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62c7ea0201
* tmp commit * move tests to the right class * remove ALL all_generative_model_classes = ... * skip tf roberta * skip InstructBlipForConditionalGenerationDecoderOnlyTest * videollava * reduce diff * reduce diff * remove on vlms * fix a few more * manual rebase bits * more manual rebase * remove all manual generative model class test entries * fix up to ernie * a few more removals * handle remaining cases * recurrent gemma * it's better here * make fixup * tf idefics is broken * tf bert + generate is broken * don't touch tf :() * don't touch tf :( * make fixup * better comments for test skips * revert tf changes * remove empty line removal * one more * missing one
477 lines
19 KiB
Python
477 lines
19 KiB
Python
# coding=utf-8
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# Copyright 2024 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 Granite model."""
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import unittest
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from parameterized import parameterized
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from transformers import GraniteConfig, is_torch_available, set_seed
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from transformers.testing_utils import (
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require_read_token,
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require_torch,
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require_torch_gpu,
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slow,
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torch_device,
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)
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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
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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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GraniteForCausalLM,
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GraniteModel,
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)
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from transformers.models.granite.modeling_granite import (
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GraniteRotaryEmbedding,
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)
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class GraniteModelTester:
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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 = torch.tril(torch.ones_like(input_ids).to(torch_device))
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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 GraniteConfig(
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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 = GraniteModel(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 = GraniteModel(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 = GraniteForCausalLM(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 = GraniteForCausalLM(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 GraniteModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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GraniteModel,
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GraniteForCausalLM,
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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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pipeline_model_mapping = (
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{
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"feature-extraction": GraniteModel,
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"text-generation": GraniteForCausalLM,
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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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fx_compatible = False
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# Need to use `0.8` instead of `0.9` for `test_cpu_offload`
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# This is because we are hitting edge cases with the causal_mask buffer
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model_split_percents = [0.5, 0.7, 0.8]
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def setUp(self):
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self.model_tester = GraniteModelTester(self)
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self.config_tester = ConfigTester(self, config_class=GraniteConfig, 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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@unittest.skip("Granite 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_from_config(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 = GraniteModel(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 = GraniteModel(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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torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, 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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def test_model_rope_scaling(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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scaling_factor = 10
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short_input_length = 10
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long_input_length = int(config.max_position_embeddings * 1.5)
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# Inputs
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x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device
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position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device)
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position_ids_short = position_ids_short.unsqueeze(0)
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position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device)
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position_ids_long = position_ids_long.unsqueeze(0)
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# Sanity check original RoPE
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original_rope = GraniteRotaryEmbedding(config=config).to(torch_device)
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original_cos_short, original_sin_short = original_rope(x, position_ids_short)
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original_cos_long, original_sin_long = original_rope(x, position_ids_long)
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torch.testing.assert_close(original_cos_short, original_cos_long[:, :short_input_length, :])
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torch.testing.assert_close(original_sin_short, original_sin_long[:, :short_input_length, :])
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# Sanity check linear RoPE scaling
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# New position "x" should match original position with index "x/scaling_factor"
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config.rope_scaling = {"type": "linear", "factor": scaling_factor}
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linear_scaling_rope = GraniteRotaryEmbedding(config=config).to(torch_device)
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linear_cos_short, linear_sin_short = linear_scaling_rope(x, position_ids_short)
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linear_cos_long, linear_sin_long = linear_scaling_rope(x, position_ids_long)
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torch.testing.assert_close(linear_cos_short, linear_cos_long[:, :short_input_length, :])
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torch.testing.assert_close(linear_sin_short, linear_sin_long[:, :short_input_length, :])
