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 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_abs_add_div_mean_mul_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = 1e-10
    tmp4 = tmp0 + tmp3
    tmp5 = tmp2 / tmp4
    tmp6 = tl_math.abs(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = 256.0
    tmp11 = tmp9 / tmp10
    tmp12 = 100.0
    tmp13 = tmp11 * tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp13, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_abs_add_div_mean_mul_sub_0[grid(1)](buf1, arg0_1,
            arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class MAPELossNew(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: 0.015295
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 math as tl_math
from torch.nn.modules.loss import CrossEntropyLoss
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_clamp_log_mul_neg_sum_0(in_ptr0, in_ptr1, out_ptr0,
    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
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = 1e-07
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = 0.9999999
    tmp5 = triton_helpers.minimum(tmp3, tmp4)
    tmp6 = tl_math.log(tmp5)
    tmp7 = tmp0 * tmp6
    tmp10 = triton_helpers.maximum(tmp9, tmp2)
    tmp11 = triton_helpers.minimum(tmp10, tmp4)
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp8 * tmp12
    tmp14 = tmp7 + tmp13
    tmp17 = triton_helpers.maximum(tmp16, tmp2)
    tmp18 = triton_helpers.minimum(tmp17, tmp4)
    tmp19 = tl_math.log(tmp18)
    tmp20 = tmp15 * tmp19
    tmp21 = tmp14 + tmp20
    tmp24 = triton_helpers.maximum(tmp23, tmp2)
    tmp25 = triton_helpers.minimum(tmp24, tmp4)
    tmp26 = tl_math.log(tmp25)
    tmp27 = tmp22 * tmp26
    tmp28 = tmp21 + tmp27
    tmp29 = -tmp28
    tmp30 = tl.broadcast_to(tmp29, [XBLOCK, RBLOCK])
    tmp32 = tl.sum(tmp30, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp32, 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)
        get_raw_stream(0)
        triton_per_fused_clamp_log_mul_neg_sum_0[grid(1)](arg1_1, arg0_1,
            buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class CrossEntropyLossOneHotNew(CrossEntropyLoss):
    EPS: 'int' = 1e-07

    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: 0.015991
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex % 2
    x3 = xindex // 2
    y0 = yindex % 4
    y1 = yindex // 4
    x5 = xindex
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (2 * x2 + 4 * (y0 // 2) + 8 * x3 + 64 * y1 + 
        y0 % 2), xmask & ymask)
    tl.store(out_ptr0 + (x5 + 16 * y4), tmp0, xmask & ymask)


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, 2), (64, 16, 4, 2, 1), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 16)](arg0_1, buf0, 16, 16, XBLOCK
            =16, YBLOCK=16, num_warps=4, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf0, (4, 16, 2, 2), (64, 4, 2, 1), 0),


class ReorgNew(nn.Module):

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

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


Measured latency_ms: 0.014004
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 28
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x1 = xindex // 7
    x2 = xindex
    tmp0 = -3 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + (-3 + x0 + 4 * x1), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_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
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, 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), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 7), (7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(28)](primals_1, buf0, 28,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7
            ), (0, 7, 1), 0), primals_2, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (1, 4, 4), (16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(16)](buf2, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf2, (4, 4), (4, 1), 0
        ), primals_2, reinterpret_tensor(buf0, (1, 4, 7), (28, 7, 1), 0)


class CausalConv1dNew(torch.nn.Conv1d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        dilation=1, groups=1, bias=True):
        super(CausalConv1dNew, self).__init__(in_channels, out_channels,
            kernel_size=kernel_size, stride=stride, padding=0, dilation=
            dilation, groups=groups, bias=bias)
        self.__padding = (kernel_size - 1) * dilation

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


Measured latency_ms: 0.035149
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_glu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


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


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

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

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

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


Measured latency_ms: 0.092217
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 math as tl_math
import torch.nn.functional
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_eq_masked_fill_mean_smooth_l1_loss_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)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp1 = 4.0
    tmp2 = tmp0 == tmp1
    tmp4 = tmp3 - tmp0
    tmp5 = tl_math.abs(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 < tmp6
    tmp8 = tmp5 * tmp5
    tmp9 = 0.5
    tmp10 = tmp8 * tmp9
    tmp11 = tmp10 * tmp6
    tmp12 = tmp5 - tmp9
    tmp13 = tl.where(tmp7, tmp11, tmp12)
    tmp14 = 0.0
    tmp15 = tl.where(tmp2, tmp14, tmp13)
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp19 = 256.0
    tmp20 = tmp18 / tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp20, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_eq_masked_fill_mean_smooth_l1_loss_0[grid(1)](buf1,
            arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class SmoothL1LossWithIgnoreNew(nn.Module):

    def __init__(self, ignore_value: 'int', fraction: 'float'=1.0):
        super().__init__()
        self.ignore_value = ignore_value
        self.fraction = fraction

