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_per_fused_linalg_vector_norm_mean_mul_pow_sub_0(in_out_ptr0,
    in_ptr0, in_ptr1, out_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
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr1 + 0)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp14 = tmp11 - tmp13
    tmp15 = 2.0
    tmp16 = tmp14 * tmp15
    tmp17 = tmp14 * tmp14
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp20 = tl.sum(tmp18, 1)[:, None]
    tmp21 = 64.0
    tmp22 = tmp20 / tmp21
    tmp23 = 1.0
    tmp24 = tmp22 * tmp23
    tl.store(out_ptr1 + tl.broadcast_to(r2, [XBLOCK, RBLOCK]), tmp16, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp24, None)


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, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_linalg_vector_norm_mean_mul_pow_sub_0[grid(1)](buf3,
            primals_1, primals_2, buf2, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_1
        del primals_2
    return buf3, buf2


class RingLossNew(nn.Module):
    """Ring loss.
    
    Reference:
    Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
    """

    def __init__(self, weight_ring=1.0):
        super(RingLossNew, self).__init__()
        self.radius = nn.Parameter(torch.ones(1, dtype=torch.float))
        self.weight_ring = weight_ring

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


Measured latency_ms: 0.016877
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_clamp_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class FakeReLU(torch.autograd.Function):

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

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output


class FakeReLUMNew(nn.Module):

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


Measured latency_ms: 0.01395
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_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp3 = tmp1 * tmp2
    tmp6 = tmp3 + tmp5
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(256)](primals_1, primals_2,
            primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_3
    return buf0, primals_2


class LinRegModelNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.a = nn.Parameter(torch.randn(1))
        self.b = nn.Parameter(torch.randn(1))

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


Measured latency_ms: 0.016038
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
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_abs_le_mean_mul_pow_sub_where_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_math.abs(tmp2)
    tmp4 = 0.1111111111111111
    tmp5 = tmp3 <= tmp4
    tmp6 = tmp3 * tmp3
    tmp7 = 4.5
    tmp8 = tmp6 * tmp7
    tmp9 = 0.05555555555555555
    tmp10 = tmp3 - tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.broadcast_to(tmp11, [RBLOCK])
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0))
    tmp15 = 256.0
    tmp16 = tmp14 / tmp15
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp16, 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_le_mean_mul_pow_sub_where_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 SigmaL1SmoothLossNew(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: 0.013799
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: 0.014186
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](arg1_1, arg0_1, buf0, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class TorchAddNew(torch.nn.Module):

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

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


Measured latency_ms: 0.01396
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = libdevice.atan(tmp5)
    tmp7 = 1.4925373134328357
    tmp8 = tmp6 * tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp14 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp11 &
        xmask, other=0.0)
    tmp15 = libdevice.atan(tmp14)
    tmp16 = tmp15 * tmp15
    tmp17 = 1.6666666666666667
    tmp18 = tmp16 * tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp11, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp10, tmp20)
    tl.store(out_ptr0 + x3, tmp21, 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, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](arg0_1, buf0, 512, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class CompositeActivationNew(torch.nn.Module):

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


Measured latency_ms: 0.015208
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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused__to_copy_le_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 = 4.0
    tmp2 = tmp0 <= tmp1
    tmp3 = tmp2.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp3, 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__to_copy_le_0[grid(256)](arg0_1, buf0, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class ThresholdNew(torch.nn.Module):
    CAST_OPS = {'float': lambda t: t.float(), 'byte': lambda t: t.byte()}

    def __init__(self, value: 'float', comparison: 'str'='lower', dtype:
        'str'='float'):
        super(ThresholdNew, self).__init__()
        self.threshold = value
        self.comp_op = (torch.le if comparison == 'lower' else torch.ge if 
            comparison == 'greater' else torch.ge)
        if dtype not in ThresholdNew.CAST_OPS:
            log.error(
                'Casting operation type for Threshold monad should be either float or byte'
                )
        self.cast_op = ThresholdNew.CAST_OPS[dtype]

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


Measured latency_ms: 0.014183
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_leaky_relu_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 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


def call(args):
    primals_1, primals_2 = 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))
    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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(256)](buf0, buf1, buf2, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
    return buf2, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), buf1


class FullyConnectedNew(nn.Module):

    def __init__(self, hidden_size, output_size, bias=False):
        super(FullyConnectedNew, self).__init__()
        self.lrelu = nn.LeakyReLU(0.1)
        self.linear_layer = nn.Linear(hidden_size, output_size, bias=bias)

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


Measured latency_ms: 0.023786
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.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_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 = tmp1 + tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class SimpleTypeasModelNew(torch.nn.Module):

