2019-08-08 12:42:38 +03:00
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#!/usr/bin/env python3
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import argparse
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import os
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import unittest
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from inspect import currentframe, getframeinfo
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import numpy as np
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import torch
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2020-10-19 04:13:29 +03:00
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from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
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2019-08-08 12:42:38 +03:00
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from fairseq.data import data_utils as fairseq_data_utils
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from fairseq.data.dictionary import Dictionary
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from fairseq.models import (
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BaseFairseqModel,
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FairseqDecoder,
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FairseqEncoder,
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FairseqEncoderDecoderModel,
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FairseqEncoderModel,
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FairseqModel,
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)
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2020-09-10 03:00:56 +03:00
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from fairseq.tasks.fairseq_task import LegacyFairseqTask
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2019-08-08 12:42:38 +03:00
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DEFAULT_TEST_VOCAB_SIZE = 100
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# ///////////////////////////////////////////////////////////////////////////
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# utility function to setup dummy dict/task/input
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# ///////////////////////////////////////////////////////////////////////////
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def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE):
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dummy_dict = Dictionary()
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# add dummy symbol to satisfy vocab size
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for id, _ in enumerate(range(vocab_size)):
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dummy_dict.add_symbol("{}".format(id), 1000)
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return dummy_dict
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2020-09-10 03:00:56 +03:00
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class DummyTask(LegacyFairseqTask):
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2019-08-08 12:42:38 +03:00
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def __init__(self, args):
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super().__init__(args)
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self.dictionary = get_dummy_dictionary()
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if getattr(self.args, "ctc", False):
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self.dictionary.add_symbol("<ctc_blank>")
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self.tgt_dict = self.dictionary
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@property
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def target_dictionary(self):
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return self.dictionary
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def get_dummy_task_and_parser():
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"""
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to build a fariseq model, we need some dummy parse and task. This function
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is used to create dummy task and parser to faciliate model/criterion test
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Note: we use FbSpeechRecognitionTask as the dummy task. You may want
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to use other task by providing another function
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"""
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parser = argparse.ArgumentParser(
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description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
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)
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DummyTask.add_args(parser)
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args = parser.parse_args([])
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task = DummyTask.setup_task(args)
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return task, parser
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def get_dummy_input(T=100, D=80, B=5, K=100):
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forward_input = {}
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# T max sequence length
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# D feature vector dimension
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# B batch size
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# K target dimension size
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feature = torch.randn(B, T, D)
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# this (B, T, D) layout is just a convention, you can override it by
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# write your own _prepare_forward_input function
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src_lengths = torch.from_numpy(
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2020-01-24 21:28:44 +03:00
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np.random.randint(low=1, high=T, size=B, dtype=np.int64)
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2019-08-08 12:42:38 +03:00
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)
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src_lengths[0] = T # make sure the maximum length matches
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prev_output_tokens = []
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for b in range(B):
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token_length = np.random.randint(low=1, high=src_lengths[b].item() + 1)
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2020-01-24 21:28:44 +03:00
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tokens = np.random.randint(low=0, high=K, size=token_length, dtype=np.int64)
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2019-08-08 12:42:38 +03:00
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prev_output_tokens.append(torch.from_numpy(tokens))
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prev_output_tokens = fairseq_data_utils.collate_tokens(
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prev_output_tokens,
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pad_idx=1,
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eos_idx=2,
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left_pad=False,
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move_eos_to_beginning=False,
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)
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src_lengths, sorted_order = src_lengths.sort(descending=True)
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forward_input["src_tokens"] = feature.index_select(0, sorted_order)
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forward_input["src_lengths"] = src_lengths
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forward_input["prev_output_tokens"] = prev_output_tokens
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return forward_input
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def get_dummy_encoder_output(encoder_out_shape=(100, 80, 5)):
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"""
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This only provides an example to generate dummy encoder output
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"""
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(T, B, D) = encoder_out_shape
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encoder_out = {}
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encoder_out["encoder_out"] = torch.from_numpy(
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np.random.randn(*encoder_out_shape).astype(np.float32)
