# 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/wi/cwibqvrnbfx7xhnfzzckhfwxbmmaeepyx4l2irzdxw23feqjr3lp.py
# Topologically Sorted Source Nodes: [long], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   long => convert_element_type
# Graph fragment:
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%primals_1, torch.int64), kwargs = {})
triton_poi_fused__to_copy_0 = async_compile.triton('triton_poi_fused__to_copy_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 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_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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_0(in_ptr0, out_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_ptr0 + (x0), xmask)
    tmp1 = tmp0.to(tl.int64)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sj/csjoftoytknoektasbm3r3u6wambt2s3chynhpfivuojebg4btx7.py
# Topologically Sorted Source Nodes: [embedding, sum_1, out], Original ATen: [aten.embedding, aten.sum, aten.add]
# Source node to ATen node mapping:
#   embedding => embedding
#   out => add
#   sum_1 => sum_1
# Graph fragment:
#   %embedding : [num_users=1] = call_function[target=torch.ops.aten.embedding.default](args = (%primals_2, %convert_element_type, 0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%embedding, [1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %primals_3), kwargs = {})
triton_poi_fused_add_embedding_sum_1 = async_compile.triton('triton_poi_fused_add_embedding_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_embedding_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_add_embedding_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp14 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp21 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp28 = tl.load(in_ptr2 + (0))
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK])
    tmp1 = tl.full([XBLOCK], 5, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 5)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 5")
    tmp6 = tl.load(in_ptr1 + (tmp4), xmask, eviction_policy='evict_last')
    tmp8 = tmp7 + tmp1
    tmp9 = tmp7 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp7)
    tl.device_assert(((0 <= tmp10) & (tmp10 < 5)) | ~(xmask), "index out of bounds: 0 <= tmp10 < 5")
    tmp12 = tl.load(in_ptr1 + (tmp10), xmask, eviction_policy='evict_last')
    tmp13 = tmp6 + tmp12
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tl.device_assert(((0 <= tmp17) & (tmp17 < 5)) | ~(xmask), "index out of bounds: 0 <= tmp17 < 5")
    tmp19 = tl.load(in_ptr1 + (tmp17), xmask, eviction_policy='evict_last')
    tmp20 = tmp13 + tmp19
    tmp22 = tmp21 + tmp1
    tmp23 = tmp21 < 0
    tmp24 = tl.where(tmp23, tmp22, tmp21)
    tl.device_assert(((0 <= tmp24) & (tmp24 < 5)) | ~(xmask), "index out of bounds: 0 <= tmp24 < 5")
    tmp26 = tl.load(in_ptr1 + (tmp24), xmask, eviction_policy='evict_last')
    tmp27 = tmp20 + tmp26
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + (x2), tmp30, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (5, 1), (1, 1))
    assert_size_stride(primals_3, (1, ), (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: [long], Original ATen: [aten._to_copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embedding, sum_1, out], Original ATen: [aten.embedding, aten.sum, aten.add]
        triton_poi_fused_add_embedding_sum_1.run(buf0, primals_2, primals_3, buf1, 64, grid=grid(64), stream=stream0)
        del primals_2
        del primals_3
    return (buf1, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((5, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    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 math
import torch
from torch import Tensor
from torch import nn


class Wide(nn.Module):
    """wide (linear) component

    Linear model implemented via an Embedding layer connected to the output
    neuron(s).

    Parameters
    -----------
    wide_dim: int
        size of the Embedding layer. `wide_dim` is the summation of all the
        individual values for all the features that go through the wide
        component. For example, if the wide component receives 2 features with
        5 individual values each, `wide_dim = 10`
    pred_dim: int, default = 1
        size of the ouput tensor containing the predictions

    Attributes
    -----------
    wide_linear: ``nn.Module``
        the linear layer that comprises the wide branch of the model

