[cleanup] Hoist ModelTester objects to top level (#4939)
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
This commit is contained in:
Amil Khare
@@ -30,6 +30,268 @@ if is_torch_available():
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from transformers.tokenization_t5 import T5Tokenizer
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class T5ModelTester:
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def __init__(self, parent):
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self.parent = parent
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self.batch_size = 13
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self.encoder_seq_length = 7
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self.decoder_seq_length = 9
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self.is_training = True
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self.use_attention_mask = True
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self.use_labels = True
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self.vocab_size = 99
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self.n_positions = 14
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.d_ff = 37
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self.relative_attention_num_buckets = 8
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self.dropout_rate = 0.1
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self.initializer_factor = 0.002
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self.eos_token_id = 1
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self.pad_token_id = 0
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self.decoder_start_token_id = 0
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self.scope = None
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
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decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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attention_mask = None
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decoder_attention_mask = None
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if self.use_attention_mask:
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attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
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decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
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lm_labels = None
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if self.use_labels:
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lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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config = T5Config(
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vocab_size=self.vocab_size,
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n_positions=self.n_positions,
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d_model=self.hidden_size,
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d_ff=self.d_ff,
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d_kv=self.hidden_size // self.num_attention_heads,
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num_layers=self.num_hidden_layers,
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num_heads=self.num_attention_heads,
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relative_attention_num_buckets=self.relative_attention_num_buckets,
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dropout_rate=self.dropout_rate,
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initializer_factor=self.initializer_factor,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.pad_token_id,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.decoder_start_token_id,
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)
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return (
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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)
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def check_loss_output(self, result):
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self.parent.assertListEqual(list(result["loss"].size()), [])
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def check_prepare_lm_labels_via_shift_left(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5Model(config=config)
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model.to(torch_device)
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model.eval()
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# make sure that lm_labels are correctly padded from the right
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lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)
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# add casaul pad token mask
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triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
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lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
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decoder_input_ids = model._shift_right(lm_labels)
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for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
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# first item
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self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
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if i < decoder_input_ids_slice.shape[-1]:
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if i < decoder_input_ids.shape[-1] - 1:
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# items before diagonal
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self.parent.assertListEqual(
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decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
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)
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# pad items after diagonal
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if i < decoder_input_ids.shape[-1] - 2:
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self.parent.assertListEqual(
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decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
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)
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else:
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# all items after square
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self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
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def create_and_check_t5_model(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5Model(config=config)
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model.to(torch_device)
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model.eval()
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decoder_output, decoder_past, encoder_output = model(
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input_ids=input_ids,
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decoder_input_ids=decoder_input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=decoder_attention_mask,
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)
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decoder_output, decoder_past, encoder_output = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
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result = {
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"encoder_output": encoder_output,
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"decoder_output": decoder_output,
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"decoder_past": decoder_past,
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}
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self.parent.assertListEqual(
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list(result["encoder_output"].size()), [self.batch_size, self.encoder_seq_length, self.hidden_size]
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)
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self.parent.assertListEqual(
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list(result["decoder_output"].size()), [self.batch_size, self.decoder_seq_length, self.hidden_size]
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)
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self.parent.assertEqual(len(decoder_past), 2)
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# decoder_past[0] should correspond to encoder output
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self.parent.assertTrue(torch.all(decoder_past[0][0] == encoder_output))
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# There should be `num_layers` key value embeddings stored in decoder_past[1]
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self.parent.assertEqual(len(decoder_past[1]), config.num_layers)
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# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past[1] tuple
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self.parent.assertEqual(len(decoder_past[1][0]), 4)
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def create_and_check_t5_with_lm_head(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5ForConditionalGeneration(config=config)
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model.to(torch_device)
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model.eval()
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outputs = model(
