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https://github.com/huggingface/transformers.git
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2ce02b98bf
* fix * fix * fix * fix --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
480 lines
20 KiB
Python
480 lines
20 KiB
Python
# 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 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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DeviceProperties,
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Expectations,
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backend_empty_cache,
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cleanup,
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get_device_properties,
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require_bitsandbytes,
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require_flash_attn,
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require_read_token,
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require_torch,
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require_torch_accelerator,
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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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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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MistralForQuestionAnswering,
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MistralForSequenceClassification,
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MistralForTokenClassification,
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MistralModel,
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)
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from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
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class MistralModelTester(CausalLMModelTester):
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config_class = MistralConfig
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if is_torch_available():
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base_model_class = MistralModel
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causal_lm_class = MistralForCausalLM
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sequence_class = MistralForSequenceClassification
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token_class = MistralForTokenClassification
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question_answering_class = MistralForQuestionAnswering
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@require_torch
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class MistralModelTest(CausalLMModelTest, unittest.TestCase):
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all_model_classes = (
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(
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MistralModel,
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MistralForCausalLM,
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MistralForSequenceClassification,
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MistralForTokenClassification,
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MistralForQuestionAnswering,
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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": 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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"question-answering": MistralForQuestionAnswering,
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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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model_tester_class = MistralModelTester
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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,
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pipeline_test_case_name,
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config_class,
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model_architecture,
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tokenizer_name,
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image_processor_name,
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feature_extractor_name,
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processor_name,
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):
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return True
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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_inference_equivalence_right_padding(self):
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self.skipTest(reason="Mistral flash attention does not support right padding")
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@require_torch_accelerator
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@require_read_token
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class MistralIntegrationTest(unittest.TestCase):
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# This variable is used to determine which accelerator are we using for our runners (e.g. A10 or T4)
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# Depending on the hardware we get different logits / generations
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device_properties: DeviceProperties = (None, None, None)
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@classmethod
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def setUpClass(cls):
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cls.device_properties = get_device_properties()
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def setUp(self):
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cleanup(torch_device, gc_collect=True)
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def tearDown(self):
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cleanup(torch_device, gc_collect=True)
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@slow
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def test_model_7b_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = MistralForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16
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)
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input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
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with torch.no_grad():
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out = model(input_ids).logits.float().cpu()
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2)
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# ("cuda", 8) for A100/A10, and ("cuda", 7) 7 for T4.
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# considering differences in hardware processing and potential deviations in output.
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# fmt: off
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EXPECTED_SLICES = Expectations(
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{
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("cuda", 7): torch.tensor([-5.8828, -5.8633, -0.1042, -4.7266, -5.8828, -5.8789, -5.8789, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -1.0801, 1.7598, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828, -5.8828]),
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("cuda", 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]),
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("rocm", 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]),
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}
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)
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# fmt: on
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expected_slice = EXPECTED_SLICES.get_expectation()
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torch.testing.assert_close(out[0, 0, :30], expected_slice, atol=1e-4, rtol=1e-4)
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@slow
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@require_bitsandbytes
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def test_model_7b_generation(self):
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EXPECTED_TEXT_COMPLETION = "My favourite condiment is 100% ketchup. I’m not a fan of mustard, mayo,"
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prompt = "My favourite condiment is "
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
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model = MistralForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.1", device_map={"": torch_device}, load_in_4bit=True
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)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
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# greedy generation outputs
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generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
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text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
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@slow
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def test_model_7b_dola_generation(self):
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# ground truth text generated with dola_layers="low", repetition_penalty=1.2
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EXPECTED_TEXT_COMPLETION = (
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"""My favourite condiment is 100% ketchup. I love it on everything, and I’m not ash"""
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)
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prompt = "My favourite condiment is "
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
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model = MistralForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16
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)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
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# greedy generation outputs
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generated_ids = model.generate(
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input_ids, max_new_tokens=20, temperature=0, dola_layers="low", repetition_penalty=1.2
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)
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text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
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del model
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backend_empty_cache(torch_device)
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gc.collect()
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@require_flash_attn
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@require_bitsandbytes
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@slow
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@pytest.mark.flash_attn_test
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def test_model_7b_long_prompt(self):
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EXPECTED_OUTPUT_TOKEN_IDS = [306, 338]
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# An input with 4097 tokens that is above the size of the sliding window
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input_ids = [1] + [306, 338] * 2048
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model = MistralForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.1",
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device_map={"": torch_device},
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load_in_4bit=True,
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attn_implementation="flash_attention_2",
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)
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input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
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generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
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self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
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# Assisted generation
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assistant_model = model
