# 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_6/inductor_cache/7e/c7e6nde7zpogw2al4p3oaiveec6rvoay2qieuq2h2ihah2vqbmnq.py
# Topologically Sorted Source Nodes: [z], Original ATen: [aten.sub, aten.transpose]
# Source node to ATen node mapping:
#   z => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand, %permute_5), kwargs = {})
#   %permute_12 : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%view_4, [0, 2, 1]), kwargs = {})
#   %permute_13 : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%view_5, [0, 2, 1]), kwargs = {})
triton_poi_fused_sub_transpose_0 = async_compile.triton('triton_poi_fused_sub_transpose_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, 4], tile_hint=TileHint.DEFAULT,
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sub_transpose_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_sub_transpose_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x3 = xindex
    y5 = (yindex // 4)
    y0 = yindex % 4
    y2 = (yindex // 16)
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y5 + (16*x3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (y0 + (4*y2) + (16*x3)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tl.store(out_ptr0 + (x3 + (4*y4)), tmp2, xmask & ymask)
    tl.store(out_ptr1 + (y4 + (64*x3)), tmp2, xmask & ymask)
    tl.store(out_ptr2 + (y4 + (64*x3)), tmp2, xmask & ymask)
''', 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (1, 4, 16), (0, 1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((64, 4, 1), (1, 64, 256), torch.float32)
        buf4 = empty_strided_cuda((64, 1, 4), (1, 256, 64), torch.float32)
        # Topologically Sorted Source Nodes: [z], Original ATen: [aten.sub, aten.transpose]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sub_transpose_0.run(buf0, buf1, buf3, buf4, 64, 4, grid=grid(64, 4), stream=stream0)
        buf2 = reinterpret_tensor(buf0, (64, 1, 1), (1, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf1, (64, 1, 4), (4, 0, 1), 0), reinterpret_tensor(buf1, (64, 4, 1), (4, 1, 0), 0), out=buf2)
        del buf1
    return (reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0), buf3, buf4, reinterpret_tensor(primals_1, (1, 16, 4), (4, 4, 1), 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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (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


class InnerProductProbe(nn.Module):

    def __init__(self, length: 'int', max_rank: 'int'=None):
        super().__init__()
        self.length = length
        if max_rank is None:
            max_rank = length
        self.b = nn.Parameter(torch.empty(max_rank, length, dtype=torch.
            float32).uniform_(-0.05, 0.05), requires_grad=True)

    def forward(self, x):
        seq_len = x.size(1)
        x = torch.einsum('gh,bih->big', self.b, x)
        x = x.unsqueeze(2).expand(-1, -1, seq_len, -1)
        y = x.clone().permute(0, 2, 1, 3)
        z = x - y
        return torch.einsum('bijg,bijg->bij', z, z)


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


def get_init_inputs():
    return [[], {'length': 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
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_poi_fused_sub_transpose_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x3 = xindex
    y5 = yindex // 4
    y0 = yindex % 4
    y2 = yindex // 16
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y5 + 16 * x3), xmask & ymask, eviction_policy
        ='evict_last')
    tmp1 = tl.load(in_ptr0 + (y0 + 4 * y2 + 16 * x3), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tl.store(out_ptr0 + (x3 + 4 * y4), tmp2, xmask & ymask)
    tl.store(out_ptr1 + (y4 + 64 * x3), tmp2, xmask & ymask)
    tl.store(out_ptr2 + (y4 + 64 * x3), tmp2, xmask & ymask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 16), (64, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(primals_1, (1, 4, 16), (0, 1, 4), 0),
            out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((64, 4, 1), (1, 64, 256), torch.float32)
        buf4 = empty_strided_cuda((64, 1, 4), (1, 256, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sub_transpose_0[grid(64, 4)](buf0, buf1, buf3,
            buf4, 64, 4, XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        buf2 = reinterpret_tensor(buf0, (64, 1, 1), (1, 1, 1), 0)
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf1, (64, 1, 4), (4, 0, 1), 
            0), reinterpret_tensor(buf1, (64, 4, 1), (4, 1, 0), 0), out=buf2)
        del buf1
    return reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0
        ), buf3, buf4, reinterpret_tensor(primals_1, (1, 16, 4), (4, 4, 1), 0)


class InnerProductProbeNew(nn.Module):

    def __init__(self, length: 'int', max_rank: 'int'=None):
        super().__init__()
        self.length = length
        if max_rank is None:
            max_rank = length
        self.b = nn.Parameter(torch.empty(max_rank, length, dtype=torch.
            float32).uniform_(-0.05, 0.05), requires_grad=True)

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