wehub-resource-sync
325 lines
12 KiB
Python
325 lines
12 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2025 MiniMax AI. 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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from __future__ import annotations
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import gc
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import unittest
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import paddle
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from paddlenlp.transformers import (
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MiniMaxText01Config,
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MiniMaxText01ForCausalLM,
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MiniMaxText01ForSequenceClassification,
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)
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from tests.transformers.test_configuration_common import ConfigTester
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from tests.transformers.test_generation_utils import GenerationTesterMixin
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from tests.transformers.test_modeling_common import (
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ModelTesterMixin,
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ids_tensor,
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random_attention_mask,
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)
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from ...testing_utils import require_gpu
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class MiniMaxText01ModelTester:
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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=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=99,
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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="silu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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rope_theta=1e6,
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sliding_window=32,
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attention_dropout=0.0,
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num_experts_per_tok=2,
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num_local_experts=2,
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rms_norm_eps=1e-5,
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scope=None,
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attn_type_list=["0", "1"],
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):
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self.parent: MiniMaxText01ModelTest = 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.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.rope_theta = rope_theta
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self.sliding_window = sliding_window
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self.attention_dropout = attention_dropout
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self.num_experts_per_tok = num_experts_per_tok
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self.num_local_experts = num_local_experts
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self.rms_norm_eps = rms_norm_eps
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self.scope = scope
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self.attn_type_list = attn_type_list
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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, dtype=paddle.int64)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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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) -> MiniMaxText01Config:
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return MiniMaxText01Config(
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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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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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rope_theta=self.rope_theta,
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sliding_window=self.sliding_window,
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attention_dropout=self.attention_dropout,
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num_experts_per_tok=self.num_experts_per_tok,
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num_local_experts=self.num_local_experts,
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rms_norm_eps=self.rms_norm_eps,
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attn_type_list=self.attn_type_list,
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)
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def create_and_check_model(
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self,
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config: MiniMaxText01Config,
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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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):
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model = MiniMaxText01ForCausalLM(config=config)
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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[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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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=None,
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encoder_attention_mask=None,
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):
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model = MiniMaxText01ForCausalLM(config=config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels, return_dict=True)
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self.parent.assertEqual(result.logits.shape, [self.batch_size, self.seq_length, self.vocab_size])
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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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class MiniMaxText01ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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base_model_class = None
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return_dict = False
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use_labels = False
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use_test_model_name_list = False
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all_model_classes = (MiniMaxText01ForCausalLM, MiniMaxText01ForSequenceClassification)
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all_generative_model_classes = {MiniMaxText01ForCausalLM: {None, "minimax_text01"}}
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pipeline_model_mapping = {
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"text-classification": MiniMaxText01ForSequenceClassification,
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"text-generation": MiniMaxText01ForCausalLM,
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"zero-shot": MiniMaxText01ForSequenceClassification,
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}
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def setUp(self):
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super().setUp()
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self.model_tester = MiniMaxText01ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=MiniMaxText01Config, hidden_size=768)
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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_MiniMaxText01_sequence_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 = paddle.not_equal(input_ids, paddle.ones_like(input_ids))
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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 = MiniMaxText01ForSequenceClassification(config)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True)
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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_MiniMaxText01_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 = paddle.not_equal(input_ids, paddle.ones_like(input_ids))
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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 = MiniMaxText01ForSequenceClassification(config)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True)
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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_MiniMaxText01_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 = paddle.not_equal(input_ids, paddle.ones_like(input_ids))
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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(paddle.float32)
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model = MiniMaxText01ForSequenceClassification(config)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True)
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self.assertEqual(result.logits.shape, [self.model_tester.batch_size, self.model_tester.num_labels])
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class MiniMaxText01IntegrationTest(unittest.TestCase):
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@require_gpu(1)
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def test_model_tiny_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = MiniMaxText01ForCausalLM.from_pretrained(
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"__internal_testing__/MiniMax-Text-01-l2-tiny-pd", dtype="float32"
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)
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model.eval()
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input_ids = paddle.to_tensor([input_ids])
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with paddle.no_grad():
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out = model(input_ids, return_dict=True).logits
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EXPECTED_MEAN = paddle.to_tensor(
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[[-0.00203404, 0.00172575, 0.00171089, 0.00109741, -0.00046862, 0.00017896, -0.00002699, -0.00206279]]
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)
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paddle.allclose(out.mean(-1), EXPECTED_MEAN, atol=1e-6, rtol=1e-6)
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EXPECTED_SLICE = paddle.to_tensor(
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[
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-0.45604241,
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0.44674566,
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0.01559911,
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-0.22750290,
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0.46994418,
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-0.39009440,
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-0.58710217,
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-0.65201938,
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1.06324077,
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0.28406841,
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0.22498111,
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0.36873919,
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0.22047190,
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-0.47585970,
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-0.16434811,
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0.20234424,
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-0.32718620,
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0.32738528,
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0.36627784,
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-0.76008093,
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-0.15530412,
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0.63310510,
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0.49225768,
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0.57552850,
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-0.15108462,
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-0.71018273,
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0.11868254,
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-0.06228763,
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0.08378446,
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-0.84608293,
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]
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)
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paddle.allclose(out[0, 0, :30], EXPECTED_SLICE, atol=1e-6, rtol=1e-6)
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del model
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paddle.device.cuda.empty_cache()
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gc.collect()
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