619 lines
27 KiB
Python
619 lines
27 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 StableLm model."""
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import unittest
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from parameterized import parameterized
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from transformers import StableLmConfig, is_torch_available, set_seed
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from transformers.testing_utils import (
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is_flaky,
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require_bitsandbytes,
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require_flash_attn,
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require_torch,
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require_torch_sdpa,
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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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AutoTokenizer,
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StableLmForCausalLM,
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StableLmForSequenceClassification,
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StableLmForTokenClassification,
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StableLmModel,
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)
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from transformers.models.stablelm.modeling_stablelm import (
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StableLmDynamicNTKScalingRotaryEmbedding,
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StableLmLinearScalingRotaryEmbedding,
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StableLmRotaryEmbedding,
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)
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# Copied from transformers.tests.models.persimmon.test_modeling_persimmon.PersimmonModelTester with Persimmon -> StableLm
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class StableLmModelTester:
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# Ignore copy
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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=64,
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num_hidden_layers=2,
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num_attention_heads=4,
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num_key_value_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.num_key_value_heads = num_key_value_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(self.batch_size, self.seq_length)).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 StableLmConfig(
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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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num_key_value_heads=self.num_key_value_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 = StableLmModel(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 = StableLmModel(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 = StableLmForCausalLM(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 = StableLmForCausalLM(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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# Copied from transformers.tests.persimmon.test_modeling_persimmon.PersimmonModelTest with Persimmon -> StableLm
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class StableLmModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(StableLmModel, StableLmForCausalLM, StableLmForSequenceClassification, StableLmForTokenClassification)
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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": StableLmModel,
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"text-classification": StableLmForSequenceClassification,
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"token-classification": StableLmForTokenClassification,
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# TODO (ydshieh): check why these two fail. Fix them or skip them in a better way.
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# "text-generation": StableLmForCausalLM,
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# "zero-shot": StableLmForSequenceClassification,
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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 = (StableLmForCausalLM,) if is_torch_available() else ()
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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 = StableLmModelTester(self)
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self.config_tester = ConfigTester(self, config_class=StableLmConfig, 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_stablelm_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 = StableLmForSequenceClassification(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_stablelm_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 = StableLmForSequenceClassification(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_stablelm_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 = StableLmForSequenceClassification(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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_token_classification_model with Llama->StableLm,llama->stablelm
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def test_stablelm_token_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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token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels)