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)
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seq_lengths = torch.from_numpy(np.random.randint(low=1, high=T, size=B))
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# some dummy mask
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encoder_out["encoder_padding_mask"] = torch.arange(T).view(1, T).expand(
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B, -1
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) >= seq_lengths.view(B, 1).expand(-1, T)
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encoder_out["encoder_padding_mask"].t_()
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# encoer_padding_mask is (T, B) tensor, with (t, b)-th element indicate
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# whether encoder_out[t, b] is valid (=0) or not (=1)
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return encoder_out
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def _current_postion_info():
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cf = currentframe()
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frameinfo = " (at {}:{})".format(
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os.path.basename(getframeinfo(cf).filename), cf.f_back.f_lineno
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)
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return frameinfo
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def check_encoder_output(encoder_output, batch_size=None):
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"""we expect encoder_output to be a dict with the following
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key/value pairs:
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- encoder_out: a Torch.Tensor
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- encoder_padding_mask: a binary Torch.Tensor
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"""
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if not isinstance(encoder_output, dict):
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msg = (
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"FairseqEncoderModel.forward(...) must be a dict" + _current_postion_info()
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)
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return False, msg
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if "encoder_out" not in encoder_output:
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msg = (
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"FairseqEncoderModel.forward(...) must contain encoder_out"
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+ _current_postion_info()
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)
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return False, msg
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if "encoder_padding_mask" not in encoder_output:
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msg = (
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"FairseqEncoderModel.forward(...) must contain encoder_padding_mask"
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+ _current_postion_info()
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)
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return False, msg
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if not isinstance(encoder_output["encoder_out"], torch.Tensor):
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msg = "encoder_out must be a torch.Tensor" + _current_postion_info()
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return False, msg
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if encoder_output["encoder_out"].dtype != torch.float32:
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msg = "encoder_out must have float32 dtype" + _current_postion_info()
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return False, msg
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mask = encoder_output["encoder_padding_mask"]
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if mask is not None:
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if not isinstance(mask, torch.Tensor):
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msg = (
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"encoder_padding_mask must be a torch.Tensor" + _current_postion_info()
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)
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return False, msg
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2020-10-19 04:13:29 +03:00
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if mask.dtype != torch.uint8 and (
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not hasattr(torch, "bool") or mask.dtype != torch.bool
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2019-08-14 06:33:07 +03:00
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):
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2019-08-08 12:42:38 +03:00
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msg = (
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"encoder_padding_mask must have dtype of uint8"
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+ _current_postion_info()
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)
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return False, msg
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if mask.dim() != 2:
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msg = (
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"we expect encoder_padding_mask to be a 2-d tensor, in shape (T, B)"
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+ _current_postion_info()
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)
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return False, msg
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if batch_size is not None and mask.size(1) != batch_size:
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msg = (
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"we expect encoder_padding_mask to be a 2-d tensor, with size(1)"
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+ " being the batch size"
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+ _current_postion_info()
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)
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return False, msg
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return True, None
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def check_decoder_output(decoder_output):
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"""we expect output from a decoder is a tuple with the following constraint:
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- the first element is a torch.Tensor
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- the second element can be anything (reserved for future use)
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"""
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if not isinstance(decoder_output, tuple):
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msg = "FariseqDecoder output must be a tuple" + _current_postion_info()
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return False, msg
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if len(decoder_output) != 2:
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msg = "FairseqDecoder output must be 2-elem tuple" + _current_postion_info()
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return False, msg
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if not isinstance(decoder_output[0], torch.Tensor):
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msg = (
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"FariseqDecoder output[0] must be a torch.Tensor" + _current_postion_info()
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)
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return False, msg
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return True, None
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# ///////////////////////////////////////////////////////////////////////////
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# Base Test class
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# ///////////////////////////////////////////////////////////////////////////
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class TestBaseFairseqModelBase(unittest.TestCase):
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"""
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This class is used to facilitate writing unittest for any class derived from
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`BaseFairseqModel`.
