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015f8e110d
* add all * Revert "Delete .github directory" This reverts commit 9b0ff7b052e2b20b629a26fb13606b78a42944d1. * make conversion script backward compatible * fixup * more styling * copy to llama changes * fix repo consistency * nits * document correct classes * updates * more fixes * nits * update auto mappings * add readmes * smallupdates * llama-code replace with llama_code * make fixup * updates to the testsing suite * fix fast nits * more small fixes * fix decode * fix template processing * properly reset the normalizer * nits processor * tokenization tests pass * styling * last tests * additional nits * one test is left * nits Co-authored-by faabian <faabian@users.noreply.github.com> * update failing test * fixup * remove decode infilling users should handle it on their onw after generation, padding can be a problem * update * make test slow and more meaningfull * fixup * doc update * fixup * Apply suggestions from code review * add kwargs doc * tokenizer requires `requires_backend` * type requires_backends * CodeLlama instead of LlamaCode * more name cahnges * nits * make doctests happy * small pipeline nits * last nit * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * update * add codellama to toctree --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
541 lines
26 KiB
Python
541 lines
26 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch LLaMA model. """
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import unittest
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from parameterized import parameterized
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from transformers import LlamaConfig, is_torch_available, set_seed
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from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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CodeLlamaTokenizer,
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LlamaForCausalLM,
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LlamaForSequenceClassification,
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LlamaModel,
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LlamaTokenizer,
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)
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class LlamaModelTester:
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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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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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pad_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.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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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.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.pad_token_id = pad_token_id
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return LlamaConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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)
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = LlamaModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = LlamaModel(config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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)
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = LlamaForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.is_decoder = True
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config.add_cross_attention = True
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model = LlamaForCausalLM(config=config)
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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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outputs = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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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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next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_hidden_states=True,
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)["hidden_states"][0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)["hidden_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[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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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 prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
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all_generative_model_classes = (LlamaForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": LlamaModel,
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"text-classification": LlamaForSequenceClassification,
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"text-generation": LlamaForCausalLM,
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"zero-shot": LlamaForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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test_headmasking = False
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test_pruning = False
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def setUp(self):
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self.model_tester = LlamaModelTester(self)
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self.config_tester = ConfigTester(self, config_class=LlamaConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_llama_sequence_classification_model(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = LlamaForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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def test_llama_sequence_classification_model_for_single_label(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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config.problem_type = "single_label_classification"
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = LlamaForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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def test_llama_sequence_classification_model_for_multi_label(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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config.problem_type = "multi_label_classification"
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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sequence_labels = ids_tensor(
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[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
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).to(torch.float)
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model = LlamaForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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@unittest.skip("LLaMA buffers include complex numbers, which breaks this test")
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def test_save_load_fast_init_from_base(self):
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pass
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@parameterized.expand([("linear",), ("dynamic",)])
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def test_model_rope_scaling(self, scaling_type):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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short_input = ids_tensor([1, 10], config.vocab_size)
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long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
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set_seed(42) # Fixed seed at init time so the two models get the same random weights
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original_model = LlamaModel(config)
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original_model.to(torch_device)
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original_model.eval()
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original_short_output = original_model(short_input).last_hidden_state
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original_long_output = original_model(long_input).last_hidden_state
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set_seed(42) # Fixed seed at init time so the two models get the same random weights
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config.rope_scaling = {"type": scaling_type, "factor": 10.0}
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scaled_model = LlamaModel(config)
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scaled_model.to(torch_device)
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scaled_model.eval()
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scaled_short_output = scaled_model(short_input).last_hidden_state
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scaled_long_output = scaled_model(long_input).last_hidden_state
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# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
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# maximum sequence length, so the outputs for the short input should match.
