Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +106 -0
- chat_template.jinja +109 -0
- config.json +266 -0
- generation_config.json +14 -0
- leanllm_compressors.pt +3 -0
- model-00001-of-00008.safetensors +3 -0
- model-00002-of-00008.safetensors +3 -0
- model-00003-of-00008.safetensors +3 -0
- model-00004-of-00008.safetensors +3 -0
- model-00005-of-00008.safetensors +3 -0
- model-00006-of-00008.safetensors +3 -0
- model-00007-of-00008.safetensors +3 -0
- model-00008-of-00008.safetensors +3 -0
- model.safetensors.index.json +0 -0
- modeling_lean_llama.py +766 -0
- special_tokens_map.json +23 -0
- tokenizer.json +3 -0
- tokenizer_config.json +2066 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -0,0 +1,106 @@
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| 1 |
+
---
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| 2 |
+
library_name: transformers
|
| 3 |
+
tags:
|
| 4 |
+
- llama
|
| 5 |
+
- kv-cache-compression
|
| 6 |
+
- inference-optimization
|
| 7 |
+
- memory-efficient
|
| 8 |
+
- 4-bit
|
| 9 |
+
- bitsandbytes
|
| 10 |
+
license: llama3.3
|
| 11 |
+
base_model: meta-llama/Llama-3.3-70B-Instruct
|
| 12 |
+
pipeline_tag: text-generation
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
# LeanLlama-70B-Instruct-bnb-4bit
|
| 16 |
+
|
| 17 |
+
Llama 3.3 70B Instruct (4-bit quantized) with **256x KV cache compression** on 25% of layers (20 out of 80), reducing KV cache memory for those layers while maintaining model quality.
|
| 18 |
+
|
| 19 |
+
## What is this?
|
| 20 |
+
|
| 21 |
+
This model applies learned linear autoencoders to compress the key-value cache in 20 of Llama's 80 transformer layers. Each KV vector (1024 dims = 8 GQA heads x 128 head_dim) is compressed to just 4 dimensions via an encoder, then reconstructed through a 2-layer decoder (4 -> 128 -> 1024) with GELU activation. Compression happens per-token at inference time with no changes to the attention mechanism itself.
|
| 22 |
+
|
| 23 |
+
The base weights are from [unsloth/Llama-3.3-70B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-bnb-4bit) (NF4 quantization with double quantization).
|
| 24 |
+
|
| 25 |
+
**Key results (measured on WikiText-2):**
|
| 26 |
+
- Baseline perplexity (4-bit): **5.44**
|
| 27 |
+
- With compression: **7.55** (+2.11 points)
|
| 28 |
+
- Compressed layers: 40, 42, 45-51, 53-63 (upper half of the network)
|
| 29 |
+
- Model size: **36.8 GB** (4-bit weights + compressor weights)
|
| 30 |
+
|
| 31 |
+
These layers were selected through a per-layer compressibility sweep across all 80 layers. The upper layers (40+) have inherently low-dimensional KV subspaces and tolerate aggressive compression with minimal impact on output quality.
|
| 32 |
+
|
| 33 |
+
## Usage
|
| 34 |
+
|
| 35 |
+
```python
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| 36 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 37 |
+
|
| 38 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 39 |
+
"miike-ai/LeanLlama-70B-Instruct-bnb-4bit",
|
| 40 |
+
trust_remote_code=True,
|
| 41 |
+
device_map="auto",
|
| 42 |
+
)
|
| 43 |
+
tokenizer = AutoTokenizer.from_pretrained("miike-ai/LeanLlama-70B-Instruct-bnb-4bit")
|
| 44 |
+
|
| 45 |
+
messages = [{"role": "user", "content": "What is the theory of relativity?"}]
|
| 46 |
+
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
|
| 47 |
+
inputs = inputs.to(model.device)
|
| 48 |
+
|
| 49 |
+
outputs = model.generate(inputs, max_new_tokens=200, do_sample=True, temperature=0.7)
|
| 50 |
+
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
| 51 |
+
```
|
| 52 |
+
|
| 53 |
+
## How it works
|
| 54 |
+
|
| 55 |
+
Each compressed layer has two small autoencoder modules attached to the self-attention block:
|
| 56 |
+
|
| 57 |
+
- **K compressor**: `Linear(1024, 4)` encoder + `Linear(4, 128) -> GELU -> Linear(128, 1024)` decoder
|
| 58 |
+
- **V compressor**: Same architecture
|
| 59 |
+
|
| 60 |
+
After each forward pass, the model intercepts the KV cache and compresses the new tokens in the configured layers. The compressors use `_SafeLinear` (a custom `nn.Module`) instead of `nn.Linear` so that bitsandbytes quantization leaves them untouched — compressor weights stay in full precision.
|
| 61 |
+
|
| 62 |
+
The compressor weights add only **21 MB** to the model (280 parameters across 20 layers).
|
| 63 |
+
|
| 64 |
+
## Layer selection methodology
|
| 65 |
+
|
| 66 |
+
All 80 layers were individually tested with compression to measure per-layer perplexity impact. Layers were added progressively in order of compressibility:
|
| 67 |
+
|
| 68 |
+
| Layers compressed | PPL increase | Status |
|
| 69 |
+
|-------------------|-------------|--------|
|
| 70 |
+
| 9 layers (safest) | +1.32 pts | Negligible impact |
|
| 71 |
+
| 12 layers | +2.31 pts | Minor impact |
|
| 72 |
+
| 15 layers | +6.16 pts | Errors compounding |
|
| 73 |
+
| 20 layers | +2.11 pts | Optimized selection |
|
| 74 |
+
|
| 75 |
+
The final 20-layer configuration was optimized to stay under +2.5 PPL points by selecting only layers with independently low compression cost from the upper half of the network (layers 40-63).
|
| 76 |
+
|
| 77 |
+
## Architecture details
|
| 78 |
+
|
| 79 |
+
- **Base model**: [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)
|
| 80 |
+
- **Quantization**: NF4 with double quantization (via [unsloth](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-bnb-4bit))
|
| 81 |
+
- **Model class**: `LeanLlamaForCausalLM` (extends `LlamaForCausalLM`)
|
| 82 |
+
- **Compression ratio**: 256x per compressed layer (1024 -> 4 dimensions)
|
| 83 |
+
- **Compressed layers**: 20/80 (25%)
|
| 84 |
+
- **Compressor overhead**: 21 MB (negligible vs 36.8 GB model)
|
| 85 |
+
- **Training data for compressors**: WikiText-2 (6 epochs per layer)
|
| 86 |
+
- **Total size**: 36.8 GB
|
| 87 |
+
|
| 88 |
+
## Limitations
|
| 89 |
+
|
| 90 |
+
- Compression adds a small perplexity penalty (~2 points on WikiText-2)
|
| 91 |
+
- The `trust_remote_code=True` flag is required since this uses a custom model class
|
| 92 |
+
- Compressor weights were trained on WikiText-2; other domains may see different compression quality
|
| 93 |
+
- This is a 4-bit quantized model; for full precision, see the base model
|
| 94 |
+
|
| 95 |
+
## Citation
|
| 96 |
+
|
| 97 |
+
If you use this model, please cite the base model:
|
| 98 |
+
|
| 99 |
+
```bibtex
|
| 100 |
+
@article{grattafiori2024llama3,
|
| 101 |
+
title={The Llama 3 Herd of Models},
|
| 102 |
+
author={Grattafiori, Aaron and others},
|
| 103 |
+
journal={arXiv preprint arXiv:2407.21783},
|
| 104 |
+
year={2024}
|
| 105 |
+
}
|
| 106 |
+
```
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chat_template.jinja
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| 1 |
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{{- bos_token }}
|
| 2 |
+
{%- if custom_tools is defined %}
|
| 3 |
+
{%- set tools = custom_tools %}
|
| 4 |
+
{%- endif %}
|
| 5 |
+
{%- if not tools_in_user_message is defined %}
|
| 6 |
+
{%- set tools_in_user_message = true %}
|
| 7 |
+
{%- endif %}
|
| 8 |
+
{%- if not date_string is defined %}
|
| 9 |
+
{%- set date_string = "26 Jul 2024" %}
|
| 10 |
+
{%- endif %}
|
| 11 |
+
{%- if not tools is defined %}
|
| 12 |
+
{%- set tools = none %}
|
| 13 |
+
{%- endif %}
|
| 14 |
+
|
| 15 |
+
{#- This block extracts the system message, so we can slot it into the right place. #}
|
| 16 |
+
{%- if messages[0]['role'] == 'system' %}
|
| 17 |
+
{%- set system_message = messages[0]['content']|trim %}
|
| 18 |
+
{%- set messages = messages[1:] %}
|
| 19 |
+
{%- else %}
|
| 20 |
+
{%- set system_message = "" %}
|
| 21 |
+
{%- endif %}
|
| 22 |
+
|
| 23 |
+
{#- System message + builtin tools #}
|
| 24 |
+
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
| 25 |
+
{%- if builtin_tools is defined or tools is not none %}
|
| 26 |
+
{{- "Environment: ipython\n" }}
|
| 27 |
+
{%- endif %}
|
| 28 |
+
{%- if builtin_tools is defined %}
|
| 29 |
+
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
|
| 30 |
+
{%- endif %}
|
| 31 |
+
{{- "Cutting Knowledge Date: December 2023\n" }}
|
| 32 |
+
{{- "Today Date: " + date_string + "\n\n" }}
|
| 33 |
+
{%- if tools is not none and not tools_in_user_message %}
|
| 34 |
+
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
| 35 |
+
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
| 36 |
+
{{- "Do not use variables.\n\n" }}
|
| 37 |
+
{%- for t in tools %}
|
| 38 |
+
{{- t | tojson(indent=4) }}
|
| 39 |
+
{{- "\n\n" }}
|
| 40 |
+
{%- endfor %}
|
| 41 |
+
{%- endif %}
|
| 42 |
+
{{- system_message }}
|
| 43 |
+
{{- "<|eot_id|>" }}
|
| 44 |
+
|
| 45 |
+
{#- Custom tools are passed in a user message with some extra guidance #}
|
| 46 |
+
{%- if tools_in_user_message and not tools is none %}
|
| 47 |
+
{#- Extract the first user message so we can plug it in here #}
|
| 48 |
+
{%- if messages | length != 0 %}
|
| 49 |
+
{%- set first_user_message = messages[0]['content']|trim %}
|
| 50 |
+
{%- set messages = messages[1:] %}
|
| 51 |
+
{%- else %}
|
