# AOT ID: ['0_inference']
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/hd/chd4rxbi63w5iepye5w4zqxwpml3db4bngunfmfplycvf2qoeigi.py
# Topologically Sorted Source Nodes: [b1, mul, dot_prod], Original ATen: [aten.div, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   b1 => div
#   dot_prod => sum_2
#   mul => mul
# Graph fragment:
#   %div : [num_users=5] = call_function[target=torch.ops.aten.div.Tensor](args = (%select, %expand), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %select_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1], True), kwargs = {})
triton_poi_fused_div_mul_sum_0 = async_compile.triton('triton_poi_fused_div_mul_sum_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_mul_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (6*x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (2 + (6*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (4 + (6*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (1 + (6*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + (6*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (5 + (6*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = 1e-12
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = tmp0 / tmp10
    tmp13 = tmp11 * tmp12
    tmp14 = tmp2 / tmp10
    tmp16 = tmp14 * tmp15
    tmp17 = tmp13 + tmp16
    tmp18 = tmp5 / tmp10
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tl.store(out_ptr0 + (x0), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/or/corli2hhegxx5f3pgn3genmxh3u3xzhbqybdatbsgwql66rinl3a.py
# Topologically Sorted Source Nodes: [b1, mul, dot_prod, mul_1, sub], Original ATen: [aten.div, aten.mul, aten.sum, aten.sub]
# Source node to ATen node mapping:
#   b1 => div
#   dot_prod => sum_2
#   mul => mul
#   mul_1 => mul_1
#   sub => sub
# Graph fragment:
#   %div : [num_users=5] = call_function[target=torch.ops.aten.div.Tensor](args = (%select, %expand), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %select_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1], True), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, %div), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_2, %mul_1), kwargs = {})
triton_poi_fused_div_mul_sub_sum_1 = async_compile.triton('triton_poi_fused_div_mul_sub_sum_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: '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_div_mul_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_mul_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 3)
    tmp0 = tl.load(in_ptr0 + (1 + (2*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (2*x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (6*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + (6*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (4 + (6*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tmp3 * tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = 1e-12
    tmp13 = triton_helpers.maximum(tmp11, tmp12)
    tmp14 = tmp2 / tmp13
    tmp15 = tmp1 * tmp14
    tmp16 = tmp0 - tmp15
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6u/c6ujw2gsz4fiffrddbz6pylpsw3ourbw2ir2dtfko3n5r3gxebkj.py
# Topologically Sorted Source Nodes: [b1, b2, b3], Original ATen: [aten.div, aten.linalg_cross]
# Source node to ATen node mapping:
#   b1 => div
#   b2 => div_1
#   b3 => index, index_1, index_2, index_3, mul_2, mul_3
# Graph fragment:
#   %div : [num_users=5] = call_function[target=torch.ops.aten.div.Tensor](args = (%select, %expand), kwargs = {})
#   %div_1 : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %expand_1), kwargs = {})
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%div, [None, %remainder]), kwargs = {})
#   %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%div_1, [None, %remainder_1]), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index, %index_1), kwargs = {})
#   %index_2 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%div, [None, %remainder_2]), kwargs = {})
#   %index_3 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%div_1, [None, %remainder_3]), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index_2, %index_3), kwargs = {})
triton_poi_fused_div_linalg_cross_2 = async_compile.triton('triton_poi_fused_div_linalg_cross_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_linalg_cross_2', '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_div_linalg_cross_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3
    x1 = (xindex // 3)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*((1 + x0) % 3)) + (6*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (6*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (6*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (4 + (6*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + ((3*x1) + ((2 + x0) % 3)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (3*x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + (3*x1)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (2 + (3*x1)), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr0 + ((2*((2 + x0) % 3)) + (6*x1)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + ((3*x1) + ((1 + x0) % 3)), xmask)
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = 1e-12
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tmp0 / tmp11
    tmp15 = tmp14 * tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = libdevice.sqrt(tmp21)
    tmp23 = triton_helpers.maximum(tmp22, tmp10)
    tmp24 = tmp13 / tmp23
    tmp25 = tmp12 * tmp24
    tmp27 = tmp26 / tmp11
    tmp29 = tmp28 / tmp23
    tmp30 = tmp27 * tmp29
    tl.store(out_ptr0 + (x2), tmp25, xmask)
    tl.store(out_ptr1 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6w/c6woiqqq2vb4nax2oe4lql3ogk6yfdkg35mdxnytclkct4emsqlb.py
# Topologically Sorted Source Nodes: [stack], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   stack => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2], -1), kwargs = {})
triton_poi_fused_stack_3 = async_compile.triton('triton_poi_fused_stack_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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_stack_3', '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_stack_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 36
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3
    x3 = (xindex // 3)
    x2 = (xindex // 9)
    x5 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2*x3), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (6*x2), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp6 * tmp6
    tmp8 = tl.load(in_ptr0 + (2 + (6*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = tl.load(in_ptr0 + (4 + (6*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = libdevice.sqrt(tmp13)
    tmp15 = 1e-12
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = tmp5 / tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp4, tmp17, tmp18)
    tmp20 = tmp0 >= tmp3
    tmp21 = tl.full([1], 2, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr1 + (x3), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tl.load(in_ptr1 + (3*x2), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp25 * tmp25
    tmp27 = tl.load(in_ptr1 + (1 + (3*x2)), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp26 + tmp28
    tmp30 = tl.load(in_ptr1 + (2 + (3*x2)), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp31 = tmp30 * tmp30
    tmp32 = tmp29 + tmp31
    tmp33 = libdevice.sqrt(tmp32)
    tmp34 = triton_helpers.maximum(tmp33, tmp15)
    tmp35 = tmp24 / tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp23, tmp35, tmp36)
    tmp38 = tmp0 >= tmp21
    tmp39 = tl.full([1], 3, tl.int64)
    tmp40 = tmp0 < tmp39
    tmp41 = tl.load(in_ptr2 + (x3), tmp38 & xmask, eviction_policy='evict_last', other=0.0)
    tmp42 = tl.load(in_ptr3 + (x3), tmp38 & xmask, eviction_policy='evict_last', other=0.0)
    tmp43 = tmp41 - tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp38, tmp43, tmp44)
    tmp46 = tl.where(tmp23, tmp37, tmp45)
    tmp47 = tl.where(tmp4, tmp19, tmp46)
    tl.store(out_ptr0 + (x5), tmp47, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 3, 2), (6, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [b1, mul, dot_prod], Original ATen: [aten.div, aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_mul_sum_0.run(arg0_1, buf0, 4, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [b1, mul, dot_prod, mul_1, sub], Original ATen: [aten.div, aten.mul, aten.sum, aten.sub]
        triton_poi_fused_div_mul_sub_sum_1.run(arg0_1, buf0, buf1, 12, grid=grid(12), stream=stream0)
        del buf0
        buf2 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [b1, b2, b3], Original ATen: [aten.div, aten.linalg_cross]
        triton_poi_fused_div_linalg_cross_2.run(arg0_1, buf1, buf2, buf3, 12, grid=grid(12), stream=stream0)
        buf4 = empty_strided_cuda((4, 3, 3), (9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [stack], Original ATen: [aten.stack]
        triton_poi_fused_stack_3.run(arg0_1, buf1, buf2, buf3, buf4, 36, grid=grid(36), stream=stream0)
        del arg0_1
        del buf1
        del buf2
        del buf3
    return (buf4, )


