# 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_6/inductor_cache/5i/c5iybmnijeaxq3pumkl5crtkns462pwdrh72bxy4lcvnlh3r4364.py
# Topologically Sorted Source Nodes: [ne, mask, cumsum], Original ATen: [aten.ne, aten._to_copy, aten.cumsum]
# Source node to ATen node mapping:
#   cumsum => cumsum
#   mask => convert_element_type
#   ne => ne
# Graph fragment:
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%primals_1, 4), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ne, torch.int32), kwargs = {})
#   %cumsum : [num_users=1] = call_function[target=torch.ops.aten.cumsum.default](args = (%convert_element_type, 1), kwargs = {})
triton_per_fused__to_copy_cumsum_ne_0 = async_compile.triton('triton_per_fused__to_copy_cumsum_ne_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.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0

@triton_heuristics.persistent_reduction(
    size_hints=[64, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_cumsum_ne_0', '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}
)
@triton.jit
def triton_per_fused__to_copy_cumsum_ne_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x0 + (16*r2) + (64*x1)), xmask, other=0.0)
    tmp1 = 4.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.int32)
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tmp4.to(tl.int64)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp7, = tl.associative_scan((tmp6,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (x0 + (16*r2) + (64*x1)), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ft/cftxaavy7b7scxgnrhfsvfnicvimxnf3kpckow5nzkyed3meyoli.py
# Topologically Sorted Source Nodes: [ne, mask, type_as, mul, long, positions], Original ATen: [aten.ne, aten._to_copy, aten.mul, aten.add]
# Source node to ATen node mapping:
#   long => convert_element_type_2
#   mask => convert_element_type
#   mul => mul
#   ne => ne
#   positions => add
#   type_as => convert_element_type_1
# Graph fragment:
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%primals_1, 4), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ne, torch.int32), kwargs = {})
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%cumsum, torch.int32), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_1, %convert_element_type), kwargs = {})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul, torch.int64), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_2, 4), kwargs = {})
triton_poi_fused__to_copy_add_mul_ne_1 = async_compile.triton('triton_poi_fused__to_copy_add_mul_ne_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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__to_copy_add_mul_ne_1', 'mutated_arg_names': ['in_out_ptr0'], '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__to_copy_add_mul_ne_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp2 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tmp0.to(tl.int32)
    tmp3 = 4.0
    tmp4 = tmp2 != tmp3
    tmp5 = tmp4.to(tl.int32)
    tmp6 = tmp1 * tmp5
    tmp7 = tmp6.to(tl.int64)
    tmp8 = tl.full([1], 4, tl.int64)
    tmp9 = tmp7 + tmp8
    tl.store(in_out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/in/cinglqnf6mtochspmiolvr3bqay6yiivzgqlihpkdlbd5p4ccw54.py
# Topologically Sorted Source Nodes: [embedding], Original ATen: [aten.embedding]
# Source node to ATen node mapping:
#   embedding => embedding
# Graph fragment:
#   %embedding : [num_users=1] = call_function[target=torch.ops.aten.embedding.default](args = (%primals_2, %add, 4), kwargs = {})
triton_poi_fused_embedding_2 = async_compile.triton('triton_poi_fused_embedding_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_embedding_2', '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_embedding_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 9, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 9)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 9")
    tmp6 = tl.load(in_ptr1 + (x0 + (4*tmp4)), xmask)
    tl.store(out_ptr0 + (x2), tmp6, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (9, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [ne, mask, cumsum], Original ATen: [aten.ne, aten._to_copy, aten.cumsum]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy_cumsum_ne_0.run(primals_1, buf0, 64, 4, grid=grid(64), stream=stream0)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [ne, mask, type_as, mul, long, positions], Original ATen: [aten.ne, aten._to_copy, aten.mul, aten.add]
        triton_poi_fused__to_copy_add_mul_ne_1.run(buf1, primals_1, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embedding], Original ATen: [aten.embedding]
        triton_poi_fused_embedding_2.run(buf1, primals_2, buf2, 1024, grid=grid(1024), stream=stream0)
        del primals_2
    return (buf2, buf1, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((9, 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 nn
import torch.nn.functional as F


class LearnedPositionalEmbedding(nn.Embedding):
    """
    This module learns positional embeddings up to a fixed maximum size.
    Padding ids are ignored by either offsetting based on padding_idx
    or by setting padding_idx to None and ensuring that the appropriate
    position ids are passed to the forward function.
    """

    def __init__(self, num_embeddings: 'int', embedding_dim: 'int',
        padding_idx: 'int'):
        if padding_idx is not None:
            num_embeddings_ = num_embeddings + padding_idx + 1
        else:
            num_embeddings_ = num_embeddings
        super().__init__(num_embeddings_, embedding_dim, padding_idx)
        self.max_positions = num_embeddings

    def forward(self, input: 'torch.Tensor'):
        """Input is expected to be of size [bsz x seqlen]."""
        if input.size(1) > self.max_positions:
            raise ValueError(
                f'Sequence length {input.size(1)} above maximum  sequence length of {self.max_positions}'
                )
        mask = input.ne(self.padding_idx).int()
        positions = (torch.cumsum(mask, dim=1).type_as(mask) * mask).long(
            ) + self.padding_idx
        return F.embedding(positions, self.weight, self.padding_idx, self.
            max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse)


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


def get_init_inputs():
    return [[], {'num_embeddings': 4, 'embedding_dim': 4, 'padding_idx': 4}]

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
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_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused__to_copy_cumsum_ne_0(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = 4.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.int32)
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tmp4.to(tl.int64)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp7, = tl.associative_scan((tmp6,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (x0 + 16 * r2 + 64 * x1), tmp7, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_mul_ne_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.int32)
    tmp3 = 4.0
    tmp4 = tmp2 != tmp3
    tmp5 = tmp4.to(tl.int32)
    tmp6 = tmp1 * tmp5
    tmp7 = tmp6.to(tl.int64)
    tmp8 = tl.full([1], 4, tl.int64)
    tmp9 = tmp7 + tmp8
    tl.store(in_out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_poi_fused_embedding_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 9, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 9) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 9')
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * tmp4), xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (9, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.int64)
        get_raw_stream(0)
        triton_per_fused__to_copy_cumsum_ne_0[grid(64)](primals_1, buf0, 64,
            4, XBLOCK=8, num_warps=2, num_stages=1)
        buf1 = buf0
        del buf0
        triton_poi_fused__to_copy_add_mul_ne_1[grid(256)](buf1, primals_1, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_embedding_2[grid(1024)](buf1, primals_2, buf2, 
            1024, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf2, buf1


class LearnedPositionalEmbeddingNew(nn.Embedding):
    """
    This module learns positional embeddings up to a fixed maximum size.
    Padding ids are ignored by either offsetting based on padding_idx
    or by setting padding_idx to None and ensuring that the appropriate
    position ids are passed to the forward function.
    """

    def __init__(self, num_embeddings: 'int', embedding_dim: 'int',
        padding_idx: 'int'):
        if padding_idx is not None:
            num_embeddings_ = num_embeddings + padding_idx + 1
        else:
            num_embeddings_ = num_embeddings
        super().__init__(num_embeddings_, embedding_dim, padding_idx)
        self.max_positions = num_embeddings

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