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
from torch import nn
import torch.autograd
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_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)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-06
    tmp14 = tmp12 + tmp13
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x3, tmp15, 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_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def l2n(x, eps=1e-06):
    return x / (torch.norm(x, p=2, dim=1, keepdim=True) + eps).expand_as(x)


class L2NNew(nn.Module):

    def __init__(self, eps=1e-06):
        super(L2NNew, self).__init__()
        self.eps = eps

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'eps=' + str(self.eps) + ')'

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


Measured latency_ms: 0.014029
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_sigmoid_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_sigmoid_0[grid(256)](buf1, primals_2, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_2
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1


class GeneratorNew(nn.Module):

    def __init__(self, input_length: 'int'):
        super(GeneratorNew, self).__init__()
        self.dense_layer = nn.Linear(int(input_length), int(input_length))
        self.activation = nn.Sigmoid()

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


Measured latency_ms: 0.022164
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: 0.015133
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
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_eq_gt_maximum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp0 == tmp1
    tmp4 = tmp0 > tmp1
    tl.store(out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr1 + x3, tmp3, xmask)
    tl.store(out_ptr2 + x3, tmp4, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4, 1, 1), (4, 1, 1, 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)
        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.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_gt_maximum_0[grid(256)](primals_2, primals_1,
            buf0, buf1, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
    return buf0, buf1, buf2


class TLUNew(nn.Module):

    def __init__(self, num_features):
        """max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
        super(TLUNew, self).__init__()
        self.num_features = num_features
        self.tau = nn.parameter.Parameter(torch.Tensor(1, num_features, 1, 
            1), requires_grad=True)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.zeros_(self.tau)

    def extra_repr(self):
        return 'num_features={num_features}'.format(**self.__dict__)

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


Measured latency_ms: 0.018146
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (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_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(256)](buf1, primals_2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_tanh_0[grid(256)](buf3, primals_5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_5
    return buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf1, buf3, primals_4


class SENetNew(nn.Module):
    """support estimation network"""

    def __init__(self, input_size: 'int', hidden_size: 'int', output_dims:
        'int') ->None:
        super(SENetNew, self).__init__()
        self.l_1 = nn.Linear(input_size, hidden_size)
        self.l_2 = nn.Linear(hidden_size, output_dims)
        self.act = nn.Tanh()

    def forward(self, input_0):
        primals_1 = self.l_1.weight
        primals_2 = self.l_1.bias
        primals_4 = self.l_2.weight
        primals_5 = self.l_2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.069044
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    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, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        triton_poi_fused_add_1[grid(64)](buf2, primals_3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
    return buf2, reinterpret_tensor(buf0, (16, 4), (4, 1), 0)


class GeneratorNew(nn.Module):
    """Define standard linear + softmax generation step."""

    def __init__(self, size, vocab):
        super(GeneratorNew, self).__init__()
        self.size = size
        self.proj = nn.Linear(self.size, vocab)

    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: 0.035673
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, 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 = libdevice.exp2(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class RpowIntNew(torch.nn.Module):

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

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


Measured latency_ms: 0.013903
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_lift_fresh_mean_minimum_mul_neg_sub_0(in_out_ptr0,
    in_out_ptr1, 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)
    tmp5 = tl.load(in_ptr1 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 - tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.minimum(tmp3, tmp2)
    tmp6 = tmp4 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = -tmp0
    tmp11 = tmp10 - tmp1
    tmp12 = triton_helpers.minimum(tmp3, tmp11)
    tmp13 = -tmp5
    tmp14 = tmp13 + tmp1
    tmp15 = tmp12 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp19 = 256.0
    tmp20 = tmp9 / tmp19
    tmp21 = -tmp20
    tmp22 = tmp18 / tmp19
    tmp23 = -tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp21, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([1], 0, tl.int32), tmp23, 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 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0
        del buf0
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_lift_fresh_mean_minimum_mul_neg_sub_0[grid(1)](
            buf2, buf3, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2, buf3


class HingeDiscriminatorLossCutMixNew(nn.Module):
    """
    This class implements the hinge gan loss for the discriminator network when utilizing cut mix augmentation.
    """

    def __init__(self) ->None:
        """
        Constructor method
        """
        super(HingeDiscriminatorLossCutMixNew, 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], output[1]


Measured latency_ms: 0.016622
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


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_0(in_out_ptr0,
    in_ptr0, 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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(16)](buf1,
            primals_2, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3, buf2


class Conv2dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        relu=True, same_padding=False, bn=False):
        super(Conv2dNew, self).__init__()
        padding = int((kernel_size - 1) / 2) if same_padding else 0
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding=padding)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0,
            affine=True) if bn else None
        self.relu = nn.ReLU(inplace=True) if relu else None

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


Measured latency_ms: 0.02421
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_clamp_ge_le_logical_and_0(in_ptr0, out_ptr0, out_ptr1,
    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_ptr0 + x0, xmask)
    tmp1 = 0.04
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 2.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp0 >= tmp1
    tmp6 = tmp0 <= tmp3
    tmp7 = tmp5 & tmp6
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp7, xmask)


def call(args):
    primals_1, = args
    args.clear()
    assert_size_stride(primals_1, (1, 1, 4, 4), (16, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((1, 1, 4, 4), (16, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_le_logical_and_0[grid(16)](primals_1,
            buf0, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
    return buf0, buf1


class InvDepthNew(nn.Module):

    def __init__(self, height, width, min_depth=0.5, max_depth=25.0):
        super(InvDepthNew, self).__init__()
        self._min_range = 1.0 / max_depth
        self._max_range = 1.0 / min_depth
        self.w = nn.Parameter(self._init_weights(height, width))

    def _init_weights(self, height, width):
        r1 = self._min_range
        r2 = self._min_range + (self._max_range - self._min_range) * 0.1
        w_init = (r1 - r2) * torch.rand(1, 1, height, width) + r2
        return w_init

