# 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_1/inductor_cache/ia/ciayqeqzx5aa7nfdwxamntj3iy4xmzooazzst6o6kdmglfu2uckl.py
# Topologically Sorted Source Nodes: [max_1, logit], Original ATen: [aten.max, aten.sub]
# Source node to ATen node mapping:
#   logit => sub
#   max_1 => max_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%arg0_1, 3, True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %getitem), kwargs = {})
triton_poi_fused_max_sub_0 = async_compile.triton('triton_poi_fused_max_sub_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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_sub_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
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/66/c66il2i3eecgrwjjvtmjmavgkqkvozokg43embnqmual5yqwin4k.py
# Topologically Sorted Source Nodes: [exp_logit, exp_sum, log_exp_sum, neg_log_prob, mul, out, mean], Original ATen: [aten.exp, aten.sum, aten.log, aten.sub, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   exp_logit => exp
#   exp_sum => sum_1
#   log_exp_sum => log
#   mean => mean
#   mul => mul
#   neg_log_prob => sub_1
#   out => sum_2
# Graph fragment:
#   %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, [3], 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 = (%log, %sub), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %arg1_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [3]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_per_fused_exp_log_mean_mul_sub_sum_1 = async_compile.triton('triton_per_fused_exp_log_mean_mul_sub_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_exp_log_mean_mul_sub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_exp_log_mean_mul_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 + tmp3
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp11 - tmp0
    tmp14 = tmp12 * tmp13
    tmp15 = tmp11 - tmp2
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp19 = tmp11 - tmp5
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = tmp11 - tmp8
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = 64.0
    tmp31 = tmp29 / tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp31, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (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, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_1, logit], Original ATen: [aten.max, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_max_sub_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [exp_logit, exp_sum, log_exp_sum, neg_log_prob, mul, out, mean], Original ATen: [aten.exp, aten.sum, aten.log, aten.sub, aten.mul, aten.mean]
        triton_per_fused_exp_log_mean_mul_sub_sum_1.run(buf2, buf0, arg1_1, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
    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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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


def soft_cross_entropy(logit, label, weight=None, reduce=None, reduction='mean'
    ):
    if weight is not None and weight.requires_grad:
        raise RuntimeError('gradient for weight is not supported')
    losses = SoftCrossEntropyFunction.apply(logit, label, weight)
    reduction = {(True): 'mean', (False): 'none', None: reduction}[reduce]
    if reduction == 'mean':
        return losses.mean()
    elif reduction == 'sum':
        return losses.sum()
    elif reduction == 'none':
        return losses
    else:
        raise ValueError('invalid value for reduction: {}'.format(reduction))


class SoftCrossEntropyFunction(torch.autograd.Function):

    @staticmethod
    def forward(ctx, logit, label, weight=None):
        assert logit.size() == label.size(), 'logit.size() != label.size()'
        dim = logit.dim()
        max_logit = logit.max(dim - 1, keepdim=True)[0]
        logit = logit - max_logit
        exp_logit = logit.exp()
        exp_sum = exp_logit.sum(dim - 1, keepdim=True)
        prob = exp_logit / exp_sum
        log_exp_sum = exp_sum.log()
        neg_log_prob = log_exp_sum - logit
        if weight is None:
            weighted_label = label
        else:
            if weight.size() != (logit.size(-1),):
                raise ValueError(
                    'since logit.size() = {}, weight.size() should be ({},), but got {}'
                    .format(logit.size(), logit.size(-1), weight.size()))
            size = [1] * label.dim()
            size[-1] = label.size(-1)
            weighted_label = label * weight.view(size)
        ctx.save_for_backward(weighted_label, prob)
        out = (neg_log_prob * weighted_label).sum(dim - 1)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        weighted_label, prob = ctx.saved_tensors
        old_size = weighted_label.size()
        K = old_size[-1]
        B = weighted_label.numel() // K
        grad_output = grad_output.view(B, 1)
        weighted_label = weighted_label.view(B, K)
        prob = prob.view(B, K)
        grad_input = grad_output * (prob * weighted_label.sum(1, True) -
            weighted_label)
        grad_input = grad_input.view(old_size)
        return grad_input, None, None


class SoftCrossEntropyLoss(torch.nn.Module):

    def __init__(self, weight=None, reduce=None, reduction='mean'):
        super(SoftCrossEntropyLoss, self).__init__()
        self.weight = weight
        self.reduce = reduce
        self.reduction = reduction

    def forward(self, logit, label, weight=None):
        if weight is None:
            weight = self.weight
        return soft_cross_entropy(logit, label, weight, self.reduce, self.
            reduction)


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


def get_init_inputs():
    return [[], {}]

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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
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_max_sub_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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused_exp_log_mean_mul_sub_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 + tmp3
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp11 - tmp0
    tmp14 = tmp12 * tmp13
    tmp15 = tmp11 - tmp2
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp19 = tmp11 - tmp5
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = tmp11 - tmp8
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = 64.0
    tmp31 = tmp29 / tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp31, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_sub_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_exp_log_mean_mul_sub_sum_1[grid(1)](buf2, buf0,
            arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf2,


def soft_cross_entropy(logit, label, weight=None, reduce=None, reduction='mean'
    ):
    if weight is not None and weight.requires_grad:
        raise RuntimeError('gradient for weight is not supported')
    losses = SoftCrossEntropyFunction.apply(logit, label, weight)
    reduction = {(True): 'mean', (False): 'none', None: reduction}[reduce]
    if reduction == 'mean':
        return losses.mean()
    elif reduction == 'sum':
        return losses.sum()
    elif reduction == 'none':
        return losses
    else:
        raise ValueError('invalid value for reduction: {}'.format(reduction))


class SoftCrossEntropyFunction(torch.autograd.Function):

    @staticmethod
    def forward(ctx, logit, label, weight=None):
        assert logit.size() == label.size(), 'logit.size() != label.size()'
        dim = logit.dim()
        max_logit = logit.max(dim - 1, keepdim=True)[0]
        logit = logit - max_logit
        exp_logit = logit.exp()
        exp_sum = exp_logit.sum(dim - 1, keepdim=True)
        prob = exp_logit / exp_sum
        log_exp_sum = exp_sum.log()
        neg_log_prob = log_exp_sum - logit
        if weight is None:
            weighted_label = label
        else:
            if weight.size() != (logit.size(-1),):
                raise ValueError(
                    'since logit.size() = {}, weight.size() should be ({},), but got {}'
                    .format(logit.size(), logit.size(-1), weight.size()))
            size = [1] * label.dim()
            size[-1] = label.size(-1)
            weighted_label = label * weight.view(size)
        ctx.save_for_backward(weighted_label, prob)
        out = (neg_log_prob * weighted_label).sum(dim - 1)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        weighted_label, prob = ctx.saved_tensors
        old_size = weighted_label.size()
        K = old_size[-1]
        B = weighted_label.numel() // K
        grad_output = grad_output.view(B, 1)
        weighted_label = weighted_label.view(B, K)
        prob = prob.view(B, K)
        grad_input = grad_output * (prob * weighted_label.sum(1, True) -
            weighted_label)
        grad_input = grad_input.view(old_size)
        return grad_input, None, None


class SoftCrossEntropyLossNew(torch.nn.Module):

    def __init__(self, weight=None, reduce=None, reduction='mean'):
        super(SoftCrossEntropyLossNew, self).__init__()
        self.weight = weight
        self.reduce = reduce
        self.reduction = reduction

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
