Merge pull request #4 from tomaarsen/model/pythia

Add GPT-NeoX/Pythia support + benchmark results

README.md

@@ -1,20 +1,25 @@
 
 # Attention Sinks in Transformers for Infinite-length LLMs
 
-| Llama 2 7B | Falcon 7B | MPT 7B |
-| ------------- | ------------- | ---- |
-| ![llama_2_7b_ppl_vram_plotted](https://github.com/tomaarsen/attention_sinks/assets/37621491/8d2e5b88-7158-41ac-8b3a-5a7abe38020d)  | ![falcon_7b_ppl_vram_plotted](https://github.com/tomaarsen/attention_sinks/assets/37621491/1be07370-6de7-4a7e-b5ab-3092a5ecb412)  | ![mpt_7b_ppl_vram_plotted](https://github.com/mit-han-lab/streaming-llm/assets/37621491/c96cff66-92a3-43ab-bc21-40232f2740a0) |
+| Llama 2 7B | Falcon 7B |
+|:-------------:|:-------------:|
+| ![llama_2_7b_ppl_vram_plotted](https://github.com/tomaarsen/attention_sinks/assets/37621491/8d2e5b88-7158-41ac-8b3a-5a7abe38020d)  | ![falcon_7b_ppl_vram_plotted](https://github.com/tomaarsen/attention_sinks/assets/37621491/1be07370-6de7-4a7e-b5ab-3092a5ecb412)  |
+| **MPT 7B** | **Pythia 6.9B** |
+| ![mpt_7b_ppl_vram_plotted](https://github.com/mit-han-lab/streaming-llm/assets/37621491/c96cff66-92a3-43ab-bc21-40232f2740a0) | ![pythia_6 8b_ppl_vram_plotted](https://github.com/tomaarsen/attention_sinks/assets/37621491/b0fee168-fa5a-457d-9e27-8395eb6dfb38) |
+
 