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

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


Measured latency_ms: 0.025914
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_log_mse_loss_sqrt_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)
    tmp4 = tl.load(in_ptr1 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.log(tmp2)
    tmp5 = tmp4 + tmp1
    tmp6 = tl_math.log(tmp5)
    tmp7 = tmp3 - tmp6
    tmp8 = tmp7 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp11 = triton_helpers.promote_to_tensor(tl.sum(tmp9, 0))
    tmp12 = 256.0
    tmp13 = tmp11 / tmp12
    tmp14 = 1e-08
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp16, 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_log_mse_loss_sqrt_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 RMSLELossNew(nn.Module):

    def __init__(self, eps=1e-08):
        super(RMSLELossNew, self).__init__()
        self.mse = nn.MSELoss()
        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: 0.014939
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
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_mean_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 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = 256.0
    tmp8 = tmp6 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp8, 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_mean_sub_0[grid(1)](buf1, arg1_1, arg0_1, 1, 
            256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class L1LossNew(torch.nn.Module):

    def __init__(self):
        super(L1LossNew, self).__init__()
        self.loss = torch.nn.L1Loss(reduction='mean')

    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: 0.015327
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.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_floor_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.floor(tmp2)
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_floor_0[grid(256)](arg0_1, arg1_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class SimpleFloorModuleNew(torch.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: 0.014311
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 math
from torch import Tensor
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_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (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_0[grid(256)](primals_1, primals_2,
            primals_3, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        del primals_3
    return buf0, primals_1


class DiagLinearNew(nn.Module):
    """Applies a diagonal linear transformation to the incoming data: :math:`y = xD^T + b`"""
    __constants__ = ['features']

    def __init__(self, features, bias=True):
        super(DiagLinearNew, self).__init__()
        self.features = features
        self.weight = nn.Parameter(Tensor(features))
        if bias:
            self.bias = nn.Parameter(Tensor(features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        bound = 1 / math.sqrt(self.features)
        nn.init.uniform_(self.weight, -bound, bound)
        if self.bias is not None:
            bound = 1 / math.sqrt(self.features)
            nn.init.uniform_(self.bias, -bound, bound)

    def extra_repr(self):
        return 'features={}, bias={}'.format(self.features, self.bias is not
            None)

    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: 0.015955
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_clamp_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -0.1
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 0.1
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class TorchClampOptionMaxMinNew(torch.nn.Module):

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


Measured latency_ms: 0.01421
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data.dataloader
import torch.utils.data
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_div_hardtanh_mul_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 3.0
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 6.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = 0.16666666666666666
    tmp8 = tmp6 * tmp7
    tmp9 = tmp8 * tmp0
    tl.store(out_ptr0 + x0, tmp9, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_hardtanh_mul_0[grid(256)](arg0_1, buf0, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class h_swishNew(nn.Module):

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

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


Measured latency_ms: 0.013857
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_div_max_relu_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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp3 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = 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')
    tmp8 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp10 = tmp2 / tmp9
    tl.store(out_ptr0 + x3, 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_div_max_relu_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class normreluNew(nn.Module):

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

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


Measured latency_ms: 0.013948
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
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_exp_log_log_sigmoid_forward_mean_mul_neg_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)
    tmp2 = tl.load(in_ptr1 + r0, None)
    tmp1 = -tmp0
    tmp3 = 2.0
    tmp4 = tmp2 * tmp3
    tmp5 = 1.0
    tmp6 = tmp4 - tmp5
    tmp7 = tmp1 * tmp6
    tmp8 = 0.0
    tmp9 = triton_helpers.minimum(tmp8, tmp7)
    tmp10 = tl_math.abs(tmp7)
    tmp11 = -tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp13 = libdevice.log1p(tmp12)
    tmp14 = tmp9 - tmp13
    tmp15 = tmp14 * tmp3
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp0 * tmp2
    tmp18 = tmp0 - tmp17
    tmp19 = triton_helpers.maximum(tmp1, tmp8)
    tmp20 = tmp18 + tmp19
    tmp21 = -tmp19
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp1 - tmp19
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tl_math.log(tmp25)
    tmp27 = tmp20 + tmp26
    tmp28 = tmp16 * tmp27
    tmp29 = tl.broadcast_to(tmp28, [RBLOCK])
    tmp31 = triton_helpers.promote_to_tensor(tl.sum(tmp29, 0))
    tmp32 = 256.0
    tmp33 = tmp31 / tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([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)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_clamp_exp_log_log_sigmoid_forward_mean_mul_neg_sub_0[
            grid(1)](buf1, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class FocalLossNew(nn.Module):

    def __init__(self, gamma=2):
        super().__init__()
        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: 0.015016
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
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused_cumsum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 4
    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)
    r2 = rindex
    x0 = xindex % 16
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = tmp0.to(tl.float32)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp3, = tl.associative_scan((tmp2,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (x0 + 16 * r2 + 64 * x1), tmp3, xmask)


