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.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_sqrt_sum_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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-10
    tmp14 = tmp12 + tmp13
    tmp15 = tmp0 / tmp14
    tmp17 = tmp15 * tmp16
    tl.store(out_ptr0 + x3, tmp17, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (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_sqrt_sum_0[grid(256)](primals_1,
            primals_2, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf0, primals_1


class L2NormNew(nn.Module):

    def __init__(self, n_channels, scale=1.0):
        super(L2NormNew, self).__init__()
        self.n_channels = n_channels
        self.scale = scale
        self.eps = 1e-10
        self.weight = nn.Parameter(torch.Tensor(self.n_channels))
        self.weight.data *= 0.0
        self.weight.data += self.scale

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


Measured latency_ms: 0.015588
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_per_fused_add_exp_mean_pow_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp2 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp24 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp26 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = tmp0 * tmp0
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 + tmp3
    tmp5 = tmp4 - tmp2
    tmp6 = 1.0
    tmp7 = tmp5 - tmp6
    tmp9 = tmp8 * tmp8
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp13 = tmp12 - tmp10
    tmp14 = tmp13 - tmp6
    tmp15 = tmp7 + tmp14
    tmp17 = tmp16 * tmp16
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp20 - tmp18
    tmp22 = tmp21 - tmp6
    tmp23 = tmp15 + tmp22
    tmp25 = tmp24 * tmp24
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp28 - tmp26
    tmp30 = tmp29 - tmp6
    tmp31 = tmp23 + tmp30
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.sum(tmp32, 1)[:, None]
    tmp35 = 64.0
    tmp36 = tmp34 / tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp36, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_exp_mean_pow_sub_sum_0[grid(1)](buf2, arg0_1,
            arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class selfLatentLossNew(nn.Module):

    def __init__(self):
        super(selfLatentLossNew, 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: 0.040365
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_leaky_relu_leaky_relu_backward_0(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.01
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_leaky_relu_backward_0[grid(16)](buf1,
            buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf1, primals_1, primals_2, buf2


class BasicConv2dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
        super(BasicConv2dNew, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=
            False, **kwargs)

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


Measured latency_ms: 0.022453
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 itertools import chain as chain
import torch.utils.data
from torch.nn.modules.loss import _Loss
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_with_logits_0(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp3 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr0 + (64 + r2), None)
    tmp18 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.minimum(tmp5, tmp3)
    tmp7 = tl_math.abs(tmp3)
    tmp8 = -tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = libdevice.log1p(tmp9)
    tmp11 = tmp6 - tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp17 = tmp1 - tmp16
    tmp19 = tmp17 * tmp18
    tmp20 = triton_helpers.minimum(tmp5, tmp18)
    tmp21 = tl_math.abs(tmp18)
    tmp22 = -tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = libdevice.log1p(tmp23)
    tmp25 = tmp20 - tmp24
    tmp26 = tmp19 - tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = 64.0
    tmp31 = tmp15 / tmp30
    tmp32 = tmp29 / tmp30
    tmp33 = tmp31 + tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp33, 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_with_logits_0[grid(1)](buf2,
            arg1_1, arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class StendLossNew(_Loss):

    def __init__(self, size_average=None, reduce=None, reduction='mean'):
        super(StendLossNew, self).__init__()
        self.reduction = reduction

    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: 0.015452
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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 256
    x4 = xindex // 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x5, 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, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(1024)](arg1_1, arg0_1, buf0, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class AttentionNew(nn.Module):

    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: 0.014051
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):
    xnumel = 2859936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 238328 % 3
    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, (3, 1, 3, 3, 3), (27, 27, 9, 3, 1))
    assert_size_stride(primals_2, (3,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64, 64), (262144, 262144, 4096,
        64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1, 1), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 3, 62, 62, 62), (714984, 238328, 3844,
            62, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(2859936)](buf1, primals_2, 
            2859936, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3


class NetworkNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=3)

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


Measured latency_ms: 0.279388
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
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_view_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x4, tmp2, 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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_view_0[grid(256)](buf2, primals_3, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_3
    return buf2, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0)


class LinearRegressionNew(nn.Module):

    def __init__(self, in_features: 'int', out_features: 'int', bias:
        'bool'=True):
        super().__init__()
        self.weights = nn.Parameter(torch.randn(out_features, in_features))
        self.bias = bias
        if bias:
            self.bias_term = nn.Parameter(torch.randn(out_features))

