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* remove it from all py files * remove it from the doc * remove it from examples * style * remove traces of _fast_init * Update test_peft_integration.py * CIs
282 lines
11 KiB
Python
282 lines
11 KiB
Python
# Copyright 2025 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 MiniMax model."""
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import unittest
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import pytest
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from transformers import MiniMaxConfig, is_torch_available
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from transformers.cache_utils import Cache
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from transformers.testing_utils import (
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require_flash_attn,
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require_torch,
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require_torch_accelerator,
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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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if is_torch_available():
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import torch
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from transformers import (
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MiniMaxForCausalLM,
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MiniMaxForQuestionAnswering,
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MiniMaxForSequenceClassification,
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MiniMaxForTokenClassification,
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MiniMaxModel,
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)
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from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
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class MiniMaxModelTester(CausalLMModelTester):
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config_class = MiniMaxConfig
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if is_torch_available():
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base_model_class = MiniMaxModel
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causal_lm_class = MiniMaxForCausalLM
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sequence_class = MiniMaxForSequenceClassification
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token_class = MiniMaxForTokenClassification
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question_answering_class = MiniMaxForQuestionAnswering
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def __init__(self, parent, layer_types=None, block_size=3):
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super().__init__(parent)
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self.layer_types = layer_types
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self.block_size = block_size
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@require_torch
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class MiniMaxModelTest(CausalLMModelTest, unittest.TestCase):
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all_model_classes = (
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(
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MiniMaxModel,
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MiniMaxForCausalLM,
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MiniMaxForSequenceClassification,
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MiniMaxForTokenClassification,
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MiniMaxForQuestionAnswering,
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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": MiniMaxModel,
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"text-classification": MiniMaxForSequenceClassification,
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"token-classification": MiniMaxForTokenClassification,
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"text-generation": MiniMaxForCausalLM,
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"question-answering": MiniMaxForQuestionAnswering,
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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 = MiniMaxModelTester
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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="MiniMax flash attention does not support right padding")
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def test_load_balancing_loss(self):
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r"""
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Let's make sure we can actually compute the loss and do a backward on it.
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"""
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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.num_local_experts = 8
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config.output_router_logits = True
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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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model = MiniMaxForCausalLM(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)
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self.assertEqual(result.router_logits[0].shape, (91, config.num_local_experts))
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torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2)
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# First, we make sure that adding padding tokens doesn't change the loss
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# loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding)
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pad_length = 1000
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# Add padding tokens (assume that pad_token_id=1) to input_ids
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padding_block = torch.ones(input_ids.shape[0], pad_length, dtype=torch.int32).to(torch_device)
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padded_input_ids = torch.cat((padding_block, input_ids), dim=1) # this is to simulate padding to the left
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padded_attention_mask = padded_input_ids.ne(1).to(torch_device)
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padded_result = model(padded_input_ids, attention_mask=padded_attention_mask)
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torch.testing.assert_close(result.aux_loss.cpu(), padded_result.aux_loss.cpu(), rtol=1e-4, atol=1e-4)
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# We make sure that the loss of including padding tokens != the loss without padding tokens
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# if attention_mask=None --> we don't exclude padding tokens
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include_padding_result = model(padded_input_ids, attention_mask=None)
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# This is to mimic torch.testing.assert_not_close
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self.assertNotAlmostEqual(include_padding_result.aux_loss.item(), result.aux_loss.item())
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def _check_attentions_for_generate(
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self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values
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):
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self.assertIsInstance(attentions, tuple)
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self.assertListEqual(
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[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
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)
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self.assertEqual(len(attentions), (output_length - prompt_length))
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use_cache = decoder_past_key_values is not None
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for generated_length, iter_attentions in enumerate(attentions):
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# regardless of using cache, the first forward pass will have the full prompt as input
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if use_cache and generated_length > 0:
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model_input_length = 1
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else:
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model_input_length = prompt_length + generated_length
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expected_shape = (
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batch_size,
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config.num_attention_heads,
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model_input_length,
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prompt_length + generated_length,
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)
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for layer_idx, layer_attention in enumerate(iter_attentions):
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if config.layer_types[layer_idx] == "full_attention":
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self.assertEqual(layer_attention.shape, expected_shape)
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def _check_past_key_values_for_generate(self, batch_size, decoder_past_key_values, cache_length, config):
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self.assertIsInstance(decoder_past_key_values, (tuple, Cache))
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# (batch, head, seq_length, head_features)
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key_value_cache_expected_shape = (
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batch_size,
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config.num_key_value_heads,
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cache_length,
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config.hidden_size // config.num_attention_heads,
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)
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# (batch, head, head_features, head_features)
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linear_cache_expected_shape = (
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batch_size,
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config.num_attention_heads,
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config.hidden_size // config.num_attention_heads,
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config.hidden_size // config.num_attention_heads,
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)
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for layer_idx in range(config.num_hidden_layers):
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if config.layer_types[layer_idx] == "full_attention":
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self.assertEqual(decoder_past_key_values[layer_idx][0].shape, key_value_cache_expected_shape)
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self.assertEqual(decoder_past_key_values[layer_idx][1].shape, key_value_cache_expected_shape)
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else:
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self.assertEqual(decoder_past_key_values[layer_idx][0].shape, linear_cache_expected_shape)
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@pytest.mark.generate
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def test_past_key_values_format(self, custom_all_cache_shapes=None):
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"""
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Test that the KV cache is formatted correctly.