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"""
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@classmethod
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def setUpClass(cls):
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if cls is TestBaseFairseqModelBase:
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raise unittest.SkipTest("Skipping test case in base")
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super().setUpClass()
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def setUpModel(self, model):
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self.assertTrue(isinstance(model, BaseFairseqModel))
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self.model = model
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def setupInput(self):
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pass
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def setUp(self):
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self.model = None
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self.forward_input = None
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pass
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class TestFairseqEncoderDecoderModelBase(TestBaseFairseqModelBase):
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"""
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base code to test FairseqEncoderDecoderModel (formally known as
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`FairseqModel`) must be derived from this base class
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"""
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@classmethod
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def setUpClass(cls):
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if cls is TestFairseqEncoderDecoderModelBase:
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raise unittest.SkipTest("Skipping test case in base")
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super().setUpClass()
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def setUpModel(self, model_cls, extra_args_setters=None):
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self.assertTrue(
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issubclass(model_cls, (FairseqEncoderDecoderModel, FairseqModel)),
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msg="This class only tests for FairseqModel subclasses",
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)
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task, parser = get_dummy_task_and_parser()
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model_cls.add_args(parser)
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args = parser.parse_args([])
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2020-10-20 10:31:00 +03:00
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2019-08-08 12:42:38 +03:00
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if extra_args_setters is not None:
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for args_setter in extra_args_setters:
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args_setter(args)
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model = model_cls.build_model(args, task)
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self.model = model
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def setUpInput(self, input=None):
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self.forward_input = get_dummy_input() if input is None else input
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def setUp(self):
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super().setUp()
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def test_forward(self):
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if self.model and self.forward_input:
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forward_output = self.model.forward(**self.forward_input)
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# for FairseqEncoderDecoderModel, forward returns a tuple of two
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# elements, the first one is a Torch.Tensor
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succ, msg = check_decoder_output(forward_output)
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if not succ:
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self.assertTrue(succ, msg=msg)
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self.forward_output = forward_output
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def test_get_normalized_probs(self):
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if self.model and self.forward_input:
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forward_output = self.model.forward(**self.forward_input)
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logprob = self.model.get_normalized_probs(forward_output, log_probs=True)
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prob = self.model.get_normalized_probs(forward_output, log_probs=False)
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# in order for different models/criterion to play with each other
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# we need to know whether the logprob or prob output is batch_first
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# or not. We assume an additional attribute will be attached to logprob
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# or prob. If you find your code failed here, simply override
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# FairseqModel.get_normalized_probs, see example at
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# https://fburl.com/batch_first_example
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self.assertTrue(hasattr(logprob, "batch_first"))
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self.assertTrue(hasattr(prob, "batch_first"))
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self.assertTrue(torch.is_tensor(logprob))
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self.assertTrue(torch.is_tensor(prob))
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class TestFairseqEncoderModelBase(TestBaseFairseqModelBase):
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"""
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base class to test FairseqEncoderModel
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"""