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


Measured latency_ms: 0.022878
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
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_clamp_mul_pow_rsqrt_sum_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
    x1 = xindex // 16 % 4
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp3 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = 1e-09
    tmp15 = triton_helpers.maximum(tmp13, tmp14)
    tmp16 = libdevice.rsqrt(tmp15)
    tmp17 = tmp2 * tmp16
    tl.store(out_ptr0 + x3, tmp17, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_mul_pow_rsqrt_sum_0[grid(256)](primals_1,
            primals_2, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
    return buf0, primals_2


class L2NormNew(nn.Module):
    """Channel-wise L2 normalization."""

    def __init__(self, in_channels):
        super(L2NormNew, self).__init__()
        self.weight = nn.Parameter(torch.randn(in_channels))

    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: 0.015039
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_cat_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (64 + x2), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (128 + x2), xmask)
    tmp8 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (192 + x2), xmask)
    tmp16 = tl.load(in_ptr1 + x2, xmask)
    tmp18 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = 1.0
    tmp5 = tmp4 - tmp3
    tmp7 = tmp0 * tmp6
    tmp9 = tmp4 - tmp8
    tmp11 = tmp0 * tmp10
    tmp12 = tmp9 * tmp11
    tmp13 = tmp7 + tmp12
    tmp14 = tmp5 * tmp13
    tmp15 = tmp2 + tmp14
    tmp17 = tmp0 * tmp16
    tmp19 = tmp4 - tmp18
    tmp20 = tmp19 * tmp15
    tmp21 = tmp17 + tmp20
    tl.store(out_ptr0 + x2, tmp15, xmask)
    tl.store(out_ptr1 + x2, tmp13, xmask)
    tl.store(out_ptr2 + x2, tmp21, xmask)
    tl.store(out_ptr3 + x2, tmp11, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (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)
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        buf0 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 64)
        buf2 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 128)
        buf1 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
        buf3 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 192)
        get_raw_stream(0)
        triton_poi_fused_add_cat_mul_rsub_0[grid(64)](arg0_1, arg1_1, buf0,
            buf2, buf1, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf4,


class CPUReverseForgetMultNew(torch.nn.Module):

    def __init__(self):
        super(CPUReverseForgetMultNew, 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: 0.019487
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_add_gt_lt_mul_where_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 = -1.0
    tmp2 = tmp0 < tmp1
    tmp3 = 0.01
    tmp4 = tmp0 * tmp3
    tmp5 = tmp4 + tmp1
    tmp6 = tl.where(tmp2, tmp5, tmp0)
    tmp7 = 1.0
    tmp8 = tmp6 > tmp7
    tmp9 = tmp6 * tmp3
    tmp10 = tmp9 + tmp7
    tmp11 = tl.where(tmp8, tmp10, tmp6)
    tl.store(out_ptr0 + x0, tmp11, 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_gt_lt_mul_where_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class leaky_hardtanhNew(nn.Module):

    def __init__(self, min=-1, max=1, slope=0.01):
        super(leaky_hardtanhNew, self).__init__()
        self.min = min
        self.max = max
        self.slope = slope

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


Measured latency_ms: 0.013896
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_div_exp_sum_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
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp3 + tmp5
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 + tmp8
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp13 = tmp1 / tmp12
    tl.store(out_ptr0 + x2, tmp13, 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_exp_sum_0[grid(256)](arg0_1, buf0, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class SoftmaxNew(nn.Module):

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


Measured latency_ms: 0.014097
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 math
import torch.utils.data
import torch.cuda
import torch.utils.checkpoint
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_pow_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 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = tmp0 * tmp0
    tmp4 = tmp3 * tmp0
    tmp5 = 0.044715
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tmp8 = 0.7978845608028654
    tmp9 = tmp7 * tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tmp2 * tmp12
    tl.store(out_ptr0 + x0, tmp13, 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_mul_pow_tanh_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def agelu(x):
    SQRT_M2_PI = math.sqrt(2 / math.pi)
    COEFF = 0.044715
    return 0.5 * x * (1.0 + torch.tanh(SQRT_M2_PI * (x + COEFF * torch.pow(
        x, 3))))


class AGELUNew(torch.nn.Module):

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


Measured latency_ms: 0.012768
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
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_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 % 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 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = 0.7071067811865476
    tmp5 = tmp1 * tmp4
    tmp6 = libdevice.erf(tmp5)
    tmp7 = 1.0
    tmp8 = tmp6 + tmp7
    tmp9 = tmp3 * tmp8
    tmp10 = tmp0 * tmp9
    tl.store(out_ptr0 + x2, tmp10, 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_gelu_mul_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, 4), (128, 32, 8, 1), 0
        ), reinterpret_tensor(buf0, (4, 4, 4, 4), (128, 32, 8, 1), 4)


class GEGLUNew(nn.Module):

