diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index 93f2c96d2d..4ab637eaef 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -468,6 +468,8 @@ title: MT5 - local: model_doc/mvp title: MVP + - local: model_doc/nemotron + title: Nemotron - local: model_doc/nezha title: NEZHA - local: model_doc/nllb diff --git a/docs/source/en/index.md b/docs/source/en/index.md index 92cbdd44d7..af105a3b7b 100644 --- a/docs/source/en/index.md +++ b/docs/source/en/index.md @@ -222,6 +222,7 @@ Flax), PyTorch, and/or TensorFlow. | [MusicGen Melody](model_doc/musicgen_melody) | ✅ | ❌ | ❌ | | [MVP](model_doc/mvp) | ✅ | ❌ | ❌ | | [NAT](model_doc/nat) | ✅ | ❌ | ❌ | +| [Nemotron](model_doc/nemotron) | ✅ | ❌ | ❌ | | [Nezha](model_doc/nezha) | ✅ | ❌ | ❌ | | [NLLB](model_doc/nllb) | ✅ | ❌ | ❌ | | [NLLB-MOE](model_doc/nllb-moe) | ✅ | ❌ | ❌ | diff --git a/docs/source/en/model_doc/nemotron.md b/docs/source/en/model_doc/nemotron.md new file mode 100644 index 0000000000..1979847c43 --- /dev/null +++ b/docs/source/en/model_doc/nemotron.md @@ -0,0 +1,148 @@ + + +# Nemotron + +## Nemotron + +### License + +The use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license). + +### Description + +Nemotron-4 is a family of enterprise ready generative text models compatible with [NVIDIA NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/). + +NVIDIA NeMo is an end-to-end, cloud-native platform to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI. To get access to NeMo Framework, please sign up at [this link](https://developer.nvidia.com/nemo-framework/join). + +### References + +[Announcement Blog](https://developer.nvidia.com/blog/nvidia-ai-foundation-models-build-custom-enterprise-chatbots-and-co-pilots-with-production-ready-llms/) + +### Model Architecture + +**Architecture Type:** Transformer + +**Network Architecture:** Transformer Decoder (auto-regressive language model). + +## Minitron + +### Minitron 4B Base + +Minitron is a family of small language models (SLMs) obtained by pruning NVIDIA's [Nemotron-4 15B](https://arxiv.org/abs/2402.16819) model. We prune model embedding size, attention heads, and MLP intermediate dimension, following which, we perform continued training with distillation to arrive at the final models. + +Deriving the Minitron 8B and 4B models from the base 15B model using our approach requires up to **40x fewer training tokens** per model compared to training from scratch; this results in **compute cost savings of 1.8x** for training the full model family (15B, 8B, and 4B). Minitron models exhibit up to a 16% improvement in MMLU scores compared to training from scratch, perform comparably to other community models such as Mistral 7B, Gemma 7B and Llama-3 8B, and outperform state-of-the-art compression techniques from the literature. Please refer to our [arXiv paper](https://arxiv.org/abs/2407.14679) for more details. + +Minitron models are for research and development only. + +### HuggingFace Quickstart + +The following code provides an example of how to load the Minitron-4B model and use it to perform text generation. + +```python +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM + +# Load the tokenizer and model +model_path = 'nvidia/Minitron-4B-Base' +tokenizer = AutoTokenizer.from_pretrained(model_path) + +device = 'cuda' +dtype = torch.bfloat16 +model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device) + +# Prepare the input text +prompt = 'Complete the paragraph: our solar system is' +inputs = tokenizer.encode(prompt, return_tensors='pt').to(model.device) + +# Generate the output +outputs = model.generate(inputs, max_length=20) + +# Decode and print the output +output_text = tokenizer.decode(outputs[0]) +print(output_text) +``` + +### License + +Minitron is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf). + +### Evaluation Results + +*5-shot performance.* Language Understanding evaluated using [Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300): + +| Average | +| :---- | +| 58.6 | + +*Zero-shot performance.* Evaluated using select datasets from the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) with additions: + +| HellaSwag | Winogrande | GSM8K| ARC-C | XLSum | +| :------------- | :------------- | :------------- | :------------- | :------------- | +| 75.0 | 74.0 | 24.1 | 50.9 | 29.5 + + +*Code generation performance*. Evaluated using [HumanEval](https://github.com/openai/human-eval): + +| p@1, 0-Shot | +| :------------- | +| 23.3 | + +Please refer to our [paper](https://arxiv.org/abs/2407.14679) for the full set of results. + +### Citation + +If you find our work helpful, please consider citing our paper: +``` +@article{minitron2024, + title={Compact Language Models via Pruning and Knowledge Distillation}, + author={Saurav Muralidharan and Sharath Turuvekere Sreenivas and Raviraj Joshi and Marcin Chochowski and Mostofa Patwary and Mohammad Shoeybi and Bryan Catanzaro and Jan Kautz and Pavlo Molchanov}, + journal={arXiv preprint arXiv:2407.14679}, + year={2024}, + url={https://arxiv.org/abs/2407.14679}, +} +``` + +## NemotronConfig + +[[autodoc]] NemotronConfig + + +## NemotronModel + +[[autodoc]] NemotronModel + - forward + + +## NemotronForCausalLM + +[[autodoc]] NemotronForCausalLM + - forward + +## NemotronForSequenceClassification + +[[autodoc]] NemotronForSequenceClassification + - forward + + +## NemotronForQuestionAnswering + +[[autodoc]] NemotronForQuestionAnswering + - forward + + +## NemotronForTokenClassification + +[[autodoc]] NemotronForTokenClassification + - forward \ No newline at end of file diff --git a/docs/source/en/perf_infer_gpu_one.md b/docs/source/en/perf_infer_gpu_one.md index b0109a0e8d..089761ed6c 100644 --- a/docs/source/en/perf_infer_gpu_one.md +++ b/docs/source/en/perf_infer_gpu_one.md @@ -67,6 +67,7 @@ FlashAttention-2 is currently supported for the following architectures: * [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel) * [Musicgen](https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenModel) * [MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody#transformers.MusicgenMelodyModel) +* [Nemotron](https://huggingface.co/docs/transformers/model_doc/nemotron) * [NLLB](https://huggingface.co/docs/transformers/model_doc/nllb) * [OLMo](https://huggingface.co/docs/transformers/model_doc/olmo#transformers.OlmoModel) * [OPT](https://huggingface.co/docs/transformers/model_doc/opt#transformers.OPTModel) @@ -228,6 +229,7 @@ For now, Transformers supports SDPA inference and training for the following arc * [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel) * [Musicgen](https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenModel) * [MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody#transformers.MusicgenMelodyModel) +* [Nemotron](https://huggingface.co/docs/transformers/model_doc/nemotron) * [ViT](https://huggingface.co/docs/transformers/model_doc/vit#transformers.ViTModel) * [ViTHybrid](https://huggingface.co/docs/transformers/model_doc/vit_hybrid#transformers.ViTHybridModel) * [ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae#transformers.ViTMAEModel) diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index f971b5ffe4..d279f8c33d 100755 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -592,6 +592,7 @@ _import_structure = { "MusicgenMelodyDecoderConfig", ], "models.mvp": ["MvpConfig", "MvpTokenizer"], + "models.nemotron": ["NemotronConfig"], "models.nllb": [], "models.nllb_moe": ["NllbMoeConfig"], "models.nougat": ["NougatProcessor"], @@ -2742,6 +2743,16 @@ else: "MvpPreTrainedModel", ] ) + _import_structure["models.nemotron"].extend( + [ + "NemotronForCausalLM", + "NemotronForQuestionAnswering", + "NemotronForSequenceClassification", + "NemotronForTokenClassification", + "NemotronModel", + "NemotronPreTrainedModel", + ] + ) _import_structure["models.nllb_moe"].extend( [ "NllbMoeForConditionalGeneration", @@ -5286,6 +5297,7 @@ if TYPE_CHECKING: MusicgenMelodyDecoderConfig, ) from .models.mvp import MvpConfig, MvpTokenizer + from .models.nemotron import NemotronConfig from .models.nllb_moe import NllbMoeConfig from .models.nougat import NougatProcessor from .models.nystromformer import ( @@ -7187,6 +7199,14 @@ if TYPE_CHECKING: MvpModel, MvpPreTrainedModel, ) + from .models.nemotron import ( + NemotronForCausalLM, + NemotronForQuestionAnswering, + NemotronForSequenceClassification, + NemotronForTokenClassification, + NemotronModel, + NemotronPreTrainedModel, + ) from .models.nllb_moe import ( NllbMoeForConditionalGeneration, NllbMoeModel, diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py index cc1e41b3fc..ee921fc3e3 100644 --- a/src/transformers/models/__init__.py +++ b/src/transformers/models/__init__.py @@ -159,6 +159,7 @@ from . import ( musicgen, musicgen_melody, mvp, + nemotron, nllb, nllb_moe, nougat, diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py index 512c1eaaf5..6f49d0020a 100755 --- a/src/transformers/models/auto/configuration_auto.py +++ b/src/transformers/models/auto/configuration_auto.py @@ -177,6 +177,7 @@ CONFIG_MAPPING_NAMES = OrderedDict( ("musicgen_melody", "MusicgenMelodyConfig"), ("mvp", "MvpConfig"), ("nat", "NatConfig"), + ("nemotron", "NemotronConfig"), ("nezha", "NezhaConfig"), ("nllb-moe", "NllbMoeConfig"), ("nougat", "VisionEncoderDecoderConfig"), @@ -469,6 +470,7 @@ MODEL_NAMES_MAPPING = OrderedDict( ("musicgen_melody", "MusicGen Melody"), ("mvp", "MVP"), ("nat", "NAT"), + ("nemotron", "Nemotron"), ("nezha", "Nezha"), ("nllb", "NLLB"), ("nllb-moe", "NLLB-MOE"), diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py index d096abf434..86d3e5d8d1 100755 --- a/src/transformers/models/auto/modeling_auto.py +++ b/src/transformers/models/auto/modeling_auto.py @@ -169,6 +169,7 @@ MODEL_MAPPING_NAMES = OrderedDict( ("musicgen_melody", "MusicgenMelodyModel"), ("mvp", "MvpModel"), ("nat", "NatModel"), + ("nemotron", "NemotronModel"), ("nezha", "NezhaModel"), ("nllb-moe", "NllbMoeModel"), ("nystromformer", "NystromformerModel"), @@ -481,6 +482,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict( ("musicgen", "MusicgenForCausalLM"), ("musicgen_melody", "MusicgenMelodyForCausalLM"), ("mvp", "MvpForCausalLM"), + ("nemotron", "NemotronForCausalLM"), ("olmo", "OlmoForCausalLM"), ("open-llama", "OpenLlamaForCausalLM"), ("openai-gpt", "OpenAIGPTLMHeadModel"), @@ -902,6 +904,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( ("mra", "MraForSequenceClassification"), ("mt5", "MT5ForSequenceClassification"), ("mvp", "MvpForSequenceClassification"), + ("nemotron", "NemotronForSequenceClassification"), ("nezha", "NezhaForSequenceClassification"), ("nystromformer", "NystromformerForSequenceClassification"), ("open-llama", "OpenLlamaForSequenceClassification"), @@ -983,6 +986,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( ("mra", "MraForQuestionAnswering"), ("mt5", "MT5ForQuestionAnswering"), ("mvp", "MvpForQuestionAnswering"), + ("nemotron", "NemotronForQuestionAnswering"), ("nezha", "NezhaForQuestionAnswering"), ("nystromformer", "NystromformerForQuestionAnswering"), ("opt", "OPTForQuestionAnswering"), @@ -1078,6 +1082,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict( ("mpt", "MptForTokenClassification"), ("mra", "MraForTokenClassification"), ("mt5", "MT5ForTokenClassification"), + ("nemotron", "NemotronForTokenClassification"), ("nezha", "NezhaForTokenClassification"), ("nystromformer", "NystromformerForTokenClassification"), ("persimmon", "PersimmonForTokenClassification"), diff --git a/src/transformers/models/nemotron/__init__.py b/src/transformers/models/nemotron/__init__.py new file mode 100644 index 0000000000..bd0d1b5701 --- /dev/null +++ b/src/transformers/models/nemotron/__init__.py @@ -0,0 +1,68 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_sentencepiece_available, + is_torch_available, +) + + +_import_structure = { + "configuration_nemotron": ["NemotronConfig"], +} + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_nemotron"] = [ + "NemotronForQuestionAnswering", + "NemotronForCausalLM", + "NemotronModel", + "NemotronPreTrainedModel", + "NemotronForSequenceClassification", + "NemotronForTokenClassification", + ] + + +if TYPE_CHECKING: + from .configuration_nemotron import NemotronConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_nemotron import ( + NemotronForCausalLM, + NemotronForQuestionAnswering, + NemotronForSequenceClassification, + NemotronForTokenClassification, + NemotronModel, + NemotronPreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/src/transformers/models/nemotron/configuration_nemotron.py b/src/transformers/models/nemotron/configuration_nemotron.py new file mode 100644 index 0000000000..7690703127 --- /dev/null +++ b/src/transformers/models/nemotron/configuration_nemotron.py @@ -0,0 +1,153 @@ +# coding=utf-8 +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Nemotron model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...modeling_rope_utils import rope_config_validation +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +class NemotronConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`NemotronModel`]. It