 ## Overview
 
 * Extend existing LLMs (e.g. Llama 2) to infinite length without sacrificing efficiency and performance, without any retraining.
+  * Model perplexities were stable even after 4 million tokens!
+  * Unlike with regular `transformers`, there is no linear memory increase and no extremely slow inference due to memory issues at higher sequence lengths.
 * The `attention_sinks` API allows for a drop-in replacement of the `transformers` API:
   ```python
   from attention_sinks import AutoModel
 
   model = AutoModel.from_pretrained("meta-llama/Llama-2-7b-hf", device_map="auto")
   ```
-* Support for Llama and Falcon models.
+* Support for Llama, Falcon, MPT and GPTNeoX (Pythia) models.
 * New parameters to `AutoModel....from_pretrained`:
   * `attention_sink_size`, int, defaults to 4: The number of initial tokens to use as the attention sink. These tokens are always included in the Attention Sink KV Cache.
   * `attention_sink_window_size`, int, defaults to 1020: The size of the sliding window, i.e. the number of "recent tokens" to include in the Attention Sink KV Cache.
@@ -26,7 +31,14 @@ pip install attention_sinks
 ```
 
 ## Benchmarks
-You can run a few benchmarks to compute the perplexity of various models over time using the provided [perplexity.py](benchmark/perplexity.py) benchmarking script. For example:
+
+### Pre-prepared benchmarks
+See [benchmark/scripts](benchmark/scripts) for a collection of ready-to-go scripts for various model architectures like Llama 2, Falcon, MPT and GPT-NeoX (Pythia). Each of these scripts runs the benchmarking and plotting tools described below for pure [`transformers`](https://github.com/huggingface/transformers), [`attention_sinks`](https://github.com/tomaarsen/attention_sinks) and a third alternative: `windowed`, which involves simple windowed attention at a window size of 1024 tokens. Upon completion, the script will plot the figures that you see at the top of this README.
+
+### Benchmarking tool
+You can run a few benchmarks to compute the perplexity of various models over time using the provided [perplexity.py](benchmark/perplexity.py) benchmarking script. This is done by computing the negative log likelihood losses of the chosen model when it is provided a full book with 60k+ tokens. By default, the scripts stop after 8192 tokens, but this can be modified. An ideal solution continuously has a low log perplexity and a constant CUDA VRAM usage.
+
+To use the script, you can run:
 ```
 python benchmark/perplexity.py --experiment attention_sinks
 ```
@@ -57,7 +69,13 @@ options:
 
 This script will create a `csv` file in the output directory (`"benchmarks/outputs"` by default) for that experiment, with information about perplexities, CUDA VRAM usage and latencies.
 
-This information can be plotted using the [plot_perplexity.py](benchmark\plot_perplexity.py) script. For example:
+### Plotting tool
+The information from the benchmarking tool can be plotted using the [plot_perplexity.py](benchmark\plot_perplexity.py) script. In particular, you can plot any combination of the following features:
+* `perplexity`,
+* `vram`, i.e. CUDA VRAM usage,
+* `latency`.
+
+For example:
 ```
 python benchmark/plot_perplexity.py --features perplexity latency --title "Log perplexity & latency of Llama 2 7B as a function of input lengths"
 ```
@@ -90,7 +108,7 @@ Clear as day:
 2. `windowed`: The VRAM is constant usage due to the windowing at 1024 tokens. However, it fails as soon as the first tokens leave the window.
 3. `attention_sinks`: Constant VRAM usage due to windowing with 4 attention sink tokens + the 1020 most recent tokens. This approach never fails despite the constant VRAM usage.
 
-I've uploaded [benchmark/outputs_llama_2_7b](benchmark/outputs_llama_2_7b) so you can reproduce this graph using the former command.
+I've uploaded outputs of various benchmarks in [benchmark](benchmark) so you can reproduce this graph using the former command.
 
 ## Changelog
 

attention_sinks/__init__.py

@@ -15,6 +15,12 @@
     FalconForTokenClassification,
     FalconModel,
     FalconPreTrainedModel,
+    GPTNeoXForCausalLM,
+    GPTNeoXForQuestionAnswering,
+    GPTNeoXForSequenceClassification,
+    GPTNeoXForTokenClassification,
+    GPTNeoXModel,
+    GPTNeoXPreTrainedModel,
     LlamaForCausalLM,
     LlamaForSequenceClassification,
     LlamaModel,

attention_sinks/models/__init__.py

@@ -13,6 +13,14 @@
     FalconModel,
     FalconPreTrainedModel,
 )
+from .gpt_neox import (
+    GPTNeoXForCausalLM,
+    GPTNeoXForQuestionAnswering,
+    GPTNeoXForSequenceClassification,
+    GPTNeoXForTokenClassification,
+    GPTNeoXModel,
+    GPTNeoXPreTrainedModel,
+)
 from .llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel
 from .mpt import (
     MptForCausalLM,

attention_sinks/models/auto/modeling_auto.py

@@ -13,7 +13,7 @@
 from transformers import (
     AutoModelForTokenClassification as TAutoModelForTokenClassification,
 )
-from transformers import FalconConfig, LlamaConfig, MptConfig
+from transformers import FalconConfig, GPTNeoXConfig, LlamaConfig, MptConfig
 from transformers.models.auto.modeling_auto import (
     MODEL_FOR_CAUSAL_LM_MAPPING,
     MODEL_FOR_QUESTION_ANSWERING_MAPPING,
@@ -29,6 +29,13 @@
     FalconForTokenClassification,
     FalconModel,
 )
+from ..gpt_neox import (
+    GPTNeoXForCausalLM,
+    GPTNeoXForQuestionAnswering,
+    GPTNeoXForSequenceClassification,
+    GPTNeoXForTokenClassification,
+    GPTNeoXModel,
+)
 from ..llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel
 from ..mpt import (
     MptForCausalLM,
@@ -38,24 +45,33 @@
     MptModel,
 )
 
-MODEL_MAPPING._extra_content = {LlamaConfig: LlamaModel, FalconConfig: FalconModel, MptConfig: MptModel}
+MODEL_MAPPING._extra_content = {
+    LlamaConfig: LlamaModel,
+    FalconConfig: FalconModel,
+    MptConfig: MptModel,
+    GPTNeoXConfig: GPTNeoXModel,
