program_id(0)
    XBLOCK: tl.constexpr = 1024
    if xpid >= 0 and xpid < 1:
        xpid_offset = xpid - 0
        xnumel = 1
        xoffset = xpid_offset * XBLOCK
        xindex = xoffset + tl.arange(0, XBLOCK)[:]
        xmask = xindex < xnumel
        tmp0 = tl.load(in_ptr0 + (0))
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
        tmp2 = 1.0
        tmp3 = tmp1 + tmp2
        tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp3, None)
    elif xpid >= 1 and xpid < 2:
        xpid_offset = xpid - 1
        xnumel = 1
        xoffset = xpid_offset * XBLOCK
        xindex = xoffset + tl.arange(0, XBLOCK)[:]
        xmask = xindex < xnumel
        tmp4 = tl.load(in_ptr1 + (0))
        tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
        tmp6 = 1.0
        tmp7 = tmp5 + tmp6
        tl.store(out_ptr1 + (tl.full([XBLOCK], 0, tl.int32)), tmp7, None)
    elif xpid >= 2 and xpid < 3:
        xpid_offset = xpid - 2
        xnumel = 1
        xoffset = xpid_offset * XBLOCK
        xindex = xoffset + tl.arange(0, XBLOCK)[:]
        xmask = xindex < xnumel
        tmp8 = tl.load(in_ptr2 + (0))
        tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
        tmp10 = 1.0
        tmp11 = tmp9 + tmp10
        tl.store(out_ptr2 + (tl.full([XBLOCK], 0, tl.int32)), tmp11, None)
    else:
        pass
''', device_str='cuda')

import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream


# kernel path: /tmp/torchinductor_mlazos/f6/cf6mqlvcaho5wytbkdyareia6v2d32yctobezzb3f2ttjj6mskmc.py
# Source Nodes: [], Original ATen: []