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input_ids=input_ids,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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labels=lm_labels,
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)
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loss, prediction_scores, _, _ = outputs
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self.parent.assertEqual(len(outputs), 4)
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result = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()), [self.batch_size, self.decoder_seq_length, self.vocab_size]
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)
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self.check_loss_output(result)
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def create_and_check_t5_decoder_model_past(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5Model(config=config).get_decoder()
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model.to(torch_device)
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model.eval()
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# first forward pass
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output, past_key_value_states = model(input_ids, use_cache=True)
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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output_from_no_past = model(next_input_ids)[0]
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output_from_past = model(next_tokens, past_key_value_states=past_key_value_states)[0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_t5_decoder_model_attention_mask_past(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5Model(config=config).get_decoder()
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model.to(torch_device)
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model.eval()
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# create attention mask
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attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
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half_seq_length = input_ids.shape[-1] // 2
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attn_mask[:, half_seq_length:] = 0
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# first forward pass
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output, past_key_value_states = model(input_ids, attention_mask=attn_mask, use_cache=True)
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# change a random masked slice from input_ids
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random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
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random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
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input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
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# append to next input_ids and attn_mask
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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attn_mask = torch.cat(
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[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1,
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)
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# get two different outputs
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output_from_no_past = model(next_input_ids, attention_mask=attn_mask)[0]
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output_from_past = model(next_tokens, past_key_value_states=past_key_value_states, attention_mask=attn_mask)[0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_t5_and_check_t5_generate_with_past_key_value_states(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5ForConditionalGeneration(config=config)
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model.to(torch_device)
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model.eval()
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torch.manual_seed(0)
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output_without_past_cache = model.generate(
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input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
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)
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torch.manual_seed(0)
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output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
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self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
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def create_and_check_t5_model_fp16_forward(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5Model(config=config)
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model.to(torch_device)
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model.half()
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model.eval()
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output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)[0]
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self.parent.assertFalse(torch.isnan(output).any().item())
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"decoder_input_ids": decoder_input_ids,
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"decoder_attention_mask": decoder_attention_mask,
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"use_cache": False,
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}
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return config, inputs_dict
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@require_torch
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class T5ModelTest(ModelTesterMixin, unittest.TestCase):
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@@ -40,302 +302,8 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
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test_resize_embeddings = False
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is_encoder_decoder = True
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class T5ModelTester(object):
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def __init__(
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self,
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parent,
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batch_size=13,
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encoder_seq_length=7,
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decoder_seq_length=9,
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is_training=True,
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use_attention_mask=True,
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use_labels=True,
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vocab_size=99,
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n_positions=14,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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d_ff=37,
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relative_attention_num_buckets=8,
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dropout_rate=0.1,
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initializer_factor=0.002,
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eos_token_id=1,
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pad_token_id=0,
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decoder_start_token_id=0,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.encoder_seq_length = encoder_seq_length
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self.decoder_seq_length = decoder_seq_length
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self.is_training = is_training
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self.use_attention_mask = use_attention_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.n_positions = n_positions
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.d_ff = d_ff
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.dropout_rate = dropout_rate
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self.initializer_factor = initializer_factor
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self.scope = scope
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.decoder_start_token_id = decoder_start_token_id
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
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decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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attention_mask = None
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decoder_attention_mask = None
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if self.use_attention_mask:
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attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
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decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
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lm_labels = None
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if self.use_labels:
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lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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config = T5Config(
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vocab_size=self.vocab_size,
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n_positions=self.n_positions,
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d_model=self.hidden_size,
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d_ff=self.d_ff,
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d_kv=self.hidden_size // self.num_attention_heads,
