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700 lines
28 KiB
Python
700 lines
28 KiB
Python
# 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 Zamba model."""
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import math
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import tempfile
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import unittest
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import pytest
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from parameterized import parameterized
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from transformers import AutoTokenizer, Zamba2Config, is_torch_available
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from transformers.testing_utils import (
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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_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, _config_zero_init, 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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Zamba2ForCausalLM,
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Zamba2ForSequenceClassification,
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Zamba2Model,
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)
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from transformers.models.zamba2.modeling_zamba2 import (
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Zamba2HybridDynamicCache,
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)
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class Zamba2ModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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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_labels=True,
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vocab_size=99,
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hidden_size=16,
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mamba_d_state=2,
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chunk_size=8,
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mamba_dt_rank="auto",
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num_hidden_layers=2,
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num_attention_heads=2,
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n_mamba_heads=8,
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mamba_ngroups=8,
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intermediate_size=4,
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hidden_act="gelu",
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hidden_mamba_act="silu",
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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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layers_block_type=["mamba", "hybrid"],
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num_mem_blocks=1,
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use_mem_rope=True,
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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_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.mamba_dt_rank = mamba_dt_rank
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self.mamba_d_state = mamba_d_state
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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.n_mamba_heads = n_mamba_heads
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self.mamba_ngroups = mamba_ngroups
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self.chunk_size = chunk_size
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_mamba_act = hidden_mamba_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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self.layers_block_type = layers_block_type
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self.num_mem_blocks = num_mem_blocks
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self.use_mem_rope = use_mem_rope
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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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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, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return Zamba2Config(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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mamba_dt_rank=self.mamba_dt_rank,
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mamba_d_state=self.mamba_d_state,
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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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n_mamba_heads=self.n_mamba_heads,
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intermediate_size=self.intermediate_size,
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chunk_size=self.chunk_size,
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hidden_act=self.hidden_act,
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mamba_ngroups=self.mamba_ngroups,
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hidden_mamba_act=self.hidden_mamba_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=True,
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initializer_range=self.initializer_range,
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use_mamba_kernels=False,
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layers_block_type=self.layers_block_type,
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num_mem_blocks=self.num_mem_blocks,
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use_mem_rope=self.use_mem_rope,
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)
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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input_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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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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return (
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config,
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input_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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)
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def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = Zamba2Model(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_for_causal_lm(
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self,
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config,
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input_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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):
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model = Zamba2ForCausalLM(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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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids, labels=token_labels)
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result = model(input_ids)
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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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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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config.is_decoder = True
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config.add_cross_attention = False
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model = Zamba2ForCausalLM(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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# Attention: Zamba2 needs the cache to be initialized to return a cache!
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past_key_values = Zamba2HybridDynamicCache(config, input_ids.shape[0], model.dtype, device=model.device)
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outputs = model(
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input_ids,
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attention_mask=input_mask,
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past_key_values=past_key_values,
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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, 1), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 1), 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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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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past_key_values=past_key_values,
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output_hidden_states=True,
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cache_position=torch.arange(
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input_ids.shape[1], input_ids.shape[1] + next_tokens.shape[1], device=model.device
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),
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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[:, -1:, 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 create_and_check_for_sequence_classification(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = Zamba2ForSequenceClassification(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=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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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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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 Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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test_torchscript = False
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all_model_classes = (
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(
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Zamba2Model,
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Zamba2ForCausalLM,
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Zamba2ForSequenceClassification,
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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": Zamba2Model,
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"text-classification": Zamba2ForSequenceClassification,
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"text-generation": Zamba2ForCausalLM,
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"zero-shot": Zamba2ForSequenceClassification,
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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 = Zamba2ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Zamba2Config, hidden_size=37)
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@unittest.skip("position_ids cannot be used to pad due to Mamba2 layers")
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def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
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pass
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def test_past_key_values_format(self):
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"""
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Overwriting to pass the expected cache shapes (Zamba2 has cache shape = [batch_size, 0] for mamba layers)
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"""
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config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
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batch_size, seq_length = inputs["input_ids"].shape
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per_head_embed_dim = config.attention_head_dim # note: this one is not a common attribute name
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self_attention_cache_shape = (batch_size, config.num_key_value_heads, seq_length, per_head_embed_dim)
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# build the full cache shapes, including mamba layers
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all_cache_shapes = []
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for i in range(config.num_hidden_layers):
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if config.layers_block_type[i] == "mamba":
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all_cache_shapes.append([torch.Size([batch_size, 0]), torch.Size([batch_size, 0])])
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else:
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all_cache_shapes.append([self_attention_cache_shape, self_attention_cache_shape])
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super().test_past_key_values_format(custom_all_cache_shapes=all_cache_shapes)
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@unittest.skip(reason="Zamba2 has hybrid cache.")