triton_for_fused_1 = async_compile.triton('triton_', '''
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.foreach(
    num_warps=8,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*fp32', 11: '*fp32', 12: '*fp32', 13: '*fp32', 14: '*fp32', 15: '*fp32', 16: '*fp32', 17: '*fp32', 18: '*fp32', 19: '*fp32', 20: '*fp32', 21: '*fp32', 22: '*fp32', 23: '*fp32'}, '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, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_for_fused_1', 'backend_hash': '63937d058519033f995f0585a4aab6c8c8898fe6839dd14ce1536da9b902b160', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': 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_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10, in_ptr11, in_ptr12, in_ptr13, in_ptr14, out_ptr1, out_ptr2, out_ptr3, out_ptr5, out_ptr6, out_ptr7, out_ptr9, out_ptr10, out_ptr11):
    xpid = tl.program_id(0)
    XBLOCK: tl.constexpr = 1024
    if xpid >= 0 and xpid < 1:
        xpid_offset = xpid - 0
        xnumel = 20
        xoffset = xpid_offset * XBLOCK
        xindex = xoffset + tl.arange(0, XBLOCK)[:]
        xmask = xindex < xnumel
        x0 = xindex
        tmp0 = tl.load(in_ptr0 + (x0), xmask)
        tmp3 = tl.load(in_ptr1 + (x0), xmask)
        tmp8 = tl.load(in_ptr2 + (x0), xmask)
        tmp13 = tl.load(in_ptr3 + (x0), xmask)
        tmp15 = tl.load(in_ptr4 + (0))
        tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
        tmp1 = 0.999
        tmp2 = tmp0 * tmp1
        tmp4 = tmp3 * tmp3
        tmp5 = 0.0010000000000000009
        tmp6 = tmp4 * tmp5
        tmp7 = tmp2 + tmp6
        tmp9 = tmp3 - tmp8
        tmp10 = 0.09999999999999998
        tmp11 = tmp9 * tmp10
        tmp12 = tmp8 + tmp11
        tmp14 = libdevice.sqrt(tmp7)
        tmp17 = libdevice.pow(tmp1, tmp16)
        tmp18 = 1.0
        tmp19 = tmp17 - tmp18
        tmp20 = -tmp19
        tmp21 = libdevice.sqrt(tmp20)
        tmp22 = tmp14 / tmp21
        tmp23 = 1e-08
        tmp24 = tmp22 + tmp23
        tmp25 = 0.9
        tmp26 = libdevice.pow(tmp25, tmp16)
        tmp27 = tmp26 - tmp18
        tmp28 = 1000.0
        tmp29 = tmp27 * tmp28
        tmp30 = 1 / tmp29
        tmp31 = tmp24 / tmp30
        tmp32 = tmp12 / tmp31
        tmp33 = tmp13 + tmp32
        tl.store(out_ptr1 + (x0), tmp12, xmask)
        tl.store(out_ptr2 + (x0), tmp33, xmask)
        tl.store(out_ptr3 + (x0), tmp7, xmask)
    elif xpid >= 1 and xpid < 2:
        xpid_offset = xpid - 1
        xnumel = 20
        xoffset = xpid_offset * XBLOCK
        xindex = xoffset + tl.arange(0, XBLOCK)[:]
        xmask = xindex < xnumel
        x1 = xindex
        tmp34 = tl.load(in_ptr5 + (x1), xmask)
        tmp37 = tl.load(in_ptr6 + (x1), xmask)
        tmp42 = tl.load(in_ptr7 + (x1), xmask)
        tmp47 = tl.load(in_ptr8 + (x1), xmask)
        tmp49 = tl.load(in_ptr9 + (0))
        tmp50 = tl.broadcast_to(tmp49, [XBLOCK])
        tmp35 = 0.999
        tmp36 = tmp34 * tmp35
        tmp38 = tmp37 * tmp37
        tmp39 = 0.0010000000000000009
        tmp40 = tmp38 * tmp39
        tmp41 = tmp36 + tmp40
        tmp43 = tmp37 - tmp42
        tmp44 = 0.09999999999999998
        tmp45 = tmp43 * tmp44
        tmp46 = tmp42 + tmp45
        tmp48 = libdevice.sqrt(tmp41)
        tmp51 = libdevice.pow(tmp35, tmp50)
        tmp52 = 1.0
        tmp53 = tmp51 - tmp52
        tmp54 = -tmp53
        tmp55 = libdevice.sqrt(tmp54)
        tmp56 = tmp48 / tmp55
        tmp57 = 1e-08
        tmp58 = tmp56 + tmp57
        tmp59 = 0.9
        tmp60 = libdevice.pow(tmp59, tmp50)
        tmp61 = tmp60 - tmp52
        tmp62 = 1000.0
        tmp63 = tmp61 * tmp62
        tmp64 = 1 / tmp63
        tmp65 = tmp58 / tmp64
        tmp66 = tmp46 / tmp65
        tmp67 = tmp47 + tmp66
        tl.store(out_ptr5 + (x1), tmp46, xmask)
        tl.store(out_ptr6 + (x1), tmp67, xmask)
        tl.store(out_ptr7 + (x1), tmp41, xmask)
    elif xpid >= 2 and xpid < 3:
        xpid_offset = xpid - 2
        xnumel = 20
        xoffset = xpid_offset * XBLOCK
        xindex = xoffset + tl.arange(0, XBLOCK)[:]
        xmask = xindex < xnumel
        x2 = xindex
        tmp68 = tl.load(in_ptr10 + (x2), xmask)
        tmp71 = tl.load(in_ptr11 + (x2), xmask)
        tmp76 = tl.load(in_ptr12 + (x2), xmask)
        tmp81 = tl.load(in_ptr13 + (x2), xmask)
        tmp83 = tl.load(in_ptr14 + (0))
        tmp84 = tl.broadcast_to(tmp83, [XBLOCK])
        tmp69 = 0.999
        tmp70 = tmp68 * tmp69
        tmp72 = tmp71 * tmp71
        tmp73 = 0.0010000000000000009
        tmp74 = tmp72 * tmp73
        tmp75 = tmp70 + tmp74
        tmp77 = tmp71 - tmp76
        tmp78 = 0.09999999999999998
        tmp79 = tmp77 * tmp78
        tmp80 = tmp76 + tmp79
        tmp82 = libdevice.sqrt(tmp75)
        tmp85 = libdevice.pow(tmp69, tmp84)
        tmp86 = 1.0
        tmp87 = tmp85 - tmp86
        tmp88 = -tmp87
        tmp89 = libdevice.sqrt(tmp88)
        tmp90 = tmp82 / tmp89
        tmp91 = 1e-08
        tmp92 = tmp90 + tmp91
        tmp93 = 0.9
        tmp94 = libdevice.pow(tmp93, tmp84)
        tmp95 = tmp94 - tmp86
        tmp96 = 1000.0
        tmp97 = tmp95 * tmp96
        tmp98 = 1 / tmp97
        tmp99 = tmp92 / tmp98
        tmp100 = tmp80 / tmp99
        tmp101 = tmp81 + tmp100
        tl.store(out_ptr9 + (x2), tmp80, xmask)
        tl.store(out_ptr10 + (x2), tmp101, xmask)
        tl.store(out_ptr11 + (x2), tmp75, xmask)
    else:
        pass
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 5), (5, 1))
    assert_size_stride(arg1_1, (4, 5), (5, 1))
    assert_size_stride(arg2_1, (4, 5), (5, 1))
    assert_size_stride(arg3_1, (), ())
    assert_size_stride(arg4_1, (4, 5), (5, 1))
    assert_size_stride(arg5_1, (4, 5), (5, 1))
    assert_size_stride(arg6_1, (4, 5), (5, 1))
    assert_size_stride(arg7_1, (4, 5), (5, 1))
    assert_size_stride(arg8_1, (4, 5), (5, 1))
    assert_size_stride(arg9_1, (4, 5), (5, 1))
    assert_size_stride(arg10_1, (), ())
    assert_size_stride(arg11_1, (), ())
    assert_size_stride(arg12_1, (4, 5), (5, 1))
    assert_size_stride(arg13_1, (4, 5), (5, 1))
    assert_size_stride(arg14_1, (4, 5), (5, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_for_fused_0.run(arg10_1, arg3_1, arg11_1, arg10_1, arg3_1, arg11_1, grid=((3, 1, 1)), stream=stream0)
        # Source Nodes: [], Original ATen: []
        triton_for_fused_1.run(arg8_1, arg12_1, arg6_1, arg0_1, arg10_1, arg5_1, arg13_1, arg4_1, arg1_1, arg3_1, arg9_1, arg14_1, arg7_1, arg2_1, arg11_1, arg6_1, arg0_1, arg8_1, arg4_1, arg1_1, arg5_1, arg7_1, arg2_1, arg9_1, grid=((3, 1, 1)), stream=stream0)
        del arg0_1
        del arg10_1
        del arg11_1
        del arg12_1
        del arg13_1
        del arg14_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
        del arg5_1
        del arg6_1
        del arg7_1
        del arg8_1
        del arg9_1
    return ()


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    arg4_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg5_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg6_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg7_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg8_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg9_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg10_1 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    arg11_1 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    arg12_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg13_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    arg14_1 = rand_strided((4, 5), (5, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1])
    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)

[INFO]:Output code written to: /tmp/torchinductor_mlazos/ue/cueqxubtnm3far2kh6u4xpvwjaptxhw4kwvxjgaf4ulsgbi26rzx.py