# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. # Copyright 2025 MiniMax AI. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import gc import unittest import paddle from paddlenlp.transformers import ( MiniMaxText01Config, MiniMaxText01ForCausalLM, MiniMaxText01ForSequenceClassification, ) from tests.transformers.test_configuration_common import ConfigTester from tests.transformers.test_generation_utils import GenerationTesterMixin from tests.transformers.test_modeling_common import ( ModelTesterMixin, ids_tensor, random_attention_mask, ) from ...testing_utils import require_gpu class MiniMaxText01ModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=99, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=2, intermediate_size=37, hidden_act="silu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, pad_token_id=0, bos_token_id=1, eos_token_id=2, rope_theta=1e6, sliding_window=32, attention_dropout=0.0, num_experts_per_tok=2, num_local_experts=2, rms_norm_eps=1e-5, scope=None, attn_type_list=["0", "1"], ): self.parent: MiniMaxText01ModelTest = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.rope_theta = rope_theta self.sliding_window = sliding_window self.attention_dropout = attention_dropout self.num_experts_per_tok = num_experts_per_tok self.num_local_experts = num_local_experts self.rms_norm_eps = rms_norm_eps self.scope = scope self.attn_type_list = attn_type_list def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype=paddle.int64) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self) -> MiniMaxText01Config: return MiniMaxText01Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, rope_theta=self.rope_theta, sliding_window=self.sliding_window, attention_dropout=self.attention_dropout, num_experts_per_tok=self.num_experts_per_tok, num_local_experts=self.num_local_experts, rms_norm_eps=self.rms_norm_eps, attn_type_list=self.attn_type_list, ) def create_and_check_model( self, config: MiniMaxText01Config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = MiniMaxText01ForCausalLM(config=config) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states=None, encoder_attention_mask=None, ): model = MiniMaxText01ForCausalLM(config=config) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels, return_dict=True) self.parent.assertEqual(result.logits.shape, [self.batch_size, self.seq_length, self.vocab_size]) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict class MiniMaxText01ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): base_model_class = None return_dict = False use_labels = False use_test_model_name_list = False all_model_classes = (MiniMaxText01ForCausalLM, MiniMaxText01ForSequenceClassification) all_generative_model_classes = {MiniMaxText01ForCausalLM: {None, "minimax_text01"}} pipeline_model_mapping = { "text-classification": MiniMaxText01ForSequenceClassification, "text-generation": MiniMaxText01ForCausalLM, "zero-shot": MiniMaxText01ForSequenceClassification, } def setUp(self): super().setUp() self.model_tester = MiniMaxText01ModelTester(self) self.config_tester = ConfigTester(self, config_class=MiniMaxText01Config, hidden_size=768) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_MiniMaxText01_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = paddle.not_equal(input_ids, paddle.ones_like(input_ids)) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = MiniMaxText01ForSequenceClassification(config) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True) self.assertEqual(result.logits.shape, [self.model_tester.batch_size, self.model_tester.num_labels]) def test_MiniMaxText01_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = paddle.not_equal(input_ids, paddle.ones_like(input_ids)) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = MiniMaxText01ForSequenceClassification(config) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True) self.assertEqual(result.logits.shape, [self.model_tester.batch_size, self.model_tester.num_labels]) def test_MiniMaxText01_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = paddle.not_equal(input_ids, paddle.ones_like(input_ids)) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(paddle.float32) model = MiniMaxText01ForSequenceClassification(config) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True) self.assertEqual(result.logits.shape, [self.model_tester.batch_size, self.model_tester.num_labels]) class MiniMaxText01IntegrationTest(unittest.TestCase): @require_gpu(1) def test_model_tiny_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] model = MiniMaxText01ForCausalLM.from_pretrained( "__internal_testing__/MiniMax-Text-01-l2-tiny-pd", dtype="float32" ) model.eval() input_ids = paddle.to_tensor([input_ids]) with paddle.no_grad(): out = model(input_ids, return_dict=True).logits EXPECTED_MEAN = paddle.to_tensor( [[-0.00203404, 0.00172575, 0.00171089, 0.00109741, -0.00046862, 0.00017896, -0.00002699, -0.00206279]] ) paddle.allclose(out.mean(-1), EXPECTED_MEAN, atol=1e-6, rtol=1e-6) EXPECTED_SLICE = paddle.to_tensor( [ -0.45604241, 0.44674566, 0.01559911, -0.22750290, 0.46994418, -0.39009440, -0.58710217, -0.65201938, 1.06324077, 0.28406841, 0.22498111, 0.36873919, 0.22047190, -0.47585970, -0.16434811, 0.20234424, -0.32718620, 0.32738528, 0.36627784, -0.76008093, -0.15530412, 0.63310510, 0.49225768, 0.57552850, -0.15108462, -0.71018273, 0.11868254, -0.06228763, 0.08378446, -0.84608293, ] ) paddle.allclose(out[0, 0, :30], EXPECTED_SLICE, atol=1e-6, rtol=1e-6) del model paddle.device.cuda.empty_cache() gc.collect()