@triton.jit
def triton_poi_fused_add_atan_div_sub_1(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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + 1)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp3 = tmp0 - tmp2
    tmp6 = tmp3 / tmp5
    tmp7 = libdevice.atan(tmp6)
    tmp8 = 0.3183098861837907
    tmp9 = tmp7 * tmp8
    tmp10 = 0.5
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + x0, tmp11, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (2,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 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_per_fused_cumsum_0[grid(64)](primals_2, buf0, 64, 4, XBLOCK=
            8, num_warps=2, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_atan_div_sub_1[grid(256)](buf0, primals_1,
            buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf1, primals_1, buf0


class CDFLayerNew(nn.Module):

    def __init__(self, device='cpu'):
        super(CDFLayerNew, self).__init__()
        self.loc_scale = Parameter(torch.FloatTensor([0.0, 1.0]))

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


Measured latency_ms: 0.023801
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, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y1 = yindex // 16 % 4
    x4 = xindex % 4
    x5 = xindex // 4
    y0 = yindex % 16
    y6 = yindex // 64
    y3 = yindex // 256
    y7 = yindex // 16 % 16
    tmp0 = y1
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 4 * y1 + 8 * x5 + 64 * y0 + 1024 * y6), 
        tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1, 1], 4, tl.int64)
    tmp9 = tl.load(in_ptr0 + (32 + x4 + 4 * (-2 + y1) + 8 * x5 + 64 * y0 + 
        1024 * y6), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (y0 + 16 * x4 + 64 * y7 + 1024 * x5 + 4096 * y3),
        tmp10, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 64, 16, 4), (4096, 64, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4, 4, 4), (4096, 1024, 256, 
            64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1024, 16)](arg0_1, buf0, 1024, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class DecoderNew(nn.Module):
    """ Encoder
    """

    def __init__(self, n_levels, n_color, n_eccentricity, n_azimuth,
        n_theta, n_phase):
        super(DecoderNew, self).__init__()
        self.n_levels = n_levels
        self.n_color = n_color
        self.n_eccentricity = n_eccentricity
        self.n_azimuth = n_azimuth
        self.n_theta = n_theta
        self.n_phase = n_phase
        self.h_size = (n_levels * n_color * n_eccentricity * n_azimuth *
            n_theta * n_phase)

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


Measured latency_ms: 0.014922
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: 0.013673
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_div_log_mean_mul_rsub_sum_0(in_out_ptr0, in_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 % 16
    r1 = rindex // 16
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp7 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp13 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp19 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp28 = tl.load(in_ptr0 + r2, None)
    tmp29 = tl.load(in_ptr0 + (64 + r2), None)
    tmp31 = tl.load(in_ptr0 + (128 + r2), None)
    tmp33 = tl.load(in_ptr0 + (192 + r2), None)
    tmp1 = 1e-08
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.log(tmp2)
    tmp4 = tmp0 * tmp3
    tmp5 = 0.7213475204444817
    tmp6 = tmp4 * tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = tl_math.log(tmp8)
    tmp10 = tmp7 * tmp9
    tmp11 = tmp10 * tmp5
    tmp12 = tmp6 + tmp11
    tmp14 = tmp13 + tmp1
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp13 * tmp15
    tmp17 = tmp16 * tmp5
    tmp18 = tmp12 + tmp17
    tmp20 = tmp19 + tmp1
    tmp21 = tl_math.log(tmp20)
    tmp22 = tmp19 * tmp21
    tmp23 = tmp22 * tmp5
    tmp24 = tmp18 + tmp23
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp34 = tmp32 + tmp33
    tmp35 = 4.0
    tmp36 = tmp34 / tmp35
    tmp37 = tmp36 + tmp1
    tmp38 = tl_math.log(tmp37)
    tmp39 = tmp36 * tmp38
    tmp40 = tmp39 * tmp5
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp44 = 64.0
    tmp45 = tmp27 / tmp44
    tmp46 = 1.0
    tmp47 = tmp46 - tmp45
    tmp48 = tmp47 + tmp43
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp48, 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_log_mean_mul_rsub_sum_0[grid(1)](buf2,
            arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class JSDNew(nn.Module):

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

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


Measured latency_ms: 0.014412
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](arg1_1, arg0_1, buf0, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class TorchAddNew(torch.nn.Module):

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

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


Measured latency_ms: 0.014291