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if scaling_type == "dynamic":
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self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
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else:
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self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
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# The output should be different for long inputs
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self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
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@require_torch
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class LlamaIntegrationTest(unittest.TestCase):
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@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
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@slow
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def test_model_7b_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", device_map="auto")
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out = model(torch.tensor([input_ids]))
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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# slicing logits[0, 0, 0:30]
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# fmt: off
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EXPECTED_SLICE = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,])
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# fmt: on
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
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@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
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@slow
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def test_model_13b_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", device_map="auto")
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out = model(torch.tensor(input_ids))
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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# slicing logits[0, 0, 0:30]
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# fmt: off
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EXPECTED_SLICE = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273])
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# fmt: on
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
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|
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@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
|
|
@slow
|
|
def test_model_13bf_logits(self):
|
|
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
|
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model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf", device_map="auto")
|
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out = model(torch.tensor(input_ids))
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# Expected mean on dim = -1
|
|
EXPECTED_MEAN = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
|
|
# slicing logits[0, 0, 0:30]
|
|
# fmt: off
|
|
EXPECTED_SLICE = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513])
|
|
# fmt: on
|
|
torch.testing.assert_close(out.mean(-1), EXPECTED_SLICE, atol=1e-2, rtol=1e-2)
|
|
|
|
@unittest.skip(
|
|
"Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"
|
|
)
|
|
@slow
|
|
def test_model_70b_logits(self):
|
|
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
|
|
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf", device_map="auto")
|
|
out = model(torch.tensor(input_ids))
|
|
|
|
EXPECTED_MEAN = torch.tensor(
|
|
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]], dtype=torch.float32
|
|
)
|
|
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
|
|
# fmt: off
|
|
EXPECTED_SLICE = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312])
|
|
# fmt: on
|
|
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
|
|
|
|
@unittest.skip("Model is curently gated")
|
|
@slow
|
|
def test_model_13b_greedy_generation(self):
|
|
EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi"""
|
|
prompt = "Simply put, the theory of relativity states that "
|
|
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
|
model = LlamaForCausalLM.from_pretrained(
|
|
"meta-llama/Llama-2-13b-chat-hf", device_map="sequential", use_safetensors=False
|
|
)
|
|
|
|
# greedy generation outputs
|
|
generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
|
|
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
|
|
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
|
|
|
|
|
|
@require_torch
|
|
class CodeLlamaIntegrationTest(unittest.TestCase):