+}
 MODEL_FOR_CAUSAL_LM_MAPPING._extra_content = {
     LlamaConfig: LlamaForCausalLM,
     FalconConfig: FalconForCausalLM,
     MptConfig: MptForCausalLM,
+    GPTNeoXConfig: GPTNeoXForCausalLM,
 }
 MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING._extra_content = {
     LlamaConfig: LlamaForSequenceClassification,
     FalconConfig: FalconForSequenceClassification,
     MptConfig: MptForSequenceClassification,
+    GPTNeoXConfig: GPTNeoXForSequenceClassification,
 }
 MODEL_FOR_QUESTION_ANSWERING_MAPPING._extra_content = {
     FalconConfig: FalconForQuestionAnswering,
     MptConfig: MptForQuestionAnswering,
+    GPTNeoXConfig: GPTNeoXForQuestionAnswering,
 }
 MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING._extra_content = {
     FalconConfig: FalconForTokenClassification,
     MptConfig: MptForTokenClassification,
+    GPTNeoXConfig: GPTNeoXForTokenClassification,
 }
 
 

attention_sinks/models/falcon/pos_shift.py

@@ -1,4 +1,3 @@
-import math
 import types
 from typing import Optional, Tuple
 

attention_sinks/models/gpt_neox/__init__.py

@@ -0,0 +1,8 @@
+from .modeling_gpt_neox import (
+    GPTNeoXForCausalLM,
+    GPTNeoXForQuestionAnswering,
+    GPTNeoXForSequenceClassification,
+    GPTNeoXForTokenClassification,
+    GPTNeoXModel,
+    GPTNeoXPreTrainedModel,
+)

attention_sinks/models/gpt_neox/modeling_gpt_neox.py

@@ -0,0 +1,137 @@
+import os
+import types
+from typing import List, Optional, Tuple, Union
+
+from transformers import (
+    GPTNeoXForCausalLM as TGPTNeoXForCausalLM,
+)
+from transformers import (
+    GPTNeoXForQuestionAnswering as TGPTNeoXForQuestionAnswering,
+)
+from transformers import (
+    GPTNeoXForSequenceClassification as TGPTNeoXForSequenceClassification,
+)
+from transformers import (
+    GPTNeoXForTokenClassification as TGPTNeoXForTokenClassification,
+)
+from transformers import (
+    GPTNeoXModel as TGPTNeoXModel,
+)
+from transformers import (
+    GPTNeoXPreTrainedModel as TGPTNeoXPreTrainedModel,
+)
+from transformers import (
+    PretrainedConfig,
+)
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.gpt_neox.modeling_gpt_neox import (
+    _CHECKPOINT_FOR_DOC,
+    _CONFIG_FOR_DOC,
+    _REAL_CHECKPOINT_FOR_DOC,
+    GPT_NEOX_INPUTS_DOCSTRING,
+)
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings_to_model_forward,
+)
+
+from attention_sinks.attention_sink_kv_cache import AttentionSinkKVCache
+from attention_sinks.models.gpt_neox.pos_shift import (
+    enable_gpt_neox_pos_shift_attention,
+)
+
+
+class GPTNeoXPreTrainedModel(TGPTNeoXPreTrainedModel):
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        ignore_mismatched_sizes: bool = False,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        revision: str = "main",
+        use_safetensors: bool = None,
+        **kwargs,
+    ) -> None:
+        # Separate Attention Sink kwargs from regular kwargs
+        attention_sink_kwargs = {key: value for key, value in kwargs.items() if key.startswith("attention_sink")}
+        for key in attention_sink_kwargs:
+            kwargs.pop(key)
+
+        model = super(GPTNeoXPreTrainedModel, cls).from_pretrained(
+            pretrained_model_name_or_path,
+            *model_args,
+            config=config,
+            cache_dir=cache_dir,
+            ignore_mismatched_sizes=ignore_mismatched_sizes,
+            force_download=force_download,
+            local_files_only=local_files_only,
+            token=token,
+            revision=revision,
+            use_safetensors=use_safetensors,
+            **kwargs,
+        )
+        # Enable position shifting attention for GPTNeoX
+        enable_gpt_neox_pos_shift_attention(model)
+
+        # Hackishly attach the Attention Sink KV Cache to the model
+        cls._attach_attention_sink_kv_cache(model, **attention_sink_kwargs)
+
+        return model
+
+    @classmethod
+    def _attach_attention_sink_kv_cache(cls, module, **attention_sink_kwargs):
+        if isinstance(module, TGPTNeoXModel):
+            # Create the new cache
+            module.attention_sink_kv_cache = AttentionSinkKVCache(
+                **attention_sink_kwargs,
+                k_seq_dim=2,
+                v_seq_dim=2,
+            )
+
+            # Keep track of the old forward method, we need it in the wrapped one
+            old_forward = module.forward
+
+            # Wrap the forward by overriding the past_key_values using the cache
+            @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+            @add_code_sample_docstrings(
+                checkpoint=_CHECKPOINT_FOR_DOC,
+                real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
+                output_type=BaseModelOutputWithPast,
+                config_class=_CONFIG_FOR_DOC,
+            )
+            def wrapped_forward(self, *args, **kwargs) -> Union[Tuple, BaseModelOutputWithPast]:
+                outputs = old_forward(*args, **kwargs)
+                outputs.past_key_values = self.attention_sink_kv_cache(outputs.past_key_values)
+                return outputs
+
+            module.forward = types.MethodType(wrapped_forward, module)
+
+        # Recursively call this to find all GPTNeoXModels
+        for module in reversed(module._modules.values()):
+            if len(list(module.children())) > 0:
+                cls._attach_attention_sink_kv_cache(module, **attention_sink_kwargs)
+
+
+class GPTNeoXModel(GPTNeoXPreTrainedModel, TGPTNeoXModel):
+    pass
+
+
+class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, TGPTNeoXForCausalLM):
+    pass
+
+
+class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel, TGPTNeoXForSequenceClassification):
+    pass
+
+
+class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel, TGPTNeoXForTokenClassification):
+    pass
+
+
+class GPTNeoXForQuestionAnswering(GPTNeoXPreTrainedModel, TGPTNeoXForQuestionAnswering):
+    pass