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@classmethod
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def setUpClass(cls):
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if cls is TestFairseqEncoderModelBase:
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raise unittest.SkipTest("Skipping test case in base")
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super().setUpClass()
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def setUpModel(self, model_cls, extra_args_setters=None):
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self.assertTrue(
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issubclass(model_cls, FairseqEncoderModel),
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msg="This class is only used for testing FairseqEncoderModel",
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)
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task, parser = get_dummy_task_and_parser()
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model_cls.add_args(parser)
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args = parser.parse_args([])
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if extra_args_setters is not None:
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for args_setter in extra_args_setters:
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args_setter(args)
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model = model_cls.build_model(args, task)
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self.model = model
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def setUpInput(self, input=None):
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self.forward_input = get_dummy_input() if input is None else input
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# get_dummy_input() is originally for s2s, here we delete extra dict
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# items, so it can be used for EncoderModel / Encoder as well
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self.forward_input.pop("prev_output_tokens", None)
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def setUp(self):
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super().setUp()
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def test_forward(self):
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if self.forward_input and self.model:
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bsz = self.forward_input["src_tokens"].size(0)
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forward_output = self.model.forward(**self.forward_input)
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# we expect forward_output to be a dict with the following
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# key/value pairs:
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# - encoder_out: a Torch.Tensor
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# - encoder_padding_mask: a binary Torch.Tensor
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succ, msg = check_encoder_output(forward_output, batch_size=bsz)
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if not succ:
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self.assertTrue(succ, msg=msg)
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self.forward_output = forward_output
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def test_get_normalized_probs(self):
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if self.model and self.forward_input:
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forward_output = self.model.forward(**self.forward_input)
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logprob = self.model.get_normalized_probs(forward_output, log_probs=True)
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prob = self.model.get_normalized_probs(forward_output, log_probs=False)
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# in order for different models/criterion to play with each other
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# we need to know whether the logprob or prob output is batch_first
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|
# or not. We assume an additional attribute will be attached to logprob
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# or prob. If you find your code failed here, simply override
|
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|
# FairseqModel.get_normalized_probs, see example at
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|
# https://fburl.com/batch_first_example
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self.assertTrue(hasattr(logprob, "batch_first"))
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self.assertTrue(hasattr(prob, "batch_first"))
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self.assertTrue(torch.is_tensor(logprob))
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self.assertTrue(torch.is_tensor(prob))
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class TestFairseqEncoderBase(unittest.TestCase):
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"""
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|
base class to test FairseqEncoder
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|
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|
"""
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@classmethod
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def setUpClass(cls):
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if cls is TestFairseqEncoderBase:
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|
raise unittest.SkipTest("Skipping test case in base")
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super().setUpClass()
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def setUpEncoder(self, encoder):
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self.assertTrue(
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|
isinstance(encoder, FairseqEncoder),
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|
msg="This class is only used for test FairseqEncoder",
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|
)
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|
self.encoder = encoder
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def setUpInput(self, input=None):
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self.forward_input = get_dummy_input() if input is None else input
