wehub-resource-sync
480 lines
20 KiB
Python
480 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace 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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import math
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import unittest
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from typing import Dict, List, Tuple
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from unittest.util import safe_repr
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import paddle
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from paddlenlp.transformers import (
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AutoTokenizer,
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MambaConfig,
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MambaForCausalLM,
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MambaModel,
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)
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from paddlenlp.transformers.mamba.modeling import MambaCache
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from ...testing_utils import slow
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from ..test_configuration_common import ConfigTester
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from ..test_modeling_common import ModelTesterMixin, ids_tensor
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class MambaModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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seq_length=7,
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is_training=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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intermediate_size=32,
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hidden_act="silu",
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hidden_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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num_labels=3,
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num_choices=4,
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scope=None,
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tie_word_embeddings=True,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_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.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.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.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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self.pad_token_id = vocab_size - 1
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self.tie_word_embeddings = tie_word_embeddings
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def get_large_model_config(self):
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return MambaConfig.from_pretrained("state-spaces/mamba-2.8b-hf")
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def prepare_config_and_inputs(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.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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gradient_checkpointing=gradient_checkpointing,
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scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
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reorder_and_upcast_attn=reorder_and_upcast_attn,
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)
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return (
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config,
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input_ids,
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None,
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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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def get_config(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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return MambaConfig(
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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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intermediate_size=self.intermediate_size,
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activation_function=self.hidden_act,
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n_positions=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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use_cache=True,
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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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pad_token_id=self.pad_token_id,
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gradient_checkpointing=gradient_checkpointing,
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tie_word_embeddings=self.tie_word_embeddings,
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)
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def get_pipeline_config(self):
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config = self.get_config()
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config.vocab_size = 300
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return config
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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input_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = self.prepare_config_and_inputs()
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return (
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config,
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input_ids,
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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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def create_and_check_mamba_model(self, config, input_ids, *args):
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config.output_hidden_states = True
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model = MambaModel(config=config)
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model.eval()
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(len(result.hidden_states), config.num_hidden_layers + 1)
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def create_and_check_causal_lm(self, config, input_ids, *args):
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model = MambaForCausalLM(config)
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model.eval()
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result = model(input_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.ndim, 0)
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self.parent.assertEqual(result.logits.shape, [self.batch_size, self.seq_length, self.vocab_size])
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def create_and_check_state_equivalency(self, config, input_ids, *args):
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model = MambaModel(config=config)
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model.eval()
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outputs = model(input_ids)
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output_whole = outputs.last_hidden_state
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outputs = model(
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input_ids[:, :-1],
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use_cache=True,
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)
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output_one = outputs.last_hidden_state
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# Using the state computed on the first inputs, we will get the same output
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outputs = model(
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input_ids[:, -1:],
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use_cache=True,
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cache=outputs.cache,
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)
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output_two = outputs.last_hidden_state
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self.parent.assertTrue(
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paddle.allclose(paddle.concat([output_one, output_two], axis=1), output_whole, atol=1e-5)
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)
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def create_and_check_mamba_cached_slow_forward_and_backwards(
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self, config, input_ids, *args, gradient_checkpointing=False
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):
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model = MambaModel(config)
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if gradient_checkpointing:
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model.enable_recompute = gradient_checkpointing
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# create cache
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cache = model(input_ids, use_cache=True).cache
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cache.reset()
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# use cache
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token_emb = model.embeddings(input_ids)
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outputs = model.layers[0].mixer.slow_forward(
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token_emb,
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cache,
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)
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loss = paddle.log(1 + paddle.abs(outputs.sum()))
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self.parent.assertEqual(loss.ndim, 0)
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self.parent.assertEqual(outputs.shape, [self.batch_size, self.seq_length, self.hidden_size])
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loss.backward()
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def create_and_check_mamba_lm_head_forward_and_backwards(
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self, config, input_ids, *args, gradient_checkpointing=False
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):
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model = MambaForCausalLM(config)
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if gradient_checkpointing:
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model.backbone.enable_recompute = gradient_checkpointing
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result = model(input_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.ndim, 0)
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self.parent.assertEqual(result.logits.shape, [self.batch_size, self.seq_length, self.vocab_size])
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result.loss.backward()
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def prepare_config_and_inputs_for_common(self):
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(
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config,
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input_ids,
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_,
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sequence_labels,
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token_labels,
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choice_labels,
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) = self.prepare_config_and_inputs()
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inputs_dict = {"input_ids": input_ids}
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return config, inputs_dict
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class MambaModelTest(ModelTesterMixin, unittest.TestCase):
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use_test_model_name_list = False
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all_model_classes = (MambaModel, MambaForCausalLM)
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all_generative_model_classes = (MambaForCausalLM,)
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has_attentions = False # Mamba does not support attentions
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fx_compatible = False # FIXME let's try to support this @ArthurZucker
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test_torchscript = False # FIXME let's try to support this @ArthurZucker
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test_missing_keys = False
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test_model_parallel = False
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test_pruning = False
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test_head_masking = False # Mamba does not have attention heads
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def setUp(self):
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self.model_tester = MambaModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=MambaConfig, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
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)
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def assertInterval(self, member, container, msg=None):
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r"""
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Simple utility function to check if a member is inside an interval.
