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input = torch.randn(2,2)

part = torch.tensor([[1, 2]])

ind0 = torch.arange(0,1)
ind1 = torch.arange(0,2)

input = torch.index_put(input, (ind0, ind1), part)

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import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import itertools
import tempfile


def pack_pad_seq(seq_tensor, seq_lengths):

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import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import itertools
import tempfile


def pack_pad_seq(seq_tensor, seq_lengths):

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import torch

class Test(torch.nn.Module):
    def __init__(self):
        super(Test, self).__init__()

    def forward(self, input):
        # y = input.size(0) + 1
        y = 0
        for i in range(10):

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import torch

a = torch.tensor([[-0.5689,  1.3550, -1.7742, -0.2412,  0.2400],
    [-1.1720,  0.6153,  0.0285,  0.7397,  0.3760],
    [ 1.0568, -0.9253, -0.5579,  0.1791,  1.3932 ],
    [ 0.4966,  0.9272, -1.3335, -0.2913,  0.8120 ],
    [-0.5048, -0.9092,  0.2757,  1.3891,  1.1164]])


print("% output:")

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import torch

def remove_sentence_boundary(tensor):
    tensor_shape = list(tensor.data.shape)
    new_shape = list(tensor_shape)
    new_shape[1] = tensor_shape[1] - 2
    tensor_without_boundary_tokens = torch.zeros(new_shape, device=tensor.device)
    return tensor_without_boundary_tokens

traced_fn = torch.jit.trace(remove_sentence_boundary, torch.rand(10, 20, 30))

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import torch
import torch.nn as nn
import torch.nn.functional as F


class LeNet(nn.Module):

    def __init__(self):
        super(LeNet, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution

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import torch


def fn(input):
    return torch.log(input + 1e-8)


input = torch.rand(5, 5)

output = fn(input)

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import torch

class Test(torch.jit.ScriptModule):
  def __init__(self, b = None):
    self.b = b
  def forward(self, input):
    x = input
    if self.b is not None:
      x = self.b(input)
      

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ATen/JIT Function Schema

This doc is to talk about the difference in ATen/JIT function schema and find a way to align each other.

List syntax

Currently in ATen function schema when we want to define a list, we have: