851 lines
36 KiB
Python
851 lines
36 KiB
Python
# coding=utf-8
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# Copyright 2023 Mistral AI and 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 Mistral model."""
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import gc
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import tempfile
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import unittest
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import pytest
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from packaging import version
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from transformers import AutoTokenizer, MistralConfig, is_torch_available, set_seed
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from transformers.testing_utils import (
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backend_empty_cache,
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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_gpu,
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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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MistralForCausalLM,
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MistralForSequenceClassification,
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MistralForTokenClassification,
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MistralModel,
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)
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class MistralModelTester:
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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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num_key_value_heads=2,
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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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs
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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 MistralConfig(
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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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Mistral
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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 = MistralModel(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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Mistral
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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 = MistralModel(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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Mistral
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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 = MistralForCausalLM(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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Mistral
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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 = MistralForCausalLM(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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common
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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 MistralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(MistralModel, MistralForCausalLM, MistralForSequenceClassification, MistralForTokenClassification)
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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 = (MistralForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": MistralModel,
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"text-classification": MistralForSequenceClassification,
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"token-classification": MistralForTokenClassification,
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"text-generation": MistralForCausalLM,
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"zero-shot": MistralForSequenceClassification,
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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 = True
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# TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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return True
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# TODO: @Fxmarty
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@is_flaky(max_attempts=3, description="flaky on some models.")
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@require_torch_sdpa
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@slow
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def test_eager_matches_sdpa_generate(self):
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super().test_eager_matches_sdpa_generate()
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def setUp(self):
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self.model_tester = MistralModelTester(self)
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self.config_tester = ConfigTester(self, config_class=MistralConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_Mistral_sequence_classification_model(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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print(config)
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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 = MistralForSequenceClassification(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_Mistral_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 = MistralForSequenceClassification(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_Mistral_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 = MistralForSequenceClassification(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->Mistral,llama->Mistral
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def test_Mistral_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 = MistralForTokenClassification(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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@unittest.skip("Mistral 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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@unittest.skip("Mistral uses GQA on all models so the KV cache is a non standard format")
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def test_past_key_values_format(self):
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pass
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@require_flash_attn
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@require_torch_gpu
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@pytest.mark.flash_attn_test
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@slow
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def test_flash_attn_2_generate_padding_right(self):
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import torch
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for model_class in self.all_generative_model_classes:
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(
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torch_device
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)
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dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device)
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dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device)
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model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False)
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model = model_class.from_pretrained(
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tmpdirname,
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torch_dtype=torch.float16,
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attn_implementation="flash_attention_2",
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low_cpu_mem_usage=True,
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).to(torch_device)
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with self.assertRaises(ValueError):
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_ = model.generate(
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dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False
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)
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@require_flash_attn
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@require_torch_gpu
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@pytest.mark.flash_attn_test
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@slow
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def test_flash_attn_2_generate_use_cache(self):
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import torch
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max_new_tokens = 30
|
||
|
||
for model_class in self.all_generative_model_classes:
|
||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||
|
||
dummy_input = inputs_dict[model_class.main_input_name]
|
||
if dummy_input.dtype in [torch.float32, torch.bfloat16]:
|
||
dummy_input = dummy_input.to(torch.float16)
|
||
|
||
# make sure that all models have enough positions for generation
|
||
if hasattr(config, "max_position_embeddings"):
|
||
config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1
|
||
|
||
model = model_class(config)
|
||
|
||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||
model.save_pretrained(tmpdirname)
|
||
|
||
dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
|
||
# NOTE: Mistral apparently 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,
|
||
).to(torch_device)
|
||
|
||
# Just test that a large cache works as expected
|
||
_ = model.generate(
|
||
dummy_input,
|
||
attention_mask=dummy_attention_mask,
|
||
max_new_tokens=max_new_tokens,
|
||
do_sample=False,
|
||
use_cache=True,
|
||
)
|
||
|
||
@require_flash_attn
|
||
@require_torch_gpu
|
||
@pytest.mark.flash_attn_test
|
||
@slow
|
||
def test_flash_attn_2_inference_equivalence_right_padding(self):
|
||
self.skipTest("Mistral flash attention does not support right padding")
|
||
|
||
|
||
@require_torch_gpu
|
||
class MistralIntegrationTest(unittest.TestCase):
|
||
# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
|
||
# Depending on the hardware we get different logits / generations
|
||
cuda_compute_capability_major_version = None
|
||
|
||
@classmethod
|
||
def setUpClass(cls):
|
||
if is_torch_available() and torch.cuda.is_available():
|
||
# 8 is for A100 / A10 and 7 for T4
|
||
cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
|
||
|
||
def tearDown(self):
|
||
torch.cuda.empty_cache()
|
||
gc.collect()
|
||
|
||
@slow
|
||
def test_model_7b_logits(self):
|
||
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
|
||
model = MistralForCausalLM.from_pretrained(
|
||
"mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16
|
||
)
|
||
input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
|
||
with torch.no_grad():
|
||
out = model(input_ids).logits.cpu()
|
||
# Expected mean on dim = -1
|
||
EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]])