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def test_generate_continue_from_inputs_embeds(self):
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pass
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@unittest.skip(reason="A large mamba2 would be necessary (and costly) for that")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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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_for_causal_lm(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_for_causal_lm(*config_and_inputs)
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def test_for_sequence_classification(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_for_sequence_classification(*config_and_inputs)
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def test_decoder_model_past_with_large_inputs(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
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def test_initialization(self):
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r"""
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Overriding the test_initialization test as the A_log and D params of the Mamba block are initialized differently
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"""
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.requires_grad:
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if "A_log" in name:
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A = torch.arange(1, config.n_mamba_heads + 1, dtype=torch.float32)[None, :]
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self.assertTrue(torch.allclose(param.data, torch.log(A), atol=1e-5, rtol=1e-5))
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elif "D" in name:
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# check if it's a ones like
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self.assertTrue(torch.allclose(param.data, torch.ones_like(param.data), atol=1e-5, rtol=1e-5))
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elif "dt_bias" in name:
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dt = torch.exp(
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torch.tensor([0, 1]) * (math.log(config.time_step_max) - math.log(config.time_step_min))
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+ math.log(config.time_step_min)
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).clamp(min=config.time_step_floor)
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inv_dt = dt + torch.log(-torch.expm1(-dt))
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if param.requires_grad:
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self.assertTrue(param.data.max().item() <= inv_dt[1])
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self.assertTrue(param.data.min().item() >= inv_dt[0])
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else:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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@unittest.skip(reason="Cumbersome and redundant for Zamba2")
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def test_mismatched_shapes_have_properly_initialized_weights(self):
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r"""
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Overriding the test_mismatched_shapes_have_properly_initialized_weights test because A_log and D params of the
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Mamba block are initialized differently and we tested that in test_initialization
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"""
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pass
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def test_attention_outputs(self):
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r"""
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Overriding the test_attention_outputs test as the Zamba2 model outputs attention only for its attention layers
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"""
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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seq_len = getattr(self.model_tester, "seq_length", None)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class._from_config(config, attn_implementation="eager")
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config = model.config
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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added_hidden_states = 1
|
|
self.assertEqual(out_len + added_hidden_states, len(outputs))
|
|
|
|
self_attentions = outputs.attentions
|
|
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
)
|
|
|
|
def _get_input_ids_and_config(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
(
|
|
config,
|
|
input_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
) = config_and_inputs
|
|
return config, input_ids, input_mask
|
|
|
|
def test_left_padding_compatibility(self):
|
|
r"""
|
|
Overriding the test_left_padding_compatibility test as the mamba layers accentuate the numerical differences
|
|
effect of the left padding discussed in the issue in the note. Using a more permissive tolerance value.
|
|
"""
