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
import torch.nn as nn
from itertools import product as product
import torch.nn.parallel
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_add_div_hardtanh_mul_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 3.0
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 6.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = 0.16666666666666666
    tmp8 = tmp6 * tmp7
    tmp9 = tmp0 * tmp8
    tl.store(out_ptr0 + x0, tmp9, xmask)


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


class h_sigmoid(nn.Module):

    def __init__(self, inplace=True):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)

    def forward(self, x):
        return self.relu(x + 3) / 6


class h_swishNew(nn.Module):

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

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


Measured latency_ms: 0.014113
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.onnx
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_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 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tmp6 * tmp1
    tmp8 = tl.where(tmp4, tmp0, tmp7)
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_softplus_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class SoftplusNew(nn.Module):

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


Measured latency_ms: 0.014172
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


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


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


class GaussianSmearingNew(nn.Module):

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

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


Measured latency_ms: 0.013991
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 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__to_copy_add_div_eq_log_mul_ne_neg_rsub_sigmoid_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)
    tmp4 = tl.load(in_ptr1 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = tl_math.log(tmp5)
    tmp7 = tmp3 * tmp6
    tmp8 = tmp1 - tmp3
    tmp9 = tmp1 - tmp5
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp8 * tmp10
    tmp12 = tmp7 + tmp11
    tmp13 = -tmp12
    tmp14 = 0.0
    tmp15 = tmp0 != tmp14
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp13 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = tl.broadcast_to(tmp16, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tmp24 = tmp20 / 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)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused__to_copy_add_div_eq_log_mul_ne_neg_rsub_sigmoid_sum_0[
            grid(1)](buf2, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class MaskedMultiTaskCrossEntropyNew(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: 0.015202
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 = tmp8 + tmp11
    tmp17 = tmp16 + tmp14
    tmp18 = tmp15 / tmp17
    tmp19 = -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 SoftDiceLossNew(nn.Module):

    def __init__(self):
        super(SoftDiceLossNew, 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: 0.01529
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 numpy as np
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_mul_sin_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = 0.2
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.float64)
    tmp5 = tmp0 * tmp4
    tmp6 = libdevice.sin(tmp5)
    tl.store(out_ptr0 + x2, tmp6, 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, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 16), (1024, 256, 64, 16, 1),
            torch.float64)
        get_raw_stream(0)
        triton_poi_fused_mul_sin_0[grid(4096)](arg1_1, arg0_1, buf0, 4096,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class RadialBesselLayerNew(nn.Module):
    """Radial Bessel functions based on the work by DimeNet: https://github.com/klicperajo/dimenet

    Args:
        n_radials (int, optional): total number of radial functions, :math:`N_g`.
        cutoff (float, optional): cutoff, :math:`\\mu_{r_c}`

    """

    def __init__(self, n_radial=16, cutoff=5.0, device=None):
        super(RadialBesselLayerNew, self).__init__()
        self.inv_cutoff = 1 / cutoff
        self.frequencies = nn.Parameter(torch.tensor(np.arange(1, n_radial +
            1) * np.pi, device=device), requires_grad=False)

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


Measured latency_ms: 0.015053
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
import torch.utils.model_zoo
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_mul_neg_rsub_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp2 = tl.load(in_ptr1 + r0, None)
    tmp14 = tl.load(in_ptr2 + r0, None)
    tmp1 = -tmp0
    tmp3 = 1e-14
    tmp4 = tmp2 + tmp3
    tmp5 = tl_math.log(tmp4)
    tmp6 = tmp1 * tmp5
    tmp7 = 1.0
    tmp8 = tmp7 - tmp0
    tmp9 = tmp7 - tmp2
    tmp10 = tmp9 + tmp3
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp8 * tmp11
    tmp13 = tmp6 - tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp19 = 0.00390625
    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, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_div_log_mul_neg_rsub_sub_sum_0[grid(1)](buf1,
            arg1_1, arg0_1, arg2_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf1,


class penalty_bce_lossNew(nn.Module):

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

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


Measured latency_ms: 0.014249
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.functional
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_binary_cross_entropy_with_logits_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 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp17, 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_0[grid(1)](buf1,
            arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


class BCELossNew(nn.Module):

    def __init__(self):
        super(BCELossNew, self).__init__()
        self.bce = nn.BCEWithLogitsLoss()