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


Measured latency_ms: 0.01448
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 as T
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_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


def call(args):
    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, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, arg0_1, 16, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        del arg0_1
    return buf1,


class Avg2dNew(T.nn.Module):

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


Measured latency_ms: 0.014564
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


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


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 7), (7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(28)](primals_1, buf0, 28,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7
            ), (0, 7, 1), 0), primals_2, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (1, 4, 4), (16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(16)](buf2, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf2, (4, 4), (4, 1), 0
        ), primals_2, reinterpret_tensor(buf0, (1, 4, 7), (28, 7, 1), 0)


class CausalConv1dNew(torch.nn.Conv1d):

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

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


Measured latency_ms: 0.036805
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
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf1 = extern_kernels.convolution(buf0, primals_3, 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, 2, 2), (16, 4, 2, 1))
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(64)](buf2, primals_4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_4
    return buf2, primals_1, primals_2, primals_3, buf0


class DepthwiseSeparableConv2dNew(nn.Module):

    def __init__(self, input, output, padding=0, bias=False):
        super(DepthwiseSeparableConv2dNew, self).__init__()
        self.depthwise = nn.Conv2d(input, input, kernel_size=3, padding=
            padding, groups=input, bias=bias)
        self.pointwise = nn.Conv2d(input, output, kernel_size=1)

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


Measured latency_ms: 0.015207
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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
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_triu_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 % 4
    y3 = yindex
    y1 = yindex // 4
    tmp3 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp0 = x2 + -1 * y0
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = y0 + -1 * x2
    tmp7 = tl.full([1, 1], 1, tl.int64)
    tmp8 = tmp6 >= tmp7
    tmp10 = tl.where(tmp8, tmp9, tmp4)
    tmp11 = tmp5 + tmp10
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp11, xmask & ymask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_triu_0[grid(64, 4)](arg0_1, buf0, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class SymmetricNew(nn.Module):

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


Measured latency_ms: 0.014236
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_abs_add_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 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp10 = tmp8 - tmp9
    tmp11 = tl_math.abs(tmp10)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(rmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = tmp7 + tmp15
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, 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_abs_add_sub_sum_0[grid(1)](buf2, arg0_1, 1, 192,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class TVLossNew(nn.Module):

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

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


Measured latency_ms: 0.0143
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.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_log_sigmoid_forward_mean_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)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.minimum(tmp3, tmp2)
    tmp5 = tl_math.abs(tmp2)
    tmp6 = -tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = libdevice.log1p(tmp7)
    tmp9 = tmp4 - tmp8
    tmp10 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0))
    tmp13 = 256.0
    tmp14 = tmp12 / tmp13
    tmp15 = -tmp14
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp15, 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_log_sigmoid_forward_mean_neg_sub_0[grid(1)](buf1,
            arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class BPRLossNew(nn.Module):

    def __init__(self):
        nn.Module.__init__(self)
        self.m = nn.LogSigmoid()

    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: 0.015009
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
import torch.quantization.quantize_fx
import torch.utils.data
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 = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (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=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 8, 8), (256, 64, 8, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(1024)](buf1, primals_2, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3


class KeypointRCNNPredictorNoUpscaleNew(nn.Module):

    def __init__(self, in_channels, num_keypoints):
        super(KeypointRCNNPredictorNoUpscaleNew, self).__init__()
        input_features = in_channels
        deconv_kernel = 4
        self.kps_score_lowres = nn.ConvTranspose2d(input_features,
            num_keypoints, deconv_kernel, stride=2, padding=deconv_kernel //
            2 - 1)
        nn.init.kaiming_normal_(self.kps_score_lowres.weight, mode=
            'fan_out', nonlinearity='relu')
        nn.init.constant_(self.kps_score_lowres.bias, 0)
        self.out_channels = num_keypoints

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


Measured latency_ms: 0.023798
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
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
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_convolution_relu_threshold_backward_0(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 2112 % 33
    x0 = xindex % 2112
    x3 = xindex // 2112
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr0 + (x0 + 2176 * x3), tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (33, 16, 3, 5), (240, 15, 5, 1))
    assert_size_stride(primals_2, (33,), (1,))
    assert_size_stride(primals_3, (4, 16, 64, 64), (65536, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            1), padding=(4, 2), dilation=(3, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 33, 33, 64), (69696, 2112, 64, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 33, 33, 64), (71808, 2176, 64, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(278784)](
            buf1, primals_2, buf2, 278784, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3, buf2


class ConvReluNew(nn.Module):

    def __init__(self):
        super(ConvReluNew, self).__init__()
        self.conv = torch.nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=
            (4, 2), dilation=(3, 1))

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


Measured latency_ms: 0.023163
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
from torch import nn
import torch.hub
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 = 4
    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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_2, (1,
            4, 4), (16, 4, 1), 0), primals_1, stride=(2,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (1, 4, 1), (4, 1, 1))
        buf1 = reinterpret_tensor(buf0, (4, 1), (1, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(4)](buf1, buf2, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
    return buf1, primals_1, reinterpret_tensor(primals_2, (1, 4, 4), (16, 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, audio_channels):
        super(EncoderNew, self).__init__()
        self.L, self.N = L, N
        self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=
            L // 2, bias=False)

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


Measured latency_ms: 0.060892