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, 3, 2), (6, 2, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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)

import torch
import torch.nn as nn
import torch.nn.functional as F


class ContinousRotReprDecoder(nn.Module):

    def __init__(self):
        super(ContinousRotReprDecoder, self).__init__()

    def forward(self, module_input):
        reshaped_input = module_input.view(-1, 3, 2)
        b1 = F.normalize(reshaped_input[:, :, 0], dim=1)
        dot_prod = torch.sum(b1 * reshaped_input[:, :, 1], dim=1, keepdim=True)
        b2 = F.normalize(reshaped_input[:, :, 1] - dot_prod * b1, dim=-1)
        b3 = torch.cross(b1, b2, dim=1)
        return torch.stack([b1, b2, b3], dim=-1)


def get_inputs():
    return [torch.rand([4, 3, 2])]


def get_init_inputs():
    return [[], {}]

import torch
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


@triton.jit
def triton_poi_fused_div_mul_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 6 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (2 + 6 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (4 + 6 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (1 + 6 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (3 + 6 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr0 + (5 + 6 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = 1e-12
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = tmp0 / tmp10
    tmp13 = tmp11 * tmp12
    tmp14 = tmp2 / tmp10
    tmp16 = tmp14 * tmp15
    tmp17 = tmp13 + tmp16
    tmp18 = tmp5 / tmp10
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tl.store(out_ptr0 + x0, tmp21, xmask)