|
||
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
|
||
|
||
# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
|
||
#
|
||
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
|
||
# considering differences in hardware processing and potential deviations in output.
|
||
EXPECTED_SLICE = {
|
||
7: torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781]),
|
||
8: torch.tensor([-5.8711, -5.8555, -0.1050, -4.7148, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -1.0781, 1.7568, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711]),
|
||
9: torch.tensor([-5.8750, -5.8594, -0.1047, -4.7188, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -1.0781, 1.7578, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750]),
|
||
} # fmt: skip
|
||
|
||
torch.testing.assert_close(
|
||
out[0, 0, :30], EXPECTED_SLICE[self.cuda_compute_capability_major_version], atol=1e-4, rtol=1e-4
|
||
)
|
||
|
||
del model
|
||
backend_empty_cache(torch_device)
|
||
gc.collect()
|
||
|
||
@slow
|
||
@require_bitsandbytes
|
||
def test_model_7b_generation(self):
|
||
EXPECTED_TEXT_COMPLETION = {
|
||
7: "My favourite condiment is 100% ketchup. I love it on everything. I'm not a big",
|
||
8: "My favourite condiment is 100% ketchup. I’m not a fan of mustard, mayo,",
|
||
}
|
||
|
||
prompt = "My favourite condiment is "
|
||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
|
||
model = MistralForCausalLM.from_pretrained(
|
||
"mistralai/Mistral-7B-v0.1", device_map={"": torch_device}, load_in_4bit=True
|
||
)
|
||
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
|
||
|
||
# greedy generation outputs
|
||
generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
|
||
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
|
||
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text)
|
||
|
||
del model
|
||
backend_empty_cache(torch_device)
|
||
gc.collect()
|
||
|
||
@require_bitsandbytes
|
||
@slow
|
||
@require_flash_attn
|
||
def test_model_7b_long_prompt(self):
|
||
EXPECTED_OUTPUT_TOKEN_IDS = [306, 338]
|
||
# An input with 4097 tokens that is above the size of the sliding window
|
||
input_ids = [1] + [306, 338] * 2048
|
||
model = MistralForCausalLM.from_pretrained(
|
||
"mistralai/Mistral-7B-v0.1",
|
||
device_map={"": torch_device},
|
||
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][-2:].tolist())
|
||
|
||
# Assisted generation
|
||
assistant_model = model
|
||
assistant_model.generation_config.num_assistant_tokens = 2
|
||
assistant_model.generation_config.num_assistant_tokens_schedule = "constant"
|
||
generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
|
||
self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
|
||
|
||
del assistant_model
|
||
del model
|
||
backend_empty_cache(torch_device)
|
||
gc.collect()
|
||
|
||
@slow
|
||
@require_torch_sdpa
|
||
def test_model_7b_long_prompt_sdpa(self):
|
||
EXPECTED_OUTPUT_TOKEN_IDS = [306, 338]
|
||
# An input with 4097 tokens that is above the size of the sliding window
|
||
input_ids = [1] + [306, 338] * 2048
|
||
model = MistralForCausalLM.from_pretrained(
|
||
"mistralai/Mistral-7B-v0.1", device_map="auto", attn_implementation="sdpa", torch_dtype=torch.float16
|
||
)
|
||
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][-2:].tolist())
|
||
|
||
# Assisted generation
|
||
assistant_model = model
|
||
assistant_model.generation_config.num_assistant_tokens = 2
|
||
assistant_model.generation_config.num_assistant_tokens_schedule = "constant"
|
||
generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
|
||
self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
|
||
|
||
del assistant_model
|
||
|
||
backend_empty_cache(torch_device)
|
||
gc.collect()
|
||
|
||
EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big"""
|
||
prompt = "My favourite condiment is "
|
||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
|
||
|
||
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
|
||
|
||
# greedy generation outputs
|
||
generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
|
||
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
|
||
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
|
||
|
||
@slow
|
||
def test_speculative_generation(self):
