akhauriyash/Code-Regression huggingface dataset:
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 dataclasses import dataclass
from collections import defaultdict
import torch.optim
from torch import nn
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_0(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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg0_1
    return buf0,


class Base(nn.Module):
    registered = defaultdict(dict)

    @dataclass
    class Config:
        pass

    @property
    def config(self):
        return self._config

    def __init__(self, *args, config: Config=None, **kwargs):
        super().__init__(*args, **kwargs)
        self._config = config

    def __str__(self) ->str:
        return self.__name__

    @classmethod
    def module(Child, Impl):
        try:
            Impl.name
        except AttributeError:
            msg = 'Class {Impl} has no attribute .name'
            raise irtm.IRTMError(msg)
        Base.registered[Child.__name__][Impl.name] = Impl
        return Impl

    @classmethod
    def init(Child, *, name: str=None, **kwargs):
        try:
            if name is None:
                name = 'noop'
            A = Base.registered[Child.__name__][name]
        except KeyError:
            dicrep = yaml.dump(Base.registered, default_flow_style=False)
            msg = (
                f'could not find module "{name}"\n\navailable modules:\n{dicrep}'
                )
            raise irtm.IRTMError(msg)
        config = A.Config(**kwargs)
        log.info(f'! initializing {A.__name__} with {config}')
        return A(config=config)


class Aggregator(Base):
    pass


@Aggregator.module
class MaxPoolingAggregator_1New(Aggregator):
    name = 'max 1'

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


Measured latency_ms: 0.01397
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
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_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


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_add_0[grid(256)](arg1_1, arg0_1, buf0, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class TorchAddNew(torch.nn.Module):

    def __init__(self):
        super(TorchAddNew, self).__init__()

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


Measured latency_ms: 0.015149
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
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2 % 4
    x3 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + x4, tmp0, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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, 2, 4, 2), (256, 64, 16, 8, 2, 1
            ), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(1024)](arg0_1, buf0, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf0, (4, 4, 8, 8), (256, 64, 8, 1), 0),


class UpsampleNew(nn.Module):

    def __init__(self, stride=2):
        super(UpsampleNew, self).__init__()
        self.stride = stride

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


Measured latency_ms: 0.014558
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
import torch.nn as nn
import torch.jit
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_sum_0(in_ptr0, out_ptr0, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_sum_0[grid(1)](arg0_1, buf0, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
    return buf0,


class _SumNew(nn.Module):

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


Measured latency_ms: 0.014021
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_glu_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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_glu_0[grid(256)](buf0, buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf0, (4, 4, 4, 8), (128, 32, 8, 1), 0)


class GatedLinearUnitNew(nn.Module):
    """Gated Linear Unit"""

    def __init__(self, input_size: 'int', hidden_size: 'int'=None, dropout:
        'float'=None):
        super().__init__()
        if dropout is not None:
            self.dropout = nn.Dropout(dropout)
        else:
            self.dropout = dropout
        self.hidden_size = hidden_size or input_size
        self.fc = nn.Linear(input_size, self.hidden_size * 2)
        self.init_weights()

    def init_weights(self):
        for n, p in self.named_parameters():
            if 'bias' in n:
                torch.nn.init.zeros_(p)
            elif 'fc' in n:
                torch.nn.init.xavier_uniform_(p)

    def forward(self, input_0):
        primals_1 = self.fc.weight
        primals_2 = self.fc.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.03837
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
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_prod_sigmoid_0(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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp7 = tl.sigmoid(tmp6)
    tl.store(out_ptr0 + x2, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (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(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_prod_sigmoid_0[grid(64)](buf0, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf1


class PSNNew(torch.nn.Module):

    def __init__(self, input_size, hidden_size, output_size):
        super(PSNNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.fc = torch.nn.Linear(self.input_size, self.hidden_size)

    def forward(self, input_0):
        primals_1 = self.fc.weight
        primals_2 = self.fc.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.023994
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 import 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_per_fused_add_div_mul_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 1e-07
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp0 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tl.store(in_out_ptr0 + x0, tmp12, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_div_mul_sum_0[grid(16)](buf1, arg0_1, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf1,


class LinearPoolNew(nn.Module):

    def __init__(self):
        super(LinearPoolNew, self).__init__()

