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 torch._inductor.runtime.triton_helpers import libdevice, 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_per_fused_add_mean_mul_sub_xlogy_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)
    tmp9 = tl.load(in_ptr1 + r0, None)
    tmp1 = libdevice.isnan(tmp0).to(tl.int1)
    tmp2 = 0.0
    tmp3 = tmp0 == tmp2
    tmp4 = tl_math.log(tmp0)
    tmp5 = tmp0 * tmp4
    tmp6 = tl.where(tmp3, tmp2, tmp5)
    tmp7 = float('nan')
    tmp8 = tl.where(tmp1, tmp7, tmp6)
    tmp10 = tmp0 * tmp9
    tmp11 = tmp8 - tmp10
    tmp12 = tl.broadcast_to(tmp11, [RBLOCK])
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0))
    tmp15 = libdevice.isnan(tmp9).to(tl.int1)
    tmp16 = tmp9 == tmp2
    tmp17 = tl_math.log(tmp9)
    tmp18 = tmp9 * tmp17
    tmp19 = tl.where(tmp16, tmp2, tmp18)
    tmp20 = tl.where(tmp15, tmp7, tmp19)
    tmp21 = tmp9 * tmp0
    tmp22 = tmp20 - tmp21
    tmp23 = tl.broadcast_to(tmp22, [RBLOCK])
    tmp25 = triton_helpers.promote_to_tensor(tl.sum(tmp23, 0))
    tmp26 = 256.0
    tmp27 = tmp14 / tmp26
    tmp28 = tmp25 / tmp26
    tmp29 = tmp27 + tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp29, 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)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_mean_mul_sub_xlogy_0[grid(1)](buf2, arg0_1,
            arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class JS_DivergenceNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.engine = nn.KLDivLoss()

    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: 0.020166
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 torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
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_mul_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
    tmp9 = tmp0 * tmp8
    tl.store(out_ptr0 + x0, tmp9, 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_mul_0[grid(256)](arg0_1, buf0, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def hard_swish(x: 'torch.Tensor', inplace: 'bool'=False):
    """Hard swish."""
    inner = F.relu6(x + 3.0).div_(6.0)
    return x.mul_(inner) if inplace else x.mul(inner)


class HardSwishNew(nn.Module):
    """Custom hardswish to work with onnx."""

    def __init__(self, inplace: 'bool'=False):
        """Initialize."""
        super().__init__()
        self.inplace = inplace

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


Measured latency_ms: 0.013685
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
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_mean_std_0(in_ptr0, out_ptr2, out_ptr3, 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
    x2 = xindex % 4
    x3 = xindex // 4
    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]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp4 / tmp19
    tmp21 = 15.0
    tmp22 = tmp18 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tl.store(out_ptr2 + (x2 + 8 * x3), tmp20, xmask)
    tl.store(out_ptr3 + (x2 + 8 * x3), tmp23, 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)
        buf6 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        buf4 = reinterpret_tensor(buf6, (4, 4), (8, 1), 0)
        buf5 = reinterpret_tensor(buf6, (4, 4), (8, 1), 4)
        get_raw_stream(0)
        triton_per_fused_mean_std_0[grid(16)](arg0_1, buf4, buf5, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf6,


class MeanStdExtractorNew(nn.Module):

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

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


Measured latency_ms: 0.016437
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 typing import *
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_add_mul_rsub_stack_0(in_ptr0, in_ptr1, out_ptr1,
    out_ptr2, out_ptr3, out_ptr4, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp1 = tl.load(in_ptr1 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp6 = tl.load(in_ptr1 + (64 + x0), xmask)
    tmp9 = tl.load(in_ptr0 + x0, xmask)
    tmp10 = tl.load(in_ptr1 + x0, xmask)
    tmp16 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp17 = tl.load(in_ptr1 + (192 + x0), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 - tmp5
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 * tmp11
    tmp13 = tmp7 + tmp12
    tmp14 = tmp4 * tmp13
    tmp15 = tmp2 + tmp14
    tmp18 = tmp16 * tmp17
    tmp19 = tmp3 - tmp16
    tmp20 = tmp19 * tmp15
    tmp21 = tmp18 + tmp20
    tl.store(out_ptr1 + x0, tmp13, xmask)
    tl.store(out_ptr2 + x0, tmp11, xmask)
    tl.store(out_ptr3 + x0, tmp15, xmask)
    tl.store(out_ptr4 + x0, tmp21, 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)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 64)
        buf1 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0)
        buf3 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 128)
        buf4 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 192)
        get_raw_stream(0)
        triton_poi_fused_add_mul_rsub_stack_0[grid(64)](arg0_1, arg1_1,
            buf2, buf1, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0),


class CPUForgetMultNew(torch.nn.Module):

    def __init__(self):
        super(CPUForgetMultNew, 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: 0.015342
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 5 % 5
    x0 = xindex % 5
    x2 = xindex // 25
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_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_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, 5, 5), (100, 25, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(400)](primals_1, buf0, 400,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 2, 2), (16, 4, 2, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(64)](buf2, primals_3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
    return buf2, primals_2, buf0


class DownsampleNew(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=2, padding=0)