    Examples
    --------
    >>> import torch
    >>> from pytorch_widedeep.models import Wide
    >>> X = torch.empty(4, 4).random_(6)
    >>> wide = Wide(wide_dim=X.unique().size(0), pred_dim=1)
    >>> out = wide(X)
    """

    def __init__(self, wide_dim: 'int', pred_dim: 'int'=1):
        super(Wide, self).__init__()
        self.wide_linear = nn.Embedding(wide_dim + 1, pred_dim, padding_idx=0)
        self.bias = nn.Parameter(torch.zeros(pred_dim))
        self._reset_parameters()

    def _reset_parameters(self) ->None:
        """initialize Embedding and bias like nn.Linear. See `original
        implementation
        <https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear>`_.
        """
        nn.init.kaiming_uniform_(self.wide_linear.weight, a=math.sqrt(5))
        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.wide_linear.
            weight)
        bound = 1 / math.sqrt(fan_in)
        nn.init.uniform_(self.bias, -bound, bound)

    def forward(self, X: 'Tensor') ->Tensor:
        """Forward pass. Simply connecting the Embedding layer with the ouput
        neuron(s)"""
        out = self.wide_linear(X.long()).sum(dim=1) + self.bias
        return out


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


def get_init_inputs():
    return [[], {'wide_dim': 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 math
from torch import 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__to_copy_0(in_ptr0, out_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_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.int64)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_add_embedding_sum_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp28 = tl.load(in_ptr2 + 0)
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK])
    tmp1 = tl.full([XBLOCK], 5, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 5) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 5')
    tmp6 = tl.load(in_ptr1 + tmp4, xmask, eviction_policy='evict_last')
    tmp8 = tmp7 + tmp1
    tmp9 = tmp7 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp7)
    tl.device_assert((0 <= tmp10) & (tmp10 < 5) | ~xmask,
        'index out of bounds: 0 <= tmp10 < 5')
    tmp12 = tl.load(in_ptr1 + tmp10, xmask, eviction_policy='evict_last')
    tmp13 = tmp6 + tmp12
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tl.device_assert((0 <= tmp17) & (tmp17 < 5) | ~xmask,
        'index out of bounds: 0 <= tmp17 < 5')
    tmp19 = tl.load(in_ptr1 + tmp17, xmask, eviction_policy='evict_last')
    tmp20 = tmp13 + tmp19
    tmp22 = tmp21 + tmp1
    tmp23 = tmp21 < 0
    tmp24 = tl.where(tmp23, tmp22, tmp21)
    tl.device_assert((0 <= tmp24) & (tmp24 < 5) | ~xmask,
        'index out of bounds: 0 <= tmp24 < 5')
    tmp26 = tl.load(in_ptr1 + tmp24, xmask, eviction_policy='evict_last')
    tmp27 = tmp20 + tmp26
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + x2, tmp30, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (5, 1), (1, 1))
    assert_size_stride(primals_3, (1,), (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_poi_fused__to_copy_0[grid(256)](primals_1, buf0, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_add_embedding_sum_1[grid(64)](buf0, primals_2,
            primals_3, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        del primals_3
    return buf1, buf0


class WideNew(nn.Module):
    """wide (linear) component

    Linear model implemented via an Embedding layer connected to the output
    neuron(s).

    Parameters
    -----------
    wide_dim: int
        size of the Embedding layer. `wide_dim` is the summation of all the
        individual values for all the features that go through the wide
        component. For example, if the wide component receives 2 features with
        5 individual values each, `wide_dim = 10`
    pred_dim: int, default = 1
        size of the ouput tensor containing the predictions

    Attributes
    -----------
    wide_linear: ``nn.Module``
        the linear layer that comprises the wide branch of the model

    Examples
    --------
    >>> import torch
    >>> from pytorch_widedeep.models import Wide
    >>> X = torch.empty(4, 4).random_(6)
    >>> wide = Wide(wide_dim=X.unique().size(0), pred_dim=1)
    >>> out = wide(X)
    """

    def __init__(self, wide_dim: 'int', pred_dim: 'int'=1):
        super(WideNew, self).__init__()
        self.wide_linear = nn.Embedding(wide_dim + 1, pred_dim, padding_idx=0)
        self.bias = nn.Parameter(torch.zeros(pred_dim))
        self._reset_parameters()

    def _reset_parameters(self) ->None:
        """initialize Embedding and bias like nn.Linear. See `original
        implementation
        <https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear>`_.
        """
        nn.init.kaiming_uniform_(self.wide_linear.weight, a=math.sqrt(5))
        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.wide_linear.
            weight)
        bound = 1 / math.sqrt(fan_in)
        nn.init.uniform_(self.bias, -bound, bound)

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