From 89077bc67d5f89c2548a7063be903dcfef72ed51 Mon Sep 17 00:00:00 2001 From: weak-kajuma Date: Fri, 11 Oct 2024 08:03:01 +0000 Subject: [PATCH] first adding diffllama --- docs/source/en/_toctree.yml | 4 +- docs/source/en/model_doc/diffllama.md | 63 + src/transformers/__init__.py | 28 + src/transformers/models/__init__.py | 1 + .../models/auto/configuration_auto.py | 2 + src/transformers/models/auto/modeling_auto.py | 5 + .../models/auto/tokenization_auto.py | 6 + src/transformers/models/diffllama/__init__.py | 63 + .../diffllama/configuration_diffllama.py | 206 +++ .../models/diffllama/modeling_diffllama.py | 1554 +++++++++++++++++ tests/models/diffllama/__init__.py | 0 .../diffllama/test_modeling_diffllama.py | 1141 ++++++++++++ 12 files changed, 3072 insertions(+), 1 deletion(-) create mode 100644 docs/source/en/model_doc/diffllama.md create mode 100644 src/transformers/models/diffllama/__init__.py create mode 100644 src/transformers/models/diffllama/configuration_diffllama.py create mode 100644 src/transformers/models/diffllama/modeling_diffllama.py create mode 100644 tests/models/diffllama/__init__.py create mode 100644 tests/models/diffllama/test_modeling_diffllama.py diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index 02595f30db2893..2314dcd08d5f94 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -376,6 +376,8 @@ title: DeBERTa-v2 - local: model_doc/dialogpt title: DialoGPT + - local: model_doc/diffllama + title: DiffLlama - local: model_doc/distilbert title: DistilBERT - local: model_doc/dpr @@ -969,4 +971,4 @@ - local: internal/time_series_utils title: Utilities for Time Series title: Internal Helpers - title: API \ No newline at end of file + title: API diff --git a/docs/source/en/model_doc/diffllama.md b/docs/source/en/model_doc/diffllama.md new file mode 100644 index 00000000000000..7ca12f4663fd96 --- /dev/null +++ b/docs/source/en/model_doc/diffllama.md @@ -0,0 +1,63 @@ + + +# DiffLlama + +## Overview + +The DiffLlama model was proposed in []() by . + + +The abstract from the paper is the following: + +** + +Tips: + + + +This model was contributed by [INSERT YOUR HF USERNAME HERE](https://huggingface.co/). +The original code can be found [here](). + + +## DiffLlamaConfig + +[[autodoc]] DiffLlamaConfig + +## DiffLlamaModel + +[[autodoc]] DiffLlamaModel + - forward + +## DiffLlamaForCausalLM + +[[autodoc]] DiffLlamaForCausalLM + - forward + +## DiffLlamaForSequenceClassification + +[[autodoc]] DiffLlamaForSequenceClassification + - forward + +## DiffLlamaForQuestionAnswering + +[[autodoc]] DiffLlamaForQuestionAnswering + - forward + +## DiffLlamaForTokenClassification + +[[autodoc]] DiffLlamaForTokenClassification + - forward diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index ab829c6894c0f9..500ee853cbf79d 100755 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -524,6 +524,7 @@ "models.levit": ["LevitConfig"], "models.lilt": ["LiltConfig"], "models.llama": ["LlamaConfig"], + "models.diffllama": ["DiffLlamaConfig"], "models.llava": [ "LlavaConfig", "LlavaProcessor", @@ -1007,6 +1008,7 @@ _import_structure["models.gpt_sw3"].append("GPTSw3Tokenizer") _import_structure["models.layoutxlm"].append("LayoutXLMTokenizer") _import_structure["models.llama"].append("LlamaTokenizer") + _import_structure["models.diffllama"].append("DiffLlamaTokenizer") _import_structure["models.m2m_100"].append("M2M100Tokenizer") _import_structure["models.marian"].append("MarianTokenizer") _import_structure["models.mbart"].append("MBartTokenizer") @@ -2554,6 +2556,16 @@ "LlamaPreTrainedModel", ] ) + _import_structure["models.diffllama"].extend( + [ + "DiffLlamaForCausalLM", + "DiffLlamaForQuestionAnswering", + "DiffLlamaForSequenceClassification", + "DiffLlamaForTokenClassification", + "DiffLlamaModel", + "DiffLlamaPreTrainedModel", + ] + ) _import_structure["models.llava"].extend( [ "LlavaForConditionalGeneration", @@ -4728,6 +4740,7 @@ ) _import_structure["models.gptj"].extend(["FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTJPreTrainedModel"]) _import_structure["models.llama"].extend(["FlaxLlamaForCausalLM", "FlaxLlamaModel", "FlaxLlamaPreTrainedModel"]) + _import_structure["models.diffllama"].extend(["FlaxDiffLlamaForCausalLM", "FlaxDiffLlamaModel", "FlaxDiffLlamaPreTrainedModel"]) _import_structure["models.gemma"].extend(["FlaxGemmaForCausalLM", "FlaxGemmaModel", "FlaxGemmaPreTrainedModel"]) _import_structure["models.longt5"].extend( [ @@ -5364,6 +5377,7 @@ from .models.levit import LevitConfig from .models.lilt import LiltConfig from .models.llama import LlamaConfig + from .models.diffllama import DiffLlamaConfig from .models.llava import ( LlavaConfig, LlavaProcessor, @@ -5903,6 +5917,7 @@ from .models.gpt_sw3 import GPTSw3Tokenizer from .models.layoutxlm import LayoutXLMTokenizer from .models.llama import LlamaTokenizer + from .models.diffllama import DiffLlamaTokenizer from .models.m2m_100 import M2M100Tokenizer from .models.marian import MarianTokenizer from .models.mbart import MBartTokenizer @@ -7207,6 +7222,14 @@ LlamaModel, LlamaPreTrainedModel, ) + from .models.diffllama import ( + DiffLlamaForCausalLM, + DiffLlamaForQuestionAnswering, + DiffLlamaForSequenceClassification, + DiffLlamaForTokenClassification, + DiffLlamaModel, + DiffLlamaPreTrainedModel, + ) from .models.llava import ( LlavaForConditionalGeneration, LlavaPreTrainedModel, @@ -8956,6 +8979,11 @@ FlaxLlamaModel, FlaxLlamaPreTrainedModel, ) + from .models.diffllama import ( + FlaxDiffLlamaForCausalLM, + FlaxDiffLlamaModel, + FlaxDiffLlamaPreTrainedModel, + ) from .models.longt5 import ( FlaxLongT5ForConditionalGeneration, FlaxLongT5Model, diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py index 804957c0a551ae..f53e5ec79c8928 100644 --- a/src/transformers/models/__init__.py +++ b/src/transformers/models/__init__.py @@ -131,6 +131,7 @@ levit, lilt, llama, + diffllama, llava, llava_next, llava_next_video, diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py index 17219570684d53..a7f22d055443a4 100644 --- a/src/transformers/models/auto/configuration_auto.py +++ b/src/transformers/models/auto/configuration_auto.py @@ -149,6 +149,7 @@ ("levit", "LevitConfig"), ("lilt", "LiltConfig"), ("llama", "LlamaConfig"), + ("diffllama", "DiffLlamaConfig"), ("llava", "LlavaConfig"), ("llava_next", "LlavaNextConfig"), ("llava_next_video", "LlavaNextVideoConfig"), @@ -452,6 +453,7 @@ ("levit", "LeViT"), ("lilt", "LiLT"), ("llama", "LLaMA"), + ("diffllama", "DiffDiffLlama"), ("llama2", "Llama2"), ("llama3", "Llama3"), ("llava", "LLaVa"), diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py index aa0d59de52ff4c..a86c7bff6f453f 100644 --- a/src/transformers/models/auto/modeling_auto.py +++ b/src/transformers/models/auto/modeling_auto.py @@ -144,6 +144,7 @@ ("levit", "LevitModel"), ("lilt", "LiltModel"), ("llama", "LlamaModel"), + ("diffllama", "DiffLlamaModel"), ("longformer", "LongformerModel"), ("longt5", "LongT5Model"), ("luke", "LukeModel"), @@ -497,6 +498,7 @@ ("jamba", "JambaForCausalLM"), ("jetmoe", "JetMoeForCausalLM"), ("llama", "LlamaForCausalLM"), + ("diffllama", "DiffLlamaForCausalLM"), ("mamba", "MambaForCausalLM"), ("mamba2", "Mamba2ForCausalLM"), ("marian", "MarianForCausalLM"), @@ -954,6 +956,7 @@ ("led", "LEDForSequenceClassification"), ("lilt", "LiltForSequenceClassification"), ("llama", "LlamaForSequenceClassification"), + ("diffllama", "DiffLlamaForSequenceClassification"), ("longformer", "LongformerForSequenceClassification"), ("luke", "LukeForSequenceClassification"), ("markuplm", "MarkupLMForSequenceClassification"), @@ -1039,6 +1042,7 @@ ("led", "LEDForQuestionAnswering"), ("lilt", "LiltForQuestionAnswering"), ("llama", "LlamaForQuestionAnswering"), + ("diffllama", "DiffLlamaForQuestionAnswering"), ("longformer", "LongformerForQuestionAnswering"), ("luke", "LukeForQuestionAnswering"), ("lxmert", "LxmertForQuestionAnswering"), @@ -1136,6 +1140,7 @@ ("layoutlmv3", "LayoutLMv3ForTokenClassification"), ("lilt", "LiltForTokenClassification"), ("llama", "LlamaForTokenClassification"), + ("diffllama", "DiffLlamaForTokenClassification"), ("longformer", "LongformerForTokenClassification"), ("luke", "LukeForTokenClassification"), ("markuplm", "MarkupLMForTokenClassification"), diff --git a/src/transformers/models/auto/tokenization_auto.py b/src/transformers/models/auto/tokenization_auto.py index 8c3a7a82a60a51..ad6fbc52ed9579 100644 --- a/src/transformers/models/auto/tokenization_auto.py +++ b/src/transformers/models/auto/tokenization_auto.py @@ -257,6 +257,12 @@ "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), + ( + "diffllama", + ( + "LlamaTokenizer" if is_sentencepiece_available() else None, + "LlamaTokenizerFast" if is_tokenizers_available() else None, + ), ("llava", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("llava-onevision", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("llava_next", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)), diff --git a/src/transformers/models/diffllama/__init__.py b/src/transformers/models/diffllama/__init__.py new file mode 100644 index 00000000000000..2f30b85ecabf4c --- /dev/null +++ b/src/transformers/models/diffllama/__init__.py @@ -0,0 +1,63 @@ +# Copyright 2024 EleutherAI and The HuggingFace Inc. team. 