    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: 0.015084
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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_mul_sin_0(in_ptr0, in_ptr1, in_ptr2, 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
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp3 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl_math.sin(tmp4)
    tl.store(out_ptr0 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sin_0[grid(64)](primals_4, buf0, primals_5,
            buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf1, primals_4, primals_5, reinterpret_tensor(primals_3, (16, 4
        ), (4, 1), 0), buf0


class FiLMLayerNew(nn.Module):

    def __init__(self, input_dim, hidden_dim):
        super().__init__()
        self.layer = nn.Linear(input_dim, hidden_dim)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.layer.weight
        primals_2 = self.layer.bias
        primals_3 = input_0
        primals_4 = input_1
        primals_5 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.033727
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__unsafe_index_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
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp9, xmask)


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__unsafe_index_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class UpsampleNew(nn.Module):
    """ nn.Upsample is deprecated """

    def __init__(self, scale_factor, mode='nearest'):
        super(UpsampleNew, self).__init__()
        self.scale_factor = scale_factor
        self.mode = mode

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


Measured latency_ms: 0.013935
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.nn as nn
import torch.utils.data
import torch.nn.functional as F
from torch.distributions.utils import probs_to_logits
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_neg_sigmoid_softplus_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = -tmp0
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = -tmp7
    tmp9 = tmp0 > tmp3
    tmp10 = tl_math.exp(tmp0)
    tmp11 = libdevice.log1p(tmp10)
    tmp12 = tl.where(tmp9, tmp0, tmp11)
    tmp13 = -tmp12
    tmp14 = tmp8 + tmp13
    tl.store(out_ptr0 + x0, tmp1, xmask)
    tl.store(out_ptr1 + x0, tmp14, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_neg_sigmoid_softplus_0[grid(256)](arg0_1, buf0,
            buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0, buf1


class Bijection(nn.Module):
    """
    An invertible transformation.
    """

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

    def forward(self, inputs, context):
        """
        :param inputs: [..., D]
        :param context: [..., H] or None
        :return: [..., D] outputs and [..., D] log determinant of Jacobian
        """
        raise NotImplementedError('Implement me!')

    def inverse(self, outputs, context):
        """
        :param outputs: [..., D]
        :param context: [..., H] or None
        :return: [..., D] inputs and [..., D] log determinant of Jacobian of inverse transform
        """
        raise NotImplementedError('Implement me!')


class SigmoidTransformerNew(Bijection):
    """
    Maps inputs from R to (0, 1) using a sigmoid.
    """

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

    def inverse(self, outputs, context=None):
        inputs = probs_to_logits(outputs, is_binary=True)
        log_p, log_q = -F.softplus(-inputs), -F.softplus(inputs)
        return inputs, -log_p - log_q

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0], output[1]


Measured latency_ms: 0.016057
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.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_per_fused_div_linalg_vector_norm_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = tmp0 * tmp0
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp11 = libdevice.sqrt(tmp6)
    tmp12 = libdevice.sqrt(tmp10)
    tmp13 = tmp11 / tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp13, 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_div_linalg_vector_norm_sub_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 SpectralConvergengeLossNew(torch.nn.Module):
    """Spectral convergence loss module."""

    def __init__(self):
        """Initilize spectral convergence loss module."""
        super(SpectralConvergengeLossNew, 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: 0.015352
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.utils.data
import torch
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_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_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, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_1
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
    return reinterpret_tensor(buf2, (4, 4), (4, 1), 0), primals_2, buf1


def autopad(k, p=None):
    if p is None:
        p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
    return p


class Flatten(nn.Module):

    @staticmethod
    def forward(x):
        return x.view(x.size(0), -1)


class ClassifyNew(nn.Module):

    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
        super(ClassifyNew, self).__init__()
        self.aap = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False
            )
        self.flat = Flatten()

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


Measured latency_ms: 0.013937
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 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_per_fused_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_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)
    tmp3 = tl.load(in_ptr1 + r0, 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 = -tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp1 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = 0.5
    tmp18 = tmp16 * tmp17
    tmp19 = tmp18 * tmp12
    tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
    tmp22 = triton_helpers.promote_to_tensor(tl.sum(tmp20, 0))
    tmp23 = 256.0
    tmp24 = tmp22 / tmp23
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp24, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_0[
            grid(1)](buf1, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class FocalLossNew(nn.Module):
    """Focal loss function for imbalanced dataset. 
    Args:
        alpha (float): weighing factor between 0 and 1. Alpha may be set by inverse 
                       class frequency
        gamma (float):  modulating factor reduces the loss contribution from easy 
                        examples and extends the range in which an example receives 
                        low loss. Usually between 0 - 5.  
    """

    def __init__(self, alpha=0.5, gamma=2):
        super().__init__()
        self.alpha = alpha
        self.gamma = gamma

    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: 0.01494
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_neg_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
    tmp9 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp11 = triton_helpers.promote_to_tensor(tl.sum(tmp9, 0))
    tmp12 = 2.0
    tmp13 = tmp5 * tmp12
    tmp14 = 1.0
    tmp15 = tmp13 + tmp14
    tmp16 = -tmp15
    tmp17 = tmp8 + tmp11
    tmp18 = tmp17 + tmp14
    tmp19 = tmp16 / tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp19, 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_neg_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):
    """DiceLoss.