    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: 0.033339
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
from torch.testing._internal.common_utils import *
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 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tl.store(out_ptr0 + x0, tmp2, 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=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class MyRelu2(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input):
        ctx.save_for_backward(input)
        return input.clamp(min=0)


class Net2New(nn.Module):

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

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


Measured latency_ms: 0.01364
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.triton_helpers import libdevice, math as tl_math
from torch.utils.data import Dataset as Dataset
import torch.nn.init as init
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_softplus_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 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_convolution_div_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.015625
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, 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,), (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((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_softplus_0[grid(256)](primals_1, buf0, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(primals_3, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 1), (4, 1, 1, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_div_1[grid(16)](buf2, primals_2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf2, primals_1, primals_3, buf0


class PosConv2dNew(torch.nn.Conv2d):

    def reset_parameters(self) ->None:
        super().reset_parameters()
        self.fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)

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


Measured latency_ms: 0.035244
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


@triton.jit
def triton_poi_fused_pow_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
    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)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tl.store(out_ptr0 + x2, tmp18, 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), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_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 EltwiseSubEmbedNew(nn.Module):

    def __init__(self, nonlinearity='square', use_batch_norm=False,
        use_classifier=False, num_features=0, num_classes=0):
        super(EltwiseSubEmbedNew, self).__init__()
        self.nonlinearity = nonlinearity
        if nonlinearity is not None and nonlinearity not in ['square', 'abs']:
            raise KeyError('Unknown nonlinearity:', nonlinearity)
        self.use_batch_norm = use_batch_norm
        self.use_classifier = use_classifier
        if self.use_batch_norm:
            self.bn = nn.BatchNorm1d(num_features)
            self.bn.weight.data.fill_(1)
            self.bn.bias.data.zero_()
        if self.use_classifier:
            assert num_features > 0 and num_classes > 0
            self.classifier = nn.Linear(num_features, num_classes)
            self.classifier.weight.data.normal_(0, 0.001)
            self.classifier.bias.data.zero_()

    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: 0.01506
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: 0.015125
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.nn import Module
import torch.nn.functional
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
from torch.nn import Parameter
from torch.nn import Module
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_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 16777216
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x2, tmp4, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16777216,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 16777216), (268435456, 67108864,
        16777216, 1))
    assert_size_stride(primals_3, (16777216,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 16777216), (268435456, 67108864,
            16777216, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(1073741824)](primals_2, primals_1,
            primals_3, buf0, 1073741824, XBLOCK=1024, num_warps=4, num_stages=1
            )
    return buf0, primals_1, primals_2, primals_3


class ModelNew(Module):

    def __init__(self):
        super(ModelNew, self).__init__()
        self.a = Parameter(torch.FloatTensor(4096 * 4096).fill_(1.0))
        self.b = Parameter(torch.FloatTensor(4096 * 4096).fill_(2.0))

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


Measured latency_ms: 0.013759
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


@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 // 784 % 32
    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, (32, 4, 9, 9), (324, 81, 9, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 28, 28), (25088, 784, 28, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(100352)](buf1, primals_2, 
            100352, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3


class ConvUnitNew(nn.Module):

    def __init__(self, in_channels):
        super(ConvUnitNew, self).__init__()
        self.conv0 = nn.Conv2d(in_channels=in_channels, out_channels=32,
            kernel_size=9, stride=2, bias=True)

    def forward(self, input_0):
        primals_1 = self.conv0.weight
        primals_2 = self.conv0.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.031102
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_div_0(in_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_out_ptr0 + x0, xmask)
    tmp1 = 0.00390625
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, 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((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 0),
            reinterpret_tensor(arg0_1, (16, 16), (1, 16), 0), out=buf0)
        del arg0_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(256)](buf1, 256, XBLOCK=256, num_warps=
            4, num_stages=1)
    return buf1,


class GramMatrixNew(nn.Module):

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


Measured latency_ms: 0.019237
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_gelu_mul_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 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp9 = 1.7015043497085571
    tmp10 = tmp8 * tmp9
    tl.store(out_ptr0 + x0, tmp10, 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_gelu_mul_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class VPGELUNew(nn.Module):

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


Measured latency_ms: 0.014083
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 import 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_clamp_mul_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 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 2.0
    tmp4 = tmp0 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = triton_helpers.minimum(tmp6, tmp3)
    tmp8 = tmp2 * 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_clamp_mul_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def hard_mish(x, inplace: 'bool'=False):
    """ Hard Mish
    Experimental, based on notes by Mish author Diganta Misra at
      https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
    """
    if inplace:
        return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
    else:
        return 0.5 * x * (x + 2).clamp(min=0, max=2)


class HardMishNew(nn.Module):

    def __init__(self, inplace: 'bool'=False):
        super(HardMishNew, self).__init__()
        self.inplace = inplace