|
|
import inspect
|
|
# NOTE: left-padding results in small numerical differences. This is expected.
|
|
# See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535
|
|
|
|
# First, filter out models that don't support left padding - generative and decoder-only.
|
|
# Zamba2 is a decoder-only architecture
|
|
decoder_only_classes = self.all_generative_model_classes
|
|
|
|
# Then, test left-padding
|
|
def _prepare_model_kwargs(input_ids, attention_mask, signature):
|
|
model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
|
|
if "position_ids" in signature:
|
|
position_ids = torch.cumsum(attention_mask, dim=-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
model_kwargs["position_ids"] = position_ids
|
|
if "cache_position" in signature:
|
|
cache_position = torch.arange(input_ids.shape[-1], device=torch_device)
|
|
model_kwargs["cache_position"] = cache_position
|
|
return model_kwargs
|
|
|
|
for model_class in decoder_only_classes:
|
|
config, input_ids, attention_mask = self._get_input_ids_and_config()
|
|
model = model_class(config).to(torch_device).eval()
|
|
signature = inspect.signature(model.forward).parameters.keys()
|
|
|
|
# Without padding
|
|
model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature)
|
|
next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]
|
|
|
|
# With left-padding (length 32)
|
|
pad_size = (input_ids.shape[0], 32)
|
|
padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.pad_token_id
|
|
padded_input_ids = torch.cat((padding, input_ids), dim=1)
|
|
padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
|
|
model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature)
|
|
next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]
|
|
|
|
# They should result in very similar logits
|
|
self.assertTrue(torch.allclose(next_logits_wo_padding, next_logits_with_padding, atol=3e-3))
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@require_bitsandbytes
|
|
@pytest.mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_fp32_ln(self):
|
|
r"""
|
|
Overriding the test_flash_attn_2_fp32_ln test as the Zamba2 model, like Mixtral, doesn't support
|
|
right padding + use cache with FA2
|
|
"""
|
|
for model_class in self.all_generative_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
model = model_class(config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
|
|
dummy_input = inputs_dict[model.main_input_name]
|
|
dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
|
|
# NOTE: Zamba2 does not support right padding + use_cache with FA2.
|
|
dummy_attention_mask[:, -1] = 1
|
|
|
|
model = model_class.from_pretrained(
|
|
tmpdirname,
|
|
torch_dtype=torch.float16,
|
|
attn_implementation="flash_attention_2",
|
|
low_cpu_mem_usage=True,
|
|
load_in_4bit=True,
|
|
)
|
|
|
|
for _, param in model.named_parameters():
|
|
# upcast only layer norms
|
|
if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
|
|
param.data = param.data.to(torch.float32)
|
|
|
|
_ = model(dummy_input)
|
|
# with attention mask
|
|
_ = model(dummy_input, attention_mask=dummy_attention_mask)
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@pytest.mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_inference_equivalence_right_padding(self):
|
|
r"""
|
|
Overriding the test_flash_attn_2_inference_padding_right test as the Zamba2 model, like Mixtral, doesn't support
|
|
right padding + use cache with FA2
|
|
"""
|
|
self.skipTest(reason="Zamba2 flash attention does not support right padding")
|
|
|
|
@unittest.skip(reason="Zamba2 has its own special cache type")
|
|
@parameterized.expand([(1, False), (1, True), (4, False)])
|
|
def test_new_cache_format(self, num_beams, do_sample):
|
|
pass
|
|
|
|
@require_torch_gpu
|
|
def test_flex_attention_with_grads(self):
|
|
"""
|
|
Overwriting as the base hidden size is big enough for compile.
|
|
Manipulation of dims causes issues due to other constraints not being satisfied anymore.
|
|
"""
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config._attn_implementation = "flex_attention"
|
|
|
|
model = model_class(config).to(device=torch_device)
|
|
self.assertTrue(model.config._attn_implementation == "flex_attention")
|
|
|
|