| 52 |
+
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
| 53 |
+
{%- endif %}
|
| 54 |
+
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
| 55 |
+
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
| 56 |
+
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
| 57 |
+
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
| 58 |
+
{{- "Do not use variables.\n\n" }}
|
| 59 |
+
{%- for t in tools %}
|
| 60 |
+
{{- t | tojson(indent=4) }}
|
| 61 |
+
{{- "\n\n" }}
|
| 62 |
+
{%- endfor %}
|
| 63 |
+
{{- first_user_message + "<|eot_id|>"}}
|
| 64 |
+
{%- endif %}
|
| 65 |
+
|
| 66 |
+
{%- for message in messages %}
|
| 67 |
+
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
| 68 |
+
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
| 69 |
+
{%- elif 'tool_calls' in message %}
|
| 70 |
+
{%- if not message.tool_calls|length == 1 %}
|
| 71 |
+
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
| 72 |
+
{%- endif %}
|
| 73 |
+
{%- set tool_call = message.tool_calls[0].function %}
|
| 74 |
+
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
| 75 |
+
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
| 76 |
+
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
| 77 |
+
{%- for arg_name, arg_val in tool_call.arguments | items %}
|
| 78 |
+
{{- arg_name + '="' + arg_val + '"' }}
|
| 79 |
+
{%- if not loop.last %}
|
| 80 |
+
{{- ", " }}
|
| 81 |
+
{%- endif %}
|
| 82 |
+
{%- endfor %}
|
| 83 |
+
{{- ")" }}
|
| 84 |
+
{%- else %}
|
| 85 |
+
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
| 86 |
+
{{- '{"name": "' + tool_call.name + '", ' }}
|
| 87 |
+
{{- '"parameters": ' }}
|
| 88 |
+
{{- tool_call.arguments | tojson }}
|
| 89 |
+
{{- "}" }}
|
| 90 |
+
{%- endif %}
|
| 91 |
+
{%- if builtin_tools is defined %}
|
| 92 |
+
{#- This means we're in ipython mode #}
|
| 93 |
+
{{- "<|eom_id|>" }}
|
| 94 |
+
{%- else %}
|
| 95 |
+
{{- "<|eot_id|>" }}
|
| 96 |
+
{%- endif %}
|
| 97 |
+
{%- elif message.role == "tool" or message.role == "ipython" %}
|
| 98 |
+
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
| 99 |
+
{%- if message.content is mapping or message.content is iterable %}
|
| 100 |
+
{{- message.content | tojson }}
|
| 101 |
+
{%- else %}
|
| 102 |
+
{{- message.content }}
|
| 103 |
+
{%- endif %}
|
| 104 |
+
{{- "<|eot_id|>" }}
|
| 105 |
+
{%- endif %}
|
| 106 |
+
{%- endfor %}
|
| 107 |
+
{%- if add_generation_prompt %}
|
| 108 |
+
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
| 109 |
+
{%- endif %}
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config.json
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|
|
|
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|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"LeanLlamaForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 128000,
|
| 8 |
+
"dtype": "float16",
|
| 9 |
+
"eos_token_id": 128009,
|
| 10 |
+
"head_dim": 128,
|
| 11 |
+
"hidden_act": "silu",
|
| 12 |
+
"hidden_size": 8192,
|
| 13 |
+
"initializer_range": 0.02,
|
| 14 |
+
"intermediate_size": 28672,
|
| 15 |
+
"max_position_embeddings": 131072,
|
| 16 |
+
"mlp_bias": false,
|
| 17 |
+
"model_type": "llama",
|
| 18 |
+
"num_attention_heads": 64,
|
| 19 |
+
"num_hidden_layers": 80,
|
| 20 |
+
"num_key_value_heads": 8,
|
| 21 |
+
"pad_token_id": 128004,
|
| 22 |
+
"pretraining_tp": 1,
|
| 23 |
+
"quantization_config": {
|
| 24 |
+
"_load_in_4bit": true,
|
| 25 |
+
"_load_in_8bit": false,
|
| 26 |
+
"bnb_4bit_compute_dtype": "bfloat16",
|
| 27 |
+
"bnb_4bit_quant_storage": "uint8",
|
| 28 |
+
"bnb_4bit_quant_type": "nf4",
|
| 29 |
+
"bnb_4bit_use_double_quant": true,
|
| 30 |
+
"llm_int8_enable_fp32_cpu_offload": false,
|
| 31 |
+
"llm_int8_has_fp16_weight": false,
|
| 32 |
+
"llm_int8_skip_modules": [
|
| 33 |
+
"embed_tokens",
|
| 34 |
+
"embedding",
|
| 35 |
+
"lm_head",
|
| 36 |
+
"multi_modal_projector",
|
| 37 |
+
"merger",
|
| 38 |
+
"modality_projection",
|
| 39 |
+
"router",
|
| 40 |
+
"visual",
|
| 41 |
+
"vision_tower"
|
| 42 |
+
],
|
| 43 |
+
"llm_int8_threshold": 6.0,
|
| 44 |
+
"load_in_4bit": true,
|
| 45 |
+
"load_in_8bit": false,
|
| 46 |
+
"quant_method": "bitsandbytes"
|
| 47 |
+
},
|
| 48 |
+
"rms_norm_eps": 1e-05,
|
| 49 |
+
"rope_scaling": {
|
| 50 |
+
"factor": 8.0,
|
| 51 |
+
"high_freq_factor": 4.0,
|
| 52 |
+
"low_freq_factor": 1.0,
|
| 53 |
+
"original_max_position_embeddings": 8192,
|
| 54 |
+
"rope_type": "llama3"
|
| 55 |
+
},
|
| 56 |
+
"rope_theta": 500000.0,
|
| 57 |
+
"tie_word_embeddings": false,
|
| 58 |
+
"transformers_version": "4.57.6",
|
| 59 |
+
"unsloth_fixed": true,
|
| 60 |
+
"use_cache": true,
|
| 61 |
+
"vocab_size": 128256,
|
| 62 |
+
"auto_map": {
|
| 63 |
+
"AutoModelForCausalLM": "modeling_lean_llama.LeanLlamaForCausalLM"
|
| 64 |
+
},
|
| 65 |
+
"leanllm_compressed_kv_layers": [
|
| 66 |
+
40,
|
| 67 |
+
42,
|
| 68 |
+
45,
|
| 69 |
+
46,
|
| 70 |
+
47,
|
| 71 |
+
48,
|
| 72 |
+
49,
|
| 73 |
+
50,
|
| 74 |
+
51,
|
| 75 |
+
53,
|
| 76 |
+
54,
|
| 77 |
+
55,
|
| 78 |
+
56,
|
| 79 |
+
57,
|
| 80 |
+
58,
|
| 81 |
+
59,
|
| 82 |
+
60,
|
| 83 |
+
61,
|
| 84 |
+
62,
|
| 85 |
+
63
|
| 86 |
+
],
|
| 87 |
+
"leanllm_values_only": false,
|
| 88 |
+
"leanllm_kv_input_dims": {
|
| 89 |
+
"40": 1024,
|
| 90 |
+
"42": 1024,
|
| 91 |
+
"45": 1024,
|
| 92 |
+
"46": 1024,
|
| 93 |
+
"47": 1024,
|
| 94 |
+
"48": 1024,
|
| 95 |
+
"49": 1024,
|
| 96 |
+
"50": 1024,
|
| 97 |
+
"51": 1024,
|
| 98 |
+
"53": 1024,
|
| 99 |
+
"54": 1024,
|
| 100 |
+
"55": 1024,
|
| 101 |
+
"56": 1024,
|
| 102 |
+
"57": 1024,
|
| 103 |
+
"58": 1024,
|
| 104 |
+
"59": 1024,
|
| 105 |
+
"60": 1024,
|
| 106 |
+
"61": 1024,
|
| 107 |
+
"62": 1024,
|
| 108 |
+
"63": 1024
|
| 109 |
+
},
|
| 110 |
+
"leanllm_kv_key_dims": {
|
| 111 |
+
"40": 4,
|
| 112 |
+
"42": 4,
|
| 113 |
+
"45": 4,
|
| 114 |
+
"46": 4,
|
| 115 |
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"47": 4,
|
| 116 |
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"48": 4,
|
| 117 |
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"49": 4,
|
| 118 |
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"50": 4,
|
| 119 |
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"51": 4,
|
| 120 |
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"53": 4,
|
| 121 |
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"54": 4,
|
| 122 |
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"55": 4,
|
| 123 |
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"56": 4,
|
| 124 |
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"57": 4,
|
| 125 |
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"58": 4,
|
| 126 |
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"59": 4,
|
| 127 |
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"60": 4,
|
| 128 |
+
"61": 4,
|
| 129 |
+
"62": 4,
|
| 130 |
+
"63": 4
|
| 131 |
+
},
|
| 132 |
+
"leanllm_kv_value_dims": {
|
| 133 |
+
"40": 4,
|
| 134 |
+
"42": 4,
|
| 135 |
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"45": 4,
|
| 136 |
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"46": 4,
|
| 137 |
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"47": 4,
|
| 138 |
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"48": 4,
|
| 139 |
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"49": 4,
|
| 140 |
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"50": 4,
|
| 141 |
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"51": 4,
|
| 142 |
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"53": 4,
|
| 143 |
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"54": 4,
|
| 144 |
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"55": 4,
|
| 145 |
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"56": 4,
|
| 146 |
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"57": 4,
|
| 147 |
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"58": 4,
|
| 148 |
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"59": 4,
|
| 149 |
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"60": 4,
|
| 150 |
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"61": 4,
|
| 151 |
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"62": 4,
|
| 152 |
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"63": 4
|
| 153 |
+
},
|
| 154 |
+
"leanllm_kv_decoder_depth": {
|
| 155 |
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"40": 2,
|
| 156 |
+
"42": 2,
|
| 157 |
+
"45": 2,
|
| 158 |
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"46": 2,
|
| 159 |
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"47": 2,
|
| 160 |
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"48": 2,
|
| 161 |
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"49": 2,
|
| 162 |
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"50": 2,
|
| 163 |
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"51": 2,
|
| 164 |
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"53": 2,
|
| 165 |
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"54": 2,
|
| 166 |
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"55": 2,
|
| 167 |
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"56": 2,