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num_layers=self.num_hidden_layers,
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num_heads=self.num_attention_heads,
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relative_attention_num_buckets=self.relative_attention_num_buckets,
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dropout_rate=self.dropout_rate,
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initializer_factor=self.initializer_factor,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.pad_token_id,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.decoder_start_token_id,
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)
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return (
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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)
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def check_loss_output(self, result):
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self.parent.assertListEqual(list(result["loss"].size()), [])
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def check_prepare_lm_labels_via_shift_left(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5Model(config=config)
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model.to(torch_device)
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model.eval()
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# make sure that lm_labels are correctly padded from the right
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lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)
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# add casaul pad token mask
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triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
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lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
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decoder_input_ids = model._shift_right(lm_labels)
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for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
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# first item
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self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
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if i < decoder_input_ids_slice.shape[-1]:
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if i < decoder_input_ids.shape[-1] - 1:
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# items before diagonal
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self.parent.assertListEqual(
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decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
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)
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# pad items after diagonal
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if i < decoder_input_ids.shape[-1] - 2:
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self.parent.assertListEqual(
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decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
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)
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else:
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# all items after square
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self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
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def create_and_check_t5_model(
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self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
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):
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model = T5Model(config=config)
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model.to(torch_device)
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model.eval()
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decoder_output, decoder_past, encoder_output = model(
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input_ids=input_ids,
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decoder_input_ids=decoder_input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=decoder_attention_mask,
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)
|
||||
decoder_output, decoder_past, encoder_output = model(
|
||||
input_ids=input_ids, decoder_input_ids=decoder_input_ids
|
||||
)
|
||||
|
||||
result = {
|
||||
"encoder_output": encoder_output,
|
||||
"decoder_output": decoder_output,
|
||||
"decoder_past": decoder_past,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["encoder_output"].size()), [self.batch_size, self.encoder_seq_length, self.hidden_size]
|
||||
)
|
||||
self.parent.assertListEqual(
|
||||
list(result["decoder_output"].size()), [self.batch_size, self.decoder_seq_length, self.hidden_size]
|
||||
)
|
||||
self.parent.assertEqual(len(decoder_past), 2)
|
||||
# decoder_past[0] should correspond to encoder output
|
||||
self.parent.assertTrue(torch.all(decoder_past[0][0] == encoder_output))
|
||||
# There should be `num_layers` key value embeddings stored in decoder_past[1]
|
||||
self.parent.assertEqual(len(decoder_past[1]), config.num_layers)
|
||||
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past[1] tuple
|
||||
self.parent.assertEqual(len(decoder_past[1][0]), 4)
|
||||
|
||||
def create_and_check_t5_with_lm_head(
|
||||
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
|
||||
):
|
||||
model = T5ForConditionalGeneration(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
outputs = model(
|
||||
input_ids=input_ids,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
labels=lm_labels,
|
||||
)
|
||||
loss, prediction_scores, _, _ = outputs
|
||||
self.parent.assertEqual(len(outputs), 4)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"prediction_scores": prediction_scores,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["prediction_scores"].size()), [self.batch_size, self.decoder_seq_length, self.vocab_size]
|
||||
)
|
||||
self.check_loss_output(result)
|
||||
|
||||
def create_and_check_t5_decoder_model_past(
|
||||
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
|
||||
):
|
||||
model = T5Model(config=config).get_decoder()
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# first forward pass
|
||||
output, past_key_value_states = model(input_ids, use_cache=True)
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
||||
|
||||
# append to next input_ids and
|
||||
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
||||
|
||||
output_from_no_past = model(next_input_ids)[0]
|
||||
output_from_past = model(next_tokens, past_key_value_states=past_key_value_states)[0]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
|
||||
|
||||
def create_and_check_t5_decoder_model_attention_mask_past(
|
||||
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
|
||||
):
|
||||
model = T5Model(config=config).get_decoder()
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# create attention mask
|
||||
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
|
||||
|
||||
half_seq_length = input_ids.shape[-1] // 2
|
||||
attn_mask[:, half_seq_length:] = 0
|
||||
|
||||
# first forward pass
|
||||
output, past_key_value_states = model(input_ids, attention_mask=attn_mask, use_cache=True)
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
||||
|
||||
# change a random masked slice from input_ids
|
||||
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
|
||||
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
|
||||
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
|
||||
|
||||
# append to next input_ids and attn_mask
|
||||
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
||||
attn_mask = torch.cat(
|
||||
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1,
|
||||
)
|
||||
|
||||
# get two different outputs
|
||||
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)[0]
|
||||
output_from_past = model(
|
||||
next_tokens, past_key_value_states=past_key_value_states, attention_mask=attn_mask
|
||||
)[0]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
|
||||
|
||||
def create_t5_and_check_t5_generate_with_past_key_value_states(
|
||||
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
|
||||
):
|
||||
model = T5ForConditionalGeneration(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
torch.manual_seed(0)
|
||||
output_without_past_cache = model.generate(
|
||||
input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
|
||||
)
|
||||
torch.manual_seed(0)
|
||||
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
|
||||
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
|
||||
|
||||
def create_and_check_t5_model_fp16_forward(
|
||||
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels,
|
||||
):
|
||||
model = T5Model(config=config)
|
||||
model.to(torch_device)
|
||||
model.half()
|
||||
model.eval()
|
||||
output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)[0]
|
||||
self.parent.assertFalse(torch.isnan(output).any().item())
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
decoder_input_ids,
|
||||
attention_mask,
|
||||
decoder_attention_mask,
|
||||
lm_labels,
|
||||
) = config_and_inputs
|
||||
|
||||
inputs_dict = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": decoder_attention_mask,
|
||||
"use_cache": False,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = T5ModelTest.T5ModelTester(self)
|
||||
self.model_tester = T5ModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
|
||||
|
||||
def test_config(self):
|
||||
|
||||
Reference in New Issue
Block a user