# 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/am/camfuqgvxy6bycif5dr657ji4zny6dgxoml6xmwbj4la6mrka2dq.py
# Topologically Sorted Source Nodes: [mul, sub, mul_1, utils_3, log_softmax], Original ATen: [aten.mul, aten.rsub, aten.add, aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => amax, exp, sub_1, sum_1
#   mul => mul
#   mul_1 => mul_1
#   sub => sub
#   utils_3 => add
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %view_8), 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=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_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__log_softmax_add_mul_rsub_0 = async_compile.triton('triton_poi_fused__log_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__log_softmax_add_mul_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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__log_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
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp8 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp9 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp15 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp16 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp22 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp23 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = -16.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp10 = tmp8 * tmp9
    tmp11 = tmp3 - tmp8
    tmp12 = tmp11 * tmp5
    tmp13 = tmp10 + tmp12
    tmp14 = triton_helpers.maximum(tmp7, tmp13)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp3 - tmp15
    tmp19 = tmp18 * tmp5
    tmp20 = tmp17 + tmp19
    tmp21 = triton_helpers.maximum(tmp14, tmp20)
    tmp24 = tmp22 * tmp23
    tmp25 = tmp3 - tmp22
    tmp26 = tmp25 * tmp5
    tmp27 = tmp24 + tmp26
    tmp28 = triton_helpers.maximum(tmp21, tmp27)
    tmp29 = tmp7 - tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp13 - tmp28
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp20 - tmp28
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp33 + tmp35
    tmp37 = tmp27 - tmp28
    tmp38 = tl_math.exp(tmp37)
    tmp39 = tmp36 + tmp38
    tl.store(out_ptr0 + (x2), tmp28, xmask)
    tl.store(out_ptr1 + (x2), tmp39, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ci/cciwcvrwy24tefjdtz5snbseko4ourit3a6lgazrjpzviddyu4tb.py
# Topologically Sorted Source Nodes: [mul, sub, mul_1, utils_3, log_softmax], Original ATen: [aten.mul, aten.rsub, aten.add, aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => amax, log, sub_1, sub_2
#   mul => mul
#   mul_1 => mul_1
#   sub => sub
#   utils_3 => add
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %view_8), 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=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, %log), kwargs = {})
triton_poi_fused__log_softmax_add_mul_rsub_1 = async_compile.triton('triton_poi_fused__log_softmax_add_mul_rsub_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: '*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__log_softmax_add_mul_rsub_1', 'mutated_arg_names': ['in_out_ptr0'], '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__log_softmax_add_mul_rsub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp8 = tl.load(in_ptr1 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (x0 + (16*x2)), xmask, 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
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp9 - tmp11
    tl.store(in_out_ptr0 + (x3), tmp12, xmask)
''', 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, 4, 4, 4), (64, 16, 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((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [utils], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [utils_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf0, reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0), out=buf1)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [utils_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf1, reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0), out=buf2)
        del buf1
        buf3 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, sub, mul_1, utils_3, log_softmax], Original ATen: [aten.mul, aten.rsub, aten.add, aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_add_mul_rsub_0.run(primals_2, buf2, buf3, buf4, 64, grid=grid(64), stream=stream0)
        buf5 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [mul, sub, mul_1, utils_3, log_softmax], Original ATen: [aten.mul, aten.rsub, aten.add, aten._log_softmax]
        triton_poi_fused__log_softmax_add_mul_rsub_1.run(buf5, primals_2, buf3, buf4, 256, grid=grid(256), stream=stream0)
        del buf3
        del buf4
    return (buf5, primals_2, buf5, )


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, 4), (64, 16, 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 BP(nn.Module):
    """
    Implementation of the Bastell-Polking k-th order model as a pytorch module
    """

    def __init__(self, n, k, d):
        """
        Initializes a k-th order Batsell-Polking model

        Args:
        n- number of items in universe
        k- order of the model
        d- rank of the model
        """
        super(BP, self).__init__()
        shape = tuple([n] * k)
        self.U = nn.Parameter(torch.nn.init.normal_(torch.Tensor(*shape)))
        self.k = k
        self.n = n
        self.d = d
        self.m = nn.LogSoftmax()

    def forward(self, x):
        """
        Computes choice probabilities for k-th order BP model
        """
        utils = self.U
        for _ in range(self.k - 1):
            utils = torch.matmul(utils, torch.squeeze(x))
        utils = x * utils + (1 - x) * -16
        return self.m(utils)

    def __str__(self):
        return 'BP:k=' + str(self.k)


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


def get_init_inputs():
    return [[], {'n': 4, 'k': 4, 'd': 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.utils.data
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__log_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
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp22 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = -16.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp10 = tmp8 * tmp9
    tmp11 = tmp3 - tmp8
    tmp12 = tmp11 * tmp5
    tmp13 = tmp10 + tmp12
    tmp14 = triton_helpers.maximum(tmp7, tmp13)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp3 - tmp15
    tmp19 = tmp18 * tmp5
    tmp20 = tmp17 + tmp19
    tmp21 = triton_helpers.maximum(tmp14, tmp20)
    tmp24 = tmp22 * tmp23
    tmp25 = tmp3 - tmp22
    tmp26 = tmp25 * tmp5
    tmp27 = tmp24 + tmp26
    tmp28 = triton_helpers.maximum(tmp21, tmp27)
    tmp29 = tmp7 - tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp13 - tmp28
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp20 - tmp28
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp33 + tmp35
    tmp37 = tmp27 - tmp28
    tmp38 = tl_math.exp(tmp37)
    tmp39 = tmp36 + tmp38
    tl.store(out_ptr0 + x2, tmp28, xmask)
    tl.store(out_ptr1 + x2, tmp39, xmask)


@triton.jit
def triton_poi_fused__log_softmax_add_mul_rsub_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp8 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, 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
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp9 - tmp11
    tl.store(in_out_ptr0 + x3, tmp12, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 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((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0
            ), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf0, reinterpret_tensor(primals_2, (16, 4, 4),
            (16, 4, 1), 0), out=buf1)
        buf2 = buf0
        del buf0
        extern_kernels.bmm(buf1, reinterpret_tensor(primals_2, (16, 4, 4),
            (16, 4, 1), 0), out=buf2)
        del buf1
        buf3 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_add_mul_rsub_0[grid(64)](primals_2,
            buf2, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused__log_softmax_add_mul_rsub_1[grid(256)](buf5,
            primals_2, buf3, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del buf4
    return buf5, primals_2, buf5


class BPNew(nn.Module):
    """
    Implementation of the Bastell-Polking k-th order model as a pytorch module
    """

    def __init__(self, n, k, d):
        """
        Initializes a k-th order Batsell-Polking model

        Args:
        n- number of items in universe
        k- order of the model
        d- rank of the model
        """
        super(BPNew, self).__init__()
        shape = tuple([n] * k)
        self.U = nn.Parameter(torch.nn.init.normal_(torch.Tensor(*shape)))
        self.k = k
        self.n = n
        self.d = d
        self.m = nn.LogSoftmax()

    def __str__(self):
        return 'BP:k=' + str(self.k)

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