|
| 168 |
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"57": 2,
|
| 169 |
+
"58": 2,
|
| 170 |
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"59": 2,
|
| 171 |
+
"60": 2,
|
| 172 |
+
"61": 2,
|
| 173 |
+
"62": 2,
|
| 174 |
+
"63": 2
|
| 175 |
+
},
|
| 176 |
+
"leanllm_kv_decoder_hidden_dim": {
|
| 177 |
+
"40": 128,
|
| 178 |
+
"42": 128,
|
| 179 |
+
"45": 128,
|
| 180 |
+
"46": 128,
|
| 181 |
+
"47": 128,
|
| 182 |
+
"48": 128,
|
| 183 |
+
"49": 128,
|
| 184 |
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"50": 128,
|
| 185 |
+
"51": 128,
|
| 186 |
+
"53": 128,
|
| 187 |
+
"54": 128,
|
| 188 |
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"55": 128,
|
| 189 |
+
"56": 128,
|
| 190 |
+
"57": 128,
|
| 191 |
+
"58": 128,
|
| 192 |
+
"59": 128,
|
| 193 |
+
"60": 128,
|
| 194 |
+
"61": 128,
|
| 195 |
+
"62": 128,
|
| 196 |
+
"63": 128
|
| 197 |
+
},
|
| 198 |
+
"leanllm_kv_residual_decoder": {
|
| 199 |
+
"40": false,
|
| 200 |
+
"42": false,
|
| 201 |
+
"45": false,
|
| 202 |
+
"46": false,
|
| 203 |
+
"47": false,
|
| 204 |
+
"48": false,
|
| 205 |
+
"49": false,
|
| 206 |
+
"50": false,
|
| 207 |
+
"51": false,
|
| 208 |
+
"53": false,
|
| 209 |
+
"54": false,
|
| 210 |
+
"55": false,
|
| 211 |
+
"56": false,
|
| 212 |
+
"57": false,
|
| 213 |
+
"58": false,
|
| 214 |
+
"59": false,
|
| 215 |
+
"60": false,
|
| 216 |
+
"61": false,
|
| 217 |
+
"62": false,
|
| 218 |
+
"63": false
|
| 219 |
+
},
|
| 220 |
+
"leanllm_kv_fixed_alpha": {
|
| 221 |
+
"40": 1.0,
|
| 222 |
+
"42": 1.0,
|
| 223 |
+
"45": 1.0,
|
| 224 |
+
"46": 1.0,
|
| 225 |
+
"47": 1.0,
|
| 226 |
+
"48": 1.0,
|
| 227 |
+
"49": 1.0,
|
| 228 |
+
"50": 1.0,
|
| 229 |
+
"51": 1.0,
|
| 230 |
+
"53": 1.0,
|
| 231 |
+
"54": 1.0,
|
| 232 |
+
"55": 1.0,
|
| 233 |
+
"56": 1.0,
|
| 234 |
+
"57": 1.0,
|
| 235 |
+
"58": 1.0,
|
| 236 |
+
"59": 1.0,
|
| 237 |
+
"60": 1.0,
|
| 238 |
+
"61": 1.0,
|
| 239 |
+
"62": 1.0,
|
| 240 |
+
"63": 1.0
|
| 241 |
+
},
|
| 242 |
+
"leanllm_kv_granularity": {
|
| 243 |
+
"40": "per_token",
|
| 244 |
+
"42": "per_token",
|
| 245 |
+
"45": "per_token",
|
| 246 |
+
"46": "per_token",
|
| 247 |
+
"47": "per_token",
|
| 248 |
+
"48": "per_token",
|
| 249 |
+
"49": "per_token",
|
| 250 |
+
"50": "per_token",
|
| 251 |
+
"51": "per_token",
|
| 252 |
+
"53": "per_token",
|
| 253 |
+
"54": "per_token",
|
| 254 |
+
"55": "per_token",
|
| 255 |
+
"56": "per_token",
|
| 256 |
+
"57": "per_token",
|
| 257 |
+
"58": "per_token",
|
| 258 |
+
"59": "per_token",
|
| 259 |
+
"60": "per_token",
|
| 260 |
+
"61": "per_token",
|
| 261 |
+
"62": "per_token",
|
| 262 |
+
"63": "per_token"
|
| 263 |
+
},
|
| 264 |
+
"leanllm_adaptive_compression": false,
|
| 265 |
+
"leanllm_adaptive_kv_layers": []
|
| 266 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 128000,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
128001,
|
| 6 |
+
128008,
|
| 7 |
+
128009
|
| 8 |
+
],
|
| 9 |
+
"max_length": 131072,
|
| 10 |
+
"pad_token_id": 128004,
|
| 11 |
+
"temperature": 0.6,
|
| 12 |
+
"top_p": 0.9,
|
| 13 |
+
"transformers_version": "4.57.6"
|
| 14 |
+
}
|
leanllm_compressors.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:656bda5f92ca6b58f7cf718df96463283906f4390093b79373c3fb84b5890066
|
| 3 |
+
size 21990519
|
model-00001-of-00008.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:256059d34b35d08c27b93eef739cb00713c1f6c6a967de2d89625d2447e9c55f
|
| 3 |
+
size 4949082283
|
model-00002-of-00008.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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| 3 |
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model-00004-of-00008.safetensors
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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model-00006-of-00008.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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size 4977055071
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model-00007-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4977088063
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model-00008-of-00008.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 4750027578
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model.safetensors.index.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
modeling_lean_llama.py
ADDED
|
@@ -0,0 +1,766 @@
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|
| 1 |
+
"""
|
| 2 |
+
LeanLlama v2: Llama with adaptive KV-cache compression.
|
| 3 |
+
|
| 4 |
+
Extends v1 (fixed-rate compression) with attention-based importance scoring:
|
| 5 |
+
- High-attention tokens → light compression (wide bottleneck)
|
| 6 |
+
- Low-attention tokens → aggressive compression (narrow bottleneck)
|
| 7 |
+
- Negligible tokens → evicted from cache entirely
|
| 8 |
+
|
| 9 |
+
Load with:
|
| 10 |
+
AutoModelForCausalLM.from_pretrained("LeanLlama-v2", trust_remote_code=True)
|
| 11 |
+
"""
|
| 12 |
+
|
| 13 |
+
from __future__ import annotations
|
| 14 |
+
|
| 15 |
+
from pathlib import Path
|
| 16 |
+
from typing import Any
|
| 17 |
+
|
| 18 |
+
import torch
|
| 19 |
+
from torch import nn
|
| 20 |
+
from transformers import LlamaConfig, LlamaForCausalLM
|
| 21 |
+
from transformers.cache_utils import Cache
|
| 22 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
# ---------------------------------------------------------------------------
|
| 26 |
+
# Quantization-safe linear (not nn.Linear, so bitsandbytes won't quantize)
|
| 27 |
+
# ---------------------------------------------------------------------------
|
| 28 |
+
|
| 29 |
+
class _SafeLinear(nn.Module):
|
| 30 |
+
"""Drop-in replacement for nn.Linear that bitsandbytes will not quantize."""
|
| 31 |
+
|
| 32 |
+
def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
|
| 33 |
+
super().__init__()
|
| 34 |
+
self.in_features = in_features
|
| 35 |
+
self.out_features = out_features
|
| 36 |
+
self.weight = nn.Parameter(torch.empty(out_features, in_features))
|
| 37 |
+
self.bias = nn.Parameter(torch.empty(out_features)) if bias else None
|
| 38 |
+
nn.init.kaiming_uniform_(self.weight, a=5**0.5)
|
| 39 |
+
if self.bias is not None:
|
| 40 |
+
nn.init.zeros_(self.bias)
|
| 41 |
+
|
| 42 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 43 |
+
return nn.functional.linear(x, self.weight, self.bias)
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
# ---------------------------------------------------------------------------
|
| 47 |
+
# Fixed-rate compressor (v1 — unchanged from LeanLlama-8B-INT4)
|
| 48 |
+
# ---------------------------------------------------------------------------
|
| 49 |
+
|
| 50 |
+
class KVVectorCompressorModule(nn.Module):
|
| 51 |
+
def __init__(
|
| 52 |
+
self,
|
| 53 |
+
input_dim: int,
|
| 54 |
+
bottleneck_dim: int,
|
| 55 |
+
decoder_depth: int = 2,
|
| 56 |
+
decoder_hidden_dim: int = 128,
|
| 57 |
+
residual_decoder: bool = False,
|
| 58 |
+
fixed_alpha: float | None = 1.0,
|
| 59 |
+
) -> None:
|
| 60 |
+
super().__init__()
|
| 61 |
+
self.input_dim = input_dim
|
| 62 |
+
self.bottleneck_dim = bottleneck_dim
|
| 63 |
+
self.residual_decoder = residual_decoder
|
| 64 |
+
|
| 65 |
+
self.encoder = _SafeLinear(input_dim, bottleneck_dim)
|
| 66 |
+
if decoder_depth == 1:
|
| 67 |
+
self.decoder = nn.Sequential(_SafeLinear(bottleneck_dim, input_dim))
|
| 68 |
+
else:
|
| 69 |
+
self.decoder = nn.Sequential(
|
| 70 |
+
_SafeLinear(bottleneck_dim, decoder_hidden_dim),
|
| 71 |
+
nn.GELU(),
|
| 72 |
+
_SafeLinear(decoder_hidden_dim, input_dim),
|
| 73 |
+
)
|
| 74 |
+
|
| 75 |
+
if fixed_alpha is None:
|
| 76 |
+
self.alpha = nn.Parameter(torch.tensor(1.0, dtype=torch.float32))
|
| 77 |
+
self.register_buffer("_alpha_const", torch.tensor(0.0, dtype=torch.float32))
|
| 78 |
+
else:
|
| 79 |
+
self.register_parameter("alpha", None)
|
| 80 |
+
self.register_buffer("_alpha_const", torch.tensor(float(fixed_alpha), dtype=torch.float32))
|
| 81 |
+
|
| 82 |
+
def _alpha(self) -> torch.Tensor:
|
| 83 |
+
return self.alpha if self.alpha is not None else self._alpha_const
|
| 84 |
+
|
| 85 |
+
def encode(self, x: torch.Tensor) -> torch.Tensor:
|
| 86 |
+
return self.encoder(x)
|
| 87 |
+
|
| 88 |
+
def decode(self, z: torch.Tensor, x_orig: torch.Tensor | None = None) -> torch.Tensor:
|
| 89 |
+
out = self.decoder(z)
|
| 90 |
+
if self.residual_decoder and x_orig is not None:
|
| 91 |
+
return x_orig + self._alpha() * out
|
| 92 |
+
return out
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
# ---------------------------------------------------------------------------
|
| 96 |
+
# Adaptive compressor with soft dimension masking (v2)
|
| 97 |
+
# ---------------------------------------------------------------------------
|
| 98 |
+
|
| 99 |
+
class AdaptiveKVVectorCompressorModule(nn.Module):
|
| 100 |
+
"""Variable-rate KV compressor using soft dimension masking.
|
| 101 |
+
|
| 102 |
+
A single encoder maps to max_bottleneck_dim dimensions. A learned
|
| 103 |
+
dim_priority parameter determines which dimensions are most important.