is used to instantiate an Nemotron + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the Nemotron-8B. + e.g. [nvidia/nemotron-3-8b-base-4k-hf](https://huggingface.co/nvidia/nemotron-3-8b-base-4k-hf). + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 256000): + Vocabulary size of the Nemotron model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`NemotronModel`] + hidden_size (`int`, *optional*, defaults to 6144): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 24576): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer decoder. + num_attention_heads (`int`, *optional*, defaults to 48): + Number of attention heads for each attention layer in the Transformer decoder. + head_dim (`int`, *optional*): + Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if None + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 4096): + The maximum sequence length that this model might ever be used with. + initializer_range (`float`, *optional*, defaults to 0.0134): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + pad_token_id (`int`, *optional*): + Padding token id. + bos_token_id (`int`, *optional*, defaults to 2): + Beginning of stream token id. + eos_token_id (`int`, *optional*, defaults to 3): + End of stream token id. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + partial_rotary_factor (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding. + attention_bias (`bool`, *optional*, defaults to `False`): + Whether to use a bias in the query, key, value and output projection layers during self-attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + mlp_bias (`bool`, *optional*, defaults to `False`): + Whether to use a bias in up_proj and down_proj layers in the MLP layers. + + ```python + >>> from transformers import NemotronModel, NemotronConfig + + >>> # Initializing a Nemotron nemotron-15b style configuration + >>> configuration = NemotronConfig() + + >>> # Initializing a model from the nemotron-15b style configuration + >>> model = NemotronModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "nemotron" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=256000, + hidden_size=6144, + intermediate_size=24576, + num_hidden_layers=32, + num_attention_heads=48, + head_dim=None, + num_key_value_heads=None, + hidden_act="relu2", + max_position_embeddings=4096, + initializer_range=0.0134, + norm_eps=1e-5, + use_cache=True, + pad_token_id=None, + bos_token_id=2, + eos_token_id=3, + tie_word_embeddings=False, + rope_theta=10000.0, + partial_rotary_factor=0.5, + attention_bias=False, + attention_dropout=0.0, + mlp_bias=False, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.norm_eps = norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.partial_rotary_factor = partial_rotary_factor + rope_config_validation(self) + self.attention_bias = attention_bias + self.attention_dropout = attention_dropout + self.mlp_bias = mlp_bias + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/src/transformers/models/nemotron/convert_nemotron_nemo_to_hf.py b/src/transformers/models/nemotron/convert_nemotron_nemo_to_hf.py new file mode 100644 index 0000000000..b9b1e9c56b --- /dev/null +++ b/src/transformers/models/nemotron/convert_nemotron_nemo_to_hf.py @@ -0,0 +1,346 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import shutil +from argparse import ArgumentParser +from collections import OrderedDict + +import torch +from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.utils import logging +from pytorch_lightning import Trainer + +from transformers import LlamaTokenizer, PreTrainedTokenizerFast +from transformers.convert_slow_tokenizer import LlamaConverter + + +""" +Script to convert a nemotron checkpoint in nemo (mcore path) into a HuggingFace checkpoint. +This script can be used to 1) generate only the HF weights, or 2) generate an entire HF model folder. + +1) Generate only HF weights from a nemo file: + + python convert_nemotron_nemo_to_hf.py \ + --input_name_or_path /path/to/file.nemo or /path/to/extracted_folder \ + --output_path /path/to/pytorch_model.bin + +2) Generate the full HF model folder + + python convert_nemotron_nemo_to_hf.py \ + --input_name_or_path /path/to/file.nemo or /path/to/extracted_folder \ + --hf_input_path /path/to/input_hf_folder \ + --hf_output_path /path/to/output_hf_folder \ + + Use the --cpu-only flag if the model cannot fit in the GPU (e.g. Nemotron4 340b). + However this option makes the conversion script significantly slower. +""" + + +def get_args(): + parser = ArgumentParser() + parser.add_argument( + "--input_name_or_path", + type=str, + default=None, + required=True, + help="Path to .nemo file or extracted folder", + ) + parser.add_argument("--output_path", type=str, default=None, required=False, help="Path to HF .bin file") + parser.add_argument( + "--hf_input_path", + type=str, + default=None, + help="A HF model path, " "e.g. a folder containing https://huggingface.co/nvidia/Minitron-8B-Base", + ) + parser.add_argument( + "--hf_output_path", + type=str, + default=None, + help="Output HF model path, " "with the same format as above but user's own weights", + ) + parser.add_argument( + "--precision", + type=str, + default=None, + help="Precision of output weights." + "Defaults to precision of the input nemo weights (model.cfg.trainer.precision)", + ) + parser.add_argument( + "--cpu-only", + action="store_true", + help="Load model in cpu only. Useful if the model cannot fit in GPU memory, " + "but this option makes the conversion script significantly slower.", + ) + args = parser.parse_args() + return args + + +def convert_hf_config(nemo_config, tokenizer, vocab_size, dtype, hf_output_path, hf_url="nvidia/Minitron-8B-Base"): + """ + Convert NeMo config to HF config + """ + NEMO_ACT2HF = { + "squared-relu": "relu2", + "fast-swiglu": "silu", + } + DTYPE2HF = { + torch.bfloat16: "bfloat16", + torch.float16: "float16", + torch.float32: "float32", + } + hf_config = { + "_name_or_path": hf_url, + "architectures": ["NemotronForCausalLM"], + "bos_token_id": tokenizer.bos_id, + "eos_token_id": tokenizer.eos_id, + "hidden_act": NEMO_ACT2HF[nemo_config.activation], + "hidden_size": nemo_config.hidden_size, + "initializer_range": nemo_config.init_method_std, + "intermediate_size": nemo_config.ffn_hidden_size, + "max_position_embeddings": nemo_config.max_position_embeddings, + "model_type": "nemotron", + "num_attention_heads": nemo_config.num_attention_heads, + "num_hidden_layers": nemo_config.num_layers, + "num_key_value_heads": nemo_config.get("num_query_groups", nemo_config.num_attention_heads), + "norm_eps": nemo_config.layernorm_epsilon, + "rope_theta": nemo_config.get("rotary_base", 