    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    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: 0.014966
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.autograd import Function
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_sign_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 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tl.store(out_ptr0 + x0, tmp7, 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_sign_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class SignFunction(Function):

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

    @staticmethod
    def forward(ctx, input, is_training=True):
        if is_training:
            prob = input.new(input.size()).uniform_()
            x = input.clone()
            x[(1 - input) / 2 <= prob] = 1
            x[(1 - input) / 2 > prob] = -1
            return x
        else:
            return input.sign()

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output, None


class SignNew(nn.Module):

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

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


Measured latency_ms: 0.014146
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


@triton.jit
def triton_poi_fused__to_copy_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.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp1, 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.float16)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class tofp16New(nn.Module):
    """
    Model wrapper that implements::
        def forward(self, input):
            return input.half()
    """

    def __init__(self):
        super(tofp16New, self).__init__()

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


Measured latency_ms: 0.016219
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_per_fused_add_div_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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, :]
    rmask = rindex < rnumel
    r0 = rindex % 12
    r1 = rindex // 12
    r2 = rindex % 3
    r3 = rindex // 3
    tmp0 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, other=0.0)
    tmp8 = tl.load(in_ptr0 + (1 + r2 + 4 * r3), rmask, other=0.0)
    tmp9 = tl.load(in_ptr0 + (r2 + 4 * r3), rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp10 = tmp8 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(rmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = tmp7 + tmp15
    tmp17 = 1.0
    tmp18 = tmp16 * tmp17
    tmp19 = 0.00390625
    tmp20 = tmp18 * tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp20, 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)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_div_mul_pow_sub_sum_0[grid(1)](buf2, arg0_1, 1,
            192, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class TVLossNew(nn.Module):

    def __init__(self, tvloss_weight=1):
        super(TVLossNew, self).__init__()
        self.tvloss_weight = tvloss_weight

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


Measured latency_ms: 0.014287
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_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 = 1.0
    tmp2 = tmp0 + tmp1
    tmp3 = 0.1
    tmp4 = tmp2 * tmp3
    tmp5 = tmp4 - tmp1
    tmp6 = tmp0 - tmp1
    tmp7 = tmp6 * tmp3
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.minimum(tmp8, tmp0)
    tmp10 = triton_helpers.maximum(tmp5, 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_add_maximum_minimum_mul_sub_0[grid(256)](arg0_1,
            buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class PLUNew(nn.Module):
    """
    y = max(alpha*(x+c)−c, min(alpha*(x−c)+c, x))
    from PLU: The Piecewise Linear Unit Activation Function
    """

    def __init__(self, alpha=0.1, c=1):
        super().__init__()
        self.alpha = alpha
        self.c = c

    def __repr__(self):
        s = '{name} ({alhpa}, {c})'
        return s.format(name=self.__class__.__name__, **self.__dict__)

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


Measured latency_ms: 0.015722
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
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
import torch.fft
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_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + 4 * x1, tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tl_math.cos(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp14 = tl.load(in_ptr0 + 4 * x1, tmp11 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.load(in_ptr0 + (1 + 4 * x1), tmp11 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tl_math.sin(tmp15)
    tmp17 = tmp14 * tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp11, tmp17, tmp18)
    tmp20 = tl.where(tmp4, tmp10, tmp19)
    tl.store(out_ptr0 + x2, tmp20, 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), (32, 8, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(128)](arg0_1, buf0, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class CartesianNew(nn.Module):

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


Measured latency_ms: 0.014085
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (4 * x1 + (-1 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 6, tl.int64)
    tmp14 = tl.load(in_ptr0 + 4 * x1, tmp11 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x2, tmp16, 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, 6), (96, 24, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(384)](arg0_1, buf0, 384, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def lr_pad(x, padding=1):
    """ Pad left/right-most to each other instead of zero padding """
    return torch.cat([x[..., -padding:], x, x[..., :padding]], dim=3)


class LR_PADNew(nn.Module):
    """ Pad left/right-most to each other instead of zero padding """

    def __init__(self, padding=1):
        super(LR_PADNew, self).__init__()
        self.padding = padding

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


Measured latency_ms: 0.013892
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
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_abs_pow_sub_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 % 4
    x2 = xindex // 16
    x3 = xindex // 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tmp3 * tmp3
    tl.store(out_ptr0 + x4, tmp4, 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, (4, 4), (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_abs_pow_sub_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 PairwiseDistanceNew(torch.nn.Module):

    def __init__(self, p=2):
        super().__init__()
        self.p = p

    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: 0.014936
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: 0.013771