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assistant_model.generation_config.num_assistant_tokens = 2
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assistant_model.generation_config.num_assistant_tokens_schedule = "constant"
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generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
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self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
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@slow
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@require_torch_sdpa
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def test_model_7b_long_prompt_sdpa(self):
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EXPECTED_OUTPUT_TOKEN_IDS = [306, 338]
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# An input with 4097 tokens that is above the size of the sliding window
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input_ids = [1] + [306, 338] * 2048
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model = MistralForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.1", device_map="auto", attn_implementation="sdpa", torch_dtype=torch.float16
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)
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input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
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generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
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self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
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# Assisted generation
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assistant_model = model
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assistant_model.generation_config.num_assistant_tokens = 2
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assistant_model.generation_config.num_assistant_tokens_schedule = "constant"
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generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
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self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
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del assistant_model
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backend_empty_cache(torch_device)
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gc.collect()
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EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big"""
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prompt = "My favourite condiment is "
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
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# greedy generation outputs
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generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
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text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
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@slow
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def test_speculative_generation(self):
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EXPECTED_TEXT_COMPLETION = "My favourite condiment is 100% Sriracha. I love it on everything. I have it on my"
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prompt = "My favourite condiment is "
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
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model = MistralForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16
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)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
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# greedy generation outputs
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set_seed(0)
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generated_ids = model.generate(
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input_ids, max_new_tokens=20, do_sample=True, temperature=0.3, assistant_model=model
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)
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text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
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@slow
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def test_compile_static_cache(self):
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# `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2
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# work as intended. See https://github.com/pytorch/pytorch/issues/121943
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if version.parse(torch.__version__) < version.parse("2.3.0"):
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self.skipTest(reason="This test requires torch >= 2.3 to run.")
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if self.device_properties[0] == "cuda" and self.device_properties[1] == 7:
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self.skipTest(reason="This test is failing (`torch.compile` fails) on Nvidia T4 GPU.")
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NUM_TOKENS_TO_GENERATE = 40
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EXPECTED_TEXT_COMPLETION = [
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"My favourite condiment is 100% ketchup. I love it on everything. "
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"I’m not a big fan of mustard, mayo, or relish. I’m not a fan of pickles"
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]
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prompts = ["My favourite condiment is "]
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
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tokenizer.pad_token = tokenizer.eos_token
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model = MistralForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.1", device_map=torch_device, torch_dtype=torch.float16
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)
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inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
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# Dynamic Cache
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generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
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dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text)
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# Static Cache
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generated_ids = model.generate(
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**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
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)
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static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text)
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# Sliding Window Cache
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generated_ids = model.generate(
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**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window"
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)
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static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text)
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# Static Cache + compile
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forward_function = model.forward
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model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True)
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generated_ids = model.generate(
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**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
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)
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static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)
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# Sliding Window Cache + compile
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torch._dynamo.reset()
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model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True)
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generated_ids = model.generate(
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**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window"
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)
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static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)
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@slow
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@require_torch_accelerator
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class Mask4DTestHard(unittest.TestCase):
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model_name = "mistralai/Mistral-7B-v0.1"
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model = None
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model_dtype = None
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@classmethod
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def setUpClass(cls):
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cleanup(torch_device, gc_collect=True)
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if cls.model_dtype is None:
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cls.model_dtype = torch.float16
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if cls.model is None:
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cls.model = MistralForCausalLM.from_pretrained(cls.model_name, torch_dtype=cls.model_dtype).to(
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torch_device
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)
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@classmethod
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def tearDownClass(cls):
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del cls.model_dtype
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del cls.model
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cleanup(torch_device, gc_collect=True)
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def setUp(self):
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cleanup(torch_device, gc_collect=True)
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=False)
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def tearDown(self):
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cleanup(torch_device, gc_collect=True)
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def get_test_data(self):
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template = "my favorite {}"
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items = ("pet is a", "artist plays a", "name is L") # same number of tokens in each item
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batch_separate = [template.format(x) for x in items] # 3 separate lines
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batch_shared_prefix = template.format(" ".join(items)) # 1 line with options concatenated
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input_ids = self.tokenizer(batch_separate, return_tensors="pt").input_ids.to(torch_device)
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input_ids_shared_prefix = self.tokenizer(batch_shared_prefix, return_tensors="pt").input_ids.to(torch_device)
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mask_shared_prefix = torch.tensor(
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[
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[
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[
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[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
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[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
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[1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
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[1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0],
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[1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
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[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)
|