10000), + "partial_rotary_factor": nemo_config.get("rotary_percentage", 1.0), + "tie_word_embeddings": False, + "torch_dtype": DTYPE2HF[dtype], + "transformers_version": "4.32.0.dev0", # TODO + "use_cache": True, + "vocab_size": vocab_size, + } + if nemo_config.kv_channels is not None: + hf_config["kv_channels"] = nemo_config.kv_channels + json.dump(hf_config, open(f"{hf_output_path}/config.json", "w"), indent=2) + + +def convert(input_nemo_file, output_hf_file, precision=None, cpu_only=False) -> None: + """ + Convert NeMo weights to HF weights + """ + dummy_trainer = Trainer(devices=1, accelerator="cpu", strategy=NLPDDPStrategy()) + model_config = MegatronGPTModel.restore_from(input_nemo_file, trainer=dummy_trainer, return_config=True) + model_config.tensor_model_parallel_size = 1 + model_config.pipeline_model_parallel_size = 1 + model_config.sequence_parallel = False + model_config.transformer_engine = True + if cpu_only: + map_location = torch.device("cpu") + model_config.use_cpu_initialization = True + model_config.dist_ckpt_load_on_device = False + else: + map_location = None + + if cpu_only: + logging.info("******** Loading model on CPU. This will take a significant amount of time.") + + model = MegatronGPTModel.restore_from( + input_nemo_file, trainer=dummy_trainer, override_config_path=model_config, map_location=map_location + ) + + vocab_size = model.padded_vocab_size + + if precision is None: + precision = model.cfg.precision + if precision in [32, "32"]: + dtype = torch.float32 + elif precision in [16, "16", "16-mixed"]: + dtype = torch.float16 + elif precision in ["bf16", "bf16-mixed"]: + dtype = torch.bfloat16 + else: + logging.warning(f"Precision string {precision} is not recognized, falling back to fp32") + dtype = torch.float32 # fallback + logging.info(f"Using precision {dtype}") + + def param_to_weights(param): + return param.to(dtype) + + checkpoint = OrderedDict() + + hidden_size = model.cfg.hidden_size + head_num = model.cfg.num_attention_heads + num_layers = model.cfg.num_layers + ffn_hidden_size = model.cfg.ffn_hidden_size + num_query_groups = model.cfg.get("num_query_groups", head_num) # different num_query_groups for 70B + if num_query_groups is None: + num_query_groups = head_num + heads_per_group = head_num // num_query_groups + qkv_total_dim = head_num + 2 * num_query_groups + + # Embedding + embed_weight = model.state_dict()["model.embedding.word_embeddings.weight"] + embed_weights_base_name = "model.embed_tokens.weight" + checkpoint[embed_weights_base_name] = param_to_weights(embed_weight) + + for l in range(int(num_layers)): + print(f"converting layer {l}") + + qkv_weights = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.weight"] + qkv_weights = qkv_weights.reshape([qkv_total_dim, -1, hidden_size]) + + q_slice = torch.cat( + [ + torch.arange((heads_per_group + 2) * i, (heads_per_group + 2) * i + heads_per_group) + for i in range(num_query_groups) + ] + ) + k_slice = torch.arange(heads_per_group, qkv_total_dim, (heads_per_group + 2)) + v_slice = torch.arange(heads_per_group + 1, qkv_total_dim, (heads_per_group + 2)) + ## Example of slices + ## (without GQA): num_query_groups = head_num = 32, + ## q_slice = [0, 3, 6, 9 , ... 90, 93] + ## k_slice = [1, 4, 7, 10, ... 91, 94] + ## v_slice = [2, 5, 8, 11, ... 92, 95] + ## (with GQA): num_query_groups = 8, head_num = 64 + ## q_slice = [0, 1, .. 6, 7, 10, 11, .. 16, 17, 20, 21, .. 67, 70, ... 76, 77] + ## k_slice = [8, 18, 28, ... 68, 78] + ## v_slice = [9, 19, 29, ... 69, 79] + + q_weights_base_name = f"model.layers.{l}.self_attn.q_proj.weight" + k_weights_base_name = f"model.layers.{l}.self_attn.k_proj.weight" + v_weights_base_name = f"model.layers.{l}.self_attn.v_proj.weight" + + checkpoint[q_weights_base_name] = param_to_weights(qkv_weights[q_slice].reshape(-1, hidden_size)) + checkpoint[k_weights_base_name] = param_to_weights(qkv_weights[k_slice].reshape(-1, hidden_size)) + checkpoint[v_weights_base_name] = param_to_weights(qkv_weights[v_slice].reshape(-1, hidden_size)) + + # attention dense + o_weight = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_proj.weight"] + o_weight_base_name = f"model.layers.{l}.self_attn.o_proj.weight" + checkpoint[o_weight_base_name] = param_to_weights(o_weight) + + # mlp + mlp_weights = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.weight"] + mlp_up_proj_weight = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc2.weight"] + + if mlp_weights.shape[0] != mlp_up_proj_weight.shape[1]: + # Has projection (used for swi-glu) + logging.warning( + "Gated projection layers detected in NeMo checkpoint. Currently Nemotron HF does not support gated MLP." + ) + assert mlp_weights.shape[0] == 2 * mlp_up_proj_weight.shape[1] + + mlp_down_proj_weight = mlp_weights[:ffn_hidden_size, :] + mlp_gate_proj_weight = mlp_weights[ffn_hidden_size:, :] + + mlp_down_proj_base_name = f"model.layers.{l}.mlp.gate_proj.weight" + mlp_gate_proj_base_name = f"model.layers.{l}.mlp.up_proj.weight" + + checkpoint[mlp_down_proj_base_name] = param_to_weights(mlp_down_proj_weight) + checkpoint[mlp_gate_proj_base_name] = param_to_weights(mlp_gate_proj_weight) + else: + mlp_down_proj_weight = mlp_weights + mlp_down_proj_base_name = f"model.layers.{l}.mlp.up_proj.weight" + checkpoint[mlp_down_proj_base_name] = param_to_weights(mlp_down_proj_weight) + + mlp_up_proj_base_name = f"model.layers.{l}.mlp.down_proj.weight" + checkpoint[mlp_up_proj_base_name] = param_to_weights(mlp_up_proj_weight) + + # layernorm + input_ln_weight = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_weight"] + input_ln_base_name = f"model.layers.{l}.input_layernorm.weight" + checkpoint[input_ln_base_name] = param_to_weights(input_ln_weight) + if ( + model.state_dict().get(f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_bias", None) + is not None + ): + input_ln_bias = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_bias"] + input_ln_bias_name = f"model.layers.{l}.input_layernorm.bias" + checkpoint[input_ln_bias_name] = param_to_weights(input_ln_bias) + + post_attn_ln_weight = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_weight"] + post_attn_ln_base_name = f"model.layers.{l}.post_attention_layernorm.weight" + checkpoint[post_attn_ln_base_name] = param_to_weights(post_attn_ln_weight) + if model.state_dict().get(f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_bias", None) is not None: + post_attn_ln_bias = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_bias"] + post_attn_ln_bias_name = f"model.layers.