Falcon port (#24523)
* Initial commit * Update src/transformers/models/falcon/configuration_falcon.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/falcon/configuration_falcon.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Cleanup config docstring * Update src/transformers/models/falcon/configuration_falcon.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Convert to relative imports * Remove torch < 1.8 warning * Restructure cos_sin header * qkv -> query, key, value * Refactor attention calculation * Add a couple of config variables to account for the different checkpoints * Successful merging of the code paths! * Fix misplaced line in the non-parallel attention path * Update config and tests * Add a pad_token_id when testing * Support output_attentions when alibi is None * make fixup * Skip KV cache shape test * No more _keys_to_ignore_on_load_missing * Simplify self attention a bit * Simplify self attention a bit * make fixup * stash commit * Some more attention mask updates * Should pass all tests except assisted generation! * Add big model generation test * make fixup * Add temporary workaround for test * Test overrides for assisted generation * Update src/transformers/models/falcon/modeling_falcon.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/falcon/modeling_falcon.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/falcon/modeling_falcon.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update tests/models/falcon/test_modeling_falcon.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Test overrides for assisted generation * Add generation demo * Update copyright * Make the docstring model actually small * Add module-level docstring * Remove all assertions * Add copied from bloom * Reformat the QKV layer * Add copied from bloom * Update src/transformers/models/falcon/modeling_falcon.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Remove unused line and reformat * No single letter variables * Cleanup return names * Add copied from line * Remove the deprecated arguments blocks * Change the embeddings test to an alibi on/off test * Remove position_ids from FalconForQA * Remove old check for token type IDs * Fix the alibi path when multi_query is False * Update src/transformers/models/falcon/modeling_falcon.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/falcon/modeling_falcon.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/falcon/test_modeling_falcon.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update config naming * Fix typo for new_decoder_architecture * Add some comments * Fix docstring * Fix docstring * Create range in the right dtype from the start * Review comment cleanup * n_head_kv -> num_kv_heads * self.alibi -> self.use_alibi * self.num_kv -> self.num_kv_heads * Reorder config args * Made alibi arguments Optional * Add all model docstrings * Add extra checkpoints * Add author info for Falcon * Stop removing token_type_ids because our checkpoints shouldn't return it anymore * Add one hopeful comment for the future * Fix typo * Update tests, fix cache issue for generation * Use -1e9 instead of -inf to avoid float overflow * Recompute the rotary embeddings much less often * Re-enable disabled tests * One final fix to attention mask calculation, and update tests * Cleanup targeting falcon-40b equivalency * Post-rebase docs update * Update docstrings, especially in the config * More descriptive variable names, and comments where we can't rename them --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Matt
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tests/models/falcon/__init__.py
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tests/models/falcon/__init__.py
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tests/models/falcon/test_modeling_falcon.py
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tests/models/falcon/test_modeling_falcon.py
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# coding=utf-8
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# Copyright 2023 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 Falcon model. """
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import unittest
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from transformers import AutoTokenizer, FalconConfig, is_torch_available
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from transformers.testing_utils import require_torch, 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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FalconForCausalLM,
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FalconForQuestionAnswering,
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FalconForSequenceClassification,
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FalconForTokenClassification,
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FalconModel,
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)
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class FalconModelTester:
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def __init__(
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self,
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parent,
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batch_size=3,
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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=5,
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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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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.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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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 FalconConfig(
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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=1,
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new_decoder_architecture=True,
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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 = FalconModel(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 = FalconModel(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 = FalconForCausalLM(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 = FalconForCausalLM(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 FalconModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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FalconModel,
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FalconForCausalLM,
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FalconForSequenceClassification,
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FalconForTokenClassification,
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FalconForQuestionAnswering,
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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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all_generative_model_classes = (FalconForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": FalconModel,
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"text-classification": FalconForSequenceClassification,
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"text-generation": FalconForCausalLM,
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"question-answering": FalconForQuestionAnswering,
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"token-classification": FalconForTokenClassification,
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"zero-shot": FalconForSequenceClassification,
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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 = FalconModelTester(self)
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self.config_tester = ConfigTester(self, config_class=FalconConfig, 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_position_embedding_types(self):
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config, *inputs = self.model_tester.prepare_config_and_inputs()
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for alibi in [True, False]:
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config.alibi = alibi
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self.model_tester.create_and_check_model(config, *inputs)
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def test_falcon_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 = FalconForSequenceClassification(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_falcon_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 = FalconForSequenceClassification(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_cache_conversions(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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input_ids = input_dict["input_ids"]
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model = FalconForCausalLM(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, use_cache=True)
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batch_size = input_ids.shape[0]
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rw_cache = model._convert_to_rw_cache(result.past_key_values)
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standard_cache = model._convert_cache_to_standard_format(rw_cache, batch_size)
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for layer in range(len(rw_cache)):
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for tensor_idx in range(2):
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self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3)
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self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4)
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self.assertTrue(
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torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx])
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)
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def test_falcon_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 = FalconForSequenceClassification(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_past_key_values_format(self):
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# Falcon can have different numbers of KV-heads than the number of query heads, so we need
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# to override this test to use the right head counts.
