# 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/j4/cj4rzuuszv3uecdumz6l776uuhxdahbpifl74f5qoc42wjjzlvjk.py
# Topologically Sorted Source Nodes: [mul, sub, mul_1, u, p], Original ATen: [aten.mul, aten.rsub, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   p => amax, exp, sub_1, sum_1
#   sub => sub
#   u => add
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %primals_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %primals_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -16), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__softmax_add_mul_rsub_0 = async_compile.triton('triton_poi_fused__softmax_add_mul_rsub_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 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__softmax_add_mul_rsub_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_add_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (64*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask)
    tmp14 = tl.load(in_ptr0 + (32 + x3 + (64*x2)), xmask)
    tmp20 = tl.load(in_ptr0 + (48 + x3 + (64*x2)), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = -16.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tmp8 * tmp1
    tmp10 = tmp3 - tmp8
    tmp11 = tmp10 * tmp5
    tmp12 = tmp9 + tmp11
    tmp13 = triton_helpers.maximum(tmp7, tmp12)
    tmp15 = tmp14 * tmp1
    tmp16 = tmp3 - tmp14
    tmp17 = tmp16 * tmp5
    tmp18 = tmp15 + tmp17
    tmp19 = triton_helpers.maximum(tmp13, tmp18)
    tmp21 = tmp20 * tmp1
    tmp22 = tmp3 - tmp20
    tmp23 = tmp22 * tmp5
    tmp24 = tmp21 + tmp23
    tmp25 = triton_helpers.maximum(tmp19, tmp24)
    tmp26 = tmp7 - tmp25
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp12 - tmp25
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp31 = tmp18 - tmp25
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp24 - tmp25
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp33 + tmp35
    tl.store(out_ptr0 + (x4), tmp25, xmask)
    tl.store(out_ptr1 + (x4), tmp36, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gc/cgczns5ri4wjc5eyelqtmakr7cyybwdqua77zznhsmahqlw53mkh.py
# Topologically Sorted Source Nodes: [mul, sub, mul_1, u, p, sum_1, truediv, log], Original ATen: [aten.mul, aten.rsub, aten.add, aten._softmax, aten.sum, aten.div, aten.log]
# Source node to ATen node mapping:
#   log => log
#   mul => mul
#   mul_1 => mul_1
#   p => amax, div, exp, sub_1
#   sub => sub
#   sum_1 => sum_2
#   truediv => div_1
#   u => add
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %primals_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %primals_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -16), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.default](args = (%div,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div, %sum_2), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div_1,), kwargs = {})
triton_per_fused__softmax_add_div_log_mul_rsub_sum_1 = async_compile.triton('triton_per_fused__softmax_add_div_log_mul_rsub_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, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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': {6: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=(6,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_add_div_log_mul_rsub_sum_1', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 4, '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__softmax_add_div_log_mul_rsub_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r4 = rindex
    r0 = rindex % 4
    r3 = (rindex // 64)
    r5 = rindex % 16
    tmp0 = tl.load(in_ptr0 + (r4), None)
    tmp1 = tl.load(in_ptr1 + (r0), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (r5 + (16*r3)), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + (r5 + (16*r3)), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = -16.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 / tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp12 / tmp15
    tmp17 = tl_math.log(tmp16)
    tl.store(out_ptr1 + (tl.broadcast_to(r4, [RBLOCK])), tmp17, None)
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp15, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, sub, mul_1, u, p], Original ATen: [aten.mul, aten.rsub, aten.add, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_add_mul_rsub_0.run(primals_2, primals_1, buf0, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, sub, mul_1, u, p, sum_1, truediv, log], Original ATen: [aten.mul, aten.rsub, aten.add, aten._softmax, aten.sum, aten.div, aten.log]
        triton_per_fused__softmax_add_div_log_mul_rsub_sum_1.run(primals_2, primals_1, buf0, buf1, buf2, buf3, 1, 256, grid=grid(1), stream=stream0)
        del buf0
        del buf1
    return (buf3, primals_1, primals_2, buf2, )


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, ), (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)
    fn = lambda: call([primals_1, primals_2])
    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


class MNL(nn.Module):
    """
    Implementation of MNL choice model as a Pytorch module
    """

    def __init__(self, n):
        super(MNL, self).__init__()
        self.u = nn.Parameter(torch.nn.init.normal(torch.Tensor(n)))
        self.n = n
        self.m = nn.Softmax()

    def forward(self, x):
        """
        computes the PCMC choice probabilities P(.,S)

        Args:
        x- indicator vector for choice set S, i.e. a 'size(S)-hot' encoding of the
        choice set, or a batch of such encodings
        """
        u = x * self.u + (1 - x) * -16
        p = self.m(u)
        return torch.log(p / torch.sum(p))

    def __str__(self):
        return 'MNL'


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


def get_init_inputs():
    return [[], {'n': 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
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.utils.data
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__softmax_add_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp14 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp20 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = -16.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tmp8 * tmp1
    tmp10 = tmp3 - tmp8
    tmp11 = tmp10 * tmp5
    tmp12 = tmp9 + tmp11
    tmp13 = triton_helpers.maximum(tmp7, tmp12)
    tmp15 = tmp14 * tmp1
    tmp16 = tmp3 - tmp14
    tmp17 = tmp16 * tmp5
    tmp18 = tmp15 + tmp17
    tmp19 = triton_helpers.maximum(tmp13, tmp18)
    tmp21 = tmp20 * tmp1
    tmp22 = tmp3 - tmp20
    tmp23 = tmp22 * tmp5
    tmp24 = tmp21 + tmp23
    tmp25 = triton_helpers.maximum(tmp19, tmp24)
    tmp26 = tmp7 - tmp25
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp12 - tmp25
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp31 = tmp18 - tmp25
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp24 - tmp25
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp33 + tmp35
    tl.store(out_ptr0 + x4, tmp25, xmask)
    tl.store(out_ptr1 + x4, tmp36, xmask)


@triton.jit
def triton_per_fused__softmax_add_div_log_mul_rsub_sum_1(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r4 = rindex
    r0 = rindex % 4
    r3 = rindex // 64
    r5 = rindex % 16
    tmp0 = tl.load(in_ptr0 + r4, None)
    tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (r5 + 16 * r3), None, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr3 + (r5 + 16 * r3), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = -16.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 / tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp12 / tmp15
    tmp17 = tl_math.log(tmp16)
    tl.store(out_ptr1 + tl.broadcast_to(r4, [RBLOCK]), tmp17, None)
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp15, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_add_mul_rsub_0[grid(64)](primals_2,
            primals_1, buf0, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused__softmax_add_div_log_mul_rsub_sum_1[grid(1)](primals_2
            , primals_1, buf0, buf1, buf2, buf3, 1, 256, num_warps=2,
            num_stages=1)
        del buf0
        del buf1
    return buf3, primals_1, primals_2, buf2


class MNLNew(nn.Module):
    """
    Implementation of MNL choice model as a Pytorch module
    """

    def __init__(self, n):
        super(MNLNew, self).__init__()
        self.u = nn.Parameter(torch.nn.init.normal(torch.Tensor(n)))
        self.n = n
        self.m = nn.Softmax()

    def __str__(self):
        return 'MNL'

    def forward(self, input_0):
        primals_1 = self.u
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]