{l}.post_attention_layernorm.bias" + checkpoint[post_attn_ln_bias_name] = param_to_weights(post_attn_ln_bias) + + print(f"done layer {l}") + + final_ln_weight = model.state_dict()["model.decoder.final_layernorm.weight"] + final_ln_base_name = "model.norm.weight" + checkpoint[final_ln_base_name] = param_to_weights(final_ln_weight) + if model.state_dict().get("model.decoder.final_layernorm.bias", None) is not None: + final_ln_bias = model.state_dict()["model.decoder.final_layernorm.bias"] + final_ln_bias_name = "model.norm.bias" + checkpoint[final_ln_bias_name] = param_to_weights(final_ln_bias) + + output_layer_weight = model.state_dict()["model.output_layer.weight"] + output_layer_base_name = "lm_head.weight" + checkpoint[output_layer_base_name] = param_to_weights(output_layer_weight) + + os.makedirs(os.path.dirname(output_hf_file), exist_ok=True) + torch.save(checkpoint, output_hf_file) + logging.info(f"Weights saved to {output_hf_file}") + + return model_config, model.tokenizer, dtype, vocab_size + + +def extract_nemotron_tokenizer(nemo_file, model_config, output_hf_path, nemo_tokenizer): + tokenizer_cfg = model_config.tokenizer + if tokenizer_cfg.library == "sentencepiece": + # For sentencepiece tokenizer, we are wrapping with HF's LlamaTokenizer + # and convert it to a PreTrainedTokenizerFast + tokenizer_fn = tokenizer_cfg.model[5:] + output_tokenizer = f"{output_hf_path}/tokenizer.model" + if nemo_file.endswith(".nemo"): + import tarfile + + archive = tarfile.open(nemo_file, "r") + tokenizer_filename = "./" + tokenizer_fn # exclude 'nemo:' prefix + archive.extract(tokenizer_filename, output_hf_path) + archive.close() + os.rename(f"{output_hf_path}/{tokenizer_fn}", output_tokenizer) + elif os.path.isdir(nemo_file): + shutil.copy(f"{nemo_file}/{tokenizer_fn}", output_tokenizer) + # We use LlamaTokenizer for sentencepiece based tokenizer + tokenizer = LlamaTokenizer.from_pretrained(output_hf_path, legacy=False) + # Convert the LlamaTokenizer to a PreTrainedTokenizerFast instance + tokenizer = PreTrainedTokenizerFast( + tokenizer_object=LlamaConverter(tokenizer).converted(), model_input_names=["input_ids", "token_type_ids"] + ) + tokenizer.save_pretrained(output_hf_path) + logging.info(f"Setencepiece tokenizer has been saved to {output_tokenizer}") + elif isinstance(nemo_tokenizer, AutoTokenizer): + nemo_tokenizer.tokenizer.save_pretrained(output_hf_path) + logging.info(f"HF AutoTokenizer has been saved to {output_hf_path}") + else: + raise ValueError(f"Unsupported tokenizer type: library: {tokenizer_cfg.library}, type: {tokenizer_cfg.type}") + + +if __name__ == "__main__": + args = get_args() + if not args.hf_output_path: + assert args.output_path is not None, "Need to provide either output_path or hf_output_path" + else: + args.output_path = f"{args.hf_output_path}/pytorch_model.bin" + logging.info(f"weight will be saved to {args.output_path}") + + nemo_config, nemo_tokenizer, dtype, vocab_size = convert( + args.input_name_or_path, args.output_path, precision=args.precision, cpu_only=args.cpu_only + ) + if args.hf_input_path and args.hf_output_path: + convert_hf_config(nemo_config, nemo_tokenizer, vocab_size, dtype, args.hf_output_path, args.hf_input_path) + extract_nemotron_tokenizer(args.input_name_or_path, nemo_config, args.hf_output_path, nemo_tokenizer) + else: + logging.info("`hf_input_path` and/or `hf_output_path` not provided, not generating full HF model.") + logging.info(f".bin file is saved to {args.output_path}") diff --git a/src/transformers/models/nemotron/modeling_nemotron.py b/src/transformers/models/nemotron/modeling_nemotron.py new file mode 100644 index 0000000000..56a69a1558 --- /dev/null +++ b/src/transformers/models/nemotron/modeling_nemotron.py @@ -0,0 +1,1413 @@ +# coding=utf-8 +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Nemotron model.""" + +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import Size, Tensor, nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...cache_utils import Cache, StaticCache +from ...modeling_attn_mask_utils import AttentionMaskConverter +from ...modeling_flash_attention_utils import _flash_attention_forward +from ...modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, + TokenClassifierOutput, +) +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import ALL_LAYERNORM_LAYERS +from ...utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from .configuration_nemotron import NemotronConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "NemotronConfig" + + +def _cast_if_autocast_enabled(*args): + if not torch.is_autocast_enabled(): + return args + else: + return torch.cuda.amp.autocast_mode._cast(args, torch.get_autocast_gpu_dtype()) + + +class NemotronLayerNorm1P(nn.LayerNorm): + def __init__( + self, + normalized_shape: Union[int, List[int], Size], + eps: float = 1e-5, + elementwise_affine: bool = True, + bias: bool = True, + device=None, + dtype=None, + ): + super().__init__(normalized_shape, eps, elementwise_affine, bias, device, dtype) + + def forward(self, input: Tensor) -> Tensor: + args = _cast_if_autocast_enabled(input, self.normalized_shape, self.weight + 1, self.bias, self.eps) + with torch.cuda.amp.autocast(enabled=False): + return F.layer_norm(*args) + + +ALL_LAYERNORM_LAYERS.append(NemotronLayerNorm1P) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronRotaryEmbedding(nn.Module): + # Ignore copy + def __init__( + self, + config: NemotronConfig, + device=None, + ): + super().__init__() + + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_kwargs = None + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + def _dynamic_frequency_update(self, position_ids, device): + """ + dynamic RoPE layers should recompute `inv_freq` in the following situations: + 1 - growing beyond the cached sequence length (allow scaling) + 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) + """ + seq_len = torch.max(position_ids) + 1 + if seq_len > self.max_seq_len_cached: # growth + inv_freq, self.attention_scaling = self.rope_init_fn( + self.config, device, seq_len=seq_len, **self.rope_kwargs + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation + self.max_seq_len_cached = seq_len + + if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset + self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) + self.max_seq_len_cached = self.original_max_seq_len + + @torch.no_grad() + def forward(self, x, position_ids): + if "dynamic" in self.rope_type: + self._dynamic_frequency_update(position_ids, device=x.device) + + # Core RoPE block + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 (see https://github.com/huggingface/transformers/pull/29285) + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + + # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention + cos = cos * self.attention_scaling + sin = sin * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`, *optional*): + Deprecated and unused. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + rot_dim = cos.shape[-1] + # If q_pass/k_pass is empty, rotary pos embedding is applied to all tensor q/k + q, q_pass = q[..., :rot_dim], q[..., rot_dim:] + k, k_pass = k[..., :rot_dim], k[..., rot_dim:] + + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return torch.cat((q_embed, q_pass), dim=-1), torch.cat((k_embed, k_pass), dim=-1) + + +class NemotronMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.up_proj(x))) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class NemotronAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: NemotronConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = config.head_dim + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.partial_rotary_factor = config.partial_rotary_factor + self.is_causal = True + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.o_proj = nn.Linear(self.head_dim * self.num_heads, self.hidden_size, bias=config.attention_bias) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: Tuple[torch.Tensor, torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + attn_output = attn_output.transpose(1, 2).contiguous() + + attn_output = attn_output.reshape(bsz, q_len, -1) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronFlashAttention2(NemotronAttention): + """ + Nemotron flash attention module. This module inherits from `NemotronAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + # Ignore copy + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: Tuple[torch.Tensor, torch.Tensor], + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if isinstance(past_key_value, StaticCache): + raise ValueError( + "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` " + "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers" + ) + + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (NemotronRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + position_ids=position_ids, + dropout=dropout_rate, + sliding_window=getattr(self, "sliding_window", None), + use_top_left_mask=self._flash_attn_uses_top_left_mask, + is_causal=self.is_causal, + ) + + attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronSdpaAttention(NemotronAttention): + """ + Nemotron attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `NemotronAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Ignore copy + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: Tuple[torch.Tensor, torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "NemotronModel is using NemotronSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and causal_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + is_causal = True if causal_mask is None and q_len > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(bsz, q_len, -1) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +NEMOTRON_ATTENTION_CLASSES = { + "eager": NemotronAttention, + "flash_attention_2": NemotronFlashAttention2, + "sdpa": NemotronSdpaAttention, +} + + +# Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronDecoderLayer(nn.Module): + # Ignore copy + def __init__(self, config: NemotronConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = NEMOTRON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) + + self.mlp = NemotronMLP(config) + self.input_layernorm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps) + self.post_attention_layernorm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45 + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): + attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, + query_sequence_length, key_sequence_length)` if default attention is used. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence + position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): + Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, + with `head_dim` being the embedding dimension of each attention head. + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +NEMOTRON_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`NemotronConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Nemotron Model outputting raw hidden-states without any specific head on top.", + NEMOTRON_START_DOCSTRING, +) +class NemotronPreTrainedModel(PreTrainedModel): + config_class = NemotronConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["NemotronDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +NEMOTRON_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. +""" + + +@add_start_docstrings( + "The bare Nemotron Model outputting raw hidden-states without any specific head on top.", + NEMOTRON_START_DOCSTRING, +) +class NemotronModel(NemotronPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`NemotronDecoderLayer`] + + Args: + config: NemotronConfig + """ + + def __init__(self, config: NemotronConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [NemotronDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps) + self.rotary_emb = NemotronRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(NEMOTRON_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" + ) + + if self.gradient_checkpointing and self.training and use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." + ) + use_cache = False + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if cache_position is None: + cache_position = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + + # embed positions + hidden_states = inputs_embeds + + # create position embeddings to be shared across the decoder layers + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + causal_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + position_embeddings, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + if attention_mask is not None and attention_mask.dim() == 4: + # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing + if attention_mask.max() != 0: + raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`") + causal_mask = attention_mask + else: + causal_mask = torch.full( + (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device + ) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + + +# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronForCausalLM(NemotronPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = NemotronModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(NEMOTRON_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + # Ignore copy (doc string different) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, NemotronForCausalLM + + >>> model = NemotronForCausalLM.from_pretrained("nvidia/nemotron-3-8b-base-4k-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("nvidia/nemotron-3-8b-base-4k-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + cache_position=cache_position, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + cache_position=None, + position_ids=None, + use_cache=True, + **kwargs, + ): + # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens + # Exception 1: when passing input_embeds, input_ids may be missing entries + # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here + if past_key_values is not None: + if inputs_embeds is not None: # Exception 1 + input_ids = input_ids[:, -cache_position.shape[0] :] + elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) + input_ids = input_ids[:, cache_position] + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and cache_position[0] == 0: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids.contiguous()} # `contiguous()` needed for compilation use cases + + model_inputs.update( + { + "position_ids": position_ids, + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": use_cache, + "attention_mask": attention_mask, + } + ) + return model_inputs + + +@add_start_docstrings( + """ + The Nemotron Model transformer with a sequence classification head on top (linear layer). + + [`NemotronForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + NEMOTRON_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronForSequenceClassification(NemotronPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = NemotronModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(NEMOTRON_INPUTS_DOCSTRING) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + +@add_start_docstrings( + """ +The Nemotron Model transformer with a span classification head on top for extractive question-answering tasks like +SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). + """, + NEMOTRON_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForQuestionAnswering with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronForQuestionAnswering(NemotronPreTrainedModel): + base_model_prefix = "transformer" + + # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Nemotron + def __init__(self, config): + super().__init__(config) + self.transformer = NemotronModel(config) + self.qa_outputs = nn.Linear(config.hidden_size, 2) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.transformer.embed_tokens + + def set_input_embeddings(self, value): + self.transformer.embed_tokens = value + + @add_start_docstrings_to_model_forward(NEMOTRON_INPUTS_DOCSTRING) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, QuestionAnsweringModelOutput]: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1).to(start_logits.device) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1).to(end_logits.device) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + The Nemotron Model transformer with a token classification head on top (a linear layer on top of the hidden-states + output) e.g. for Named-Entity-Recognition (NER) tasks. + """, + NEMOTRON_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronForTokenClassification(NemotronPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = NemotronModel(config) + if getattr(config, "classifier_dropout", None) is not None: + classifier_dropout = config.classifier_dropout + elif getattr(config, "hidden_dropout", None) is not None: + classifier_dropout = config.hidden_dropout + else: + classifier_dropout = 0.1 + self.dropout = nn.Dropout(classifier_dropout) + self.score = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(NEMOTRON_INPUTS_DOCSTRING) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, TokenClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = outputs[0] + sequence_output = self.dropout(sequence_output) + logits = self.score(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/src/transformers/utils/dummy_pt_objects.py b/src/transformers/utils/dummy_pt_objects.py index 258cc5191e..9ebab35bba 100644 --- a/src/transformers/utils/dummy_pt_objects.py +++ b/src/transformers/utils/dummy_pt_objects.py @@ -6338,6 +6338,48 @@ class MvpPreTrainedModel(metaclass=DummyObject): requires_backends(self, ["torch"]) +class NemotronForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NemotronForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NemotronForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NemotronForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NemotronModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NemotronPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + class NllbMoeForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] diff --git a/tests/models/nemotron/__init__.py b/tests/models/nemotron/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/models/nemotron/test_modeling_nemotron.py b/tests/models/nemotron/test_modeling_nemotron.py new file mode 100644 index 0000000000..4f8f4cc77f --- /dev/null +++ b/tests/models/nemotron/test_modeling_nemotron.py @@ -0,0 +1,246 @@ +# coding=utf-8 +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Testing suite for the PyTorch Nemotron model.""" + +import tempfile +import unittest + +import pytest +from parameterized import parameterized + +from transformers import NemotronConfig, is_torch_available +from transformers.testing_utils import ( + is_flaky, + require_flash_attn, + require_read_token, + require_torch, + require_torch_gpu, + require_torch_sdpa, + slow, + torch_device, +) + +from ...models.gemma.test_modeling_gemma import GemmaModelTest, GemmaModelTester +from ...test_configuration_common import ConfigTester + + +if is_torch_available(): + import torch + + from transformers import ( + AutoTokenizer, + NemotronForCausalLM, + NemotronForQuestionAnswering, + NemotronForSequenceClassification, + NemotronForTokenClassification, + NemotronModel, + ) + + +class NemotronModelTester(GemmaModelTester): + if is_torch_available(): + config_class = NemotronConfig + model_class = NemotronModel + for_causal_lm_class = NemotronForCausalLM + for_sequence_class = NemotronForSequenceClassification + for_token_class = NemotronForTokenClassification + + +@require_torch +class NemotronModelTest(GemmaModelTest): + # Need to use `0.8` instead of `0.9` for `test_cpu_offload` + # This is because we are hitting edge cases with the causal_mask buffer + model_split_percents = [0.5, 0.7, 0.8] + all_model_classes = ( + ( + NemotronModel, + NemotronForCausalLM, + NemotronForSequenceClassification, + NemotronForQuestionAnswering, + NemotronForTokenClassification, + ) + if is_torch_available() + else () + ) + all_generative_model_classes = (NemotronForCausalLM,) if is_torch_available() else () + pipeline_model_mapping = ( + { + "feature-extraction": NemotronModel, + "text-classification": NemotronForSequenceClassification, + "text-generation": NemotronForCausalLM, + "zero-shot": NemotronForSequenceClassification, + "question-answering": NemotronForQuestionAnswering, + "token-classification": NemotronForTokenClassification, + } + if is_torch_available() + else {} + ) + test_headmasking = False + test_pruning = False + fx_compatible = False + + # used in `test_torch_compile` + _torch_compile_test_ckpt = "nvidia/nemotron-3-8b-base-4k-hf" + + def setUp(self): + self.model_tester = NemotronModelTester(self) + self.config_tester = ConfigTester(self, config_class=NemotronConfig, hidden_size=37) + + @require_torch_sdpa + @slow + @unittest.skip( + reason="Due to custom causal mask, there is a slightly too big difference between eager and sdpa in bfloat16." + ) + @parameterized.expand([("float16",), ("bfloat16",), ("float32",)]) + def test_eager_matches_sdpa_inference(self, torch_dtype: str): + pass + + @unittest.skip("Eager and SDPA do not produce the same outputs, thus this test fails") + def test_model_outputs_equivalence(self, **kwargs): + pass + + @require_torch_sdpa + @require_torch_gpu + @slow + def test_sdpa_equivalence(self): + for model_class in self.all_model_classes: + if not model_class._supports_sdpa: + self.skipTest(reason="Model does not support SDPA") + + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + model = model_class(config) + + with tempfile.TemporaryDirectory() as tmpdirname: + model.save_pretrained(tmpdirname) + model_sdpa = model_class.from_pretrained( + tmpdirname, torch_dtype=torch.float16, attn_implementation="sdpa" + ) + model_sdpa.to(torch_device) + + model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="eager") + model.to(torch_device) + + dummy_input = inputs_dict[model_class.main_input_name] + dummy_input = dummy_input.to(torch_device) + outputs = model(dummy_input, output_hidden_states=True) + outputs_sdpa = model_sdpa(dummy_input, output_hidden_states=True) + + logits = outputs.hidden_states[-1] + logits_sdpa = outputs_sdpa.hidden_states[-1] + + # nemotron sdpa needs a high tolerance + assert torch.allclose(logits_sdpa, logits, atol=1e-2) + + @require_flash_attn + @require_torch_gpu + @pytest.mark.flash_attn_test + @is_flaky() + @slow + def test_flash_attn_2_equivalence(self): + for model_class in self.all_model_classes: + if not model_class._supports_flash_attn_2: + self.skipTest(reason="Model does not support Flash Attention 2") + + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + model = model_class(config) + + with tempfile.TemporaryDirectory() as tmpdirname: + model.save_pretrained(tmpdirname) + model_fa = model_class.from_pretrained( + tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2" + ) + model_fa.to(torch_device) + + model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="eager") + model.to(torch_device) + + dummy_input = inputs_dict[model_class.main_input_name] + dummy_input = dummy_input.to(torch_device) + outputs = model(dummy_input, output_hidden_states=True) + outputs_fa = model_fa(dummy_input, output_hidden_states=True) + + logits = outputs.hidden_states[-1] + logits_fa = outputs_fa.hidden_states[-1] + + # nemotron flash attention 2 needs a high tolerance + assert torch.allclose(logits_fa, logits, atol=1e-2) + + +@require_torch_gpu +class NemotronIntegrationTest(unittest.TestCase): + # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) + # Depending on the hardware we get different logits / generations + cuda_compute_capability_major_version = None + + @classmethod + def setUpClass(cls): + if is_torch_available() and torch.cuda.is_available(): + # 8 is for A100 / A10 and 7 for T4 + cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] + + @slow + @require_read_token + def test_nemotron_8b_generation_sdpa(self): + text = ["What is the largest planet in solar system?"] + EXPECTED_TEXT = [ + "What is the largest planet in solar system?\nAnswer: Jupiter\n\nWhat is the answer", + ] + model_id = "thhaus/nemotron3-8b" + model = NemotronForCausalLM.from_pretrained( + model_id, torch_dtype=torch.float16, device_map="auto", attn_implementation="sdpa" + ) + tokenizer = AutoTokenizer.from_pretrained(model_id) + inputs = tokenizer(text, return_tensors="pt").to(torch_device) + + output = model.generate(**inputs, do_sample=False) + output_text = tokenizer.batch_decode(output, skip_special_tokens=True) + self.assertEqual(EXPECTED_TEXT, output_text) + + @slow + @require_read_token + def test_nemotron_8b_generation_eager(self): + text = ["What is the largest planet in solar system?"] + EXPECTED_TEXT = [ + "What is the largest planet in solar system?\nAnswer: Jupiter\n\nWhat is the answer", + ] + model_id = "thhaus/nemotron3-8b" + model = NemotronForCausalLM.from_pretrained( + model_id, torch_dtype=torch.float16, device_map="auto", attn_implementation="eager" + ) + tokenizer = AutoTokenizer.from_pretrained(model_id) + inputs = tokenizer(text, return_tensors="pt").to(torch_device) + + output = model.generate(**inputs, do_sample=False) + output_text = tokenizer.batch_decode(output, skip_special_tokens=True) + self.assertEqual(EXPECTED_TEXT, output_text) + + @slow + @require_read_token + def test_nemotron_8b_generation_fa2(self): + text = ["What is the largest planet in solar system?"] + EXPECTED_TEXT = [ + "What is the largest planet in solar system?\nAnswer: Jupiter\n\nWhat is the answer", + ] + model_id = "thhaus/nemotron3-8b" + model = NemotronForCausalLM.from_pretrained( + model_id, torch_dtype=torch.float16, device_map="auto", attn_implementation="flash_attention_2" + ) + tokenizer = AutoTokenizer.from_pretrained(model_id) + inputs = tokenizer(text, return_tensors="pt").to(torch_device) + + output = model.generate(**inputs, do_sample=False) + output_text = tokenizer.batch_decode(output, skip_special_tokens=True) + self.assertEqual(EXPECTED_TEXT, output_text)