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


Measured latency_ms: 0.015354
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
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


@triton.jit
def triton_per_fused_add_div_mul_rsub_sum_0(in_out_ptr0, in_ptr0, in_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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
    tmp9 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp11 = triton_helpers.promote_to_tensor(tl.sum(tmp9, 0))
    tmp12 = 2.0
    tmp13 = tmp5 * tmp12
    tmp14 = 1e-05
    tmp15 = tmp13 + tmp14
    tmp16 = tmp8 + tmp11
    tmp17 = tmp16 + tmp14
    tmp18 = tmp15 / tmp17
    tmp19 = 1.0
    tmp20 = tmp19 - tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp20, 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((), (), torch.float32)
        buf3 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_div_mul_rsub_sum_0[grid(1)](buf3, arg0_1,
            arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf3,


class DiceLossNew(nn.Module):

    def __init__(self, weight=None, size_average=True):
        super(DiceLossNew, self).__init__()

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


Measured latency_ms: 0.015632
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.triton_helpers import libdevice
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


@triton.jit
def triton_poi_fused_mul_tanh_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.6666666666666666
    tmp2 = tmp0 * tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = 1.7159
    tmp5 = tmp3 * tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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_mul_tanh_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class LeCunTanhNew(nn.Module):

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


Measured latency_ms: 0.014112
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.triton_helpers import math as tl_math
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


@triton.jit
def triton_poi_fused_exp_mul_pow_repeat_sub_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 200
    x1 = xindex // 200
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + x0 % 4), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = -1200.5000491943226
    tmp5 = tmp3 * tmp4
    tmp6 = tl_math.exp(tmp5)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (1, 200), (200, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 200), (200, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_exp_mul_pow_repeat_sub_0[grid(800)](arg0_1, arg1_1,
            buf0, 800, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class GaussianSmearingNew(nn.Module):

    def __init__(self, in_features, start=0, end=1, num_freqs=50):
        super(GaussianSmearingNew, self).__init__()
        self.num_freqs = num_freqs
        offset = torch.linspace(start, end, num_freqs)
        self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2
        self.offset = nn.Parameter(offset.view(-1, 1).repeat(1, in_features
            ).view(1, -1), requires_grad=False)

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


Measured latency_ms: 0.013943
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
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_clamp_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -0.1
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 0.1
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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_clamp_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class TorchClampOptionMaxMinNew(torch.nn.Module):

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


Measured latency_ms: 0.013903
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 itertools import chain as chain
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16896 % 768
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (768, 3, 1, 16, 16), (768, 256, 256, 16, 1))
    assert_size_stride(primals_2, (768,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64, 64), (786432, 262144, 4096,
        64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            4, 4), padding=(1, 7, 7), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 768, 66, 16, 16), (12976128, 16896, 
            256, 16, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(51904512)](buf1, primals_2, 
            51904512, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
    return reinterpret_tensor(buf1, (4, 16896, 768), (12976128, 1, 16896), 0
        ), primals_1, primals_3


class PatchEmbedNew(nn.Module):
    """
    PatchEmbed.
    """

    def __init__(self, dim_in=3, dim_out=768, kernel=(1, 16, 16), stride=(1,
        4, 4), padding=(1, 7, 7), conv_2d=False):
        super().__init__()
        if conv_2d:
            conv = nn.Conv2d
        else:
            conv = nn.Conv3d
        self.proj = conv(dim_in, dim_out, kernel_size=kernel, stride=stride,
            padding=padding)

    def forward(self, input_0):
        primals_1 = self.proj.weight
        primals_2 = self.proj.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 6.752131
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.triton_helpers import math as tl_math
import numpy as np
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


@triton.jit
def triton_per_fused_exp_mul_neg_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 128
    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)
    r2 = rindex
    r0 = rindex % 2
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp2 = -tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp0 * tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp7, None)