    .. seealso::
        Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for
        volumetric medical image segmentation." 2016 fourth international conference on 3D vision (3DV). IEEE, 2016.

    Args:
        smooth (float): Value to avoid division by zero when images and predictions are empty.

    Attributes:
        smooth (float): Value to avoid division by zero when images and predictions are empty.
    """

    def __init__(self, smooth=1.0):
        super(DiceLossNew, self).__init__()
        self.smooth = smooth

    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: 0.014915
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
from torch import Tensor
from abc import ABC
from abc import abstractmethod
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_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    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), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sum_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg0_1
    return buf0,


class AcquisitionObjective(Module, ABC):
    """Abstract base class for objectives."""
    ...


class MCAcquisitionObjective(AcquisitionObjective):
    """Abstract base class for MC-based objectives."""

    @abstractmethod
    def forward(self, samples: 'Tensor') ->Tensor:
        """Evaluate the objective on the samples.

        Args:
            samples: A `sample_shape x batch_shape x q x m`-dim Tensors of
                samples from a model posterior.

        Returns:
            Tensor: A `sample_shape x batch_shape x q`-dim Tensor of objective
            values (assuming maximization).

        This method is usually not called directly, but via the objectives

        Example:
            >>> # `__call__` method:
            >>> samples = sampler(posterior)
            >>> outcome = mc_obj(samples)
        """
        pass


class DummyMCObjectiveNew(MCAcquisitionObjective):

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


Measured latency_ms: 0.014154
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: 0.015817
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_mul_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 3
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (3, 4), (4, 1))
    assert_size_stride(primals_2, (3,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 3), (48, 12, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 3), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 3), (48, 12, 3, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(192)](buf1, primals_2, primals_4, 192,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return reinterpret_tensor(buf1, (64, 3), (3, 1), 0
        ), primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)


class LinearRegressionNew(nn.Module):

    def __init__(self, hidden_size):
        super(LinearRegressionNew, self).__init__()
        self.linear1 = nn.Linear(hidden_size, 3)

    def forward(self, input_0, input_1):
        primals_1 = self.linear1.weight
        primals_2 = self.linear1.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.024014
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 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_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    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, 4), (256, 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_sum_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class AggregateNew(nn.Module):
    """Pooling layer based on sum or average with optional masking.

    Args:
        axis (int): axis along which pooling is done.
        mean (bool, optional): if True, use average instead for sum pooling.
        keepdim (bool, optional): whether the output tensor has dim retained or not.

    """

    def __init__(self, axis, mean=False, keepdim=True):
        super(AggregateNew, self).__init__()
        self.average = mean
        self.axis = axis
        self.keepdim = keepdim

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


Measured latency_ms: 0.013687
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_per_fused_add_clamp_div_mean_mul_pow_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp6 = tl.load(in_ptr1 + r0, None)
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = tmp2 + tmp3
    tmp5 = tmp2 / tmp4
    tmp7 = triton_helpers.maximum(tmp6, tmp1)
    tmp8 = tmp7 + tmp3
    tmp9 = tmp7 / tmp8
    tmp10 = tmp5 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [RBLOCK])
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0))
    tmp15 = 256.0
    tmp16 = tmp14 / tmp15
    tmp17 = 0.5
    tmp18 = tmp16 * tmp17
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp18, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_clamp_div_mean_mul_pow_sub_0[grid(1)](buf1,
            arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


def _tonemap(im):
    """Helper Reinhards tonemapper.
    Args:
        im(torch.Tensor): image to tonemap.
    Returns:
        (torch.Tensor) tonemaped image.
    """
    im = torch.clamp(im, min=0)
    return im / (1 + im)


class TonemappedMSENew(torch.nn.Module):
    """Mean-squared error on tonemaped images.
    Args:
        eps(float): small number to avoid division by 0.
    """

    def __init__(self, eps=0.01):
        super(TonemappedMSENew, self).__init__()
        self.eps = eps

    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: 0.015117
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
import abc
import torch.nn as nn
import torch.utils.data.dataset
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_tanh_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
    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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (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_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(256)](buf1, primals_3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
    return reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, primals_4


class BaseHead(nn.Module, metaclass=abc.ABCMeta):
    pass


class RegressionHeadNew(BaseHead):

    def __init__(self, hidden_size, hidden_dropout_prob):
        """From RobertaClassificationHead"""
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.dropout = nn.Dropout(hidden_dropout_prob)
        self.out_proj = nn.Linear(hidden_size, 1)

    def forward(self, input_0):
        primals_2 = self.dense.weight
        primals_3 = self.dense.bias
        primals_4 = self.out_proj.weight
        primals_5 = self.out_proj.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.034594