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"""
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for model_class in self.all_generative_model_classes:
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config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
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model = model_class(config).to(torch_device)
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model = model.eval()
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if "use_cache" not in inputs:
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inputs["use_cache"] = True
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outputs = model(**inputs)
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past_kv = outputs["past_key_values"]
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batch_size, seq_length = inputs["input_ids"].shape
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self._check_past_key_values_for_generate(batch_size, past_kv, seq_length, config)
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@unittest.skip(reason="MiniMaxCache doesnot support `crop()` method")
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def test_prompt_lookup_decoding_matches_greedy_search(self):
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pass
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@unittest.skip(reason="MiniMaxCache doesnot support `crop()` method")
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def test_contrastive_generate_low_memory(self):
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pass
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@unittest.skip(reason="MiniMaxCache doesnot support `crop()` method")
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def test_assisted_decoding_sample(self):
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pass
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@unittest.skip(reason="MiniMaxCache doesnot support `crop()` method")
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def test_assisted_decoding_matches_greedy_search_0_random(self):
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pass
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@unittest.skip(reason="MiniMaxCache doesnot support `crop()` method")
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def test_assisted_decoding_matches_greedy_search_1_same(self):
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pass
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@unittest.skip(reason="MiniMaxCache doesnot support `crop()` method")
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def test_contrastive_generate_dict_outputs_use_cache(self):
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pass
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@require_torch
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@require_torch_accelerator
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@slow
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class MiniMaxIntegrationTest(unittest.TestCase):
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def test_small_model_logits(self):
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model_id = "hf-internal-testing/MiniMax-tiny"
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dummy_input = torch.LongTensor([[0, 1, 0], [0, 1, 0]]).to(torch_device)
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model = MiniMaxForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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).to(torch_device)
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expected_slice = torch.tensor(
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[[1.0312, -0.5156, -0.3262], [-0.1152, 0.4336, 0.2412], [1.2188, -0.5898, -0.0381]]
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).to(torch_device)
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with torch.no_grad():
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logits = model(dummy_input).logits
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logits = logits.float()
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torch.testing.assert_close(logits[0, :3, :3], expected_slice, atol=1e-3, rtol=1e-3)
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torch.testing.assert_close(logits[1, :3, :3], expected_slice, atol=1e-3, rtol=1e-3)
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def test_small_model_generation(self):
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model_id = "hf-internal-testing/MiniMax-tiny"
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dummy_input = torch.LongTensor([[0, 1, 0], [0, 1, 0]]).to(torch_device)
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model = MiniMaxForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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).to(torch_device)
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expected_slice = (
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torch.tensor([[0, 1, 0, 933, 307, 3102, 2457, 1208], [0, 1, 0, 933, 307, 3102, 2457, 1208]])
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.to(torch.int64)
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.to(torch_device)
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)
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outputs = model.generate(dummy_input, max_new_tokens=5, do_sample=False)
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torch.testing.assert_close(outputs, expected_slice, atol=1e-3, rtol=1e-3)
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