    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: 0.042979
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 81 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(1296)](buf1, primals_2, 1296,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3


class UpBlockNew(nn.Module):

    def __init__(self, in_f, out_f, stride=2, add_blur=False):
        super(UpBlockNew, self).__init__()
        self.shuffle = nn.ConvTranspose2d(in_f, out_f, kernel_size=3,
            stride=stride, padding=0)
        self.has_blur = add_blur
        if self.has_blur:
            self.blur = nn.AvgPool2d(2, 1)

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


Measured latency_ms: 0.023045
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    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
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_1(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
    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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_1, buf0, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 16, 4), (64, 4, 1), 0)
        del buf1
        triton_poi_fused_add_1[grid(256)](buf2, primals_3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
    return buf2, reinterpret_tensor(buf0, (64, 4), (4, 1), 0)


class MLPNew(nn.Module):

    def __init__(self, dim, embed_dim):
        super().__init__()
        self.proj = nn.Linear(dim, embed_dim)

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


Measured latency_ms: 0.045605
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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, 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, 1, 4), (4, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4), (4, 4, 1), 0), primals_2, stride=(2,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (4, 4, 1), (4, 1, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(16)](buf1, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
    return buf1, primals_2, reinterpret_tensor(primals_1, (4, 1, 4), (4, 4,
        1), 0), buf2


class EncoderNew(nn.Module):
    """Estimation of the nonnegative mixture weight by a 1-D conv layer.
    """

    def __init__(self, L, N):
        super(EncoderNew, self).__init__()
        self.L, self.N = L, N
        self.conv1d_U = nn.Conv1d(1, N, kernel_size=L, stride=L // 2, bias=
            False)

    def forward(self, input_0):
        primals_2 = self.conv1d_U.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.024991
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: 0.01379
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, xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 3
    x3 = xindex // 48
    x4 = xindex % 16
    x5 = xindex
    tmp0 = -1 + x1 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-4 + x4 + 4 * x2 + 16 * x3), tmp5 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x5, 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, 3, 4, 4), (192, 48, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(768)](arg0_1, buf0, 768, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf0, (4, 4, 3, 16), (192, 48, 16, 1), 0),


class UnfoldTemporalWindowsNew(nn.Module):

    def __init__(self, window_size, window_stride, window_dilation=1):
        super().__init__()
        self.window_size = window_size
        self.window_stride = window_stride
        self.window_dilation = window_dilation
        self.padding = (window_size + (window_size - 1) * (window_dilation -
            1) - 1) // 2
        self.unfold = nn.Unfold(kernel_size=(self.window_size, 1), dilation
            =(self.window_dilation, 1), stride=(self.window_stride, 1),
            padding=(self.padding, 0))

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


Measured latency_ms: 0.015058
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 torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_clamp_log_mul_neg_pow_rsub_sigmoid_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 = tl.sigmoid(tmp1)
    tmp3 = 1e-08
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tl_math.log(tmp4)
    tmp6 = tmp0 * tmp5
    tmp7 = 1.0
    tmp8 = tmp7 - tmp0
    tmp9 = tmp7 - tmp2
    tmp10 = 0.05
    tmp11 = tmp9 + tmp10
    tmp12 = triton_helpers.minimum(tmp11, tmp7)
    tmp13 = triton_helpers.maximum(tmp12, tmp3)
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp8 * tmp14
    tmp16 = tmp6 + tmp15
    tmp17 = tmp2 * tmp0
    tmp18 = tmp12 * tmp8
    tmp19 = tmp17 + tmp18
    tmp20 = tmp7 - tmp19
    tmp21 = tmp0 * tmp7
    tmp22 = 4.0
    tmp23 = tmp8 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = libdevice.pow(tmp20, tmp24)
    tmp26 = tmp16 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0))
    tmp30 = -tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp30, 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_log_mul_neg_pow_rsub_sigmoid_sum_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 AsymmetricLossNew(nn.Module):

    def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
        disable_torch_grad_focal_loss=True):
        super(AsymmetricLossNew, self).__init__()
        self.gamma_neg = gamma_neg
        self.gamma_pos = gamma_pos
        self.clip = clip
        self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss
        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: 0.015029
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_div_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r0 = rindex % 12
    r1 = rindex // 12
    r2 = rindex % 3
    r3 = rindex // 3
    tmp0 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, other=0.0)
    tmp8 = tl.load(in_ptr0 + (1 + r2 + 4 * r3), rmask, other=0.0)
    tmp9 = tl.load(in_ptr0 + (r2 + 4 * r3), rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp10 = tmp8 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(rmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = 0.020833333333333332
    tmp17 = tmp7 * tmp16
    tmp18 = tmp15 * tmp16
    tmp19 = tmp17 + tmp18
    tmp20 = 0.25
    tmp21 = tmp19 * tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp21, 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_pow_sub_sum_0[grid(1)](buf2, arg0_1, 1, 
            192, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class TV_L2LossNew(nn.Module):

    def __init__(self):
        super(TV_L2LossNew, self).__init__()

    def tensor_size(self, t):
        return t.size()[1] * t.size()[2] * t.size()[3]