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model = StableLmForTokenClassification(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=attention_mask, labels=token_labels)
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self.assertEqual(
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result.logits.shape,
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(self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels),
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)
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@parameterized.expand([("linear",), ("dynamic",)])
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling_from_config with Llama->StableLm
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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 = StableLmModel(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 = StableLmModel(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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# Copied from tests.models.falcon.test_modeling_falcon.FalconModelTest.test_model_rope_scaling with Falcon->StableLm
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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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hidden_size = config.hidden_size
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num_heads = config.num_attention_heads
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head_dim = hidden_size // num_heads
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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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# Sanity check original RoPE
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original_rope = StableLmRotaryEmbedding(
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head_dim,
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max_position_embeddings=config.max_position_embeddings,
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base=config.rope_theta,
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).to(torch_device)
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original_cos_short, original_sin_short = original_rope(x, short_input_length)
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original_cos_long, original_sin_long = original_rope(x, long_input_length)
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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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linear_scaling_rope = StableLmLinearScalingRotaryEmbedding(
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head_dim,
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||
max_position_embeddings=config.max_position_embeddings,
|
||
base=config.rope_theta,
|
||
scaling_factor=scaling_factor,
|
||
).to(torch_device)
|
||
linear_cos_short, linear_sin_short = linear_scaling_rope(x, short_input_length)
|
||
linear_cos_long, linear_sin_long = linear_scaling_rope(x, long_input_length)
|
||
torch.testing.assert_close(linear_cos_short, linear_cos_long[:short_input_length, :])
|
||
torch.testing.assert_close(linear_sin_short, linear_sin_long[:short_input_length, :])
|
||
for new_position in range(0, long_input_length, scaling_factor):
|
||
original_position = int(new_position // scaling_factor)
|
||
torch.testing.assert_close(linear_cos_long[new_position, :], original_cos_long[original_position, :])
|
||
torch.testing.assert_close(linear_sin_long[new_position, :], original_sin_long[original_position, :])
|
||
|
||
# Sanity check Dynamic NTK RoPE scaling
|
||
# Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
|
||
# with scaling_factor (or that `inv_freq` decreases)
|
||
ntk_scaling_rope = StableLmDynamicNTKScalingRotaryEmbedding(
|
||
head_dim,
|
||
max_position_embeddings=config.max_position_embeddings,
|
||
base=config.rope_theta,
|
||
scaling_factor=scaling_factor,
|
||
).to(torch_device)
|
||
ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, short_input_length)
|
||
ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, long_input_length)
|
||
torch.testing.assert_close(ntk_cos_short, original_cos_short)
|
||
torch.testing.assert_close(ntk_sin_short, original_sin_short)
|
||
with self.assertRaises(AssertionError):
|
||
torch.testing.assert_close(ntk_cos_long, original_cos_long)
|
||
with self.assertRaises(AssertionError):
|
||
torch.testing.assert_close(ntk_sin_long, original_sin_long)
|
||
self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())
|
||
|
||
|
||
@require_torch
|
||
class StableLmModelIntegrationTest(unittest.TestCase):
|
||
@slow
|
||
def test_model_stablelm_3b_4e1t_logits(self):
|
||
input_ids = {"input_ids": torch.tensor([[510, 8588, 310, 1900, 9386]], dtype=torch.long, device=torch_device)}
|
||
|
||
model = StableLmForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t").to(torch_device)
|
||
model.eval()
|
||
|
||
output = model(**input_ids).logits
|
||
|
||
# Expected mean on dim = -1
|
||
EXPECTED_MEAN = torch.tensor([[2.7146, 2.4245, 1.5616, 1.4424, 2.6790]]).to(torch_device)
|
||
self.assertTrue(torch.allclose(output.mean(dim=-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4))
|
||
|
||
# Expected logits sliced from [0, 0, 0:30]
|
||
EXPECTED_SLICE = torch.tensor([7.1030, -1.4195, 9.9206, 7.7008, 4.9891, 4.2169, 5.5426, 3.7878, 6.7593, 5.7360, 8.4691, 5.5448, 5.0544, 10.4129, 8.5573, 13.0405, 7.3265, 3.5868, 6.1106, 5.9406, 5.6376, 5.7490, 5.4850, 4.8124, 5.1991, 4.6419, 4.5719, 9.9588, 6.7222, 4.5070]).to(torch_device) # fmt: skip