|
||
# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
|
||
#
|
||
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
|
||
# considering differences in hardware processing and potential deviations in generated text.
|
||
EXPECTED_TEXT_COMPLETION = {
|
||
7: "My favourite condiment is 100% Sriracha. I love the heat, the tang and the fact costs",
|
||
8: "My favourite condiment is 100% ketchup. I love it on everything. I’m not a big",
|
||
9: "My favourite condiment is 100% ketchup. I love it on everything. I’m not a big",
|
||
}
|
||
prompt = "My favourite condiment is "
|
||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
|
||
model = MistralForCausalLM.from_pretrained(
|
||
"mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16
|
||
)
|
||
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
|
||
|
||
# greedy generation outputs
|
||
set_seed(0)
|
||
generated_ids = model.generate(
|
||
input_ids, max_new_tokens=20, do_sample=True, temperature=0.3, assistant_model=model
|
||
)
|
||
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
|
||
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text)
|
||
|
||
del model
|
||
backend_empty_cache(torch_device)
|
||
gc.collect()
|
||
|
||
@slow
|
||
def test_compile_static_cache(self):
|
||
# `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2
|
||
# work as intended. See https://github.com/pytorch/pytorch/issues/121943
|
||
if version.parse(torch.__version__) < version.parse("2.3.0"):
|
||
self.skipTest("This test requires torch >= 2.3 to run.")
|
||
|
||
NUM_TOKENS_TO_GENERATE = 40
|
||
EXPECTED_TEXT_COMPLETION = {
|
||
8: [
|
||
"My favourite condiment is 100% ketchup. I love it on everything. "
|
||
"I’m not a big fan of mustard, mayo, or relish. I’m not a fan of pickles"
|
||
],
|
||
7: [
|
||
"My favourite condiment is 100% ketchup. I love it on everything. "
|
||
"I’m not a big fan of mustard, mayo, or relish. I’m not a fan of pickles"
|
||
],
|
||
}
|
||
|
||
prompts = ["My favourite condiment is "]
|
||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
|
||
tokenizer.pad_token = tokenizer.eos_token
|
||
model = MistralForCausalLM.from_pretrained(
|
||
"mistralai/Mistral-7B-v0.1", device_map="sequential", torch_dtype=torch.float16
|
||
)
|
||
inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
|
||
|
||
# Dynamic Cache
|
||
generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
|
||
dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
||
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], dynamic_text)
|
||
|
||
# Static Cache
|
||
generated_ids = model.generate(
|
||
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
|
||
)
|
||
static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
||
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_text)
|
||
|
||
# Sliding Window Cache
|
||
generated_ids = model.generate(
|
||
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window"
|
||
)
|
||
static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
||
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_text)
|
||
|
||
# Static Cache + compile
|
||
forward_function = model.forward
|
||
model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True)
|
||
generated_ids = model.generate(
|
||
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
|
||
)
|
||
static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
||
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)
|
||
|
||
# Sliding Window Cache + compile
|
||
torch._dynamo.reset()
|
||
model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True)
|
||
generated_ids = model.generate(
|
||
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window"
|
||
)
|
||
static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
||
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)
|
||
|
||
del model
|
||
backend_empty_cache(torch_device)
|
||
gc.collect()
|
||
|
||
|
||
@slow
|
||
@require_torch_gpu
|
||
class Mask4DTestHard(unittest.TestCase):
|
||
def tearDown(self):
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
|
||
def setUp(self):
|
||
model_name = "mistralai/Mistral-7B-v0.1"
|
||
self.model_dtype = torch.float32
|
||
self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
|
||
self.model = MistralForCausalLM.from_pretrained(model_name, torch_dtype=self.model_dtype).to(torch_device)