@triton.jit
def triton_poi_fused_div_mul_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 3
    tmp0 = tl.load(in_ptr0 + (1 + 2 * x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + 2 * x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + 6 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 6 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (4 + 6 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tmp3 * tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = 1e-12
    tmp13 = triton_helpers.maximum(tmp11, tmp12)
    tmp14 = tmp2 / tmp13
    tmp15 = tmp1 * tmp14
    tmp16 = tmp0 - tmp15
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_div_linalg_cross_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3
    x1 = xindex // 3
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * ((1 + x0) % 3) + 6 * x1), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + 6 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 6 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (4 + 6 * x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (3 * x1 + (2 + x0) % 3), xmask,
        eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + 3 * x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + 3 * x1), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (2 + 3 * x1), xmask, eviction_policy='evict_last'
        )
    tmp26 = tl.load(in_ptr0 + (2 * ((2 + x0) % 3) + 6 * x1), xmask,
        eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (3 * x1 + (1 + x0) % 3), xmask)
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = 1e-12
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tmp0 / tmp11
    tmp15 = tmp14 * tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = libdevice.sqrt(tmp21)
    tmp23 = triton_helpers.maximum(tmp22, tmp10)
    tmp24 = tmp13 / tmp23
    tmp25 = tmp12 * tmp24
    tmp27 = tmp26 / tmp11
    tmp29 = tmp28 / tmp23
    tmp30 = tmp27 * tmp29
    tl.store(out_ptr0 + x2, tmp25, xmask)
    tl.store(out_ptr1 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused_stack_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 36
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3
    x3 = xindex // 3
    x2 = xindex // 9
    x5 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + 2 * x3, tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + 6 * x2, tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp6 * tmp6
    tmp8 = tl.load(in_ptr0 + (2 + 6 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = tl.load(in_ptr0 + (4 + 6 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = libdevice.sqrt(tmp13)
    tmp15 = 1e-12
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = tmp5 / tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp4, tmp17, tmp18)
    tmp20 = tmp0 >= tmp3
    tmp21 = tl.full([1], 2, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr1 + x3, tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tl.load(in_ptr1 + 3 * x2, tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp25 * tmp25
    tmp27 = tl.load(in_ptr1 + (1 + 3 * x2), tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp26 + tmp28
    tmp30 = tl.load(in_ptr1 + (2 + 3 * x2), tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp31 = tmp30 * tmp30
    tmp32 = tmp29 + tmp31
    tmp33 = libdevice.sqrt(tmp32)
    tmp34 = triton_helpers.maximum(tmp33, tmp15)
    tmp35 = tmp24 / tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp23, tmp35, tmp36)
    tmp38 = tmp0 >= tmp21
    tl.full([1], 3, tl.int64)
    tmp41 = tl.load(in_ptr2 + x3, tmp38 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp42 = tl.load(in_ptr3 + x3, tmp38 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp43 = tmp41 - tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp38, tmp43, tmp44)
    tmp46 = tl.where(tmp23, tmp37, tmp45)
    tmp47 = tl.where(tmp4, tmp19, tmp46)
    tl.store(out_ptr0 + x5, tmp47, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 3, 2), (6, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mul_sum_0[grid(4)](arg0_1, buf0, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        triton_poi_fused_div_mul_sub_sum_1[grid(12)](arg0_1, buf0, buf1, 12,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        triton_poi_fused_div_linalg_cross_2[grid(12)](arg0_1, buf1, buf2,
            buf3, 12, XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 3, 3), (9, 3, 1), torch.float32)
        triton_poi_fused_stack_3[grid(36)](arg0_1, buf1, buf2, buf3, buf4, 
            36, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del buf1
        del buf2
        del buf3
    return buf4,


class ContinousRotReprDecoderNew(nn.Module):

    def __init__(self):
        super(ContinousRotReprDecoderNew, self).__init__()

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