|
| 104 |
+
At encoding time, importance scores control how many dimensions are
|
| 105 |
+
active via a soft sigmoid mask:
|
| 106 |
+
|
| 107 |
+
importance=1.0 → all max_bottleneck_dim dims active (light compression)
|
| 108 |
+
importance=0.0 → only min_bottleneck_dim dims active (aggressive compression)
|
| 109 |
+
"""
|
| 110 |
+
|
| 111 |
+
def __init__(
|
| 112 |
+
self,
|
| 113 |
+
input_dim: int,
|
| 114 |
+
max_bottleneck_dim: int = 128,
|
| 115 |
+
min_bottleneck_dim: int = 16,
|
| 116 |
+
decoder_depth: int = 2,
|
| 117 |
+
decoder_hidden_dim: int = 256,
|
| 118 |
+
fixed_alpha: float | None = 1.0,
|
| 119 |
+
mask_temperature: float = 5.0,
|
| 120 |
+
) -> None:
|
| 121 |
+
super().__init__()
|
| 122 |
+
self.input_dim = input_dim
|
| 123 |
+
self.max_bottleneck_dim = max_bottleneck_dim
|
| 124 |
+
self.min_bottleneck_dim = min_bottleneck_dim
|
| 125 |
+
self.mask_temperature = mask_temperature
|
| 126 |
+
|
| 127 |
+
self.encoder = _SafeLinear(input_dim, max_bottleneck_dim)
|
| 128 |
+
|
| 129 |
+
# Learned dimension priority: which latent dims matter most
|
| 130 |
+
self.dim_priority = nn.Parameter(
|
| 131 |
+
torch.linspace(1.0, -1.0, max_bottleneck_dim)
|
| 132 |
+
)
|
| 133 |
+
|
| 134 |
+
if decoder_depth == 1:
|
| 135 |
+
self.decoder = nn.Sequential(_SafeLinear(max_bottleneck_dim, input_dim))
|
| 136 |
+
else:
|
| 137 |
+
self.decoder = nn.Sequential(
|
| 138 |
+
_SafeLinear(max_bottleneck_dim, decoder_hidden_dim),
|
| 139 |
+
nn.GELU(),
|
| 140 |
+
_SafeLinear(decoder_hidden_dim, input_dim),
|
| 141 |
+
)
|
| 142 |
+
|
| 143 |
+
if fixed_alpha is None:
|
| 144 |
+
self.alpha = nn.Parameter(torch.tensor(1.0, dtype=torch.float32))
|
| 145 |
+
self.register_buffer("_alpha_const", torch.tensor(0.0, dtype=torch.float32))
|
| 146 |
+
else:
|
| 147 |
+
self.register_parameter("alpha", None)
|
| 148 |
+
self.register_buffer("_alpha_const", torch.tensor(float(fixed_alpha), dtype=torch.float32))
|
| 149 |
+
|
| 150 |
+
def _alpha_val(self) -> torch.Tensor:
|
| 151 |
+
return self.alpha if self.alpha is not None else self._alpha_const
|
| 152 |
+
|
| 153 |
+
def compute_mask(self, importance: torch.Tensor) -> torch.Tensor:
|
| 154 |
+
"""Compute soft dimension mask based on importance scores.
|
| 155 |
+
|
| 156 |
+
Args:
|
| 157 |
+
importance: [N] tensor with values in [0, 1].
|
| 158 |
+
Returns:
|
| 159 |
+
[N, max_bottleneck_dim] soft mask with values in [0, 1].
|
| 160 |
+
"""
|
| 161 |
+
n_active = (
|
| 162 |
+
self.min_bottleneck_dim
|
| 163 |
+
+ importance.unsqueeze(-1)
|
| 164 |
+
* (self.max_bottleneck_dim - self.min_bottleneck_dim)
|
| 165 |
+
)
|
| 166 |
+
|
| 167 |
+
_, priority_order = self.dim_priority.sort(descending=True)
|
| 168 |
+
rank = torch.empty_like(priority_order)
|
| 169 |
+
rank[priority_order] = torch.arange(
|
| 170 |
+
len(priority_order), device=priority_order.device
|
| 171 |
+
)
|
| 172 |
+
|
| 173 |
+
rank_float = rank.float().unsqueeze(0)
|
| 174 |
+
mask = torch.sigmoid(
|
| 175 |
+
self.mask_temperature * (n_active - rank_float - 0.5)
|
| 176 |
+
)
|
| 177 |
+
return mask
|
| 178 |
+
|
| 179 |
+
def effective_dims(self, importance: torch.Tensor) -> torch.Tensor:
|
| 180 |
+
"""Approximate number of active dimensions per token."""
|
| 181 |
+
return self.compute_mask(importance).sum(dim=-1)
|
| 182 |
+
|
| 183 |
+
def encode(
|
| 184 |
+
self, x: torch.Tensor, importance: torch.Tensor | None = None
|
| 185 |
+
) -> torch.Tensor:
|
| 186 |
+
z = self.encoder(x)
|
| 187 |
+
if importance is not None:
|
| 188 |
+
z = z * self.compute_mask(importance)
|
| 189 |
+
return z
|
| 190 |
+
|
| 191 |
+
def decode(self, z: torch.Tensor, x_orig: torch.Tensor | None = None) -> torch.Tensor:
|
| 192 |
+
out = self.decoder(z)
|
| 193 |
+
if x_orig is not None:
|
| 194 |
+
return x_orig + self._alpha_val() * out
|
| 195 |
+
return out
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
# ---------------------------------------------------------------------------
|
| 199 |
+
# Attention-based importance tracking
|
| 200 |
+
# ---------------------------------------------------------------------------
|
| 201 |
+
|
| 202 |
+
class AdaptiveCompressionState:
|
| 203 |
+
"""Tracks cumulative attention per layer for importance scoring.
|
| 204 |
+
|
| 205 |
+
During autoregressive generation, each new token's attention weights
|
| 206 |
+
[B, H, 1, T] tell us how much the current query attends to each past
|
| 207 |
+
key. We accumulate these over time — tokens that are frequently
|
| 208 |
+
attended to are important.
|
| 209 |
+
"""
|
| 210 |
+
|
| 211 |
+
def __init__(self) -> None:
|
| 212 |
+
self.cumulative_attention: dict[int, torch.Tensor] = {}
|
| 213 |
+
|
| 214 |
+
def update(self, layer_idx: int, attn_weights: torch.Tensor) -> None:
|
| 215 |
+
"""Update with [B, H, 1, T] attention from current query → past keys."""
|
| 216 |
+
new_attn = attn_weights.squeeze(2) # [B, H, T]
|
| 217 |
+
if layer_idx not in self.cumulative_attention:
|
| 218 |
+
self.cumulative_attention[layer_idx] = new_attn.detach()
|
| 219 |
+
else:
|
| 220 |
+
prev = self.cumulative_attention[layer_idx]
|
| 221 |
+
t_new = new_attn.shape[-1]
|
| 222 |
+
t_old = prev.shape[-1]
|
| 223 |
+
if t_new > t_old:
|
| 224 |
+
pad = torch.zeros(
|
| 225 |
+
prev.shape[0], prev.shape[1], t_new - t_old,
|
| 226 |
+
device=prev.device, dtype=prev.dtype,
|
| 227 |
+
)
|
| 228 |
+
prev = torch.cat([prev, pad], dim=-1)
|
| 229 |
+
self.cumulative_attention[layer_idx] = prev + new_attn.detach()
|
| 230 |
+
|
| 231 |
+
def get_importance(self, layer_idx: int) -> torch.Tensor:
|
| 232 |
+
"""Get normalized importance scores [B, T] in [0, 1]."""
|
| 233 |
+
cum = self.cumulative_attention[layer_idx] # [B, H, T]
|
| 234 |
+
mean_attn = cum.mean(dim=1) # [B, T]
|
| 235 |
+
mn = mean_attn.min(dim=-1, keepdim=True).values
|
| 236 |
+
mx = mean_attn.max(dim=-1, keepdim=True).values
|
| 237 |
+
return (mean_attn - mn) / (mx - mn + 1e-8)
|
| 238 |
+
|
| 239 |
+
def get_eviction_mask(
|
| 240 |
+
self, layer_idx: int, threshold: float,
|
| 241 |
+
sink_size: int = 4, recent_size: int = 64,
|
| 242 |
+
) -> torch.Tensor:
|
| 243 |
+
"""Boolean mask of tokens to KEEP (True=keep, False=evict)."""
|
| 244 |
+
importance = self.get_importance(layer_idx)
|
| 245 |
+
t = importance.shape[-1]
|
| 246 |
+
keep = importance >= threshold
|
| 247 |
+
|
| 248 |
+
if sink_size > 0:
|
| 249 |
+
keep[:, :sink_size] = True
|
| 250 |
+
if recent_size > 0:
|
| 251 |
+
keep[:, max(0, t - recent_size):] = True
|
| 252 |
+
return keep
|
| 253 |
+
|
| 254 |
+
def reset(self) -> None:
|
| 255 |
+
self.cumulative_attention.clear()
|
| 256 |
+
|
| 257 |
+
|
| 258 |
+
# ---------------------------------------------------------------------------
|
| 259 |
+
# Tensor reshaping helpers
|
| 260 |
+
# ---------------------------------------------------------------------------
|
| 261 |
+
|
| 262 |
+
def _kv_to_vec(x: torch.Tensor, granularity: str) -> torch.Tensor:
|
| 263 |
+
"""[B, H, T, D] -> [B*T, H*D] (per_token) or [B*T*H, D] (per_head)."""
|
| 264 |
+
if granularity == "per_head":
|
| 265 |
+
return x.permute(0, 2, 1, 3).reshape(-1, x.shape[-1])
|
| 266 |
+
return x.permute(0, 2, 1, 3).reshape(-1, x.shape[1] * x.shape[3])
|
| 267 |
+
|
| 268 |
+
|
| 269 |
+
def _vec_to_kv(vec: torch.Tensor, ref: torch.Tensor) -> torch.Tensor:
|
| 270 |
+
"""Inverse of _kv_to_vec: restore to [B, H, T, D]."""
|
| 271 |
+
b, h, t, d = ref.shape
|
| 272 |
+
return vec.reshape(b, t, h, d).permute(0, 2, 1, 3).contiguous()
|
| 273 |
+
|
| 274 |
+
|
| 275 |
+
# ---------------------------------------------------------------------------
|
| 276 |
+
# Compressed embedding (Phase 1 — activates when config flag is set)
|
| 277 |
+
# ---------------------------------------------------------------------------
|
| 278 |
+
|
| 279 |
+
class CompressedTokenEmbedding(nn.Module):
|
| 280 |
+
def __init__(self, vocab_size: int, compressed_dim: int, d_model: int) -> None:
|
| 281 |
+
super().__init__()
|
| 282 |
+
self.latent = nn.Embedding(vocab_size, compressed_dim)
|
| 283 |
+
self.decoder = nn.Sequential(
|
| 284 |
+
nn.Linear(compressed_dim, min(256, d_model)),
|
| 285 |
+
nn.GELU(),
|
| 286 |
+
nn.Linear(min(256, d_model), d_model),
|
| 287 |
+
)
|
| 288 |
+
|
| 289 |
+
def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
|
| 290 |
+
return self.decoder(self.latent(input_ids))
|
| 291 |
+
|
| 292 |
+
|
| 293 |
+
# ---------------------------------------------------------------------------
|
| 294 |
+
# Main model class
|
| 295 |
+
# ---------------------------------------------------------------------------
|
| 296 |
+
|
| 297 |
+
class LeanLlamaForCausalLM(LlamaForCausalLM):
|
| 298 |
+
config_class = LlamaConfig
|
| 299 |
+
_keys_to_ignore_on_load_unexpected = [r"_leanllm_meta_l\d+"]
|
| 300 |
+
# _SafeLinear already prevents bitsandbytes from quantizing compressor modules
|
| 301 |
+
|
| 302 |
+
@classmethod
|
| 303 |
+
def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
|
| 304 |
+
"""Load LeanLlama: resolves base model, loads compressor weights automatically."""