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_torch_available, +) + + +_import_structure = { + "configuration_diffllama": ["DiffLlamaConfig"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_diffllama"] = [ + "DiffLlamaForCausalLM", + "DiffLlamaModel", + "DiffLlamaPreTrainedModel", + "DiffLlamaForSequenceClassification", + "DiffLlamaForQuestionAnswering", + "DiffLlamaForTokenClassification", + ] + +if TYPE_CHECKING: + from .configuration_diffllama import DiffLlamaConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_diffllama import ( + DiffLlamaForCausalLM, + DiffLlamaForQuestionAnswering, + DiffLlamaForSequenceClassification, + DiffLlamaForTokenClassification, + DiffLlamaModel, + DiffLlamaPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/src/transformers/models/diffllama/configuration_diffllama.py b/src/transformers/models/diffllama/configuration_diffllama.py new file mode 100644 index 00000000000000..6d67b2e69d3297 --- /dev/null +++ b/src/transformers/models/diffllama/configuration_diffllama.py @@ -0,0 +1,206 @@ +# coding=utf-8 +# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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. +"""DiffLlama model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...modeling_rope_utils import rope_config_validation + + +class DiffLlamaConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`DiffLlamaModel`]. It is used to instantiate an DiffLlama + 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 DiffLlama-7B. + + 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 32000): + Vocabulary size of the DiffLlama model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`DiffLlamaModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + 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 32): + Number of attention heads for each attention layer in the Transformer decoder. + 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 `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. DiffLlama 1 supports up to 2048 tokens, + DiffLlama 2 up to 4096, CodeDiffLlama up to 16384. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the rms 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 1): + Beginning of stream token id. + eos_token_id (`int`, *optional*, defaults to 2): + End of stream token id. + pretraining_tp (`int`, *optional*, defaults to 1): + Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this + document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to + understand more about it. This value is necessary to ensure exact reproducibility of the pretraining + results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). + 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. + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type + and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value + accordingly. + Expected contents: + `rope_type` (`str`): + The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', + 'diffllama3'], with 'default' being the original RoPE implementation. + `factor` (`float`, *optional*): + Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In + most scaling types, a `factor` of x will enable the model to handle sequences of length x * + original maximum pre-trained length. + `original_max_position_embeddings` (`int`, *optional*): + Used with 'dynamic', 'longrope' and 'diffllama3'. The original max position embeddings used during + pretraining. + `attention_factor` (`float`, *optional*): + Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention + computation. If unspecified, it defaults to value recommended by the implementation, using the + `factor` field to infer the suggested value. + `beta_fast` (`float`, *optional*): + Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear + ramp function. If unspecified, it defaults to 32. + `beta_slow` (`float`, *optional*): + Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear + ramp function. If unspecified, it defaults to 1. + `short_factor` (`List[float]`, *optional*): + Only used with 'longrope'. The scaling factor to be applied to short contexts (< + `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden + size divided by the number of attention heads divided by 2 + `long_factor` (`List[float]`, *optional*): + Only used with 'longrope'. The scaling factor to be applied to long contexts (< + `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden + size divided by the number of attention heads divided by 2 + `low_freq_factor` (`float`, *optional*): + Only used with 'diffllama3'. Scaling factor applied to low frequency components of the RoPE + `high_freq_factor` (`float`, *optional*): + Only used with 'diffllama3'. Scaling factor applied to high frequency components of the RoPE + 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, down_proj and gate_proj layers in the MLP layers. + head_dim (`int`, *optional*): + The attention head dimension. If None, it will default to hidden_size // num_heads + + ```python + >>> from transformers import DiffLlamaModel, DiffLlamaConfig + + >>> # Initializing a DiffLlama diffllama-7b style configuration + >>> configuration = DiffLlamaConfig() + + >>> # Initializing a model from the diffllama-7b style configuration + >>> model = DiffLlamaModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "diffllama" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=None, + bos_token_id=1, + eos_token_id=2, + pretraining_tp=1, + tie_word_embeddings=False, + rope_theta=10000.0, + rope_scaling=None, + attention_bias=False, + attention_dropout=0.0, + mlp_bias=False, + head_dim=None, + **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 + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.pretraining_tp = pretraining_tp + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + self.attention_bias = attention_bias + self.attention_dropout = attention_dropout + self.mlp_bias = mlp_bias + self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads + # Validate the correctness of rotary position embeddings parameters + # BC: if there is a 'type' field, copy it it to 'rope_type'. + if self.rope_scaling is not None and "type" in self.rope_scaling: + self.rope_scaling["rope_type"] = self.rope_scaling["type"] + rope_config_validation(self) + + 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/diffllama/modeling_diffllama.py b/src/transformers/models/diffllama/modeling_diffllama.py new file mode 100644 index 00000000000000..9663da1cfa994b --- /dev/null +++ b/src/transformers/models/diffllama/modeling_diffllama.py @@ -0,0 +1,1554 @@ +# coding=utf-8 +# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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 math +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, StaticCache +from ...generation import GenerationMixin +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_diffllama import DiffLlamaConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "DiffLlamaConfig" + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->DiffLlama +class DiffLlamaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + DiffLlamaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +ALL_LAYERNORM_LAYERS.append(DiffLlamaRMSNorm) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->DiffLlama +class DiffLlamaRotaryEmbedding(nn.Module): + def __init__( + self, + dim=None, + max_position_embeddings=2048, + base=10000, + device=None, + scaling_factor=1.0, + rope_type="default", + config: Optional[DiffLlamaConfig] = None, + ): + super().