|
|
PROMPTS = [
|
|
'''def remove_non_ascii(s: str) -> str:
|
|
""" <FILL_ME>
|
|
return result
|
|
''',
|
|
"""# Installation instructions:
|
|
```bash
|
|
<FILL_ME>
|
|
```
|
|
This downloads the LLaMA inference code and installs the repository as a local pip package.
|
|
""",
|
|
"""class InterfaceManagerFactory(AbstractManagerFactory):
|
|
def __init__(<FILL_ME>
|
|
def main():
|
|
factory = InterfaceManagerFactory(start=datetime.now())
|
|
managers = []
|
|
for i in range(10):
|
|
managers.append(factory.build(id=i))
|
|
""",
|
|
"""/-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/
|
|
theorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :
|
|
π₁ P = 0 ↔ <FILL_ME> = 0 :=
|
|
begin
|
|
split,
|
|
{ intros h f,
|
|
rw pi_1_etalisation at h,
|
|
simp [h],
|
|
refl
|
|
},
|
|
{ intro h,
|
|
have := @quasi_adjoint C D P,
|
|
simp [←pi_1_etalisation, this, h],
|
|
refl
|
|
}
|
|
end
|
|
""",
|
|
]
|
|
|
|
@require_torch_gpu
|
|
@slow
|
|
def test_model_7b_logits(self):
|
|
model = LlamaForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf").to(torch_device)
|
|
tokenizer = CodeLlamaTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
|
|
# Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
|
|
# meaning by default this supports passing splitted list of inputs
|
|
processed_text = tokenizer.batch_decode(tokenizer(self.PROMPTS)["input_ids"], add_special_tokens=False)
|
|
# fmt: off
|
|
EXPECTED_TEXT = [
|
|
'<s> <PRE> def remove_non_ascii(s: str) -> str:\n """ <SUF>\n return result\n <MID>',
|
|
'<s> <PRE> # Installation instructions:\n ```bash\n <SUF>\n ```\nThis downloads the LLaMA inference code and installs the repository as a local pip package.\n <MID>',
|
|
'<s> <PRE> class InterfaceManagerFactory(AbstractManagerFactory):\n def __init__( <SUF>\ndef main():\n factory = InterfaceManagerFactory(start=datetime.now())\n managers = []\n for i in range(10):\n managers.append(factory.build(id=i))\n <MID>',
|
|
'<s> <PRE> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n rw pi_1_etalisation at h,\n simp [h],\n refl\n},\n{ intro h,\n have := @quasi_adjoint C D P,\n simp [←pi_1_etalisation, this, h],\n refl\n}\nend\n <MID>'
|
|
]
|
|
# fmt: on
|
|
self.assertEqual(processed_text, EXPECTED_TEXT)
|
|
processed_text_suffix_first = tokenizer.batch_decode(
|
|
tokenizer(self.PROMPTS, suffix_first=True, add_special_tokens=False)["input_ids"]
|
|
)
|
|
|
|
# fmt: off
|
|
EXPECTED_TEXT = [
|
|
'<PRE> <SUF>\n return result\n <MID> def remove_non_ascii(s: str) -> str:\n """ ',
|
|
'<PRE> <SUF>\n ```\nThis downloads the LLaMA inference code and installs the repository as a local pip package.\n <MID> # Installation instructions:\n ```bash\n',
|
|
'<PRE> <SUF>\ndef main():\n factory = InterfaceManagerFactory(start=datetime.now())\n managers = []\n for i in range(10):\n managers.append(factory.build(id=i))\n <MID> class InterfaceManagerFactory(AbstractManagerFactory):\n def __init__(',
|
|
'<PRE> <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n rw pi_1_etalisation at h,\n simp [h],\n refl\n},\n{ intro h,\n have := @quasi_adjoint C D P,\n simp [←pi_1_etalisation, this, h],\n refl\n}\nend\n <MID> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ '
|
|
]
|
|
EXPECTED_IDS = torch.tensor([[ 1, 32007, 822, 3349, 29918, 5464, 29918, 294, 18869, 29898,29879, 29901, 851, 29897, 1599, 851, 29901, 13, 1678, 9995, 29871, 32008, 13, 1678, 736, 1121, 13, 32009, 15941, 1661, 29899, 28599, 2687, 4890, 515, 263, 1347, 29889, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 269, 29901, 450, 1347, 304, 3349, 1661, 29899, 28599, 2687, 4890, 515, 29889, 13, 13, 1678, 16969, 29901, 13, 4706, 450, 1347, 411, 1661, 29899, 28599, 2687, 4890, 6206, 29889, 13, 1678, 9995, 13, 1678, 1121, 353, 5124, 13, 1678, 363, 274, 297, 269, 29901, 13, 4706, 565, 4356, 29898, 29883, 29897, 529, 29871, 29896, 29906, 29947, 29901, 13, 9651, 1121, 4619, 274, 32010, 2]])
|
|
# fmt: on
|
|
self.assertEqual(processed_text_suffix_first, EXPECTED_TEXT)
|
|
input_ids = tokenizer(self.PROMPTS[0], return_tensors="pt")["input_ids"]
|
|
generated_ids = model.generate(input_ids.to(torch_device), max_new_tokens=128)
|
|
torch.testing.assert_close(generated_ids, EXPECTED_IDS)
|
|
|
|
EXPECTED_INFILLING = [
|
|
'<s> <PRE> def remove_non_ascii(s: str) -> str:\n """ <SUF>\n return result\n <MID>Remove non-ASCII characters from a string.\n\n Args:\n s: The string to remove non-ASCII characters from.\n\n Returns:\n The string with non-ASCII characters removed.\n """\n result = ""\n for c in s:\n if ord(c) < 128:\n result += c <EOT></s>'
|
|
]
|
|
infilling = tokenizer.batch_decode(generated_ids)
|
|
self.assertEqual(infilling, EXPECTED_INFILLING)
|