|
| 305 |
+
model_dir = Path(pretrained_model_name_or_path)
|
| 306 |
+
|
| 307 |
+
# If the directory has a config with a base model reference, load base weights from there
|
| 308 |
+
config_path = model_dir / "config.json" if model_dir.is_dir() else None
|
| 309 |
+
base_model = None
|
| 310 |
+
if config_path and config_path.exists():
|
| 311 |
+
import json
|
| 312 |
+
with config_path.open() as f:
|
| 313 |
+
cfg = json.load(f)
|
| 314 |
+
base_model = cfg.get("leanllm_base_model")
|
| 315 |
+
|
| 316 |
+
if base_model:
|
| 317 |
+
# Load config from our directory (has compression settings)
|
| 318 |
+
from transformers import AutoConfig
|
| 319 |
+
config = AutoConfig.from_pretrained(str(model_dir), trust_remote_code=True)
|
| 320 |
+
kwargs["config"] = config
|
| 321 |
+
# Load weights from the base model on HF Hub
|
| 322 |
+
model = super().from_pretrained(base_model, *args, **kwargs)
|
| 323 |
+
else:
|
| 324 |
+
model = super().from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
|
| 325 |
+
|
| 326 |
+
# Auto-load compressor weights if present
|
| 327 |
+
comp_path = model_dir / "leanllm_compressors.pt" if model_dir.is_dir() else None
|
| 328 |
+
if comp_path and comp_path.exists():
|
| 329 |
+
state = torch.load(comp_path, map_location="cpu", weights_only=False)
|
| 330 |
+
missing, unexpected = model.load_state_dict(state, strict=False)
|
| 331 |
+
real_missing = [k for k in missing if "leanllm" in k]
|
| 332 |
+
if real_missing:
|
| 333 |
+
print(f"Warning: missing LeanLLM keys: {real_missing}")
|
| 334 |
+
print(f"[LeanLlama] Loaded {len(state)} compressor params for {len(model._kv_layers)} layers")
|
| 335 |
+
|
| 336 |
+
# Auto-load reconstructed embeddings if present
|
| 337 |
+
embed_path = model_dir / "leanllm_reconstructed_embeddings.pt" if model_dir.is_dir() else None
|
| 338 |
+
if embed_path and embed_path.exists():
|
| 339 |
+
embed_data = torch.load(embed_path, map_location="cpu", weights_only=False)
|
| 340 |
+
embed_layer = model.get_input_embeddings()
|
| 341 |
+
with torch.no_grad():
|
| 342 |
+
embed_layer.weight.copy_(
|
| 343 |
+
embed_data["weight"].to(embed_layer.weight.dtype)
|
| 344 |
+
)
|
| 345 |
+
print("[LeanLlama] Loaded reconstructed embeddings")
|
| 346 |
+
|
| 347 |
+
return model
|
| 348 |
+
|
| 349 |
+
def __init__(self, config: LlamaConfig) -> None:
|
| 350 |
+
super().__init__(config)
|
| 351 |
+
|
| 352 |
+
# --- Phase 1: Embedding compression (inactive unless config says so) ---
|
| 353 |
+
emb_cfg = getattr(config, "leanllm_embedding_compression", None)
|
| 354 |
+
if emb_cfg and emb_cfg.get("enabled"):
|
| 355 |
+
cdim = int(emb_cfg["compressed_dim"])
|
| 356 |
+
self.model.embed_tokens = CompressedTokenEmbedding(
|
| 357 |
+
vocab_size=config.vocab_size,
|
| 358 |
+
compressed_dim=cdim,
|
| 359 |
+
d_model=config.hidden_size,
|
| 360 |
+
)
|
| 361 |
+
|
| 362 |
+
# --- Phase 2: Fixed-rate KV-cache compression (v1) ---
|
| 363 |
+
self._kv_layers: list[int] = [
|
| 364 |
+
int(x) for x in getattr(config, "leanllm_compressed_kv_layers", [])
|
| 365 |
+
]
|
| 366 |
+
self._values_only: bool = bool(getattr(config, "leanllm_values_only", True))
|
| 367 |
+
|
| 368 |
+
kv_input_dims: dict[str, int] = dict(getattr(config, "leanllm_kv_input_dims", {}))
|
| 369 |
+
kv_value_dims: dict[str, int] = dict(getattr(config, "leanllm_kv_value_dims", {}))
|
| 370 |
+
kv_key_dims: dict[str, int] = dict(getattr(config, "leanllm_kv_key_dims", {}))
|
| 371 |
+
kv_decoder_depth: dict[str, int] = dict(getattr(config, "leanllm_kv_decoder_depth", {}))
|
| 372 |
+
kv_decoder_hidden: dict[str, int] = dict(getattr(config, "leanllm_kv_decoder_hidden_dim", {}))
|
| 373 |
+
kv_residual: dict[str, bool] = dict(getattr(config, "leanllm_kv_residual_decoder", {}))
|
| 374 |
+
kv_fixed_alpha: dict[str, float | None] = dict(getattr(config, "leanllm_kv_fixed_alpha", {}))
|
| 375 |
+
kv_granularity: dict[str, str] = dict(getattr(config, "leanllm_kv_granularity", {}))
|
| 376 |
+
|
| 377 |
+
self._kv_granularity_map: dict[int, str] = {}
|
| 378 |
+
self._compressed_up_to: dict[int, int] = {}
|
| 379 |
+
|
| 380 |
+
for layer_idx in self._kv_layers:
|
| 381 |
+
key = str(layer_idx)
|
| 382 |
+
in_dim = int(kv_input_dims[key])
|
| 383 |
+
d_depth = int(kv_decoder_depth.get(key, 2))
|
| 384 |
+
d_hidden = int(kv_decoder_hidden.get(key, 128))
|
| 385 |
+
r_dec = bool(kv_residual.get(key, False))
|
| 386 |
+
f_alpha = kv_fixed_alpha.get(key, 1.0)
|
| 387 |
+
if f_alpha is not None:
|
| 388 |
+
f_alpha = float(f_alpha)
|
| 389 |
+
self._kv_granularity_map[layer_idx] = kv_granularity.get(key, "per_token")
|
| 390 |
+
self._compressed_up_to[layer_idx] = 0
|
| 391 |
+
|
| 392 |
+
v_dim = int(kv_value_dims[key])
|
| 393 |
+
v_mod = KVVectorCompressorModule(
|
| 394 |
+
input_dim=in_dim,
|
| 395 |
+
bottleneck_dim=v_dim,
|
| 396 |
+
decoder_depth=d_depth,
|
| 397 |
+
decoder_hidden_dim=d_hidden,
|
| 398 |
+
residual_decoder=r_dec,
|
| 399 |
+
fixed_alpha=f_alpha,
|
| 400 |
+
)
|
| 401 |
+
self.model.layers[layer_idx].self_attn.leanllm_v_compressor = v_mod
|
| 402 |
+
|
| 403 |
+
if not self._values_only and key in kv_key_dims:
|
| 404 |
+
k_dim = int(kv_key_dims[key])
|
| 405 |
+
k_mod = KVVectorCompressorModule(
|
| 406 |
+
input_dim=in_dim,
|
| 407 |
+
bottleneck_dim=k_dim,
|
| 408 |
+
decoder_depth=d_depth,
|
| 409 |
+
decoder_hidden_dim=d_hidden,
|
| 410 |
+
residual_decoder=r_dec,
|
| 411 |
+
fixed_alpha=f_alpha,
|
| 412 |
+
)
|
| 413 |
+
self.model.layers[layer_idx].self_attn.leanllm_k_compressor = k_mod
|
| 414 |
+
|
| 415 |
+
# --- Phase 2b: Adaptive KV-cache compression (v2) ---
|
| 416 |
+
self._adaptive_enabled: bool = bool(
|
| 417 |
+
getattr(config, "leanllm_adaptive_compression", False)
|
| 418 |
+
)
|
| 419 |
+
self._adaptive_layers: list[int] = [
|
| 420 |
+
int(x) for x in getattr(config, "leanllm_adaptive_kv_layers", [])
|
| 421 |
+
]
|
| 422 |
+
self._attn_state: AdaptiveCompressionState | None = None
|
| 423 |
+
self._adaptive_granularity_map: dict[int, str] = {}
|
| 424 |
+
self._adaptive_compressed_up_to: dict[int, int] = {}
|
| 425 |
+
|
| 426 |
+
# Eviction config
|
| 427 |
+
eviction_cfg = getattr(config, "leanllm_eviction", None) or {}
|
| 428 |
+
self._eviction_enabled: bool = bool(eviction_cfg.get("enabled", False))
|
| 429 |
+
self._eviction_threshold: float = float(eviction_cfg.get("threshold", 0.05))
|
| 430 |
+
self._eviction_sink_size: int = int(eviction_cfg.get("sink_size", 4))
|
| 431 |
+
self._eviction_recent_size: int = int(eviction_cfg.get("recent_size", 128))
|
| 432 |
+
self._eviction_interval: int = int(eviction_cfg.get("interval", 64))
|
| 433 |
+
self._generation_step: int = 0
|
| 434 |
+
|
| 435 |
+
adaptive_cfg = getattr(config, "leanllm_adaptive_kv_config", {}) or {}
|
| 436 |
+
for layer_idx in self._adaptive_layers:
|
| 437 |
+
key = str(layer_idx)
|
| 438 |
+
layer_cfg = adaptive_cfg.get(key, {})
|
| 439 |
+
in_dim = int(layer_cfg.get("input_dim", kv_input_dims.get(key, config.num_key_value_heads * config.head_dim)))
|
| 440 |
+
max_bn = int(layer_cfg.get("max_bottleneck_dim", 128))
|
| 441 |
+
min_bn = int(layer_cfg.get("min_bottleneck_dim", 16))
|
| 442 |
+
d_depth = int(layer_cfg.get("decoder_depth", 2))
|
| 443 |
+
d_hidden = int(layer_cfg.get("decoder_hidden_dim", 256))
|
| 444 |
+
f_alpha = layer_cfg.get("fixed_alpha", 1.0)
|
| 445 |
+
if f_alpha is not None:
|
| 446 |
+
f_alpha = float(f_alpha)
|
| 447 |
+
mask_temp = float(layer_cfg.get("mask_temperature", 5.0))
|
| 448 |
+