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for new_position in range(0, long_input_length, scaling_factor):
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original_position = int(new_position // scaling_factor)
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torch.testing.assert_close(linear_cos_long[:, new_position, :], original_cos_long[:, original_position, :])
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torch.testing.assert_close(linear_sin_long[:, new_position, :], original_sin_long[:, original_position, :])
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# Sanity check Dynamic NTK RoPE scaling
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# Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
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# with scaling_factor (or that `inv_freq` decreases)
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config.rope_scaling = {"type": "dynamic", "factor": scaling_factor}
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ntk_scaling_rope = GraniteRotaryEmbedding(config=config).to(torch_device)
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ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, position_ids_short)
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ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, position_ids_long)
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torch.testing.assert_close(ntk_cos_short, original_cos_short)
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torch.testing.assert_close(ntk_sin_short, original_sin_short)
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with self.assertRaises(AssertionError):
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torch.testing.assert_close(ntk_cos_long, original_cos_long)
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with self.assertRaises(AssertionError):
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torch.testing.assert_close(ntk_sin_long, original_sin_long)
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self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())
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# Sanity check Yarn RoPE scaling
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# Scaling should be over the entire input
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config.rope_scaling = {"type": "yarn", "factor": scaling_factor}
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yarn_scaling_rope = GraniteRotaryEmbedding(config=config).to(torch_device)
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yarn_cos_short, yarn_sin_short = yarn_scaling_rope(x, position_ids_short)
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yarn_cos_long, yarn_sin_long = yarn_scaling_rope(x, position_ids_long)
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torch.testing.assert_close(yarn_cos_short, yarn_cos_long[:, :short_input_length, :])
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torch.testing.assert_close(yarn_sin_short, yarn_sin_long[:, :short_input_length, :])
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with self.assertRaises(AssertionError):
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torch.testing.assert_close(yarn_cos_short, original_cos_short)
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with self.assertRaises(AssertionError):
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torch.testing.assert_close(yarn_sin_short, original_sin_short)
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with self.assertRaises(AssertionError):
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torch.testing.assert_close(yarn_cos_long, original_cos_long)
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with self.assertRaises(AssertionError):
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torch.testing.assert_close(yarn_sin_long, original_sin_long)
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@require_torch_gpu
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class GraniteIntegrationTest(unittest.TestCase):
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# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
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# Depending on the hardware we get different logits / generations
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cuda_compute_capability_major_version = None
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@classmethod
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def setUpClass(cls):
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if is_torch_available() and torch.cuda.is_available():
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# 8 is for A100 / A10 and 7 for T4
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cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
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@slow
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@require_read_token
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def test_model_3b_logits_bf16(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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|
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model = GraniteForCausalLM.from_pretrained(
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"ibm/PowerLM-3b", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="eager"
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)
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|
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with torch.no_grad():
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out = model(torch.tensor([input_ids]).to(torch_device))
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# Expected mean on dim = -1
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|
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# fmt: off
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EXPECTED_MEAN = torch.tensor([[-1.9798, -3.1626, -2.8062, -2.3777, -2.7091, -2.2338, -2.5924, -2.3974]])
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|
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torch.testing.assert_close(EXPECTED_MEAN.to(torch_device), out.logits.mean(-1), rtol=1e-2, atol=1e-2)
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|
|
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# slicing logits[0, 0, 0:15]
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EXPECTED_SLICE = torch.tensor([[4.8750, -2.1875, -2.1875, -2.1875, -2.1875, -2.8438, -2.1875, -2.1875,
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-2.1875, -2.1875, -2.1875, -2.1875, -2.1875, -2.1875, -2.1875]])
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# fmt: on
|
|
|
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self.assertTrue(
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|
torch.allclose(
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|
EXPECTED_SLICE.to(torch_device),
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out.logits[0, 0, :15].float(),
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|
atol=1e-3,
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|
rtol=1e-3,
|
|
)
|
|
)
|
|
|
|
@slow
|
|
@require_read_token
|
|
def test_model_3b_logits(self):
|
|
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
|
|
|
|
model = GraniteForCausalLM.from_pretrained("ibm/PowerLM-3b", device_map="auto", torch_dtype=torch.float16)
|
|
|
|
with torch.no_grad():
|
|
out = model(torch.tensor([input_ids]).to(torch_device))
|
|
|
|
# fmt: off
|
|
# Expected mean on dim = -1
|
|
EXPECTED_MEAN = torch.tensor([[-2.0984, -3.1294, -2.8153, -2.3568, -2.7337, -2.2624, -2.6016, -2.4022]])
|
|
|
|
torch.testing.assert_close(EXPECTED_MEAN.to(torch_device), out.logits.float().mean(-1), rtol=1e-2, atol=1e-2)
|