Upload Trinity-Nano-Preview-oQ4
Browse files- .gitattributes +1 -0
- README.md +40 -0
- chat_template.jinja +65 -0
- config.json +3894 -0
- configuration_afmoe.py +133 -0
- generation_config.json +4 -0
- model.safetensors +3 -0
- modeling_afmoe.py +680 -0
- special_tokens_map.json +23 -0
- tokenizer.json +3 -0
- tokenizer_config.json +271 -0
.gitattributes
CHANGED
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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
ADDED
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@@ -0,0 +1,40 @@
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---
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base_model: arcee-ai/Trinity-Nano-Preview
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library_name: mlx
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pipeline_tag: text-generation
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license: apache-2.0
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tags:
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- mlx
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- omlx
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- oq
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- oq4
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- quantized
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---
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# Trinity-Nano-Preview-oQ4
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oQ4 mixed-precision MLX quantization produced via [oMLX](https://github.com/jundot/omlx).
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- **Quantization:** oQ4 (sensitivity-driven mixed precision, group_size=64)
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- **Format:** MLX safetensors
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- **Compatible with:** mlx-lm, mlx-vlm, oMLX on Apple Silicon
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## Usage
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("bearzi/Trinity-Nano-Preview-oQ4")
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prompt = tokenizer.apply_chat_template(
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[{"role": "user", "content": "Hello"}],
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add_generation_prompt=True,
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)
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print(generate(model, tokenizer, prompt=prompt, max_tokens=512, verbose=True))
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```
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## About oQ
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oQ measures per-layer quantization sensitivity through calibration and allocates bits where they matter most — critical layers stay at higher precision, tolerant layers compress aggressively. Target averages of 2/3/4/6/8 bits are provided; actual per-layer bits vary by measured sensitivity.
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See [oQ documentation](https://github.com/jundot/omlx/blob/main/docs/oQ_Quantization.md).
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Comparative benchmarks and feedback welcome — please open a discussion.
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' }}
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{% generation %}
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{{- content}}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>' }}
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{% endgeneration%}
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{{- '\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"AfmoeForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_dropout": 0.0,
|
| 6 |
+
"auto_map": {
|
| 7 |
+
"AutoConfig": "configuration_afmoe.AfmoeConfig",
|
| 8 |
+
"AutoModel": "modeling_afmoe.AfmoeModel",
|
| 9 |
+
"AutoModelForCausalLM": "modeling_afmoe.AfmoeForCausalLM"
|
| 10 |
+
},
|
| 11 |
+
"dtype": "bfloat16",
|
| 12 |
+
"global_attn_every_n_layers": 4,
|
| 13 |
+
"head_dim": 128,
|
| 14 |
+
"hidden_act": "silu",
|
| 15 |
+
"hidden_size": 1024,
|
| 16 |
+
"initializer_range": 0.02,
|
| 17 |
+
"intermediate_size": 3072,
|
| 18 |
+
"layer_types": [
|
| 19 |
+
"sliding_attention",
|
| 20 |
+
"sliding_attention",
|
| 21 |
+
"sliding_attention",
|
| 22 |
+
"full_attention",
|
| 23 |
+
"sliding_attention",
|
| 24 |
+
"sliding_attention",
|
| 25 |
+
"sliding_attention",
|
| 26 |
+
"full_attention",
|
| 27 |
+
"sliding_attention",
|
| 28 |
+
"sliding_attention",
|
| 29 |
+
"sliding_attention",
|
| 30 |
+
"full_attention",
|
| 31 |
+
"sliding_attention",
|
| 32 |
+
"sliding_attention",
|
| 33 |
+
"sliding_attention",
|
| 34 |
+
"full_attention",
|
| 35 |
+
"sliding_attention",
|
| 36 |
+
"sliding_attention",
|
| 37 |
+
"sliding_attention",
|
| 38 |
+
"full_attention",
|
| 39 |
+
"sliding_attention",
|
| 40 |
+
"sliding_attention",
|
| 41 |
+
"sliding_attention",
|
| 42 |
+
"full_attention",
|
| 43 |
+
"sliding_attention",
|
| 44 |
+
"sliding_attention",
|
| 45 |
+
"sliding_attention",
|
| 46 |
+
"full_attention",
|
| 47 |
+
"sliding_attention",
|
| 48 |
+
"sliding_attention",
|
| 49 |
+
"sliding_attention",
|
| 50 |
+
"full_attention",
|
| 51 |
+
"sliding_attention",
|
| 52 |
+
"sliding_attention",
|
| 53 |
+
"sliding_attention",
|
| 54 |
+
"full_attention",
|
| 55 |
+
"sliding_attention",
|
| 56 |
+
"sliding_attention",
|
| 57 |
+
"sliding_attention",
|
| 58 |
+
"full_attention",
|
| 59 |
+
"sliding_attention",
|
| 60 |
+
"sliding_attention",
|
| 61 |
+
"sliding_attention",
|
| 62 |
+
"full_attention",
|
| 63 |
+
"sliding_attention",
|
| 64 |
+
"sliding_attention",