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"""
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if isinstance(member, paddle.Tensor):
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max_value, min_value = member.max().item(), member.min().item()
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elif isinstance(member, list) or isinstance(member, tuple):
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max_value, min_value = max(member), min(member)
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if not isinstance(container, list):
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raise TypeError("container should be a list or tuple")
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elif len(container) != 2:
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raise ValueError("container should have 2 elements")
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expected_min, expected_max = container
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is_inside_interval = (min_value >= expected_min) and (max_value <= expected_max)
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if not is_inside_interval:
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standardMsg = "%s not found in %s" % (safe_repr(member), safe_repr(container))
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self.fail(self._formatMessage(msg, standardMsg))
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_mamba_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_mamba_model(*config_and_inputs)
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def test_mamba_lm_head_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_causal_lm(*config_and_inputs)
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# def test_state_equivalency(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_state_equivalency(*config_and_inputs)
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def test_mamba_cached_slow_forward_and_backwards(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_mamba_cached_slow_forward_and_backwards(*config_and_inputs)
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def test_mamba_lm_head_forward_and_backwards(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_mamba_lm_head_forward_and_backwards(*config_and_inputs)
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def test_initialization(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config=config)
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for name, param in model.named_parameters():
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if "dt_proj.bias" in name:
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dt = paddle.exp(
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paddle.to_tensor([0, 1]) * (math.log(config.time_step_max) - math.log(config.time_step_min))
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+ math.log(config.time_step_min)
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).clip(min=config.time_step_floor)
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inv_dt = dt + paddle.log(-paddle.expm1(-dt))
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if not param.stop_gradient:
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self.assertTrue(param.data.max().item() <= inv_dt[1])
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self.assertTrue(param.data.min().item() >= inv_dt[0])
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elif "A_log" in name:
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A = paddle.arange(1, config.mamba_d_state + 1, dtype=paddle.float32)[None, :]
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self.assertTrue(
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paddle.allclose(param.data, paddle.log(A).expand_as(param), atol=1e-5, rtol=1e-5)
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)
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elif "D" in name:
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# check if it's a ones like
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self.assertTrue(
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paddle.allclose(param.data, paddle.ones_like(param.data), atol=1e-5, rtol=1e-5)
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)
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def test_model_outputs_equivalence(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
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with paddle.no_grad():
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tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
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dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
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def recursive_check(tuple_object, dict_object):
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if isinstance(tuple_object, MambaCache): # MODIFIED PART START
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recursive_check(tuple_object.conv_states, dict_object.conv_states)
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recursive_check(tuple_object.ssm_states, dict_object.ssm_states)
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elif isinstance(tuple_object, (List, Tuple)): # MODIFIED PART END
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for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif isinstance(tuple_object, Dict):
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for tuple_iterable_value, dict_iterable_value in zip(
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tuple_object.values(), dict_object.values()
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):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif tuple_object is None:
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return
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else:
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self.assertTrue(
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paddle.allclose(tuple_object, dict_object, atol=1e-5),
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msg=(
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"Tuple and dict output are not equal. Difference:"
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f" {paddle.max(paddle.abs(tuple_object - dict_object))}. Tuple has `nan`:"
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f" {paddle.isnan(tuple_object).any()} and `inf`: {paddle.isinf(tuple_object)}. Dict has"
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f" `nan`: {paddle.isnan(dict_object).any()} and `inf`: {paddle.isinf(dict_object)}."