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


Measured latency_ms: 0.015943
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
from torch.nn 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_poi_fused_add_mul_rsub_sigmoid_0(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x2, xmask)
    tmp8 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tmp6 = 1.0
    tmp7 = tmp6 - tmp3
    tmp9 = tmp7 * tmp8
    tmp10 = tmp5 + tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_rsub_sigmoid_0[grid(16)](buf0, primals_3,
            primals_4, primals_2, buf1, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return buf1, primals_2, primals_3, primals_4, buf0


class HighWayNew(torch.nn.Module):

    def __init__(self, f_in, f_out, bias=True):
        super(HighWayNew, self).__init__()
        self.w = Parameter(torch.Tensor(f_in, f_out))
        nn.init.xavier_uniform_(self.w)
        if bias:
            self.bias = Parameter(torch.Tensor(f_out))
            nn.init.constant_(self.bias, 0)
        else:
            self.register_parameter('bias', None)

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


Measured latency_ms: 0.015929
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
from torch.autograd 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_div_gather_mul_neg_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp9 = tl.load(in_ptr2 + r0, None)
    tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4),
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (tmp4 + 4 * r0), None, eviction_policy=
        'evict_last')
    tmp7 = -tmp6
    tmp8 = tmp7.to(tl.float32)
    tmp10 = tmp8 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.sum(tmp11, 1)[:, None]
    tmp14 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp16 = tl.sum(tmp14, 1)[:, None]
    tmp17 = tmp13 / tmp16
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp17, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, 4), (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_gather_mul_neg_sum_0[grid(1)](buf2, arg1_1,
            arg0_1, arg2_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2,


def to_contiguous(tensor):
    if tensor.is_contiguous():
        return tensor
    else:
        return tensor.contiguous()


class LanguageModelCriterionNew(nn.Module):

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

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


Measured latency_ms: 0.0157
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
from torch.nn import init
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_0(in_out_ptr0, in_ptr0, in_ptr1, 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 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr0 + 1)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp6 = tl.load(in_ptr1 + (64 + x0), xmask)
    tmp9 = tl.load(in_ptr0 + 2)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
    tmp11 = tl.load(in_ptr1 + (128 + x0), xmask)
    tmp14 = tl.load(in_ptr0 + 3)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp16 = tl.load(in_ptr1 + (192 + x0), xmask)
    tmp19 = tl.load(in_ptr0 + 4)
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp21 = tl.load(in_ptr1 + (256 + x0), xmask)
    tmp24 = tl.load(in_ptr0 + 5)
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK])
    tmp26 = tl.load(in_ptr1 + (320 + x0), xmask)
    tmp3 = tmp1 * tmp2
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp12 = tmp10 * tmp11
    tmp13 = tmp8 + tmp12
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tmp22 = tmp20 * tmp21
    tmp23 = tmp18 + tmp22
    tmp27 = tmp25 * tmp26
    tmp28 = tmp23 + tmp27
    tl.store(in_out_ptr0 + x0, tmp28, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (6,), (1,))
    assert_size_stride(primals_2, (6, 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)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(64)](buf1, primals_1, primals_2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
    return buf1, reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 64
        ), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 128
        ), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 192
        ), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 256
        ), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 320)


class FusionLayerNew(nn.Module):

    def __init__(self, nums=6):
        super(FusionLayerNew, self).__init__()
        self.weights = nn.Parameter(torch.randn(nums))
        self.nums = nums
        self._reset_parameters()

    def _reset_parameters(self):
        init.constant_(self.weights, 1 / self.nums)

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


Measured latency_ms: 0.018536
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.utils.data import Dataset as Dataset
import torch.nn.init as init
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_softplus_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_convolution_div_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.015625
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_softplus_0[grid(256)](primals_1, buf0, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(primals_3, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 1), (4, 1, 1, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_div_1[grid(16)](buf2, primals_2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf2, primals_1, primals_3, buf0


class PosConv2dNew(torch.nn.Conv2d):

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

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


Measured latency_ms: 0.03319