|
||
|
||
def get_test_data(self):
|
||
template = "my favorite {}"
|
||
items = ("pet is a", "artist plays a", "name is L") # same number of tokens in each item
|
||
|
||
batch_separate = [template.format(x) for x in items] # 3 separate lines
|
||
batch_shared_prefix = template.format(" ".join(items)) # 1 line with options concatenated
|
||
|
||
input_ids = self.tokenizer(batch_separate, return_tensors="pt").input_ids.to(torch_device)
|
||
input_ids_shared_prefix = self.tokenizer(batch_shared_prefix, return_tensors="pt").input_ids.to(torch_device)
|
||
|
||
mask_shared_prefix = torch.tensor(
|
||
[
|
||
[
|
||
[
|
||
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
|
||
[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
|
||
[1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
|
||
[1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0],
|
||
[1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],
|
||
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0],
|
||
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1],
|
||
]
|
||
]
|
||
],
|
||
device=torch_device,
|
||
)
|
||
|
||
position_ids = torch.arange(input_ids.shape[1]).tile(input_ids.shape[0], 1).to(torch_device)
|
||
|
||
# building custom positions ids based on custom mask
|
||
position_ids_shared_prefix = (mask_shared_prefix.sum(dim=-1) - 1).reshape(1, -1)
|
||
# effectively: position_ids_shared_prefix = torch.tensor([[0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5]]).to(device)
|
||
|
||
# inverting the mask
|
||
min_dtype = torch.finfo(self.model_dtype).min
|
||
mask_shared_prefix = (mask_shared_prefix.eq(0.0)).to(dtype=self.model_dtype) * min_dtype
|
||
|
||
return input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix
|
||
|
||
def test_stacked_causal_mask(self):
|
||
(
|
||
input_ids,
|
||
position_ids,
|
||
input_ids_shared_prefix,
|
||
mask_shared_prefix,
|
||
position_ids_shared_prefix,
|
||
) = self.get_test_data()
|
||
|
||
# regular batch
|
||
logits = self.model.forward(input_ids, position_ids=position_ids).logits
|
||
logits_last = logits[:, -1, :] # last tokens in each batch line
|
||
decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)]
|
||
|
||
# single forward run with 4D custom mask
|
||
logits_shared_prefix = self.model.forward(
|
||
input_ids_shared_prefix, attention_mask=mask_shared_prefix, position_ids=position_ids_shared_prefix
|
||
).logits
|
||
logits_shared_prefix_last = logits_shared_prefix[
|
||
0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], :
|
||
] # last three tokens
|
||
decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)]
|
||
|
||
self.assertEqual(decoded, decoded_shared_prefix)
|
||
|
||
def test_partial_stacked_causal_mask(self):
|
||
# Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks
|
||
|
||
(
|
||
input_ids,
|
||
position_ids,
|
||
input_ids_shared_prefix,
|
||
mask_shared_prefix,
|
||
position_ids_shared_prefix,
|
||
) = self.get_test_data()
|
||
|
||
# regular batch
|
||
logits = self.model.forward(input_ids, position_ids=position_ids).logits
|
||
logits_last = logits[:, -1, :] # last tokens in each batch line
|
||
decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)]
|
||
|
||
# 2 forward runs with custom 4D masks
|
||
part_a = 3 # split point
|
||
|
||
input_1a = input_ids_shared_prefix[:, :part_a]
|
||
position_ids_1a = position_ids_shared_prefix[:, :part_a]
|
||
mask_1a = mask_shared_prefix[:, :, :part_a, :part_a]
|
||
|
||
outs_1a = self.model.forward(input_1a, attention_mask=mask_1a, position_ids=position_ids_1a)
|
||
past_key_values_a = outs_1a["past_key_values"]
|
||
|
||
# Case 1: we pass a 4D attention mask regarding the current sequence length (i.e. [..., seq_len, full_len])
|
||
input_1b = input_ids_shared_prefix[:, part_a:]
|
||
position_ids_1b = position_ids_shared_prefix[:, part_a:]
|
||
mask_1b = mask_shared_prefix[:, :, part_a:, :]
|
||
outs_1b = self.model.forward(
|
||
input_1b, attention_mask=mask_1b, position_ids=position_ids_1b, past_key_values=past_key_values_a
|
||
)
|
||
decoded_1b = [
|
||
self.tokenizer.decode(t)
|
||
for t in outs_1b.logits.argmax(-1)[
|
||
0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a
|
||
]
|
||
]
|
||
self.assertEqual(decoded, decoded_1b)
|