__init__() + # TODO (joao): remove the `if` below, only used for BC + self.rope_kwargs = {} + if config is None: + logger.warning_once( + "`DiffLlamaRotaryEmbedding` can now be fully parameterized by passing the model config through the " + "`config` argument. All other arguments will be removed in v4.46" + ) + self.rope_kwargs = { + "rope_type": rope_type, + "factor": scaling_factor, + "dim": dim, + "base": base, + "max_position_embeddings": max_position_embeddings, + } + self.rope_type = rope_type + self.max_seq_len_cached = max_position_embeddings + self.original_max_seq_len = max_position_embeddings + else: + # BC: "rope_type" was originally "type" + if config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + 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_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs) + 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.LlamaLinearScalingRotaryEmbedding with Llama->DiffLlama +class DiffLlamaLinearScalingRotaryEmbedding(DiffLlamaRotaryEmbedding): + """DiffLlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, *args, **kwargs): + logger.warning_once( + "`DiffLlamaLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use " + "`DiffLlamaRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)." + ) + kwargs["rope_type"] = "linear" + super().__init__(*args, **kwargs) + + +# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DiffLlama +class DiffLlamaDynamicNTKScalingRotaryEmbedding(DiffLlamaRotaryEmbedding): + """DiffLlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, *args, **kwargs): + logger.warning_once( + "`DiffLlamaDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use " + "`DiffLlamaRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to " + "__init__)." + ) + kwargs["rope_type"] = "dynamic" + super().__init__(*args, **kwargs) + + +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) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +# Copied from transformers.models.llama.modeling_llama.LlamaMLP with Llama->DiffLlama +class DiffLlamaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + 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): + if self.config.pretraining_tp > 1: + slice = self.intermediate_size // self.config.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat( + [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1 + ) + up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [ + F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp) + ] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +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) + + +# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DiffLlama +class DiffLlamaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: DiffLlamaConfig, 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 = getattr(config, "head_dim", self.hidden_size // self.num_heads) + 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.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.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias) + + # TODO (joao): remove in v4.46 (RoPE is computed in the model, not in the decoder layers) + self.rotary_emb = DiffLlamaRotaryEmbedding(config=self.config) + + 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: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + if self.config.pretraining_tp > 1: + key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp + query_slices = self.q_proj.weight.split( + (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0 + ) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + + else: + 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 None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + 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) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + + attn_output = attn_output.reshape(bsz, q_len, -1) + + if self.config.pretraining_tp > 1: + attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)]) + else: + 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->DiffLlama +class DiffLlamaFlashAttention2(DiffLlamaAttention): + """ + DiffLlama flash attention module. This module inherits from `DiffLlamaAttention` 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() + + def forward( + self, + hidden_states: 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, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 + ) -> 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 None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + 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. (DiffLlamaRMSNorm 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->DiffLlama +class DiffLlamaSdpaAttention(DiffLlamaAttention): + """ + DiffLlama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `DiffLlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from DiffLlamaAttention.forward + 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: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 + **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( + "DiffLlamaModel is using DiffLlamaSdpaAttention, 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 None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + 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 + + +DIFFLLAMA_ATTENTION_CLASSES = { + "eager": DiffLlamaAttention, + "flash_attention_2": DiffLlamaFlashAttention2, + "sdpa": DiffLlamaSdpaAttention, +} + + +# Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with LLAMA->DIFFLLAMA,Llama->DiffLlama +class DiffLlamaDecoderLayer(nn.Module): + def __init__(self, config: DiffLlamaConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = DIFFLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) + + self.mlp = DiffLlamaMLP(config) + self.input_layernorm = DiffLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = DiffLlamaRMSNorm(config.hidden_size, eps=config.rms_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 v4.46 + **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 + + +DIFFLLAMA_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 ([`DiffLlamaConfig`]): + 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 DiffLlama Model outputting raw hidden-states without any specific head on top.", + DIFFLLAMA_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->DiffLlama +class DiffLlamaPreTrainedModel(PreTrainedModel): + config_class = DiffLlamaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["DiffLlamaDecoderLayer"] + _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_() + + +DIFFLLAMA_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, see our + [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); + - 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 DiffLlama Model outputting raw hidden-states without any specific head on top.", + DIFFLLAMA_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaModel with LLAMA->DIFFLLAMA,Llama->DiffLlama +class DiffLlamaModel(DiffLlamaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DiffLlamaDecoderLayer`] + + Args: + config: DiffLlamaConfig + """ + + def __init__(self, config: DiffLlamaConfig): + 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( + [DiffLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = DiffLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = DiffLlamaRotaryEmbedding(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(DIFFLLAMA_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 must specify exactly one of input_ids or inputs_embeds") + + 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) + + # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + return_legacy_cache = True + if past_key_values is None: + past_key_values = DynamicCache() + else: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " + "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " + "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" + ) + + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + 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 + ) + 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 return_legacy_cache: + next_cache = next_cache.to_legacy_cache() + + 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, + ) + + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + 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 + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_cache_shape() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). + causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + cache_position=cache_position, + batch_size=input_tensor.shape[0], + ) + + 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 + min_dtype = torch.finfo(dtype).min + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + + @staticmethod + def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + cache_position: torch.Tensor, + batch_size: int, + ): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape + `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, + to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + cache_position (`torch.Tensor`): + Indices depicting the position of the input sequence tokens in the sequence. + batch_size (`torch.Tensor`): + Batch size. + """ + 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. + causal_mask = attention_mask + else: + min_dtype = torch.finfo(dtype).min + 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(batch_size, 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 + ) + + return causal_mask + + +# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->DIFFLLAMA,Llama->DiffLlama,llama->diffllama +class DiffLlamaForCausalLM(DiffLlamaPreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = DiffLlamaModel(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(DIFFLLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + 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, + num_logits_to_keep: int = 0, + ) -> 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]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, DiffLlamaForCausalLM + + >>> model = DiffLlamaForCausalLM.from_pretrained("meta-diffllama/DiffLlama-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-diffllama/DiffLlama-2-7b-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] + if self.config.