gran = str(layer_cfg.get("granularity", "per_token"))
|
| 449 |
+
|
| 450 |
+
self._adaptive_granularity_map[layer_idx] = gran
|
| 451 |
+
self._adaptive_compressed_up_to[layer_idx] = 0
|
| 452 |
+
|
| 453 |
+
v_mod = AdaptiveKVVectorCompressorModule(
|
| 454 |
+
input_dim=in_dim,
|
| 455 |
+
max_bottleneck_dim=max_bn,
|
| 456 |
+
min_bottleneck_dim=min_bn,
|
| 457 |
+
decoder_depth=d_depth,
|
| 458 |
+
decoder_hidden_dim=d_hidden,
|
| 459 |
+
fixed_alpha=f_alpha,
|
| 460 |
+
mask_temperature=mask_temp,
|
| 461 |
+
)
|
| 462 |
+
self.model.layers[layer_idx].self_attn.leanllm_adaptive_v_compressor = v_mod
|
| 463 |
+
|
| 464 |
+
if self._adaptive_enabled and self._adaptive_layers:
|
| 465 |
+
self._attn_state = AdaptiveCompressionState()
|
| 466 |
+
|
| 467 |
+
# ------------------------------------------------------------------
|
| 468 |
+
# Fixed-rate compression (v1)
|
| 469 |
+
# ------------------------------------------------------------------
|
| 470 |
+
|
| 471 |
+
def _compress_values(self, v: torch.Tensor, layer_idx: int, start: int) -> torch.Tensor:
|
| 472 |
+
if start >= v.shape[2]:
|
| 473 |
+
return v
|
| 474 |
+
v_new = v[:, :, start:, :]
|
| 475 |
+
gran = self._kv_granularity_map[layer_idx]
|
| 476 |
+
v_dtype = v.dtype
|
| 477 |
+
compressor = self.model.layers[layer_idx].self_attn.leanllm_v_compressor
|
| 478 |
+
comp_dtype = compressor.encoder.weight.dtype
|
| 479 |
+
v_vec = _kv_to_vec(v_new, gran).to(comp_dtype)
|
| 480 |
+
v_rec = compressor.decode(compressor.encode(v_vec), x_orig=None)
|
| 481 |
+
v_compressed = _vec_to_kv(v_rec, v_new).to(dtype=v_dtype)
|
| 482 |
+
if start == 0:
|
| 483 |
+
return v_compressed
|
| 484 |
+
return torch.cat([v[:, :, :start, :], v_compressed], dim=2)
|
| 485 |
+
|
| 486 |
+
def _compress_keys(self, k: torch.Tensor, layer_idx: int, start: int) -> torch.Tensor:
|
| 487 |
+
if start >= k.shape[2]:
|
| 488 |
+
return k
|
| 489 |
+
k_new = k[:, :, start:, :]
|
| 490 |
+
gran = self._kv_granularity_map[layer_idx]
|
| 491 |
+
k_dtype = k.dtype
|
| 492 |
+
k_comp = self.model.layers[layer_idx].self_attn.leanllm_k_compressor
|
| 493 |
+
comp_dtype = k_comp.encoder.weight.dtype
|
| 494 |
+
k_vec = _kv_to_vec(k_new, gran).to(comp_dtype)
|
| 495 |
+
k_rec = k_comp.decode(k_comp.encode(k_vec), x_orig=None)
|
| 496 |
+
k_compressed = _vec_to_kv(k_rec, k_new).to(dtype=k_dtype)
|
| 497 |
+
if start == 0:
|
| 498 |
+
return k_compressed
|
| 499 |
+
return torch.cat([k[:, :, :start, :], k_compressed], dim=2)
|
| 500 |
+
|
| 501 |
+
def _compress_past_fixed(self, past_key_values: Any) -> Any:
|
| 502 |
+
"""Apply fixed-rate v1 compression to KV cache."""
|
| 503 |
+
if past_key_values is None or not self._kv_layers:
|
| 504 |
+
return past_key_values
|
| 505 |
+
|
| 506 |
+
if isinstance(past_key_values, Cache):
|
| 507 |
+
for layer_idx in self._kv_layers:
|
| 508 |
+
if layer_idx in self._adaptive_layers:
|
| 509 |
+
continue # handled by adaptive path
|
| 510 |
+
layer_cache = past_key_values.layers[layer_idx]
|
| 511 |
+
v = layer_cache.values
|
| 512 |
+
total_tokens = v.shape[2]
|
| 513 |
+
start = self._compressed_up_to[layer_idx]
|
| 514 |
+
|
| 515 |
+
layer_cache.values = self._compress_values(v, layer_idx, start)
|
| 516 |
+
|
| 517 |
+
if not self._values_only and hasattr(
|
| 518 |
+
self.model.layers[layer_idx].self_attn, "leanllm_k_compressor"
|
| 519 |
+
):
|
| 520 |
+
layer_cache.keys = self._compress_keys(
|
| 521 |
+
layer_cache.keys, layer_idx, start
|
| 522 |
+
)
|
| 523 |
+
self._compressed_up_to[layer_idx] = total_tokens
|
| 524 |
+
return past_key_values
|
| 525 |
+
|
| 526 |
+
if isinstance(past_key_values, tuple):
|
| 527 |
+
past_list = list(past_key_values)
|
| 528 |
+
for layer_idx in self._kv_layers:
|
| 529 |
+
if layer_idx in self._adaptive_layers:
|
| 530 |
+
continue
|
| 531 |
+
k, v = past_list[layer_idx]
|
| 532 |
+
total_tokens = v.shape[2]
|
| 533 |
+
start = self._compressed_up_to[layer_idx]
|
| 534 |
+
|
| 535 |
+
v_new = self._compress_values(v, layer_idx, start)
|
| 536 |
+
if not self._values_only and hasattr(
|
| 537 |
+
self.model.layers[layer_idx].self_attn, "leanllm_k_compressor"
|
| 538 |
+
):
|
| 539 |
+
k_new = self._compress_keys(k, layer_idx, start)
|
| 540 |
+
else:
|
| 541 |
+
k_new = k
|
| 542 |
+
past_list[layer_idx] = (k_new, v_new)
|
| 543 |
+
self._compressed_up_to[layer_idx] = total_tokens
|
| 544 |
+
return tuple(past_list)
|
| 545 |
+
|
| 546 |
+
return past_key_values
|
| 547 |
+
|
| 548 |
+
# ------------------------------------------------------------------
|
| 549 |
+
# Adaptive compression (v2)
|
| 550 |
+
# ------------------------------------------------------------------
|
| 551 |
+
|
| 552 |
+
def _compress_values_adaptive(
|
| 553 |
+
self,
|
| 554 |
+
v: torch.Tensor,
|
| 555 |
+
layer_idx: int,
|
| 556 |
+
importance: torch.Tensor | None,
|
| 557 |
+
start: int,
|
| 558 |
+
) -> torch.Tensor:
|
| 559 |
+
"""Compress values with importance-based soft masking."""
|
| 560 |
+
if start >= v.shape[2]:
|
| 561 |
+
return v
|
| 562 |
+
v_new = v[:, :, start:, :]
|
| 563 |
+
gran = self._adaptive_granularity_map[layer_idx]
|
| 564 |
+
v_dtype = v.dtype
|
| 565 |
+
compressor = self.model.layers[layer_idx].self_attn.leanllm_adaptive_v_compressor
|
| 566 |
+
comp_dtype = compressor.encoder.weight.dtype
|
| 567 |
+
v_vec = _kv_to_vec(v_new, gran).to(comp_dtype)
|
| 568 |
+
|
| 569 |
+
# Build per-vector importance from per-token importance
|
| 570 |
+
imp = None
|
| 571 |
+
if importance is not None:
|
| 572 |
+
b, h, t_new, d = v_new.shape
|
| 573 |
+
# importance is [B, T_total] — slice to match new tokens
|
| 574 |
+
imp_slice = importance[:, start:] # [B, t_new]
|
| 575 |
+
if gran == "per_head":
|
| 576 |
+
imp = imp_slice.unsqueeze(2).expand(b, t_new, h).reshape(-1)
|
| 577 |
+
else:
|
| 578 |
+
imp = imp_slice.reshape(-1)
|
| 579 |
+
imp = imp.to(comp_dtype)
|
| 580 |
+
|
| 581 |
+
z = compressor.encode(v_vec, importance=imp)
|
| 582 |
+
v_rec = compressor.decode(z, x_orig=None)
|
| 583 |
+
v_compressed = _vec_to_kv(v_rec, v_new).to(dtype=v_dtype)
|
| 584 |
+
if start == 0:
|
| 585 |
+
return v_compressed
|
| 586 |
+
return torch.cat([v[:, :, :start, :], v_compressed], dim=2)
|
| 587 |
+
|
| 588 |
+
def _get_token_importance(self, layer_idx: int) -> torch.Tensor | None:
|
| 589 |
+
"""Get importance scores for all tokens at a layer.
|
| 590 |
+
|
| 591 |
+
For the newest token (which has zero cumulative attention), uses
|
| 592 |
+
the median importance of existing tokens as a proxy.
|
| 593 |
+
"""
|
| 594 |
+
if self._attn_state is None:
|
| 595 |
+
return None
|
| 596 |
+
if layer_idx not in self._attn_state.cumulative_attention:
|
| 597 |
+
return None
|
| 598 |
+
|
| 599 |
+
imp_all = self._attn_state.get_importance(layer_idx) # [B, T]
|
| 600 |
+
if imp_all.shape[-1] <= 1:
|
| 601 |
+
return torch.full_like(imp_all, 0.5)
|
| 602 |
+
|
| 603 |
+
# The last token always has importance=0 (it's new, no future queries yet).
|
| 604 |
+
# Replace it with the median of existing tokens as a proxy.
|
| 605 |
+
imp_existing = imp_all[:, :-1]
|
| 606 |
+
median_imp = imp_existing.median(dim=-1, keepdim=True).values
|
| 607 |
+
imp_fixed = torch.cat([imp_existing, median_imp], dim=-1)
|
| 608 |
+
return imp_fixed
|
| 609 |
+
|
| 610 |
+
def _compress_past_adaptive(
|
| 611 |
+
self,
|
| 612 |
+
past_key_values: Any,
|
| 613 |
+
attentions: tuple | None,
|
| 614 |
+
) -> Any:
|
| 615 |
+
"""Apply adaptive importance-based compression to KV cache."""