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),
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)
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recursive_check(tuple_output, dict_output)
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.eval()
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs)
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# tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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# dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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# check_equivalence(model, tuple_inputs, dict_inputs)
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
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# tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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# dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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# check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
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class MambaIntegrationTests(unittest.TestCase):
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def setUp(self):
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self.model_id = "state-spaces/mamba-2.8b-hf"
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
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@slow
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def test_simple_generate(self):
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tokenizer = AutoTokenizer.from_pretrained(
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"state-spaces/mamba-130m-hf",
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)
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tokenizer.pad_token = tokenizer.eos_token
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model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf", dtype="float16")
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input_ids = tokenizer("Hey how are you doing?", return_tensors="pd")["input_ids"]
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out = model.generate(input_ids, do_sample=False, use_cache=True, max_new_tokens=10)[0]
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output_sentence = tokenizer.decode(out[0, :])
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self.assertEqual(output_sentence.strip(), "I'm so glad you're here.")
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with paddle.no_grad():
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logits = model(input_ids=input_ids).logits
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EXPECTED_LOGITS_NO_GRAD = paddle.to_tensor(
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[
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-55.5938, -69.7500, -49.8438, -51.6875, -57.5938, -57.8750, -56.9062,
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-57.8438, -54.6250, -55.8438, -55.2500, -57.9688, -60.5000, -46.9688,
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-52. , -49.7188, -55.9062, -57.8438, -56.6875, -57.0312, -57.2500,
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-58.2188, -57.7188, -58.7188, -59.5000, -59. , -58.6250, -52.8438,
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-53.3750, -57.2812, -56.8438, -55.6250, -53.2500, -55.7188, -56.9375,
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-56.8438, -56.1562, -54.6562, -56.3438, -57.4062
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], dtype=paddle.float32) # fmt: skip
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self.assertTrue(paddle.allclose(logits[0, 0, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3))
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@slow
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def test_simple_generate_cuda_kernels_tiny(self):
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expected_output = "John and I am a newbie to the world"
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input_ids = self.tokenizer("Hello my name is", return_tensors="pd").input_ids
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model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf", dtype="float16")
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output = model.generate(input_ids, max_new_tokens=10)[0]
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output_sentence = self.tokenizer.decode(output[0].tolist())
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self.assertEqual(output_sentence.strip(), expected_output)
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@slow
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def test_simple_generate_cuda_kernels_small(self):
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expected_output = "I am a\n\nI am a"
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input_ids = self.tokenizer("Hello my name is", return_tensors="pd").input_ids
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model = MambaForCausalLM.from_pretrained("state-spaces/mamba-790m-hf", dtype="float16")
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output = model.generate(input_ids, max_new_tokens=10)[0]
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output_sentence = self.tokenizer.decode(output[0].tolist())
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self.assertEqual(output_sentence.strip(), expected_output)
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@slow
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def test_simple_generate_cuda_kernels_mid(self):
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expected_output = "John and I am a\n\nI am a single father of a beautiful daughter. I am a"
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input_ids = self.tokenizer("Hello my name is", return_tensors="pd").input_ids
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model = MambaForCausalLM.from_pretrained("state-spaces/mamba-1.4b-hf", dtype="float16")
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output = model.generate(input_ids, max_new_tokens=20)[0]
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output_sentence = self.tokenizer.decode(output[0].tolist())
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self.assertEqual(output_sentence.strip(), expected_output)
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|
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@slow
|
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def test_simple_generate_cuda_kernels_big(self):
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expected_output = "John and I am a new member of this forum. I am a retired Marine and I am a member of the Marine Corps League. I am a"
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|
|
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input_ids = self.tokenizer("Hello my name is", return_tensors="pd").input_ids
|
|
model = MambaForCausalLM.from_pretrained("state-spaces/mamba-2.8b-hf", dtype="float16")
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|
|
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output = model.generate(input_ids, max_new_tokens=30)[0]
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output_sentence = self.tokenizer.decode(output[0].tolist())
|
|
|
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self.assertEqual(output_sentence.strip(), expected_output)
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|
|
|
@slow
|
|
def test_compile_mamba_cache(self):
|
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expected_output = "John and I am a\n\nI am a single father of a beautiful daughter. I am a"
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|
|
|
input_ids = self.tokenizer("Hello my name is", return_tensors="pd").input_ids
|
|
model = MambaForCausalLM.from_pretrained("state-spaces/mamba-1.4b-hf", dtype="float16")
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|
|
|
output = model.generate(input_ids, max_new_tokens=20, cache_implementation="mamba")[0]
|
|
output_sentence = self.tokenizer.decode(output[0].tolist())
|
|
self.assertEqual(output_sentence.strip(), expected_output)
|