# 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_7/inductor_cache/ys/cys6fbrc4ncrm6vnp4nursuec7gsli6t4pfg3v4fvdqv5fn6sq6g.py
# Topologically Sorted Source Nodes: [getitem], Original ATen: [aten.index]
# Source node to ATen node mapping:
#   getitem => index
# Graph fragment:
#   %index : [num_users=2] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_1, [%primals_2]), kwargs = {})
triton_poi_fused_index_0 = async_compile.triton('triton_poi_fused_index_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=[16], 
    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_index_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_index_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (x0 + (4*tmp4)), xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/lm/clmamyiyiy5hkvjs2tannkdyyjwt4urzvqw3vxavtdj2pm7d2pgv.py
# Topologically Sorted Source Nodes: [getitem_1, add, preds, diffs], Original ATen: [aten.index, aten.add, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   diffs => sub
#   getitem_1 => index_1
#   preds => add_1
# Graph fragment:
#   %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_4, [%primals_2]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm, %index_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %permute_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, %primals_6), kwargs = {})
triton_poi_fused_add_index_sub_1 = async_compile.triton('triton_poi_fused_add_index_sub_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: '*fp32', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_index_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_index_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + (x4), xmask)
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert(((0 <= tmp5) & (tmp5 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp5 < 4")
    tmp7 = tl.load(in_ptr2 + (tmp5), xmask, eviction_policy='evict_last')
    tmp8 = tmp0 + tmp7
    tmp10 = tmp8 + tmp9
    tmp12 = tmp10 - tmp11
    tl.store(out_ptr0 + (x4), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (4, 1), (1, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [getitem], Original ATen: [aten.index]
        stream0 = get_raw_stream(0)
        triton_poi_fused_index_0.run(primals_2, primals_1, buf0, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [getitem_1, add, preds, diffs], Original ATen: [aten.index, aten.add, aten.sub]
        triton_poi_fused_add_index_sub_1.run(buf1, primals_2, primals_4, primals_5, primals_6, buf2, 256, grid=grid(256), stream=stream0)
        del buf1
        del primals_4
        del primals_5
        del primals_6
    return (buf2, primals_2, buf0, primals_3, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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.utils.data
from torch.nn.init import xavier_uniform_


class TorchGloVeModel(nn.Module):

    def __init__(self, n_words, embed_dim):
        super().__init__()
        self.n_words = n_words
        self.embed_dim = embed_dim
        self.W = self._init_weights(self.n_words, self.embed_dim)
        self.C = self._init_weights(self.n_words, self.embed_dim)
        self.bw = self._init_weights(self.n_words, 1)
        self.bc = self._init_weights(self.n_words, 1)

    def _init_weights(self, m, n):
        return nn.Parameter(xavier_uniform_(torch.empty(m, n)))

    def forward(self, X_log, idx):
        """
        Parameters
        ----------
        X_log : torch.FloatTensor, shape `(batch_size, n_vocab)`.

        idx : torch.LongTensor, shape `(batch_size, )`
            Indices of the vocab items in the current batch.

        Returns
        -------
        torch.FloatTensor, shape `(n_vocab, n_vocab)`.

        """
        preds = self.W[idx].matmul(self.C.T) + self.bw[idx] + self.bc.T
        diffs = preds - X_log
        return diffs


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'n_words': 4, 'embed_dim': 4}]

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
import torch.nn as nn
import torch.utils.data
from torch.nn.init import xavier_uniform_
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_poi_fused_index_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    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], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * tmp4), xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_index_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + x4, xmask)
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp5 < 4')
    tmp7 = tl.load(in_ptr2 + tmp5, xmask, eviction_policy='evict_last')
    tmp8 = tmp0 + tmp7
    tmp10 = tmp8 + tmp9
    tmp12 = tmp10 - tmp11
    tl.store(out_ptr0 + x4, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (4, 1), (1, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_index_0[grid(16)](primals_2, primals_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (4, 4), (1, 4
            ), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_index_sub_1[grid(256)](buf1, primals_2,
            primals_4, primals_5, primals_6, buf2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        del primals_4
        del primals_5
        del primals_6
    return buf2, primals_2, buf0, primals_3


class TorchGloVeModelNew(nn.Module):

    def __init__(self, n_words, embed_dim):
        super().__init__()
        self.n_words = n_words
        self.embed_dim = embed_dim
        self.W = self._init_weights(self.n_words, self.embed_dim)
        self.C = self._init_weights(self.n_words, self.embed_dim)
        self.bw = self._init_weights(self.n_words, 1)
        self.bc = self._init_weights(self.n_words, 1)

    def _init_weights(self, m, n):
        return nn.Parameter(xavier_uniform_(torch.empty(m, n)))

    def forward(self, input_0, input_1):
        primals_1 = self.W
        primals_3 = self.C
        primals_4 = self.bw
        primals_5 = self.bc
        primals_6 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