|
| 616 |
+
if past_key_values is None or not self._adaptive_layers:
|
| 617 |
+
return past_key_values
|
| 618 |
+
if self._attn_state is None:
|
| 619 |
+
return past_key_values
|
| 620 |
+
|
| 621 |
+
# Update cumulative attention state from this forward pass
|
| 622 |
+
if attentions is not None:
|
| 623 |
+
for layer_idx in self._adaptive_layers:
|
| 624 |
+
if layer_idx < len(attentions) and attentions[layer_idx] is not None:
|
| 625 |
+
self._attn_state.update(layer_idx, attentions[layer_idx])
|
| 626 |
+
|
| 627 |
+
if isinstance(past_key_values, Cache):
|
| 628 |
+
for layer_idx in self._adaptive_layers:
|
| 629 |
+
layer_cache = past_key_values.layers[layer_idx]
|
| 630 |
+
v = layer_cache.values
|
| 631 |
+
total_tokens = v.shape[2]
|
| 632 |
+
start = self._adaptive_compressed_up_to[layer_idx]
|
| 633 |
+
|
| 634 |
+
importance = self._get_token_importance(layer_idx)
|
| 635 |
+
layer_cache.values = self._compress_values_adaptive(
|
| 636 |
+
v, layer_idx, importance, start
|
| 637 |
+
)
|
| 638 |
+
self._adaptive_compressed_up_to[layer_idx] = total_tokens
|
| 639 |
+
|
| 640 |
+
# Token eviction
|
| 641 |
+
if self._eviction_enabled and self._generation_step > 0:
|
| 642 |
+
if self._generation_step % self._eviction_interval == 0:
|
| 643 |
+
past_key_values = self._evict_tokens(past_key_values)
|
| 644 |
+
|
| 645 |
+
return past_key_values
|
| 646 |
+
|
| 647 |
+
if isinstance(past_key_values, tuple):
|
| 648 |
+
past_list = list(past_key_values)
|
| 649 |
+
for layer_idx in self._adaptive_layers:
|
| 650 |
+
k, v = past_list[layer_idx]
|
| 651 |
+
total_tokens = v.shape[2]
|
| 652 |
+
start = self._adaptive_compressed_up_to[layer_idx]
|
| 653 |
+
|
| 654 |
+
importance = self._get_token_importance(layer_idx)
|
| 655 |
+
v_new = self._compress_values_adaptive(
|
| 656 |
+
v, layer_idx, importance, start
|
| 657 |
+
)
|
| 658 |
+
past_list[layer_idx] = (k, v_new)
|
| 659 |
+
self._adaptive_compressed_up_to[layer_idx] = total_tokens
|
| 660 |
+
return tuple(past_list)
|
| 661 |
+
|
| 662 |
+
return past_key_values
|
| 663 |
+
|
| 664 |
+
# ------------------------------------------------------------------
|
| 665 |
+
# Token eviction
|
| 666 |
+
# ------------------------------------------------------------------
|
| 667 |
+
|
| 668 |
+
def _evict_tokens(self, past_key_values: Any) -> Any:
|
| 669 |
+
"""Remove low-importance tokens from the KV cache."""
|
| 670 |
+
if not isinstance(past_key_values, Cache):
|
| 671 |
+
return past_key_values
|
| 672 |
+
if self._attn_state is None:
|
| 673 |
+
return past_key_values
|
| 674 |
+
|
| 675 |
+
# Find tokens to keep across all adaptive layers (intersection)
|
| 676 |
+
keep_mask = None
|
| 677 |
+
for layer_idx in self._adaptive_layers:
|
| 678 |
+
if layer_idx not in self._attn_state.cumulative_attention:
|
| 679 |
+
continue
|
| 680 |
+
layer_mask = self._attn_state.get_eviction_mask(
|
| 681 |
+
layer_idx,
|
| 682 |
+
threshold=self._eviction_threshold,
|
| 683 |
+
sink_size=self._eviction_sink_size,
|
| 684 |
+
recent_size=self._eviction_recent_size,
|
| 685 |
+
)
|
| 686 |
+
if keep_mask is None:
|
| 687 |
+
keep_mask = layer_mask
|
| 688 |
+
else:
|
| 689 |
+
keep_mask = keep_mask & layer_mask # conservative: keep if ANY layer needs it
|
| 690 |
+
|
| 691 |
+
if keep_mask is None:
|
| 692 |
+
return past_key_values
|
| 693 |
+
|
| 694 |
+
# Only evict if we'd actually remove tokens
|
| 695 |
+
n_keep = int(keep_mask[0].sum().item())
|
| 696 |
+
n_total = keep_mask.shape[-1]
|
| 697 |
+
if n_keep >= n_total:
|
| 698 |
+
return past_key_values
|
| 699 |
+
|
| 700 |
+
# Apply eviction to ALL layers (both fixed and adaptive)
|
| 701 |
+
keep_indices = keep_mask[0].nonzero(as_tuple=True)[0] # [n_keep]
|
| 702 |
+
n_layers = len(past_key_values.layers)
|
| 703 |
+
for layer_idx in range(n_layers):
|
| 704 |
+
layer_cache = past_key_values.layers[layer_idx]
|
| 705 |
+
k = layer_cache.keys # [B, H, T, D]
|
| 706 |
+
v = layer_cache.values
|
| 707 |
+
if k.shape[2] != n_total:
|
| 708 |
+
continue # skip if size mismatch (shouldn't happen)
|
| 709 |
+
|
| 710 |
+
layer_cache.keys = k[:, :, keep_indices, :]
|
| 711 |
+
layer_cache.values = v[:, :, keep_indices, :]
|
| 712 |
+
|
| 713 |
+
# Reset compression tracking
|
| 714 |
+
for layer_idx in self._kv_layers:
|
| 715 |
+
self._compressed_up_to[layer_idx] = n_keep
|
| 716 |
+
for layer_idx in self._adaptive_layers:
|
| 717 |
+
self._adaptive_compressed_up_to[layer_idx] = n_keep
|
| 718 |
+
|
| 719 |
+
# Reset attention state (scores are invalidated by eviction)
|
| 720 |
+
self._attn_state.reset()
|
| 721 |
+
|
| 722 |
+
return past_key_values
|
| 723 |
+
|
| 724 |
+
# ------------------------------------------------------------------
|
| 725 |
+
# Forward
|
| 726 |
+
# ------------------------------------------------------------------
|
| 727 |
+
|
| 728 |
+
def forward(self, *args: Any, **kwargs: Any) -> CausalLMOutputWithPast:
|
| 729 |
+
# Reset compression tracking when starting a new sequence
|
| 730 |
+
past = kwargs.get("past_key_values", None)
|
| 731 |
+
if past is None and len(args) < 5:
|
| 732 |
+
for layer_idx in self._kv_layers:
|
| 733 |
+
self._compressed_up_to[layer_idx] = 0
|
| 734 |
+
for layer_idx in self._adaptive_layers:
|
| 735 |
+
self._adaptive_compressed_up_to[layer_idx] = 0
|
| 736 |
+
if self._attn_state is not None:
|
| 737 |
+
self._attn_state.reset()
|
| 738 |
+
self._generation_step = 0
|
| 739 |
+
|
| 740 |
+
# For adaptive compression, request attention weights during generation
|
| 741 |
+
# (single-token steps only — prefill is too expensive with eager attention)
|
| 742 |
+
need_attentions = (
|
| 743 |
+
self._adaptive_enabled
|
| 744 |
+
and self._adaptive_layers
|
| 745 |
+
and past is not None # generation step, not prefill
|
| 746 |
+
)
|
| 747 |
+
if need_attentions:
|
| 748 |
+
kwargs.setdefault("output_attentions", True)
|
| 749 |
+
|
| 750 |
+
outputs = super().forward(*args, **kwargs)
|
| 751 |
+
|
| 752 |
+
if hasattr(outputs, "past_key_values"):
|
| 753 |
+
# Apply fixed-rate compression (v1 layers)
|
| 754 |
+
outputs.past_key_values = self._compress_past_fixed(
|
| 755 |
+
outputs.past_key_values
|
| 756 |
+
)
|
| 757 |
+
|
| 758 |
+
# Apply adaptive compression (v2 layers)
|
| 759 |
+
if self._adaptive_enabled:
|
| 760 |
+
attentions = getattr(outputs, "attentions", None)
|
| 761 |
+
outputs.past_key_values = self._compress_past_adaptive(
|
| 762 |
+
outputs.past_key_values, attentions
|
| 763 |
+
)
|
| 764 |
+
|
| 765 |
+
self._generation_step += 1
|
| 766 |
+
return outputs
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|begin_of_text|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|eot_id|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "<|finetune_right_pad_id|>",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
}
|
| 23 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
|
| 3 |
+
size 17209920
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2066 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"128000": {
|
| 5 |
+
"content": "<|begin_of_text|>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"128001": {
|
| 13 |
+
"content": "<|end_of_text|>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"128002": {
|
| 21 |
+
"content": "<|reserved_special_token_0|>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"128003": {
|
| 29 |
+
"content": "<|reserved_special_token_1|>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
},
|
| 36 |
+
"128004": {
|
| 37 |
+
"content": "<|finetune_right_pad_id|>",
|
| 38 |
+
"lstrip": false,
|
| 39 |
+
"normalized": false,
|
| 40 |
+
"rstrip": false,
|
| 41 |
+
"single_word": false,
|
| 42 |
+
"special": true
|
| 43 |
+
},
|
| 44 |
+
"128005": {
|
| 45 |
+
"content": "<|reserved_special_token_2|>",
|
| 46 |
+
"lstrip": false,
|
| 47 |
+
"normalized": false,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false,
|
| 50 |
+
"special": true
|
| 51 |
+
},
|
| 52 |
+
"128006": {
|
| 53 |
+
"content": "<|start_header_id|>",
|
| 54 |
+
"lstrip": false,
|
| 55 |
+
"normalized": false,
|
| 56 |
+
"rstrip": false,
|
| 57 |
+
"single_word": false,
|
| 58 |
+
"special": true
|
| 59 |
+
},
|
| 60 |
+
"128007": {
|
| 61 |
+
"content": "<|end_header_id|>",
|
| 62 |
+
"lstrip": false,
|
| 63 |
+
"normalized": false,
|
| 64 |
+
"rstrip": false,
|
| 65 |
+
"single_word": false,
|
| 66 |
+
"special": true
|
| 67 |
+
},
|
| 68 |
+
"128008": {
|
| 69 |
+
"content": "<|eom_id|>",
|
| 70 |
+
"lstrip": false,
|
| 71 |
+
"normalized": false,
|
| 72 |
+
"rstrip": false,
|
| 73 |
+
"single_word": false,
|
| 74 |
+
"special": true
|
| 75 |
+
},
|
| 76 |
+
"128009": {
|
| 77 |
+
"content": "<|eot_id|>",
|
| 78 |
+
"lstrip": false,
|
| 79 |
+
"normalized": false,
|
| 80 |
+
"rstrip": false,
|
| 81 |
+
"single_word": false,
|
| 82 |
+
"special": true
|
| 83 |
+
},
|
| 84 |
+
"128010": {
|
| 85 |
+
"content": "<|python_tag|>",
|
| 86 |
+
"lstrip": false,
|
| 87 |
+
"normalized": false,
|
| 88 |
+
"rstrip": false,
|
| 89 |
+
"single_word": false,
|
| 90 |
+
"special": true
|
| 91 |
+
},
|
| 92 |
+
"128011": {
|
| 93 |
+
"content": "<|reserved_special_token_3|>",
|
| 94 |
+
"lstrip": false,
|
| 95 |
+
"normalized": false,
|
| 96 |
+
"rstrip": false,
|
| 97 |
+
"single_word": false,
|
| 98 |
+
"special": true
|
| 99 |
+
},
|
| 100 |
+
"128012": {
|
| 101 |
+
"content": "<|reserved_special_token_4|>",
|
| 102 |
+
"lstrip": false,
|
| 103 |
+
"normalized": false,
|
| 104 |
+
"rstrip": false,
|
| 105 |
+
"single_word": false,
|
| 106 |
+
"special": true
|
| 107 |
+
},
|
| 108 |
+
"128013": {
|
| 109 |
+
"content": "<|reserved_special_token_5|>",
|
| 110 |
+
"lstrip": false,
|
| 111 |
+
"normalized": false,
|
| 112 |
+
"rstrip": false,
|
| 113 |
+
"single_word": false,
|
| 114 |
+
"special": true
|
| 115 |
+
},
|
| 116 |
+
"128014": {
|
| 117 |
+
"content": "<|reserved_special_token_6|>",
|
| 118 |
+
"lstrip": false,
|
| 119 |
+
"normalized": false,
|
| 120 |
+
"rstrip": false,
|
| 121 |
+
"single_word": false,
|
| 122 |
+
"special": true
|
| 123 |
+
},
|
| 124 |
+
"128015": {
|
| 125 |
+
"content": "<|reserved_special_token_7|>",
|
| 126 |
+
"lstrip": false,
|
| 127 |
+
"normalized": false,
|
| 128 |
+
"rstrip": false,
|
| 129 |
+
"single_word": false,
|
| 130 |
+
"special": true
|
| 131 |
+
},
|
| 132 |
+
"128016": {
|
| 133 |
+
"content": "<|reserved_special_token_8|>",
|
| 134 |
+
"lstrip": false,
|
| 135 |
+
"normalized": false,
|
| 136 |
+
"rstrip": false,
|
| 137 |
+
"single_word": false,
|
| 138 |
+
"special": true
|
| 139 |
+
},
|
| 140 |
+
"128017": {
|
| 141 |
+
"content": "<|reserved_special_token_9|>",
|
| 142 |
+
"lstrip": false,
|
| 143 |
+
"normalized": false,
|
| 144 |
+
"rstrip": false,
|
| 145 |
+
"single_word": false,
|
| 146 |
+
"special": true
|
| 147 |
+
},
|
| 148 |
+
"128018": {
|
| 149 |
+
"content": "<|reserved_special_token_10|>",
|
| 150 |
+
"lstrip": false,
|
| 151 |
+
"normalized": false,
|
| 152 |
+
"rstrip": false,
|
| 153 |
+
"single_word": false,
|
| 154 |
+
"special": true
|
| 155 |
+
},
|
| 156 |
+
"128019": {
|
| 157 |
+
"content": "<|reserved_special_token_11|>",
|
| 158 |
+
"lstrip": false,
|
| 159 |
+
"normalized": false,
|
| 160 |
+
"rstrip": false,
|
| 161 |
+
"single_word": false,
|
| 162 |
+
"special": true
|
| 163 |
+
},
|
| 164 |
+
"128020": {
|
| 165 |
+
"content": "<|reserved_special_token_12|>",
|
| 166 |
+
"lstrip": false,
|
| 167 |
+
"normalized": false,
|
| 168 |
+
"rstrip": false,
|
| 169 |
+
"single_word": false,
|
| 170 |
+
"special": true
|
| 171 |
+
},
|
| 172 |
+
"128021": {
|
| 173 |
+
"content": "<|reserved_special_token_13|>",
|
| 174 |
+
"lstrip": false,
|
| 175 |
+
"normalized": false,
|
| 176 |
+
"rstrip": false,
|
| 177 |
+
"single_word": false,
|
| 178 |
+
"special": true
|
| 179 |
+
},
|
| 180 |
+
"128022": {
|
| 181 |
+
"content": "<|reserved_special_token_14|>",
|
| 182 |
+
"lstrip": false,
|
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| 1827 |
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|
| 1828 |
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|
| 1829 |
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|
| 1830 |
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|
| 1831 |
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| 1832 |
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| 1833 |
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|
| 1834 |
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|
| 1835 |
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|
| 1836 |
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|
| 1837 |
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|
| 1838 |
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| 1839 |
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|
| 1840 |
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| 1841 |
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|
| 1842 |
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|
| 1843 |
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|
| 1844 |
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|
| 1845 |
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|
| 1846 |
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| 1847 |
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|
| 1848 |
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| 1849 |
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|
| 1850 |
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|
| 1851 |
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|
| 1852 |
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|
| 1853 |
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|
| 1854 |
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| 1855 |
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| 1856 |
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|
| 1857 |
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|
| 1858 |
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|
| 1859 |
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|
| 1860 |
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|
| 1861 |
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|
| 1862 |
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|
| 1863 |
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|