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + + loss = None + if labels is not None: + # Upcast to float if we need to compute the loss to avoid potential precision issues + logits = logits.float() + # 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, + ) + + +@add_start_docstrings( + """ + The LLaMa Model transformer with a sequence classification head on top (linear layer). + + [`DiffLlamaForSequenceClassification`] 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). + """, + DIFFLLAMA_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->DIFFLLAMA,Llama->DiffLlama +class DiffLlamaForSequenceClassification(DiffLlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = DiffLlamaModel(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(DIFFLLAMA_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 DiffLlama 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`). + """, + DIFFLLAMA_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForQuestionAnswering with LLAMA->DIFFLLAMA,Llama->DiffLlama +class DiffLlamaForQuestionAnswering(DiffLlamaPreTrainedModel): + base_model_prefix = "transformer" + + def __init__(self, config): + super().__init__(config) + self.transformer = DiffLlamaModel(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(DIFFLLAMA_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 DiffLlama 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. + """, + DIFFLLAMA_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with LLAMA->DIFFLLAMA,Llama->DiffLlama +class DiffLlamaForTokenClassification(DiffLlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = DiffLlamaModel(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(DIFFLLAMA_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/tests/models/diffllama/__init__.py b/tests/models/diffllama/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/tests/models/diffllama/test_modeling_diffllama.py b/tests/models/diffllama/test_modeling_diffllama.py new file mode 100644 index 00000000000000..cd469abac20b60 --- /dev/null +++ b/tests/models/diffllama/test_modeling_diffllama.py @@ -0,0 +1,1141 @@ +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. 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 DiffLlama model.""" + +import gc +import tempfile +import unittest + +import pytest +from packaging import version +from parameterized import parameterized + +from transformers import AutoTokenizer, DiffLlamaConfig, StaticCache, is_torch_available, set_seed +from transformers.testing_utils import ( + backend_empty_cache, + require_bitsandbytes, + require_flash_attn, + require_read_token, + require_torch, + require_torch_accelerator, + require_torch_gpu, + require_torch_sdpa, + slow, + torch_device, +) + +from ...generation.test_utils import GenerationTesterMixin +from ...test_configuration_common import ConfigTester +from ...test_modeling_common import ModelTesterMixin, ids_tensor +from ...test_pipeline_mixin import PipelineTesterMixin + + +if is_torch_available(): + import torch + + from transformers import ( + DiffLlamaForCausalLM, + DiffLlamaForQuestionAnswering, + DiffLlamaForSequenceClassification, + DiffLlamaForTokenClassification, + DiffLlamaModel, + DiffLlamaTokenizer, + ) + from transformers.models.diffllama.modeling_diffllama import DiffLlamaLinearScalingRotaryEmbedding, DiffLlamaRotaryEmbedding + + +class DiffLlamaModelTester: + def __init__( + self, + parent, + batch_size=13, + seq_length=7, + is_training=True, + use_input_mask=True, + use_token_type_ids=False, + use_labels=True, + vocab_size=99, + hidden_size=32, + num_hidden_layers=2, + num_attention_heads=4, + intermediate_size=37, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=16, + type_sequence_label_size=2, + initializer_range=0.02, + num_labels=3, + num_choices=4, + pad_token_id=0, + scope=None, + ): + self.parent = parent + self.batch_size = batch_size + self.seq_length = seq_length + self.is_training = is_training + self.use_input_mask = use_input_mask + self.use_token_type_ids = use_token_type_ids + self.use_labels = use_labels + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.type_sequence_label_size = type_sequence_label_size + self.initializer_range = initializer_range + self.num_labels = num_labels + self.num_choices = num_choices + self.pad_token_id = pad_token_id + self.scope = scope + + def prepare_config_and_inputs(self): + input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) + + input_mask = None + if self.use_input_mask: + input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device)) + + token_type_ids = None + if self.use_token_type_ids: + token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) + + sequence_labels = None + token_labels = None + choice_labels = None + if self.use_labels: + sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) + token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) + choice_labels = ids_tensor([self.batch_size], self.num_choices) + + config = self.get_config() + + return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels + + def get_config(self): + return DiffLlamaConfig( + vocab_size=self.vocab_size, + hidden_size=self.hidden_size, + num_hidden_layers=self.num_hidden_layers, + num_attention_heads=self.num_attention_heads, + intermediate_size=self.intermediate_size, + hidden_act=self.hidden_act, + hidden_dropout_prob=self.hidden_dropout_prob, + attention_probs_dropout_prob=self.attention_probs_dropout_prob, + max_position_embeddings=self.max_position_embeddings, + type_vocab_size=self.type_vocab_size, + is_decoder=False, + initializer_range=self.initializer_range, + pad_token_id=self.pad_token_id, + ) + + def create_and_check_model( + self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels + ): + model = DiffLlamaModel(config=config) + model.to(torch_device) + model.eval() + result = model(input_ids, attention_mask=input_mask) + result = model(input_ids) + self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) + + def create_and_check_model_as_decoder( + self, + config, + input_ids, + token_type_ids, + input_mask, + sequence_labels, + token_labels, + choice_labels, + encoder_hidden_states, + encoder_attention_mask, + ): + config.add_cross_attention = True + model = DiffLlamaModel(config) + model.to(torch_device) + model.eval() + result = model( + input_ids, + attention_mask=input_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + ) + result = model( + input_ids, + attention_mask=input_mask, + encoder_hidden_states=encoder_hidden_states, + ) + result = model(input_ids, attention_mask=input_mask) + self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) + + def create_and_check_for_causal_lm( + self, + config, + input_ids, + token_type_ids, + input_mask, + sequence_labels, + token_labels, + choice_labels, + encoder_hidden_states, + encoder_attention_mask, + ): + model = DiffLlamaForCausalLM(config=config) + model.to(torch_device) + model.eval() + result = model(input_ids, attention_mask=input_mask, labels=token_labels) + self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) + + def create_and_check_decoder_model_past_large_inputs( + self, + config, + input_ids, + token_type_ids, + input_mask, + sequence_labels, + token_labels, + choice_labels, + encoder_hidden_states, + encoder_attention_mask, + ): + config.is_decoder = True + config.add_cross_attention = True + model = DiffLlamaForCausalLM(config=config) + model.to(torch_device) + model.eval() + + # first forward pass + outputs = model( + input_ids, + attention_mask=input_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=True, + ) + past_key_values = outputs.past_key_values + + # create hypothetical multiple next token and extent to next_input_ids + next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) + next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) + + # append to next input_ids and + next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) + next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) + + output_from_no_past = model( + next_input_ids, + attention_mask=next_attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_hidden_states=True, + )["hidden_states"][0] + output_from_past = model( + next_tokens, + attention_mask=next_attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + output_hidden_states=True, + )["hidden_states"][0] + + # select random slice + random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() + output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() + output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() + + self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) + + # test that outputs are equal for slice + self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) + + def prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + ( + config, + input_ids, + token_type_ids, + input_mask, + sequence_labels, + token_labels, + choice_labels, + ) = config_and_inputs + inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} + return config, inputs_dict + + +@require_torch +class DiffLlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): + all_model_classes = ( + ( + DiffLlamaModel, + DiffLlamaForCausalLM, + DiffLlamaForSequenceClassification, + DiffLlamaForQuestionAnswering, + DiffLlamaForTokenClassification, + ) + if is_torch_available() + else () + ) + all_generative_model_classes = (DiffLlamaForCausalLM,) if is_torch_available() else () + test_headmasking = False + test_pruning = False + fx_compatible = False + + # 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] + + # used in `test_torch_compile` + _torch_compile_test_ckpt = "meta-diffllama/DiffLlama-2-7b-hf" + + # used in `test_torch_compile_for_training` + _torch_compile_train_cls = DiffLlamaForCausalLM if is_torch_available() else None + + def setUp(self): + self.model_tester = DiffLlamaModelTester(self) + self.config_tester = ConfigTester(self, config_class=DiffLlamaConfig, hidden_size=37) + + def test_config(self): + self.config_tester.run_common_tests() + + def test_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_model(*config_and_inputs) + + def test_model_various_embeddings(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + for type in ["absolute", "relative_key", "relative_key_query"]: + config_and_inputs[0].position_embedding_type = type + self.model_tester.create_and_check_model(*config_and_inputs) + + def test_diffllama_sequence_classification_model(self): + config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() + config.num_labels = 3 + input_ids = input_dict["input_ids"] + attention_mask = input_ids.ne(1).to(torch_device) + sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) + model = DiffLlamaForSequenceClassification(config) + model.to(torch_device) + model.eval() + result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) + self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) + + def test_diffllama_sequence_classification_model_for_single_label(self): + config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() + config.num_labels = 3 + config.problem_type = "single_label_classification" + input_ids = input_dict["input_ids"] + attention_mask = input_ids.ne(1).to(torch_device) + sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) + model = DiffLlamaForSequenceClassification(config) + model.to(torch_device) + model.eval() + result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) + self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) + + def test_diffllama_sequence_classification_model_for_multi_label(self): + config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() + config.num_labels = 3 + config.problem_type = "multi_label_classification" + input_ids = input_dict["input_ids"] + attention_mask = input_ids.ne(1).to(torch_device) + sequence_labels = ids_tensor( + [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size + ).to(torch.float) + model = DiffLlamaForSequenceClassification(config) + model.to(torch_device) + model.eval() + result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) + self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) + + def test_diffllama_token_classification_model(self): + config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() + config.num_labels = 3 + input_ids = input_dict["input_ids"] + attention_mask = input_ids.ne(1).to(torch_device) + token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels) + model = DiffLlamaForTokenClassification(config=config) + model.to(torch_device) + model.eval() + result = model(input_ids, attention_mask=attention_mask, labels=token_labels) + self.assertEqual( + result.logits.shape, + (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels), + ) + + @unittest.skip(reason="DiffLlama buffers include complex numbers, which breaks this test") + def test_save_load_fast_init_from_base(self): + pass + + @parameterized.expand([("linear",), ("dynamic",), ("yarn",)]) + def test_model_rope_scaling_from_config(self, scaling_type): + config, _ = self.model_tester.prepare_config_and_inputs_for_common() + short_input = ids_tensor([1, 10], config.vocab_size) + long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size) + + set_seed(42) # Fixed seed at init time so the two models get the same random weights + original_model = DiffLlamaModel(config) + original_model.to(torch_device) + original_model.eval() + original_short_output = original_model(short_input).last_hidden_state + original_long_output = original_model(long_input).last_hidden_state + + set_seed(42) # Fixed seed at init time so the two models get the same random weights + config.rope_scaling = {"type": scaling_type, "factor": 10.0} + scaled_model = DiffLlamaModel(config) + scaled_model.to(torch_device) + scaled_model.eval() + scaled_short_output = scaled_model(short_input).last_hidden_state + scaled_long_output = scaled_model(long_input).last_hidden_state + + # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original + # maximum sequence length, so the outputs for the short input should match. + if scaling_type == "dynamic": + self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) + else: + self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) + + # The output should be different for long inputs + self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5)) + + def test_model_rope_scaling(self): + config, _ = self.model_tester.prepare_config_and_inputs_for_common() + scaling_factor = 10 + short_input_length = 10 + long_input_length = int(config.max_position_embeddings * 1.5) + + # Inputs + x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device + position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device) + position_ids_short = position_ids_short.unsqueeze(0) + position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device) + position_ids_long = position_ids_long.unsqueeze(0) + + # Sanity check original RoPE + original_rope = DiffLlamaRotaryEmbedding(config=config).to(torch_device) + original_cos_short, original_sin_short = original_rope(x, position_ids_short) + original_cos_long, original_sin_long = original_rope(x, position_ids_long) + torch.testing.assert_close(original_cos_short, original_cos_long[:, :short_input_length, :]) + torch.testing.assert_close(original_sin_short, original_sin_long[:, :short_input_length, :]) + + # Sanity check linear RoPE scaling + # New position "x" should match original position with index "x/scaling_factor" + config.rope_scaling = {"type": "linear", "factor": scaling_factor} + linear_scaling_rope = DiffLlamaRotaryEmbedding(config=config).to(torch_device) + linear_cos_short, linear_sin_short = linear_scaling_rope(x, position_ids_short) + linear_cos_long, linear_sin_long = linear_scaling_rope(x, position_ids_long) + torch.testing.assert_close(linear_cos_short, linear_cos_long[:, :short_input_length, :]) + torch.testing.assert_close(linear_sin_short, linear_sin_long[:, :short_input_length, :]) + for new_position in range(0, long_input_length, scaling_factor): + original_position = int(new_position // scaling_factor) + torch.testing.assert_close(linear_cos_long[:, new_position, :], original_cos_long[:, original_position, :]) + torch.testing.assert_close(linear_sin_long[:, new_position, :], original_sin_long[:, original_position, :]) + + # Sanity