|
||
self.assertTrue(torch.allclose(output[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4))
|
||
|
||
@slow
|
||
def test_model_stablelm_3b_4e1t_generation(self):
|
||
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
|
||
model = StableLmForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t")
|
||
input_ids = tokenizer.encode(
|
||
"My favorite food has always been pizza, but lately",
|
||
return_tensors="pt",
|
||
)
|
||
|
||
outputs = model.generate(input_ids, max_new_tokens=20, temperature=0)
|
||
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
||
|
||
EXPECTED_TEXT_COMPLETION = """My favorite food has always been pizza, but lately I’ve been craving something different. I’ve been trying to eat healthier and I’ve"""
|
||
self.assertEqual(text, EXPECTED_TEXT_COMPLETION)
|
||
|
||
@slow
|
||
def test_model_tiny_random_stablelm_2_logits(self):
|
||
# Check parallel residual and qk layernorm forward pass
|
||
input_ids = {"input_ids": torch.tensor([[510, 8588, 310, 1900, 9386]], dtype=torch.long, device=torch_device)}
|
||
|
||
model = StableLmForCausalLM.from_pretrained("stabilityai/tiny-random-stablelm-2").to(torch_device)
|
||
model.eval()
|
||
|
||
output = model(**input_ids).logits
|
||
|
||
# Expected mean on dim = -1
|
||
EXPECTED_MEAN = torch.tensor([[-2.7196, -3.6099, -2.6877, -3.1973, -3.9344]]).to(torch_device)
|
||
self.assertTrue(torch.allclose(output.mean(dim=-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4))
|
||
|
||
# Expected logits sliced from [0, 0, 0:30]
|
||
EXPECTED_SLICE = torch.tensor([2.8364, 5.3811, 5.1659, 7.5485, 4.3219, 6.3315, 1.3967, 6.9147, 3.9679, 6.4786, 5.9176, 3.3067, 5.2917, 0.1485, 3.9630, 7.9947,10.6727, 9.6757, 8.8772, 8.3527, 7.8445, 6.6025, 5.5786, 7.0985,6.1369, 3.4259, 1.9397, 4.6157, 4.8105, 3.1768]).to(torch_device) # fmt: skip
|
||
self.assertTrue(torch.allclose(output[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4))
|
||
|
||
@slow
|
||
def test_model_tiny_random_stablelm_2_generation(self):
|
||
# Check parallel residual and qk layernorm generation
|
||
tokenizer = AutoTokenizer.from_pretrained("stabilityai/tiny-random-stablelm-2")
|
||
model = StableLmForCausalLM.from_pretrained("stabilityai/tiny-random-stablelm-2")
|
||
input_ids = tokenizer.encode(
|
||
"My favorite ride at the amusement park",
|
||
return_tensors="pt",
|
||
)
|
||
|
||
outputs = model.generate(input_ids, max_new_tokens=20, temperature=0)
|
||
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
||
|
||
EXPECTED_TEXT_COMPLETION = """My favorite ride at the amusement park is the 2000-mile roller coaster. It's a thrilling ride filled with roller coast"""
|
||
self.assertEqual(text, EXPECTED_TEXT_COMPLETION)
|
||
|
||
@require_bitsandbytes
|
||
@slow
|
||
@require_flash_attn
|
||
def test_model_3b_long_prompt(self):
|
||
EXPECTED_OUTPUT_TOKEN_IDS = [3, 3, 3]
|
||
input_ids = [306, 338] * 2047
|
||
model = StableLmForCausalLM.from_pretrained(
|
||
"stabilityai/stablelm-3b-4e1t",
|
||
device_map="auto",
|
||
torch_dtype="auto",
|
||
load_in_4bit=True,
|
||
attn_implementation="flash_attention_2",
|
||
)
|
||
input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
|
||
generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
|
||
self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-3:].tolist())
|
||
|
||
# Copied from transformers.tests.models.llama.test_modeling_llama.LlamaModelTest.test_eager_matches_sdpa_generate with Llama->StableLm,saibo/llama-1B->stabilityai/stablelm-3b-4e1t
|
||
# TODO: @Fxmarty
|
||
@is_flaky(max_attempts=3, description="flaky on some models.")
|
||
@require_torch_sdpa
|
||
@slow
|
||
def test_eager_matches_sdpa_generate(self):
|
||
"""
|
||
Overwritting the common test as the test is flaky on tiny models
|
||
"""
|
||
max_new_tokens = 30
|
||
|
||
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
|
||
|
||
model_sdpa = StableLmForCausalLM.from_pretrained(
|
||
"stabilityai/stablelm-3b-4e1t",
|
||
torch_dtype=torch.float16,
|
||
low_cpu_mem_usage=True,
|
||
).to(torch_device)
|
||
|
||
self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
|
||
|
||
model_eager = StableLmForCausalLM.from_pretrained(
|
||
"stabilityai/stablelm-3b-4e1t",
|
||
torch_dtype=torch.float16,
|
||
low_cpu_mem_usage=True,
|
||
attn_implementation="eager",
|
||
).to(torch_device)
|
||
|
||
self.assertTrue(model_eager.config._attn_implementation == "eager")
|
||
|
||
for name, submodule in model_eager.named_modules():
|
||
if "SdpaAttention" in submodule.__class__.__name__:
|
||
raise ValueError("The eager model should not have SDPA attention layers")
|
||
|
||
has_sdpa = False
|
||
for name, submodule in model_sdpa.named_modules():
|
||
if "SdpaAttention" in submodule.__class__.__name__:
|
||
has_sdpa = True
|
||
break
|
||
if not has_sdpa:
|
||
raise ValueError("The SDPA model should have SDPA attention layers")
|
||
|
||
texts = [
|
||
"hi here's a longer context, getting longer and",
|
||
"Hello this is a very long sentence my friend, very long for real",
|
||
"Today I am in Paris and",
|
||
]
|
||
|
||
for padding_side in ["left", "right"]:
|
||
tokenizer.padding_side = padding_side
|
||
tokenizer.pad_token = tokenizer.eos_token
|
||
|
||
inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device)
|
||
|
||
res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
|
||
res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
|
||
|
||
with self.subTest(f"{padding_side}"):
|
||
torch.testing.assert_close(
|
||
res_eager,
|
||
res_sdpa,
|
||
msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}",
|
||
)
|