[Add Mamba] Adds support for the Mamba models (#28094)
* initial-commit * start cleaning * small nits * small nits * current updates * add kernels * small refactoring little step * add comments * styling * nit * nits * Style * Small changes * Push dummy mambda simple slow * nit * Use original names * Use original names and remove norm * Updates for inference params * Style nd updates * nits * Match logits * Add a test * Add expected generated text * nits doc, imports and styling * style * oups * dont install kernels, invite users to install the required kernels * let use use the original packages * styling * nits * fix some copieds * update doc * fix-copies * styling done * nits * fix import check * run but wrong cuda ress * mamba CUDA works :) * fix the fast path * config naming nits * conversion script is not required at this stage * finish fixing the fast path: generation make sense now! * nit * Let's start working on the CIs * style * better style * more nits * test nit * quick fix for now * nits * nit * nit * nit * nits * update test rest * fixup * update test * nit * some fixes * nits * update test values * fix styling * nit * support peft * integrations tests require torchg * also add slow markers * styling * chose forward wisely * nits * update tests * fix gradient checkpointing * fixup * nit * fix doc * check copies * fix the docstring * fix some more tests * style * fix beam search * add init schene * update * nit * fix * fixup the doc * fix the doc * fixup * tentative update but slow is no longer good * nit * should we always use float32? * nits * revert wrong changes * res in float32 * cleanup * skip fmt for now * update generation values * update test values running original model * fixup * update tests + rename inference_params to cache_params + make sure training does not use cache_params * small nits * more nits * fix final CIs * style * nit doc * I hope final doc nits * nit * 🫠 * final touch! * fix torch import * Apply suggestions from code review Co-authored-by: Lysandre Debut <hi@lysand.re> * Apply suggestions from code review * fix fix and fix * fix base model prefix! * nit * Update src/transformers/models/mamba/__init__.py * Update docs/source/en/model_doc/mamba.md Co-authored-by: Lysandre Debut <hi@lysand.re> * nit --------- Co-authored-by: Lysandre Debut <hi@lysand.re>
This commit is contained in:
Arthur
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tests/models/mamba/__init__.py
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tests/models/mamba/__init__.py
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tests/models/mamba/test_modeling_mamba.py
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tests/models/mamba/test_modeling_mamba.py
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# 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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from parameterized import parameterized
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from transformers import AutoTokenizer, MambaConfig, is_torch_available
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from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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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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from ...test_pipeline_mixin import PipelineTesterMixin
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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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MambaForCausalLM,
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MambaModel,
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)
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from transformers.models.mamba.modeling_mamba import MambaCache
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from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
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else:
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is_torch_greater_or_equal_than_2_0 = False
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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("hf-internal-testing/mamba-2.8b")
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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.to(torch_device)
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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_causl_lm(self, config, input_ids, *args):
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model = MambaForCausalLM(config)
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model.to(torch_device)
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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.shape, ())
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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.to(torch_device)
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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(input_ids[:, :-1], use_cache=True)
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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(input_ids[:, -1:], cache_params=outputs.cache_params)
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output_two = outputs.last_hidden_state
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self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two], dim=1), output_whole, atol=1e-5))
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# TODO the orignal mamba does not support decoding more than 1 token neither do we
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def create_and_check_forward_and_backwards(self, config, input_ids, *args, gradient_checkpointing=False):
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model = MambaForCausalLM(config)
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model.to(torch_device)
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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result = model(input_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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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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@unittest.skipIf(
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not is_torch_greater_or_equal_than_2_0, reason="See https://github.com/huggingface/transformers/pull/24204"
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)
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@require_torch
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class MambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (MambaModel, MambaForCausalLM) if is_torch_available() else ()
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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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test_model_parallel = False
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pipeline_model_mapping = (
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{"feature-extraction": MambaModel, "text-generation": MambaForCausalLM} if is_torch_available() else {}
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)
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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, torch.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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@unittest.skip("No attention in mamba")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@require_torch_multi_gpu
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def test_multi_gpu_data_parallel_forward(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# some params shouldn't be scattered by nn.DataParallel
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# so just remove them if they are present.
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blacklist_non_batched_params = ["cache_params"]
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for k in blacklist_non_batched_params:
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inputs_dict.pop(k, None)
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# move input tensors to cuda:O
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for k, v in inputs_dict.items():
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if torch.is_tensor(v):
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inputs_dict[k] = v.to(0)
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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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model.to(0)
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model.eval()
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# Wrap model in nn.DataParallel
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model = torch.nn.DataParallel(model)
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with torch.no_grad():
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_ = model(**self._prepare_for_class(inputs_dict, model_class))
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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_causl_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_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 = torch.exp(
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torch.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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).clamp(min=config.time_step_floor)
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inv_dt = dt + torch.log(-torch.expm1(-dt))
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if param.requires_grad:
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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 = torch.arange(1, config.state_size + 1, dtype=torch.float32)[None, :]
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self.assertTrue(torch.allclose(param.data, torch.log(A), atol=1e-5, rtol=1e-5))
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elif "D" in name:
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if param.requires_grad:
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# check if it's a ones like
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self.assertTrue(torch.allclose(param.data, torch.ones_like(param.data), atol=1e-5, rtol=1e-5))
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@unittest.skip("Mamba does not use attention")
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def test_attention_outputs(self):
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r"""
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Overriding the test_attention_outputs test as the attention outputs of Mamba are different from other models
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it has a shape `batch_size, seq_len, hidden_size`.