check Dynamic NTK RoPE scaling + # Scaling should only be observed after a long input is fed. We can observe that the frequencies increase + # with scaling_factor (or that `inv_freq` decreases) + config.rope_scaling = {"type": "dynamic", "factor": scaling_factor} + ntk_scaling_rope = DiffLlamaRotaryEmbedding(config=config).to(torch_device) + ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, position_ids_short) + ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, position_ids_long) + torch.testing.assert_close(ntk_cos_short, original_cos_short) + torch.testing.assert_close(ntk_sin_short, original_sin_short) + with self.assertRaises(AssertionError): + torch.testing.assert_close(ntk_cos_long, original_cos_long) + with self.assertRaises(AssertionError): + torch.testing.assert_close(ntk_sin_long, original_sin_long) + self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all()) + + # Sanity check Yarn RoPE scaling + # Scaling should be over the entire input + config.rope_scaling = {"type": "yarn", "factor": scaling_factor} + yarn_scaling_rope = DiffLlamaRotaryEmbedding(config=config).to(torch_device) + yarn_cos_short, yarn_sin_short = yarn_scaling_rope(x, position_ids_short) + yarn_cos_long, yarn_sin_long = yarn_scaling_rope(x, position_ids_long) + torch.testing.assert_close(yarn_cos_short, yarn_cos_long[:, :short_input_length, :]) + torch.testing.assert_close(yarn_sin_short, yarn_sin_long[:, :short_input_length, :]) + with self.assertRaises(AssertionError): + torch.testing.assert_close(yarn_cos_short, original_cos_short) + with self.assertRaises(AssertionError): + torch.testing.assert_close(yarn_sin_short, original_sin_short) + with self.assertRaises(AssertionError): + torch.testing.assert_close(yarn_cos_long, original_cos_long) + with self.assertRaises(AssertionError): + torch.testing.assert_close(yarn_sin_long, original_sin_long) + + def test_rope_class_retrocompatibility(self): + # Delete me when we remove compatibility for the old API :) + config, _ = self.model_tester.prepare_config_and_inputs_for_common() + scaling_factor = 10 + short_input_length = 10 + long_input_length = int(config.max_position_embeddings * 1.5) + config.rope_scaling = {"type": "linear", "factor": 10} + + # Inputs + x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device + position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device) + position_ids_short = position_ids_short.unsqueeze(0) + position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device) + position_ids_long = position_ids_long.unsqueeze(0) + + # Old API -- under the hood, "type": "linear" is set and `DiffLlamaRotaryEmbedding` is called + old_api_rope = DiffLlamaLinearScalingRotaryEmbedding( + config.hidden_size // config.num_attention_heads, + max_position_embeddings=config.max_position_embeddings, + base=config.rope_theta, + scaling_factor=scaling_factor, + ).to(torch_device) + old_cos_short, old_sin_short = old_api_rope(x, position_ids_short) + old_cos_long, old_sin_long = old_api_rope(x, position_ids_long) + + # New API + config.rope_scaling = {"type": "linear", "factor": scaling_factor} + new_api_rope = DiffLlamaRotaryEmbedding(config=config).to(torch_device) + new_cos_short, new_sin_short = new_api_rope(x, position_ids_short) + new_cos_long, new_sin_long = new_api_rope(x, position_ids_long) + + # The results should match + torch.testing.assert_close(old_cos_short, new_cos_short) + torch.testing.assert_close(old_sin_short, new_sin_short) + torch.testing.assert_close(old_cos_long, new_cos_long) + torch.testing.assert_close(old_sin_long, new_sin_long) + + def test_model_loading_old_rope_configs(self): + def _reinitialize_config(base_config, new_kwargs): + # Reinitialize the config with the new kwargs, forcing the config to go through its __init__ validation + # steps. + base_config_dict = base_config.to_dict() + new_config = DiffLlamaConfig.from_dict(config_dict={**base_config_dict, **new_kwargs}) + return new_config + + # from untouched config -> ✅ + base_config, model_inputs = self.model_tester.prepare_config_and_inputs_for_common() + original_model = DiffLlamaForCausalLM(base_config).to(torch_device) + original_model(**model_inputs) + + # from a config with the expected rope configuration -> ✅ + config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0}}) + original_model = DiffLlamaForCausalLM(config).to(torch_device) + original_model(**model_inputs) + + # from a config with the old rope configuration ('type' instead of 'rope_type') -> ✅ we gracefully handle BC + config = _reinitialize_config(base_config, {"rope_scaling": {"type": "linear", "factor": 10.0}}) + original_model = DiffLlamaForCausalLM(config).to(torch_device) + original_model(**model_inputs) + + # from a config with both 'type' and 'rope_type' -> ✅ they can coexist (and both are present in the config) + config = _reinitialize_config( + base_config, {"rope_scaling": {"type": "linear", "rope_type": "linear", "factor": 10.0}} + ) + self.assertTrue(config.rope_scaling["type"] == "linear") + self.assertTrue(config.rope_scaling["rope_type"] == "linear") + original_model = DiffLlamaForCausalLM(config).to(torch_device) + original_model(**model_inputs) + + # from a config with parameters in a bad range ('factor' should be >= 1.0) -> ⚠️ throws a warning + with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs: + config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": -999.0}}) + original_model = DiffLlamaForCausalLM(config).to(torch_device) + original_model(**model_inputs) + self.assertEqual(len(logs.output), 1) + self.assertIn("factor field", logs.output[0]) + + # from a config with unknown parameters ('foo' isn't a rope option) -> ⚠️ throws a warning + with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs: + config = _reinitialize_config( + base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0, "foo": "bar"}} + ) + original_model = DiffLlamaForCausalLM(config).to(torch_device) + original_model(**model_inputs) + self.assertEqual(len(logs.output), 1) + self.assertIn("Unrecognized keys", logs.output[0]) + + # from a config with specific rope type but missing one of its mandatory parameters -> ❌ throws exception + with self.assertRaises(KeyError): + config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear"}}) # missing "factor" + + @require_flash_attn + @require_torch_gpu + @require_bitsandbytes + @pytest.mark.flash_attn_test + @require_read_token + @slow + def test_flash_attn_2_generate_padding_right(self): + """ + Overwritting the common test as the test is flaky on tiny models + """ + model = DiffLlamaForCausalLM.from_pretrained( + "meta-diffllama/DiffLlama-2-7b-hf", + load_in_4bit=True, + device_map={"": 0}, + ) + + tokenizer = DiffLlamaTokenizer.from_pretrained("meta-diffllama/DiffLlama-2-7b-hf") + + texts = ["hi", "Hello this is a very long sentence"] + + tokenizer.padding_side = "right" + tokenizer.pad_token = tokenizer.eos_token + + inputs = tokenizer(texts, return_tensors="pt", padding=True).to(0) + + output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False) + output_native = tokenizer.batch_decode(output_native) + + model = DiffLlamaForCausalLM.from_pretrained( + "meta-diffllama/DiffLlama-2-7b-hf", load_in_4bit=True, device_map={"": 0}, attn_implementation="flash_attention_2" + ) + + output_fa_2 = model.generate(**inputs, max_new_tokens=20, do_sample=False) + output_fa_2 = tokenizer.batch_decode(output_fa_2) + + self.assertListEqual(output_native, output_fa_2) + + @require_flash_attn + @require_torch_gpu + @slow + @pytest.mark.flash_attn_test + def test_use_flash_attention_2_true(self): + """ + NOTE: this is the only test testing that the legacy `use_flash_attention=2` argument still works as intended. + """ + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + for model_class in self.all_model_classes: + with tempfile.TemporaryDirectory() as tmp_dir: + model = model_class(config) + model.save_pretrained(tmp_dir) + + new_model = DiffLlamaForCausalLM.from_pretrained( + tmp_dir, use_flash_attention_2=True, torch_dtype=torch.float16 + ).to("cuda") + + self.assertTrue(new_model.config._attn_implementation == "flash_attention_2") + + has_flash = False + for name, submodule in new_model.named_modules(): + if "FlashAttention" in submodule.__class__.