| 1864 |
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| 1865 |
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|
| 1866 |
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|
| 1867 |
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|
| 1868 |
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|
| 1869 |
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|
| 1870 |
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|
| 1871 |
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|
| 1872 |
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|
| 1873 |
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|
| 1874 |
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|
| 1875 |
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|
| 1876 |
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|
| 1877 |
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|
| 1878 |
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|
| 1879 |
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|
| 1880 |
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|
| 1881 |
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|
| 1882 |
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|
| 1883 |
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|
| 1884 |
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|
| 1885 |
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"content": "<|reserved_special_token_227|>",
|
| 1886 |
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|
| 1887 |
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|
| 1888 |
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|
| 1889 |
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|
| 1890 |
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|
| 1891 |
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|
| 1892 |
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|
| 1893 |
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|
| 1894 |
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| 1895 |
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|
| 1896 |
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|
| 1897 |
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|
| 1898 |
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|
| 1899 |
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|
| 1900 |
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|
| 1901 |
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|
| 1902 |
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|
| 1903 |
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|
| 1904 |
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|
| 1905 |
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|
| 1906 |
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|
| 1907 |
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|
| 1908 |
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|
| 1909 |
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"content": "<|reserved_special_token_230|>",
|
| 1910 |
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|
| 1911 |
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|
| 1912 |
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|
| 1913 |
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|
| 1914 |
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|
| 1915 |
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|
| 1916 |
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|
| 1917 |
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"content": "<|reserved_special_token_231|>",
|
| 1918 |
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|
| 1919 |
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|
| 1920 |
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|
| 1921 |
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|
| 1922 |
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|
| 1923 |
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|
| 1924 |
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|
| 1925 |
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"content": "<|reserved_special_token_232|>",
|
| 1926 |
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|
| 1927 |
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|
| 1928 |
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|
| 1929 |
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|
| 1930 |
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|
| 1931 |
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|
| 1932 |
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|
| 1933 |
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"content": "<|reserved_special_token_233|>",
|
| 1934 |
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|
| 1935 |
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|
| 1936 |
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|
| 1937 |
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|
| 1938 |
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|
| 1939 |
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|
| 1940 |
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|
| 1941 |
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"content": "<|reserved_special_token_234|>",
|
| 1942 |
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|
| 1943 |
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|
| 1944 |
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|
| 1945 |
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|
| 1946 |
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|
| 1947 |
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},
|
| 1948 |
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|
| 1949 |
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"content": "<|reserved_special_token_235|>",
|
| 1950 |
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|
| 1951 |
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|
| 1952 |
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|
| 1953 |
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|
| 1954 |
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|
| 1955 |
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},
|
| 1956 |
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|
| 1957 |
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"content": "<|reserved_special_token_236|>",
|
| 1958 |
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|
| 1959 |
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|
| 1960 |
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|
| 1961 |
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|
| 1962 |
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|
| 1963 |
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},
|
| 1964 |
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"128245": {
|
| 1965 |
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"content": "<|reserved_special_token_237|>",
|
| 1966 |
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|
| 1967 |
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|
| 1968 |
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|
| 1969 |
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|
| 1970 |
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"special": true
|
| 1971 |
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},
|
| 1972 |
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|
| 1973 |
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"content": "<|reserved_special_token_238|>",
|
| 1974 |
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|
| 1975 |
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|
| 1976 |
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|
| 1977 |
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|
| 1978 |
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|
| 1979 |
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},
|
| 1980 |
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|
| 1981 |
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"content": "<|reserved_special_token_239|>",
|
| 1982 |
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|
| 1983 |
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|
| 1984 |
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|
| 1985 |
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|
| 1986 |
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"special": true
|
| 1987 |
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},
|
| 1988 |
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|
| 1989 |
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"content": "<|reserved_special_token_240|>",
|
| 1990 |
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|
| 1991 |
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|
| 1992 |
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|
| 1993 |
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|
| 1994 |
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|
| 1995 |
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},
|
| 1996 |
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|
| 1997 |
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"content": "<|reserved_special_token_241|>",
|
| 1998 |
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|
| 1999 |
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|
| 2000 |
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|
| 2001 |
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|
| 2002 |
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"special": true
|
| 2003 |
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},
|
| 2004 |
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|
| 2005 |
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"content": "<|reserved_special_token_242|>",
|
| 2006 |
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|
| 2007 |
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|
| 2008 |
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|
| 2009 |
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|
| 2010 |
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"special": true
|
| 2011 |
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},
|
| 2012 |
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"128251": {
|
| 2013 |
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"content": "<|reserved_special_token_243|>",
|
| 2014 |
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|
| 2015 |
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|
| 2016 |
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|
| 2017 |
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|
| 2018 |
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"special": true
|
| 2019 |
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},
|
| 2020 |
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|
| 2021 |
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"content": "<|reserved_special_token_244|>",
|
| 2022 |
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|
| 2023 |
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|
| 2024 |
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|
| 2025 |
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|
| 2026 |
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|
| 2027 |
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},
|
| 2028 |
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|
| 2029 |
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"content": "<|reserved_special_token_245|>",
|
| 2030 |
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|
| 2031 |
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|
| 2032 |
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|
| 2033 |
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|
| 2034 |
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|
| 2035 |
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},
|
| 2036 |
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|
| 2037 |
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"content": "<|reserved_special_token_246|>",
|
| 2038 |
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|
| 2039 |
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|
| 2040 |
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|
| 2041 |
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|
| 2042 |
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|
| 2043 |
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},
|
| 2044 |
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|
| 2045 |
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"content": "<|reserved_special_token_247|>",
|
| 2046 |
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|
| 2047 |
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|
| 2048 |
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|
| 2049 |
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|
| 2050 |
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|
| 2051 |
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}
|
| 2052 |
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},
|
| 2053 |
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"bos_token": "<|begin_of_text|>",
|
| 2054 |
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"clean_up_tokenization_spaces": true,
|
| 2055 |
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"eos_token": "<|eot_id|>",
|
| 2056 |
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"extra_special_tokens": {},
|
| 2057 |
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"model_input_names": [
|
| 2058 |
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"input_ids",
|
| 2059 |
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"attention_mask"
|
| 2060 |
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],
|
| 2061 |
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|
| 2062 |
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"pad_token": "<|finetune_right_pad_id|>",
|
| 2063 |
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"padding_side": "left",
|
| 2064 |
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"tokenizer_class": "PreTrainedTokenizerFast",
|
| 2065 |
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|
| 2066 |
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}
|