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|
# get_dummy_input() is originally for s2s, here we delete extra dict
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|
# items, so it can be used for EncoderModel / Encoder as well
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|
self.forward_input.pop("prev_output_tokens", None)
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|
|
|
|
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|
def setUp(self):
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|
self.encoder = None
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|
self.forward_input = None
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|
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|
def test_forward(self):
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|
if self.encoder and self.forward_input:
|
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|
bsz = self.forward_input["src_tokens"].size(0)
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|
|
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|
forward_output = self.encoder.forward(**self.forward_input)
|
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|
succ, msg = check_encoder_output(forward_output, batch_size=bsz)
|
|
|
|
if not succ:
|
|
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|
self.assertTrue(succ, msg=msg)
|
|
|
|
self.forward_output = forward_output
|
|
|
|
|
|
|
|
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|
|
|
class TestFairseqDecoderBase(unittest.TestCase):
|
|
|
|
"""
|
|
|
|
base class to test FairseqDecoder
|
|
|
|
"""
|
|
|
|
|
|
|
|
@classmethod
|
|
|
|
def setUpClass(cls):
|
|
|
|
if cls is TestFairseqDecoderBase:
|
|
|
|
raise unittest.SkipTest("Skipping test case in base")
|
|
|
|
super().setUpClass()
|
|
|
|
|
|
|
|
def setUpDecoder(self, decoder):
|
|
|
|
self.assertTrue(
|
|
|
|
isinstance(decoder, FairseqDecoder),
|
|
|
|
msg="This class is only used for test FairseqDecoder",
|
|
|
|
)
|
|
|
|
self.decoder = decoder
|
|
|
|
|
|
|
|
def setUpInput(self, input=None):
|
|
|
|
self.forward_input = get_dummy_encoder_output() if input is None else input
|
|
|
|
|
|
|
|
def setUpPrevOutputTokens(self, tokens=None):
|
|
|
|
if tokens is None:
|
|
|
|
self.encoder_input = get_dummy_input()
|
|
|
|
self.prev_output_tokens = self.encoder_input["prev_output_tokens"]
|
|
|
|
else:
|
|
|
|
self.prev_output_tokens = tokens
|
|
|
|
|
|
|
|
def setUp(self):
|
|
|
|
self.decoder = None
|
|
|
|
self.forward_input = None
|
|
|
|
self.prev_output_tokens = None
|
|
|
|
|
|
|
|
def test_forward(self):
|
|
|
|
if (
|
|
|
|
self.decoder is not None
|
|
|
|
and self.forward_input is not None
|
|
|
|
and self.prev_output_tokens is not None
|
|
|
|
):
|
|
|
|
forward_output = self.decoder.forward(
|
|
|
|
prev_output_tokens=self.prev_output_tokens,
|
|
|
|
encoder_out=self.forward_input,
|
|
|
|
)
|
|
|
|
succ, msg = check_decoder_output(forward_output)
|
|
|
|
if not succ:
|
|
|
|
self.assertTrue(succ, msg=msg)
|
|
|
|
self.forward_input = forward_output
|
|
|
|
|
|
|
|
|
|
|
|
class DummyEncoderModel(FairseqEncoderModel):
|
|
|
|
def __init__(self, encoder):
|
|
|
|
super().__init__(encoder)
|
|
|
|
|
|
|
|
@classmethod
|
|
|
|
def build_model(cls, args, task):
|
|
|
|
return cls(DummyEncoder())
|
|
|
|
|
|
|
|
def get_logits(self, net_output):
|
|
|
|
# Inverse of sigmoid to use with BinaryCrossEntropyWithLogitsCriterion as
|
|
|
|
# F.binary_cross_entropy_with_logits combines sigmoid and CE
|
|
|
|
return torch.log(
|
|
|
|
torch.div(net_output["encoder_out"], 1 - net_output["encoder_out"])
|
|
|
|
)
|
|
|
|
|
2020-02-21 22:43:11 +03:00
|
|
|
def get_normalized_probs(self, net_output, log_probs, sample=None):
|
|
|
|
lprobs = super().get_normalized_probs(net_output, log_probs, sample=sample)
|
|
|
|
lprobs.batch_first = True
|
|
|
|
return lprobs
|
|
|
|
|
2019-08-08 12:42:38 +03:00
|
|
|
|
|
|
|
class DummyEncoder(FairseqEncoder):
|
|
|
|
def __init__(self):
|
|
|
|
super().__init__(None)
|
|
|
|
|
|
|
|
def forward(self, src_tokens, src_lengths):
|
|
|
|
mask, max_len = lengths_to_encoder_padding_mask(src_lengths)
|
|
|
|
return {"encoder_out": src_tokens, "encoder_padding_mask": mask}
|
|
|
|
|
|
|
|
|
|
|
|
class CrossEntropyCriterionTestBase(unittest.TestCase):
|
|
|
|
@classmethod
|
|
|
|
def setUpClass(cls):
|
|
|
|
if cls is CrossEntropyCriterionTestBase:
|
|
|
|
raise unittest.SkipTest("Skipping base class test case")
|
|
|
|
super().setUpClass()
|
|
|
|
|
|
|
|
def setUpArgs(self):
|
|
|
|
args = argparse.Namespace()
|
|
|
|
args.sentence_avg = False
|
|
|
|
args.threshold = 0.1 # to use with BinaryCrossEntropyWithLogitsCriterion
|
|
|
|
return args
|
|
|
|
|
|
|
|
def setUp(self):
|
|
|
|
args = self.setUpArgs()
|
|
|
|
self.model = DummyEncoderModel(encoder=DummyEncoder())
|
2020-10-20 10:31:00 +03:00
|
|
|
self.criterion = self.criterion_cls.build_criterion(args, task=DummyTask(args))
|
2019-08-08 12:42:38 +03:00
|
|
|
|
|
|
|
def get_src_tokens(self, correct_prediction, aggregate):
|
|
|
|
"""
|
2020-10-19 04:13:29 +03:00
|
|
|
correct_prediction: True if the net_output (src_tokens) should
|
|
|
|
predict the correct target
|
|
|
|
aggregate: True if the criterion expects net_output (src_tokens)
|
|
|
|
aggregated across time axis
|
2019-08-08 12:42:38 +03:00
|
|
|
"""
|
|
|
|
predicted_idx = 0 if correct_prediction else 1
|
|
|
|
if aggregate:
|
|
|
|
src_tokens = torch.zeros((2, 2), dtype=torch.float)
|
|
|
|
for b in range(2):
|
|
|
|
src_tokens[b][predicted_idx] = 1.0
|
|
|
|
else:
|
|
|
|
src_tokens = torch.zeros((2, 10, 2), dtype=torch.float)
|
|
|
|
for b in range(2):
|
|
|
|
for t in range(10):
|
|
|
|
src_tokens[b][t][predicted_idx] = 1.0
|
|
|
|
return src_tokens
|
|
|
|
|
|
|
|
def get_target(self, soft_target):
|
|
|
|
if soft_target:
|
|
|
|
target = torch.zeros((2, 2), dtype=torch.float)
|
|
|
|
for b in range(2):
|
|
|
|
target[b][0] = 1.0
|
|
|
|
else:
|
|
|
|
target = torch.zeros((2, 10), dtype=torch.long)
|
|
|
|
return target
|
|
|
|
|
|
|
|
def get_test_sample(self, correct, soft_target, aggregate):
|
|
|
|
src_tokens = self.get_src_tokens(correct, aggregate)
|
|
|
|
target = self.get_target(soft_target)
|
|
|
|
L = src_tokens.size(1)
|
|
|
|
return {
|
|
|
|
"net_input": {"src_tokens": src_tokens, "src_lengths": torch.tensor([L])},
|
|
|
|
"target": target,
|
|
|
|
"ntokens": src_tokens.size(0) * src_tokens.size(1),
|
|
|
|
}
|