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


Measured latency_ms: 0.014183
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 libdevice, math as tl_math
from torch.nn import Module
from torch.nn import Sigmoid
from torch.nn import BCELoss
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_binary_cross_entropy_sigmoid_zeros_like_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)
    tmp14 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = -tmp1
    tmp3 = libdevice.log1p(tmp2)
    tmp4 = -100.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 0.0
    tmp7 = tmp6 * tmp5
    tmp8 = tl_math.log(tmp1)
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = tmp7 - tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp15 = tl.sigmoid(tmp14)
    tmp16 = -tmp15
    tmp17 = libdevice.log1p(tmp16)
    tmp18 = triton_helpers.maximum(tmp17, tmp4)
    tmp19 = -1.0
    tmp20 = tmp19 * tmp18
    tmp21 = tl_math.log(tmp15)
    tmp22 = triton_helpers.maximum(tmp21, tmp4)
    tmp23 = tmp6 * tmp22
    tmp24 = tmp20 - tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = tmp13 + tmp27
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp28, 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)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_sigmoid_zeros_like_0[grid(1)
            ](buf2, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class BinaryCrossEntropyLossNew(Module):
    """This class implements :class:`torch.nn.Module` interface.

    """

    def __init__(self):
        super().__init__()
        self.sig = Sigmoid()
        self.loss = BCELoss(reduction='sum')

    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: 0.015296
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.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_exp_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = libdevice.exp(tmp1)
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp2, None)


def call(args):
    primals_1, = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1,), (1,), torch.float64)
        get_raw_stream(0)
        triton_poi_fused_exp_0[grid(1)](primals_1, buf0, 1, XBLOCK=1,
            num_warps=1, num_stages=1)
        del primals_1
    return buf0, buf0


class Optimizable_TemperatureNew(torch.nn.Module):

    def __init__(self, initial_temperature=None):
        super(Optimizable_TemperatureNew, self).__init__()
        self.log_temperature = torch.nn.Parameter(data=torch.zeros([1]).
            type(torch.DoubleTensor))
        if initial_temperature is not None:
            self.log_temperature.data = torch.log(torch.tensor(
                initial_temperature).type(torch.DoubleTensor))

    def forward(self):
        primals_1 = self.log_temperature
        output = call([primals_1])
        return output[0]


Measured latency_ms: 0.014151
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
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__to_copy_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
    tmp3 = 0.10000000149011612
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + x0, tmp4, 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__to_copy_add_0[grid(256)](arg0_1, arg1_1, buf0, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class FAddNew(nn.Module):

    def __init__(self):
        super(FAddNew, 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: 0.013993
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 = tmp0 * tmp0
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = tmp1 * tmp1
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 2.0
    tmp15 = tmp5 * tmp14
    tmp16 = 1.0
    tmp17 = tmp15 + tmp16
    tmp18 = tmp9 + tmp13
    tmp19 = tmp18 + tmp16
    tmp20 = tmp17 / tmp19
    tmp21 = tmp16 - tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp21, 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):
        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: 0.015345
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.nn import Module
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_div_mul_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.00392156862745098
    tmp2 = tmp0 * tmp1
    tmp3 = 0.5
    tmp4 = tmp2 - tmp3
    tmp5 = 2.0
    tmp6 = tmp4 * tmp5
    tl.store(out_ptr0 + x0, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mul_sub_0[grid(256)](arg0_1, buf0, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class NormImageUint8ToFloatNew(Module):

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


Measured latency_ms: 0.0139
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_add_div_mul_pow_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 = tmp0 * tmp0
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 + tmp2
    tmp6 = 0.75
    tmp7 = libdevice.pow(tmp5, tmp6)
    tmp8 = tmp0 / 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_mul_pow_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class LRNNew(nn.Module):

    def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
        ):
        super(LRNNew, self).__init__()
        self.ACROSS_CHANNELS = ACROSS_CHANNELS
        if ACROSS_CHANNELS:
            self.average = nn.AvgPool3d(kernel_size=(local_size, 1, 1),
                stride=1, padding=(int((local_size - 1.0) / 2), 0, 0))
        else:
            self.average = nn.AvgPool2d(kernel_size=local_size, stride=1,
                padding=int((local_size - 1.0) / 2))
        self.alpha = alpha
        self.beta = beta

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


Measured latency_ms: 0.013982
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.optim
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_div_mse_loss_mul_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, 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)
    tmp3 = tl.load(in_ptr2 + r0, None)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tmp4 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [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 = tmp8 / tmp11
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp12, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_div_mse_loss_mul_sum_0[grid(1)](buf2, arg0_1,
            arg1_1, arg2_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2,


class MaskedMSELossNew(nn.Module):

    def __init__(self):
        super(MaskedMSELossNew, self).__init__()
        self.loss = nn.MSELoss(reduction='sum')

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]


Measured latency_ms: 0.066572
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, math as tl_math
import torch.jit
import torch.onnx
import torch.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_softplus_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 = tmp0 + tmp0
    tmp2 = 20.0
    tmp3 = tmp1 > tmp2
    tmp4 = tl_math.exp(tmp1)
    tmp5 = libdevice.log1p(tmp4)
    tmp6 = tl.where(tmp3, tmp1, tmp5)
    tl.store(out_ptr0 + x0, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_softplus_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class SimpleSoftPlusModelNew(torch.nn.Module):

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

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


Measured latency_ms: 0.014294