# Elaborate workaround for encoder-decoder models as some do not specify their main input
|
|
dummy_inputs = {model.main_input_name: inputs_dict[model.main_input_name].to(torch_device)}
|
|
if config.is_encoder_decoder:
|
|
dummy_inputs["decoder_input_ids"] = inputs_dict["decoder_input_ids"].to(torch_device)
|
|
dummy_inputs["decoder_attention_mask"] = inputs_dict["decoder_attention_mask"].to(torch_device)
|
|
|
|
# If this does not raise an error, the test passes (see https://github.com/huggingface/transformers/pull/35605)
|
|
_ = model(**dummy_inputs)
|
|
|
|
|
|
@require_torch
|
|
class Zamba2ModelIntegrationTest(unittest.TestCase):
|
|
model = None
|
|
tokenizer = None
|
|
|
|
@classmethod
|
|
@slow
|
|
def setUpClass(cls):
|
|
model_id = "Zyphra/Zamba2-1.2B"
|
|
cls.model = Zamba2ForCausalLM.from_pretrained(
|
|
model_id, torch_dtype=torch.float32, low_cpu_mem_usage=True, revision="PR"
|
|
)
|
|
cls.tokenizer = AutoTokenizer.from_pretrained(model_id, revision="PR")
|
|
|
|
@parameterized.expand([(torch_device,), ("cpu",)])
|
|
@slow
|
|
def test_simple_generate(self, torch_device):
|
|
self.model.to(torch_device)
|
|
|
|
input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[
|
|
"input_ids"
|
|
].to(torch_device)
|
|
out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10)
|
|
output_sentence = self.tokenizer.decode(out[0, :])
|
|
self.assertEqual(
|
|
output_sentence,
|
|
"<s> Hey how are you doing on this lovely evening?\n\nI'm doing well, thanks for",
|
|
)
|
|
|
|
with torch.no_grad():
|
|
logits = self.model(input_ids=input_ids).logits.to(dtype=torch.float32)
|
|
|
|
EXPECTED_LOGITS_NO_GRAD = torch.tensor(
|
|
[
|
|
-5.9587, 10.5152, 7.0382, -2.8728, -4.8143, -4.8142, -4.8142, -4.8144,
|
|
-4.8143, -4.8143, -4.8142, -4.8142, 6.0185, 18.0037, -4.8142, -4.8144,
|
|
-4.8143, -4.8142, -4.8143, -4.8143, -4.8143, -4.8143, -4.8142, -4.8143,
|
|
-4.8144, -4.8143, -4.8143, -4.8141, -4.8142, -4.8142, -4.8142, -4.8144,
|
|
-4.8143, -4.8143, -4.8143, -4.8142, -4.8144, -4.8144, -4.8142, -4.8142
|
|
]
|
|
, dtype=torch.float32) # fmt: skip
|
|
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
|
|
|
|
@parameterized.expand([(torch_device,), ("cpu",)])
|
|
@slow
|
|
def test_simple_batched_generate_with_padding(self, torch_device):
|
|
self.model.to(torch_device)
|
|
|
|
inputs = self.tokenizer(
|
|
["Hey how are you doing on this lovely evening?", "When did the Roman empire "],
|
|
padding=True,
|
|
return_tensors="pt",
|
|
).to(torch_device)
|
|
out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10)
|
|
output_sentences = self.tokenizer.batch_decode(out)
|
|
self.assertEqual(
|
|
output_sentences[0],
|
|
"<s> Hey how are you doing on this lovely evening?\n\nI'm doing well, thanks for",
|
|
)
|
|
|
|
self.assertEqual(
|
|
output_sentences[1],
|
|
"[PAD][PAD][PAD][PAD]<s> When did the Roman empire 1st fall?\nThe Roman Empire fell in",
|
|
)
|
|
|
|
with torch.no_grad():
|
|
logits = self.model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]).logits.to(
|
|
dtype=torch.float32
|
|
)
|
|
|
|
EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
|
|
[
|
|
-5.9611, 10.5208, 7.0411, -2.8743, -4.8167, -4.8167, -4.8167, -4.8168,
|
|
-4.8167, -4.8167, -4.8167, -4.8166, 6.0218, 18.0062, -4.8167, -4.8168,
|
|
-4.8167, -4.8167, -4.8167, -4.8168, -4.8168, -4.8168, -4.8167, -4.8167,
|
|
-4.8168, -4.8167, -4.8167, -4.8165, -4.8167, -4.8167, -4.8167, -4.8169,
|
|
-4.8168, -4.8168, -4.8168, -4.8166, -4.8169, -4.8168, -4.8167, -4.8167
|
|
]
|
|
, dtype=torch.float32) # fmt: skip
|
|
|
|
EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
|
|
[
|
|
0.1966, 6.3449, 3.8350, -5.7291, -6.5106, -6.5104, -6.5103, -6.5104,
|
|
-6.5103, -6.5104, -6.5106, -6.5105, 7.8700, 13.5434, -6.5104, -6.5096,
|
|
-6.5106, -6.5102, -6.5106, -6.5106, -6.5105, -6.5106, -6.5104, -6.5106,
|
|
-6.5105, -6.5106, -6.5106, -6.5113, -6.5102, -6.5105, -6.5108, -6.5105,
|
|
-6.5104, -6.5106, -6.5106, -6.5104, -6.5106, -6.5107, -6.5103, -6.5105 ]
|
|
, dtype=torch.float32) # fmt: skip
|
|
|
|
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
|
|
torch.testing.assert_close(
|
|
logits[1, -1, :40].cpu(),
|
|
EXPECTED_LOGITS_NO_GRAD_1,
|
|
rtol=1e-3,
|
|
atol=6e-3 if torch_device == "cpu" else 1e-3,
|
|
)
|