@triton.jit
def triton_per_fused_add_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    RBLOCK: tl.constexpr = 2
    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 + r0, None)
    tmp4 = tl.load(in_out_ptr0 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, 1])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp6 = tmp5 + tmp3
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (2,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 2), (32, 8, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_exp_mul_neg_sum_0[grid(1)](primals_2, primals_1,
            buf0, 1, 128, XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = buf0
        del buf0
        triton_per_fused_add_sum_1[grid(1)](buf2, primals_1, 1, 2, XBLOCK=1,
            num_warps=2, num_stages=1)
    return buf2, primals_1, primals_2


class GeometricLossNew(nn.Module):

    def __init__(self, num_parameters=2, init=[0.0, -3.0]):
        self.num_parameters = num_parameters
        super(GeometricLossNew, self).__init__()
        assert len(init) == num_parameters
        self.weight = nn.Parameter(torch.Tensor(np.array(init)))

    def extra_repr(self):
        return 'num_parameters={}'.format(self.num_parameters)

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


Measured latency_ms: 0.027116
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
import torch.nn as nn
import torch.utils.data.distributed
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_add_div_hardtanh_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 3.0
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 6.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = 0.16666666666666666
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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_add_div_hardtanh_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class HSigmoidNew(nn.Module):

    def __init__(self, inplace=True):
        super(HSigmoidNew, self).__init__()
        self.inplace = inplace

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


Measured latency_ms: 0.013642
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
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_add_mul_sigmoid_sub_sum_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (64 + x3), xmask)
    tmp22 = tl.load(in_ptr0 + (128 + x3), xmask)
    tmp32 = tl.load(in_ptr0 + (192 + x3), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tmp5 = 4.0
    tmp6 = tmp4 * tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tmp8 * tmp5
    tmp10 = tl.sigmoid(tmp9)
    tmp11 = tmp7 - tmp10
    tmp13 = tmp12 - tmp1
    tmp14 = tmp13 + tmp3
    tmp15 = tmp14 * tmp5
    tmp16 = tl.sigmoid(tmp15)
    tmp17 = tmp13 - tmp3
    tmp18 = tmp17 * tmp5
    tmp19 = tl.sigmoid(tmp18)
    tmp20 = tmp16 - tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 - tmp1
    tmp24 = tmp23 + tmp3
    tmp25 = tmp24 * tmp5
    tmp26 = tl.sigmoid(tmp25)
    tmp27 = tmp23 - tmp3
    tmp28 = tmp27 * tmp5
    tmp29 = tl.sigmoid(tmp28)
    tmp30 = tmp26 - tmp29
    tmp31 = tmp21 + tmp30
    tmp33 = tmp32 - tmp1
    tmp34 = tmp33 + tmp3
    tmp35 = tmp34 * tmp5
    tmp36 = tl.sigmoid(tmp35)
    tmp37 = tmp33 - tmp3
    tmp38 = tmp37 * tmp5
    tmp39 = tl.sigmoid(tmp38)
    tmp40 = tmp36 - tmp39
    tmp41 = tmp31 + tmp40
    tl.store(out_ptr0 + x3, tmp41, xmask)


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,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sub_sum_0[grid(64)](arg0_1, arg1_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class SoftHistogramNew(torch.nn.Module):
    """
    Motivated by https://discuss.pytorch.org/t/differentiable-torch-histc/25865/3
    """

    def __init__(self, bins, min_bin_edge, max_bin_edge, sigma):
        super(SoftHistogramNew, self).__init__()
        self.sigma = sigma
        self.delta = float(max_bin_edge - min_bin_edge) / float(bins)
        self.centers = float(min_bin_edge) + self.delta * (torch.arange(
            bins).float() + 0.5)
        self.centers = torch.nn.Parameter(self.centers, requires_grad=False)

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


Measured latency_ms: 0.01511
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
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


@triton.jit
def triton_per_fused_maximum_mean_mul_sub_0(in_out_ptr0, in_ptr0, in_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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = 0.5
    tmp4 = tmp3 * tmp2
    tmp5 = -0.5
    tmp6 = tmp5 * tmp2
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp11 = 256.0
    tmp12 = tmp10 / tmp11
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp12, 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((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_maximum_mean_mul_sub_0[grid(1)](buf1, arg1_1,
            arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class PinballLossNew(nn.Module):
    """Computes the pinball loss between y and y_hat.
  y: actual values in torch tensor.
  y_hat: predicted values in torch tensor.
  tau: a float between 0 and 1 the slope of the pinball loss. In the context
  of quantile regression, the value of alpha determine the conditional
  quantile level.
  return: pinball_loss
  """

    def __init__(self, tau=0.5):
        super(PinballLossNew, self).__init__()
        self.tau = tau