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for model_class in self.all_generative_model_classes:
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config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
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|
||||
# If it doesn't support cache, pass the test
|
||||
if not hasattr(config, "use_cache"):
|
||||
return
|
||||
|
||||
model = model_class(config).to(torch_device)
|
||||
if "use_cache" not in inputs:
|
||||
inputs["use_cache"] = True
|
||||
outputs = model(**inputs)
|
||||
|
||||
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
|
||||
if "past_key_values" not in outputs:
|
||||
return
|
||||
|
||||
num_hidden_layers = (
|
||||
getattr(config, "decoder_layers", None)
|
||||
or getattr(config, "num_decoder_layers", None)
|
||||
or config.num_hidden_layers
|
||||
)
|
||||
num_attention_heads = getattr(config, "num_kv_heads", config.num_attention_heads)
|
||||
embed_dim = getattr(config, "d_model", config.hidden_size)
|
||||
per_head_embed_dim = embed_dim // num_attention_heads
|
||||
|
||||
past_kv = outputs["past_key_values"]
|
||||
self.assertEqual(len(past_kv), num_hidden_layers)
|
||||
|
||||
batch_size, seq_length = inputs["input_ids"].shape
|
||||
for i in range(num_hidden_layers):
|
||||
if config.new_decoder_architecture:
|
||||
num_attention_heads = config.num_attention_heads
|
||||
elif config.multi_query:
|
||||
num_attention_heads = 1
|
||||
self.assertEqual(len(past_kv[0]), 2) # K V for the decoder = 2
|
||||
self.assertEqual(
|
||||
past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
|
||||
)
|
||||
self.assertEqual(
|
||||
past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
|
||||
)
|
||||
|
||||
|
||||
@require_torch
|
||||
class FalconLanguageGenerationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_lm_generate_falcon(self):
|
||||
tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b")
|
||||
model = FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b")
|
||||
model.eval()
|
||||
model.to(torch_device)
|
||||
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
|
||||
|
||||
EXPECTED_OUTPUT = (
|
||||
"My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
|
||||
)
|
||||
|
||||
output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=19)
|
||||
output_str = tokenizer.batch_decode(output_ids)[0]
|
||||
|
||||
self.assertEqual(output_str, EXPECTED_OUTPUT)
|
||||
|
||||
@slow
|
||||
def test_lm_generation_big_models(self):
|
||||
# The big models are way too big for the CI, so we use tiny random models that resemble their
|
||||
# architectures but with much smaller and fewer layers
|
||||
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
|
||||
tokenizer = AutoTokenizer.from_pretrained(repo)
|
||||
model = FalconForCausalLM.from_pretrained(repo)
|
||||
model.eval()
|
||||
model.to(torch_device)
|
||||
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
|
||||
|
||||
# We just test that these run without errors - the models are randomly initialized
|
||||
# and so the actual text outputs will be garbage
|
||||
model.generate(**inputs, do_sample=False, max_new_tokens=4)
|
||||
model.generate(**inputs, do_sample=True, max_new_tokens=4)
|
||||
model.generate(**inputs, num_beams=2, max_new_tokens=4)
|
||||
|
||||
@slow
|
||||
def test_lm_generation_use_cache(self):
|
||||
# The big models are way too big for the CI, so we use tiny random models that resemble their
|
||||
# architectures but with much smaller and fewer layers
|
||||
with torch.no_grad():
|
||||
for repo in [
|
||||
"Rocketknight1/falcon-rw-1b",
|
||||
"Rocketknight1/tiny-random-falcon-7b",
|
||||
"Rocketknight1/tiny-random-falcon-40b",
|
||||
]:
|
||||
tokenizer = AutoTokenizer.from_pretrained(repo)
|
||||
model = FalconForCausalLM.from_pretrained(repo)
|
||||
model.eval()
|
||||
model.to(device=torch_device)
|
||||
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
|
||||
|
||||
# Test results are the same with and without cache
|
||||
outputs_no_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=False)
|
||||
outputs_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=True)
|
||||
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0)
|
||||
Reference in New Issue
Block a user