# 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_7/inductor_cache/wx/cwxwvlntewdrqi2r4caciy5ht4jdvafnhtiqncr4lo4aegcb4imz.py
# Topologically Sorted Source Nodes: [probs], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   probs => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_1, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_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: '*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__softmax_0', '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__softmax_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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4f/c4fohylrkpmotjodbcxka53btdcdzxeig62kpjpo2l45ahnmgqpg.py
# Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_1 => getitem_1
# Graph fragment:
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 1), kwargs = {})
triton_poi_fused_max_1 = async_compile.triton('triton_poi_fused_max_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: '*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_max_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_max_1(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp1 / tmp6
    tmp9 = tmp7 > tmp8
    tmp10 = tmp7 == tmp8
    tmp11 = tmp7 != tmp7
    tmp12 = tmp8 != tmp8
    tmp13 = tmp11 > tmp12
    tmp14 = tmp9 | tmp13
    tmp15 = tmp11 & tmp12
    tmp16 = tmp10 | tmp15
    tmp17 = tl.full([1], 0, tl.int64)
    tmp18 = tl.full([1], 1, tl.int64)
    tmp19 = tmp17 < tmp18
    tmp20 = tmp16 & tmp19
    tmp21 = tmp14 | tmp20
    tmp22 = tl.where(tmp21, tmp7, tmp8)
    tmp23 = tl.where(tmp21, tmp17, tmp18)
    tmp24 = tmp3 / tmp6
    tmp25 = tmp22 > tmp24
    tmp26 = tmp22 == tmp24
    tmp27 = tmp22 != tmp22
    tmp28 = tmp24 != tmp24
    tmp29 = tmp27 > tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tmp27 & tmp28
    tmp32 = tmp26 | tmp31
    tmp33 = tl.full([1], 2, tl.int64)
    tmp34 = tmp23 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tmp30 | tmp35
    tmp37 = tl.where(tmp36, tmp22, tmp24)
    tmp38 = tl.where(tmp36, tmp23, tmp33)
    tmp39 = tmp5 / tmp6
    tmp40 = tmp37 > tmp39
    tmp41 = tmp37 == tmp39
    tmp42 = tmp37 != tmp37
    tmp43 = tmp39 != tmp39
    tmp44 = tmp42 > tmp43
    tmp45 = tmp40 | tmp44
    tmp46 = tmp42 & tmp43
    tmp47 = tmp41 | tmp46
    tmp48 = tl.full([1], 3, tl.int64)
    tmp49 = tmp38 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tmp45 | tmp50
    tmp52 = tl.where(tmp51, tmp37, tmp39)
    tmp53 = tl.where(tmp51, tmp38, tmp48)
    tl.store(out_ptr0 + (x0), tmp53, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, ), (1, ))
    assert_size_stride(arg2_1, (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: [logits], Original ATen: [aten.addmm]
        extern_kernels.addmm(arg1_1, reinterpret_tensor(arg2_1, (64, 4), (4, 1), 0), reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del arg0_1
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [probs], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
        triton_poi_fused_max_1.run(buf1, buf2, 64, grid=grid(64), stream=stream0)
        del buf1
    return (buf2, )


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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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 OutputLayer(nn.Module):
    """
    Abstract base class for output layer. 
    Handles projection to output labels
    """

    def __init__(self, hidden_size, output_size):
        super(OutputLayer, self).__init__()
        self.output_size = output_size
        self.output_projection = nn.Linear(hidden_size, output_size)

    def loss(self, hidden, labels):
        raise NotImplementedError('Must implement {}.loss'.format(self.
            __class__.__name__))


class SoftmaxOutputLayer(OutputLayer):
    """
    Implements a softmax based output layer
    """

    def forward(self, hidden):
        logits = self.output_projection(hidden)
        probs = F.softmax(logits, -1)
        _, predictions = torch.max(probs, dim=-1)
        return predictions

    def loss(self, hidden, labels):
        logits = self.output_projection(hidden)
        log_probs = F.log_softmax(logits, -1)
        return F.nll_loss(log_probs.view(-1, self.output_size), labels.view(-1)
            )


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


def get_init_inputs():
    return [[], {'hidden_size': 4, 'output_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
import torch.nn.functional as F
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__softmax_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_max_1(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp1 / tmp6
    tmp9 = tmp7 > tmp8
    tmp10 = tmp7 == tmp8
    tmp11 = tmp7 != tmp7
    tmp12 = tmp8 != tmp8
    tmp13 = tmp11 > tmp12
    tmp14 = tmp9 | tmp13
    tmp15 = tmp11 & tmp12
    tmp16 = tmp10 | tmp15
    tmp17 = tl.full([1], 0, tl.int64)
    tmp18 = tl.full([1], 1, tl.int64)
    tmp19 = tmp17 < tmp18
    tmp20 = tmp16 & tmp19
    tmp21 = tmp14 | tmp20
    tmp22 = tl.where(tmp21, tmp7, tmp8)
    tmp23 = tl.where(tmp21, tmp17, tmp18)
    tmp24 = tmp3 / tmp6
    tmp25 = tmp22 > tmp24
    tmp26 = tmp22 == tmp24
    tmp27 = tmp22 != tmp22
    tmp28 = tmp24 != tmp24
    tmp29 = tmp27 > tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tmp27 & tmp28
    tmp32 = tmp26 | tmp31
    tmp33 = tl.full([1], 2, tl.int64)
    tmp34 = tmp23 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tmp30 | tmp35
    tmp37 = tl.where(tmp36, tmp22, tmp24)
    tmp38 = tl.where(tmp36, tmp23, tmp33)
    tmp39 = tmp5 / tmp6
    tmp40 = tmp37 > tmp39
    tmp41 = tmp37 == tmp39
    tmp42 = tmp37 != tmp37
    tmp43 = tmp39 != tmp39
    tmp44 = tmp42 > tmp43
    tmp45 = tmp40 | tmp44
    tmp46 = tmp42 & tmp43
    tmp47 = tmp41 | tmp46
    tmp48 = tl.full([1], 3, tl.int64)
    tmp49 = tmp38 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tmp45 | tmp50
    tl.where(tmp51, tmp37, tmp39)
    tmp53 = tl.where(tmp51, tmp38, tmp48)
    tl.store(out_ptr0 + x0, tmp53, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4,), (1,))
    assert_size_stride(arg2_1, (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.addmm(arg1_1, reinterpret_tensor(arg2_1, (64, 4), (4,
            1), 0), reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf0)
        del arg0_1
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](buf0, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        triton_poi_fused_max_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf1
    return buf2,


class OutputLayer(nn.Module):
    """
    Abstract base class for output layer. 
    Handles projection to output labels
    """

    def __init__(self, hidden_size, output_size):
        super(OutputLayer, self).__init__()
        self.output_size = output_size
        self.output_projection = nn.Linear(hidden_size, output_size)

    def loss(self, hidden, labels):
        raise NotImplementedError('Must implement {}.loss'.format(self.
            __class__.__name__))


class SoftmaxOutputLayerNew(OutputLayer):
    """
    Implements a softmax based output layer
    """

    def loss(self, hidden, labels):
        logits = self.output_projection(hidden)
        log_probs = F.log_softmax(logits, -1)
        return F.nll_loss(log_probs.view(-1, self.output_size), labels.view(-1)
            )

    def forward(self, input_0):
        arg0_1 = self.output_projection.weight
        arg1_1 = self.output_projection.bias
        arg2_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
