# 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/5i/c5i7cl7fzfqfwzp6copre24vliqiu7jwlux362n6zv7uvxddgs3p.py
# Topologically Sorted Source Nodes: [scores, log_probs, mul, sum_1, log_likelihood], Original ATen: [aten.add, aten._log_softmax, aten.mul, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   log_likelihood => mean
#   log_probs => amax, exp, log, sub, sub_1, sum_1
#   mul => mul
#   scores => add
#   sum_1 => sum_2
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_3), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %primals_4), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_per_fused__log_softmax_add_mean_mul_sum_0 = async_compile.triton('triton_per_fused__log_softmax_add_mean_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_add_mean_mul_sum_0', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 1, '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__log_softmax_add_mean_mul_sum_0(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = (rindex // 16)
    r4 = rindex % 16
    r0 = rindex % 4
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (r4 + (64*r2)), None)
    tmp1 = tl.load(in_ptr1 + (r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + r4 + (64*r2)), None)
    tmp6 = tl.load(in_ptr0 + (32 + r4 + (64*r2)), None)
    tmp9 = tl.load(in_ptr0 + (48 + r4 + (64*r2)), None)
    tmp25 = tl.load(in_ptr2 + (r4 + (64*r2)), None)
    tmp28 = tl.load(in_ptr2 + (16 + r4 + (64*r2)), None)
    tmp32 = tl.load(in_ptr2 + (32 + r4 + (64*r2)), None)
    tmp36 = tl.load(in_ptr2 + (48 + r4 + (64*r2)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = tmp6 + tmp1
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tmp10 = tmp9 + tmp1
    tmp11 = triton_helpers.maximum(tmp8, tmp10)
    tmp12 = tmp2 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp4 - tmp11
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tmp7 - tmp11
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp10 - tmp11
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tl_math.log(tmp22)
    tmp24 = tmp12 - tmp23
    tmp26 = tmp24 * tmp25
    tmp27 = tmp14 - tmp23
    tmp29 = tmp27 * tmp28
    tmp30 = tmp26 + tmp29
    tmp31 = tmp17 - tmp23
    tmp33 = tmp31 * tmp32
    tmp34 = tmp30 + tmp33
    tmp35 = tmp20 - tmp23
    tmp37 = tmp35 * tmp36
    tmp38 = tmp34 + tmp37
    tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41 = tl.sum(tmp39, 1)[:, None]
    tmp42 = 64.0
    tmp43 = tmp41 / tmp42
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp43, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), primals_1, out=buf0)
        del primals_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [scores, log_probs, mul, sum_1, log_likelihood], Original ATen: [aten.add, aten._log_softmax, aten.mul, aten.sum, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused__log_softmax_add_mean_mul_sum_0.run(buf5, buf0, primals_3, primals_4, 1, 64, grid=grid(1), stream=stream0)
    return (buf5, primals_3, primals_4, buf0, reinterpret_tensor(primals_2, (4, 64), (1, 4), 0), )


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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = 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])
    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


class Decoder(nn.Module):

    def __init__(self, dim_encoding, vocab_size):
        super().__init__()
        self.E = nn.Embedding(dim_encoding, vocab_size)
        self.b = nn.Parameter(torch.zeros(1, vocab_size))

    def forward(self, Z, targets):
        scores = Z @ self.E.weight + self.b
        log_probs = scores.log_softmax(dim=1)
        log_likelihood = (log_probs * targets).sum(1).mean()
        return log_likelihood


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


def get_init_inputs():
    return [[], {'dim_encoding': 4, 'vocab_size': 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused__log_softmax_add_mean_mul_sum_0(in_out_ptr1, in_ptr0,
    in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex // 16
    r4 = rindex % 16
    r0 = rindex % 4
    tmp0 = tl.load(in_ptr0 + (r4 + 64 * r2), None)
    tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + r4 + 64 * r2), None)
    tmp6 = tl.load(in_ptr0 + (32 + r4 + 64 * r2), None)
    tmp9 = tl.load(in_ptr0 + (48 + r4 + 64 * r2), None)
    tmp25 = tl.load(in_ptr2 + (r4 + 64 * r2), None)
    tmp28 = tl.load(in_ptr2 + (16 + r4 + 64 * r2), None)
    tmp32 = tl.load(in_ptr2 + (32 + r4 + 64 * r2), None)
    tmp36 = tl.load(in_ptr2 + (48 + r4 + 64 * r2), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = tmp6 + tmp1
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tmp10 = tmp9 + tmp1
    tmp11 = triton_helpers.maximum(tmp8, tmp10)
    tmp12 = tmp2 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp4 - tmp11
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tmp7 - tmp11
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp10 - tmp11
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tl_math.log(tmp22)
    tmp24 = tmp12 - tmp23
    tmp26 = tmp24 * tmp25
    tmp27 = tmp14 - tmp23
    tmp29 = tmp27 * tmp28
    tmp30 = tmp26 + tmp29
    tmp31 = tmp17 - tmp23
    tmp33 = tmp31 * tmp32
    tmp34 = tmp30 + tmp33
    tmp35 = tmp20 - tmp23
    tmp37 = tmp35 * tmp36
    tmp38 = tmp34 + tmp37
    tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41 = tl.sum(tmp39, 1)[:, None]
    tmp42 = 64.0
    tmp43 = tmp41 / tmp42
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp43, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        get_raw_stream(0)
        triton_per_fused__log_softmax_add_mean_mul_sum_0[grid(1)](buf5,
            buf0, primals_3, primals_4, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
    return buf5, primals_3, primals_4, buf0, reinterpret_tensor(primals_2,
        (4, 64), (1, 4), 0)


class DecoderNew(nn.Module):

    def __init__(self, dim_encoding, vocab_size):
        super().__init__()
        self.E = nn.Embedding(dim_encoding, vocab_size)
        self.b = nn.Parameter(torch.zeros(1, vocab_size))

    def forward(self, input_0, input_1):
        primals_3 = self.b
        primals_1 = self.E.weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