__name__: + has_flash = True + break + if not has_flash: + raise ValueError("The flash model should have flash attention layers") + + @require_torch_sdpa + @slow + def test_eager_matches_sdpa_generate(self): + """ + Overwritting the common test as the test is flaky on tiny models + """ + max_new_tokens = 30 + + tokenizer = DiffLlamaTokenizer.from_pretrained("saibo/diffllama-1B") + + model_sdpa = DiffLlamaForCausalLM.from_pretrained( + "saibo/diffllama-1B", + torch_dtype=torch.float16, + low_cpu_mem_usage=True, + ).to(torch_device) + + self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") + + model_eager = DiffLlamaForCausalLM.from_pretrained( + "saibo/diffllama-1B", + torch_dtype=torch.float16, + low_cpu_mem_usage=True, + attn_implementation="eager", + ).to(torch_device) + + self.assertTrue(model_eager.config._attn_implementation == "eager") + + for name, submodule in model_eager.named_modules(): + if "SdpaAttention" in submodule.__class__.__name__: + raise ValueError("The eager model should not have SDPA attention layers") + + has_sdpa = False + for name, submodule in model_sdpa.named_modules(): + if "SdpaAttention" in submodule.__class__.__name__: + has_sdpa = True + break + if not has_sdpa: + raise ValueError("The SDPA model should have SDPA attention layers") + + texts = [ + "hi here's a longer context, getting longer and", + "Hello this is a very long sentence my friend, very long for real", + "Today I am in Paris and", + ] + + for padding_side in ["left", "right"]: + tokenizer.padding_side = padding_side + tokenizer.pad_token = tokenizer.eos_token + + inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device) + + res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) + res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) + + with self.subTest(f"{padding_side}"): + torch.testing.assert_close( + res_eager, + res_sdpa, + msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}", + ) + + +@require_torch_gpu +class DiffLlamaIntegrationTest(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_diffllama_3_1_hard(self): + """ + An integration test for diffllama 3.1. It tests against a long output to ensure the subtle numerical differences + from diffllama 3.1.'s RoPE can be detected + """ + # diff on `EXPECTED_TEXT`: + # 2024-08-26: updating from torch 2.3.1 to 2.4.0 slightly changes the results. + EXPECTED_TEXT = ( + "Tell me about the french revolution. The french revolution was a period of radical political and social " + "upheaval in France that lasted from 1789 until 1799. It was a time of great change and upheaval, marked " + "by the overthrow of the monarchy, the rise of the middle class, and the eventual establishment of the " + "First French Republic.\nThe revolution began in 1789 with the Estates-General, a representative " + "assembly that had not met since 1614. The Third Estate, which represented the common people, " + "demanded greater representation and eventually broke away to form the National Assembly. This marked " + "the beginning of the end of the absolute monarchy and the rise of the middle class.\n" + ) + + tokenizer = AutoTokenizer.from_pretrained("meta-diffllama/Meta-DiffLlama-3.1-8B-Instruct") + model = DiffLlamaForCausalLM.from_pretrained( + "meta-diffllama/Meta-DiffLlama-3.1-8B-Instruct", device_map="auto", torch_dtype=torch.bfloat16 + ) + input_text = ["Tell me about the french revolution."] + model_inputs = tokenizer(input_text, return_tensors="pt").to(model.device) + + generated_ids = model.generate(**model_inputs, max_new_tokens=128, do_sample=False) + generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) + self.assertEqual(generated_text, EXPECTED_TEXT) + + @slow + @require_read_token + def test_model_7b_logits_bf16(self): + input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] + + model = DiffLlamaForCausalLM.from_pretrained( + "meta-diffllama/DiffLlama-2-7b-hf", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="eager" + ) + + with torch.no_grad(): + out = model(torch.tensor([input_ids]).to(torch_device)) + # Expected mean on dim = -1 + + # fmt: off + EXPECTED_MEAN = { + 7: torch.tensor([[-6.5061, -4.1147, -4.9669, -3.2038, 0.8069, -2.9694, 1.2864, -3.3786]]), + 8: torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536, 0.8127, -2.9811, 1.2918, -3.3848]]) + } + + self.assertTrue(torch.allclose(EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device), out.logits.mean(-1), atol=1e-2, rtol=1e-2)) + + # slicing logits[0, 0, 0:15] + EXPECTED_SLICE = { + 7: torch.tensor([[-12.5000, -7.0625, -0.6289, -7.8750, -6.9688, -7.8125, -6.4688, -7.4375, -7.6875, -6.9375, -6.0312, -7.0000, -1.8594, 1.8438, -8.5000]]), + 8: torch.tensor([[-12.5625, -7.1250, -0.6289, -7.8750, -6.9688, -7.8125, -6.5000, -7.4375, -7.6562, -6.9688, -6.0312, -7.0312, -1.8203, 1.8750, -8.5000]]) + } + # fmt: on + + self.assertTrue( + torch.allclose( + EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device), + out.logits[0, 0, :15], + atol=1e-2, + rtol=1e-2, + ) + ) + + @slow + @require_read_token + def test_model_7b_logits(self): + input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] + + model = DiffLlamaForCausalLM.from_pretrained( + "meta-diffllama/DiffLlama-2-7b-hf", device_map="auto", torch_dtype=torch.float16 + ) + + with torch.no_grad(): + out = model(torch.tensor([input_ids]).to(torch_device)) + + # fmt: off + # Expected mean on dim = -1 + EXPECTED_MEAN = { + 7: torch.tensor([[-6.6420, -4.1227, -4.9809, -3.2041, 0.8261, -3.0052, 1.2957, -3.3648]]), + 8: torch.tensor([[-6.6544, -4.1259, -4.9840, -3.2456, 0.8261, -3.0124, 1.2971, -3.3641]]) + } + + self.assertTrue(torch.allclose(EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device), out.logits.mean(-1), atol=1e-2, rtol=1e-2)) + + # slicing logits[0, 0, 0:15] + EXPECTED_SLICE = { + 7: torch.tensor([-12.8125, -7.3359, -0.4846, -8.0234, -7.2383, -7.9922, -6.4805, -7.7344, -7.8125, -7.0078, -6.1797, -7.1094, -1.8633, 1.9736, -8.6016]), + 8: torch.tensor([-12.8281, -7.4609, -0.4668, -8.0703, -7.2539, -8.0078, -6.4961, -7.7734, -7.8516, -7.0352, -6.2188, -7.1367, -1.8564, 1.9922, -8.6328]) + } + # fmt: on + + self.assertTrue( + torch.allclose( + EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device), + out.logits[0, 0, :15], + atol=1e-2, + rtol=1e-2, + ) + ) + + @slow + def test_model_7b_dola_generation(self): + # ground truth text generated with dola_layers="low", repetition_penalty=1.2 + EXPECTED_TEXT_COMPLETION = ( + "Simply put, the theory of relativity states that 1) time and space are relative, and 2) the laws of " + "physics are the same for all observers in uniform motion relative to one another.\n\nThe theory of " + "relativity was developed by Albert Einstein in the early 20th century, and it revolutionized our " + "understanding of space and time." + ) + prompt = "Simply put, the theory of relativity states that " + tokenizer = DiffLlamaTokenizer.from_pretrained("meta-diffllama/DiffLlama-2-7b-chat-hf") + model = DiffLlamaForCausalLM.from_pretrained( + "meta-diffllama/DiffLlama-2-7b-chat-hf", device_map="sequential", torch_dtype=torch.float16 + ) + model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device) + + # greedy generation outputs + generated_ids = model.generate( + **model_inputs, max_new_tokens=64, top_p=None, temperature=1, do_sample=False, dola_layers="low" + ) + text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) + self.assertEqual(EXPECTED_TEXT_COMPLETION, text) + + @slow + @require_torch_gpu + @require_read_token + def test_compile_static_cache(self): + # `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2 + # work as intended. See https://github.com/pytorch/pytorch/issues/121943 + if version.parse(torch.