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


Measured latency_ms: 0.014035
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.triton_helpers import libdevice
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_pow_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = libdevice.pow(tmp0, tmp1)
    tl.store(out_ptr0 + x0, tmp2, xmask)


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_pow_0[grid(256)](arg1_1, arg0_1, buf0, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class TorchPowNew(torch.nn.Module):

    def __init__(self):
        super(TorchPowNew, self).__init__()

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


Measured latency_ms: 0.01389
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
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


@triton.jit
def triton_poi_fused_add_relu_sub_threshold_backward_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 - tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp5 + tmp2
    tmp7 = 0.0
    tmp8 = tmp5 <= tmp7
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp8, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_relu_sub_threshold_backward_0[grid(256)](primals_2
            , primals_1, buf0, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_1
        del primals_2
    return buf0, buf1


class TReLUNew(nn.Module):

    def __init__(self):
        super(TReLUNew, self).__init__()
        self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
        self.alpha.data.fill_(0)

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


Measured latency_ms: 0.016615
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_add_convolution_0(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_convolution_0[grid(256)](buf1, primals_2,
            primals_3, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3


class Residual(nn.Module):

    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x, **kwargs):
        return self.fn(x, **kwargs) + x


class PEGNew(nn.Module):

    def __init__(self, dim, kernel_size=3):
        super().__init__()
        self.proj = Residual(nn.Conv2d(dim, dim, kernel_size=kernel_size,
            padding=kernel_size // 2, groups=dim, stride=1))

    def forward(self, input_0):
        primals_1 = self.proj.fn.weight
        primals_2 = self.proj.fn.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.013765
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


@triton.jit
def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x2 = xindex // 64
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_0[grid(1024)](primals_1, buf0, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(1024)](buf2, primals_3, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf2, primals_2, buf0


class UpsampleNew(nn.Module):

    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv2d(in_channels, in_channels,
                kernel_size=3, stride=1, padding=1)

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.03343
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
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_clamp_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -0.1
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 0.1
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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_clamp_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class TorchClampOptionMaxMinNew(torch.nn.Module):

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


Measured latency_ms: 0.015288
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
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


@triton.jit
def triton_poi_fused_add_maximum_minimum_mul_neg_sub_zeros_like_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp3 = tmp1 * tmp2
    tmp6 = 0.0
    tmp7 = tmp2 - tmp6
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = -tmp2
    tmp10 = 2.0
    tmp11 = tmp9 + tmp10
    tmp12 = triton_helpers.maximum(tmp11, tmp6)
    tmp13 = triton_helpers.minimum(tmp8, tmp12)
    tmp14 = 1.0
    tmp15 = tmp2 - tmp14
    tmp16 = triton_helpers.maximum(tmp15, tmp6)
    tmp17 = 3.0
    tmp18 = tmp9 + tmp17
    tmp19 = triton_helpers.maximum(tmp18, tmp6)
    tmp20 = triton_helpers.minimum(tmp16, tmp19)
    tmp21 = tmp20 * tmp10
    tmp22 = tmp13 - tmp21
    tmp23 = tmp5 * tmp22
    tmp24 = tmp3 + tmp23
    tl.store(out_ptr0 + x0, tmp24, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1,), (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_add_maximum_minimum_mul_neg_sub_zeros_like_0[grid(256)
            ](primals_1, primals_2, primals_3, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        del primals_3
    return buf0, primals_2


class SpikeNew(nn.Module):

    def __init__(self, center=1, width=1):
        super(SpikeNew, self).__init__()
        self.c = center
        self.w = width
        self.alpha = torch.nn.Parameter(torch.ones(1))
        self.beta = torch.nn.Parameter(torch.ones(1))

    def forward(self, input_0):
        primals_1 = self.alpha
        primals_3 = self.beta
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.014016