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"""
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pass
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@slow
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def test_model_from_pretrained(self):
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model = MambaModel.from_pretrained("hf-internal-testing/mamba-130m")
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self.assertIsNotNone(model)
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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 torch.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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torch.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" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
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f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
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f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.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.to(torch_device)
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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)
|
||||
check_equivalence(model, tuple_inputs, dict_inputs)
|
||||
|
||||
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
|
||||
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
|
||||
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
|
||||
|
||||
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
||||
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
||||
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
|
||||
|
||||
|
||||
@require_torch
|
||||
class MambaIntegrationTests(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.model_id = "ArthurZ/mamba-2.8b"
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
|
||||
|
||||
@parameterized.expand([(torch_device,), ("cpu",)])
|
||||
def test_simple_generate(self, device):
|
||||
tokenizer = AutoTokenizer.from_pretrained("ArthurZ/mamba-130m")
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
model = MambaForCausalLM.from_pretrained("ArthurZ/mamba-130m", torch_dtype=torch.float16)
|
||||
model.to(device)
|
||||
model.config.use_cache = True
|
||||
input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"].to(device)
|
||||
|
||||
out = model.generate(input_ids, do_sample=False, max_new_tokens=10)
|
||||
output_sentence = tokenizer.decode(out[0, :])
|
||||
self.assertEqual(output_sentence, "Hey how are you doing?\n\nI'm so glad you're here.")
|
||||
|
||||
with torch.no_grad():
|
||||
logits = model(input_ids=input_ids).logits
|
||||
|
||||
EXPECTED_LOGITS_NO_GRAD = torch.tensor(
|
||||
[
|
||||
-55.6875, -69.8750, -49.9062, -51.7500, -57.6875, -57.9375, -56.9688,
|
||||
-57.9375, -54.6875, -55.9375, -55.3125, -58.0938, -60.5625, -47.0000,
|
||||
-52.0312, -49.7812, -55.9375, -57.9062, -56.7812, -57.1250, -57.3438,
|
||||
-58.3125, -57.8125, -58.7812, -59.6250, -59.0938, -58.7188, -52.9375,
|
||||
-53.4688, -57.3750, -56.9375, -55.7500, -53.3125, -55.8438, -57.0000,
|
||||
-56.9062, -56.2188, -54.7188, -56.4375, -57.5000
|
||||
]
|
||||
,dtype=torch.float32) # fmt: skip
|
||||
|
||||
torch.testing.assert_close(logits[0, 0, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
|
||||
|
||||
@parameterized.expand([(torch_device,), ("cpu",)])
|
||||
def test_simple_generate_cuda_kernels_tiny(self, device):
|
||||
expected_output = "Hello my name is John and I am a newbie to the world"
|
||||
|
||||
input_ids = self.tokenizer("Hello my name is", return_tensors="pt").input_ids.to(device)
|
||||
model = MambaForCausalLM.from_pretrained("ArthurZ/mamba-130m", torch_dtype=torch.float16).to(device)
|
||||
|
||||
output = model.generate(input_ids, max_new_tokens=10)
|
||||
output_sentence = self.tokenizer.decode(output[0].tolist())
|
||||
|
||||
self.assertEqual(output_sentence, expected_output)
|
||||
|
||||
@parameterized.expand([(torch_device,), ("cpu",)])
|
||||
@slow
|
||||
def test_simple_generate_cuda_kernels_small(self, device):
|
||||
expected_output = "Hello my name is\n\nI am a\n\nI am a"
|
||||
|
||||
input_ids = self.tokenizer("Hello my name is", return_tensors="pt").input_ids.to(device)
|
||||
model = MambaForCausalLM.from_pretrained("ArthurZ/mamba-790m", torch_dtype=torch.float16).to(device)
|
||||
|
||||
output = model.generate(input_ids, max_new_tokens=10)
|
||||
output_sentence = self.tokenizer.decode(output[0].tolist())
|
||||
|
||||
self.assertEqual(output_sentence, expected_output)
|
||||
|
||||
@parameterized.expand([(torch_device,), ("cpu",)])
|
||||
@slow
|
||||
def test_simple_generate_cuda_kernels_mid(self, device):
|
||||
expected_output = "Hello my name is John and I am a\n\nI am a single father of a beautiful daughter. I am a"
|
||||
|
||||
input_ids = self.tokenizer("Hello my name is", return_tensors="pt").input_ids.to(device)
|
||||
model = MambaForCausalLM.from_pretrained("ArthurZ/mamba-1.4b", torch_dtype=torch.float16).to(device)
|
||||
|
||||
output = model.generate(input_ids, max_new_tokens=20)
|
||||
output_sentence = self.tokenizer.decode(output[0].tolist())
|
||||
|
||||
self.assertEqual(output_sentence, expected_output)
|
||||
|
||||
@parameterized.expand([(torch_device,), ("cpu",)])
|
||||
@slow
|
||||
def test_simple_generate_cuda_kernels_big(self, device):
|
||||
expected_output = "Hello my name is 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"
|
||||
|
||||
input_ids = self.tokenizer("Hello my name is", return_tensors="pt").input_ids.to(device)
|
||||
model = MambaForCausalLM.from_pretrained("ArthurZ/mamba-2.8b", torch_dtype=torch.float16).to(device)
|
||||
|
||||
output = model.generate(input_ids, max_new_tokens=30)
|
||||
output_sentence = self.tokenizer.decode(output[0].tolist())
|
||||
|
||||
self.assertEqual(output_sentence, expected_output)
|
||||
Reference in New Issue
Block a user