__version__) < version.parse("2.3.0"): + self.skipTest(reason="This test requires torch >= 2.3 to run.") + + NUM_TOKENS_TO_GENERATE = 40 + # Note on `EXPECTED_TEXT_COMPLETION`'s diff: the current value matches the original test if the original test + # was changed to have a cache of 53 tokens (as opposed to 4096), on Ampere GPUs. + EXPECTED_TEXT_COMPLETION = [ + "Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial " + "reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe " + "theory of relativ", + "My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, " + "my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p", + ] + + prompts = [ + "Simply put, the theory of relativity states that ", + "My favorite all time favorite condiment is ketchup.", + ] + tokenizer = DiffLlamaTokenizer.from_pretrained("meta-diffllama/DiffLlama-2-7b-hf", pad_token="", padding_side="right") + model = DiffLlamaForCausalLM.from_pretrained( + "meta-diffllama/DiffLlama-2-7b-hf", device_map=torch_device, torch_dtype=torch.float16 + ) + inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device) + + # Dynamic Cache + generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False) + dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text) + + # Static Cache + generated_ids = model.generate( + **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" + ) + static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text) + + # Static Cache + compile + model._cache = None # clear cache object, initialized when we pass `cache_implementation="static"` + model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) + generated_ids = model.generate( + **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" + ) + static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text) + + +@slow +@require_torch_accelerator +class Mask4DTestHard(unittest.TestCase): + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def setUp(self): + model_name = "TinyDiffLlama/TinyDiffLlama-1.1B-Chat-v1.0" + self.model_dtype = torch.float32 + self.tokenizer = DiffLlamaTokenizer.from_pretrained(model_name) + self.model = DiffLlamaForCausalLM.from_pretrained(model_name, torch_dtype=self.model_dtype).to(torch_device) + + def get_test_data(self): + template = "my favorite {}" + items = ("pet is a", "artist plays a", "name is L") # same number of tokens in each item + + batch_separate = [template.format(x) for x in items] # 3 separate lines + batch_shared_prefix = template.format(" ".join(items)) # 1 line with options concatenated + + input_ids = self.tokenizer(batch_separate, return_tensors="pt").input_ids.to(torch_device) + input_ids_shared_prefix = self.tokenizer(batch_shared_prefix, return_tensors="pt").input_ids.to(torch_device) + + mask_shared_prefix = torch.tensor( + [ + [ + [ + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], + [1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0], + [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0], + [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1], + ] + ] + ], + device=torch_device, + ) + + position_ids = torch.arange(input_ids.shape[1]).tile(input_ids.shape[0], 1).to(torch_device) + + # building custom positions ids based on custom mask + position_ids_shared_prefix = (mask_shared_prefix.sum(dim=-1) - 1).reshape(1, -1) + # effectively: position_ids_shared_prefix = torch.tensor([[0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5]]).to(device) + + # inverting the mask + min_dtype = torch.finfo(self.model_dtype).min + mask_shared_prefix = (mask_shared_prefix.eq(0.0)).to(dtype=self.model_dtype) * min_dtype + + return input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix + + def test_stacked_causal_mask(self): + ( + input_ids, + position_ids, + input_ids_shared_prefix, + mask_shared_prefix, + position_ids_shared_prefix, + ) = self.get_test_data() + + # regular batch + logits = self.model.forward(input_ids, position_ids=position_ids).logits + logits_last = logits[:, -1, :] # last tokens in each batch line + decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] + + # single forward run with 4D custom mask + logits_shared_prefix = self.model.forward( + input_ids_shared_prefix, attention_mask=mask_shared_prefix, position_ids=position_ids_shared_prefix + ).logits + logits_shared_prefix_last = logits_shared_prefix[ + 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], : + ] # last three tokens + decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)] + + self.assertEqual(decoded, decoded_shared_prefix) + + def test_partial_stacked_causal_mask(self): + # Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks + + ( + input_ids, + position_ids, + input_ids_shared_prefix, + mask_shared_prefix, + position_ids_shared_prefix, + ) = self.get_test_data() + + # regular batch + logits = self.model.forward(input_ids, position_ids=position_ids).logits + logits_last = logits[:, -1, :] # last tokens in each batch line + decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] + + # 2 forward runs with custom 4D masks + part_a = 3 # split point + + input_1a = input_ids_shared_prefix[:, :part_a] + position_ids_1a = position_ids_shared_prefix[:, :part_a] + mask_1a = mask_shared_prefix[:, :, :part_a, :part_a] + + outs_1a = self.model.forward(input_1a, attention_mask=mask_1a, position_ids=position_ids_1a) + past_key_values_a = outs_1a["past_key_values"] + + # Case 1: we pass a 4D attention mask regarding the current sequence length (i.e. [..., seq_len, full_len]) + input_1b = input_ids_shared_prefix[:, part_a:] + position_ids_1b = position_ids_shared_prefix[:, part_a:] + mask_1b = mask_shared_prefix[:, :, part_a:, :] + outs_1b = self.model.forward( + input_1b, + attention_mask=mask_1b, + position_ids=position_ids_1b, + past_key_values=past_key_values_a, + ) + decoded_1b = [ + self.tokenizer.decode(t) + for t in outs_1b.logits.argmax(-1)[ + 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a + ] + ] + self.assertEqual(decoded, decoded_1b) + + def test_stacked_causal_mask_static_cache(self): + """same as above but with StaticCache""" + ( + input_ids, + position_ids, + input_ids_shared_prefix, + mask_shared_prefix, + position_ids_shared_prefix, + ) = self.get_test_data() + + # regular batch + logits = self.model.forward(input_ids, position_ids=position_ids).logits + logits_last = logits[:, -1, :] # last tokens in each batch line + decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] + + # upgrade the model with StaticCache + max_cache_len = 16 # note that max_cache_len is greater than the attention_mask.shape[-1] + past_key_values = StaticCache( + config=self.model.config, + batch_size=1, + max_cache_len=max_cache_len, + device=torch_device, + dtype=self.model.dtype, + ) + + padded_attention_mask = torch.nn.functional.pad( + input=mask_shared_prefix, + pad=(0, max_cache_len - mask_shared_prefix.shape[-1]), + mode="constant", + value=torch.finfo(self.model_dtype).min, + ) + + # single forward run with 4D custom mask + logits_shared_prefix = self.model.forward( + input_ids_shared_prefix, + attention_mask=padded_attention_mask, + position_ids=position_ids_shared_prefix, + cache_position=torch.arange(input_ids_shared_prefix.shape[-1], device=torch_device), + past_key_values=past_key_values, + ).logits + logits_shared_prefix_last = logits_shared_prefix[ + 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], : + ] # last three tokens + decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)] + + self.assertEqual(decoded, decoded_shared_prefix) + + def test_partial_stacked_causal_mask_static_cache(self): + # Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks + # we pass a 4D attention mask shaped [..., seq_len, full_static_cache_len]) + ( + input_ids, + position_ids, + input_ids_shared_prefix, + mask_shared_prefix, + position_ids_shared_prefix, + ) = self.get_test_data() + + # regular batch + logits = self.model.forward(input_ids, position_ids=position_ids).logits + logits_last = logits[:, -1, :] # last tokens in each batch line + decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] + + # upgrade the model with StaticCache + max_cache_len = 16 # note that max_cache_len is greater than the attention_mask.shape[-1] + past_key_values = StaticCache( + config=self.model.config, + batch_size=1, + max_cache_len=max_cache_len, + device=torch_device, + dtype=self.model.dtype, + ) + + # forward run for the first part of input + part_a = 3 # split point + + input_1a = input_ids_shared_prefix[:, :part_a] + position_ids_1a = position_ids_shared_prefix[:, :part_a] + mask_1a = mask_shared_prefix[:, :, :part_a, :part_a] + + padded_mask_1a = torch.nn.functional.pad( + input=mask_1a, + pad=(0, max_cache_len - mask_1a.shape[-1]), + mode="constant", + value=torch.finfo(self.model_dtype).min, + ) + + _ = self.model.forward( + input_1a, + attention_mask=padded_mask_1a, + position_ids=position_ids_1a, + cache_position=torch.arange(part_a, device=torch_device), + past_key_values=past_key_values, + ) + + # forward run for the second part of input + input_1b = input_ids_shared_prefix[:, part_a:] + position_ids_1b = position_ids_shared_prefix[:, part_a:] + mask_1b = mask_shared_prefix[:, :, part_a:, :] + + padded_mask_1b = torch.nn.functional.pad( + input=mask_1b, pad=(0, max_cache_len - mask_1b.shape[-1]), mode="constant", value=0 + ) + + outs_1b = self.model.forward( + input_1b, + attention_mask=padded_mask_1b, + position_ids=position_ids_1b, + cache_position=torch.arange( + part_a, + input_ids_shared_prefix.shape[-1], + device=torch_device, + ), + past_key_values=past_key_values, + ) + decoded_1b = [ + self.tokenizer.decode(t) + for t in outs_1b.logits.argmax(-1)[ + 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a + ] + ] + self.assertEqual(decoded, decoded_1b)