|
| 65 |
+
"sliding_attention",
|
| 66 |
+
"full_attention",
|
| 67 |
+
"sliding_attention",
|
| 68 |
+
"sliding_attention",
|
| 69 |
+
"sliding_attention",
|
| 70 |
+
"full_attention",
|
| 71 |
+
"sliding_attention",
|
| 72 |
+
"sliding_attention",
|
| 73 |
+
"sliding_attention",
|
| 74 |
+
"full_attention"
|
| 75 |
+
],
|
| 76 |
+
"load_balance_coeff": 0.001,
|
| 77 |
+
"max_position_embeddings": 131072,
|
| 78 |
+
"model_type": "afmoe",
|
| 79 |
+
"moe_intermediate_size": 256,
|
| 80 |
+
"mup_enabled": true,
|
| 81 |
+
"n_group": 1,
|
| 82 |
+
"num_attention_heads": 8,
|
| 83 |
+
"num_dense_layers": 2,
|
| 84 |
+
"num_expert_groups": 1,
|
| 85 |
+
"num_experts": 128,
|
| 86 |
+
"num_experts_per_tok": 8,
|
| 87 |
+
"num_hidden_layers": 56,
|
| 88 |
+
"num_key_value_heads": 2,
|
| 89 |
+
"num_limited_groups": 1,
|
| 90 |
+
"num_shared_experts": 1,
|
| 91 |
+
"rms_norm_eps": 1e-05,
|
| 92 |
+
"rope_scaling": null,
|
| 93 |
+
"rope_theta": 10000,
|
| 94 |
+
"route_norm": true,
|
| 95 |
+
"route_scale": 2.826,
|
| 96 |
+
"score_func": "sigmoid",
|
| 97 |
+
"sliding_window": 2048,
|
| 98 |
+
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|
| 3531 |
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|
| 3532 |
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| 3533 |
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|
| 3534 |
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|
| 3535 |
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|
| 3536 |
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|
| 3537 |
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| 3538 |
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|
| 3539 |
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|
| 3540 |
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| 3541 |
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|
| 3542 |
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| 3543 |
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|
| 3544 |
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|
| 3545 |
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| 3546 |
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|
| 3547 |
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|
| 3548 |
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|
| 3549 |
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|
| 3550 |
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| 3551 |
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|
| 3552 |
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| 3553 |
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| 3554 |
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|
| 3555 |
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| 3557 |
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| 3558 |
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|
| 3559 |
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|
| 3560 |
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|
| 3561 |
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|
| 3562 |
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|
| 3563 |
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|
| 3564 |
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|
| 3565 |
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|
| 3566 |
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|
| 3567 |
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|
| 3568 |
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|
| 3569 |
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|
| 3570 |
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|
| 3571 |
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|
| 3572 |
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|
| 3573 |
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|
| 3574 |
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|
| 3575 |
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|
| 3576 |
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|
| 3577 |
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|
| 3578 |
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|
| 3579 |
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|
| 3580 |
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|
| 3581 |
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|
| 3582 |
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|
| 3583 |
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|
| 3584 |
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|
| 3585 |
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|
| 3586 |
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|
| 3587 |
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| 3588 |
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|
| 3589 |
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| 3590 |
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| 3591 |
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| 3592 |
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| 3593 |
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| 3594 |
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| 3595 |
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|
| 3596 |
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|
| 3597 |
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| 3598 |
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| 3599 |
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| 3600 |
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| 3601 |
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|
| 3602 |
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| 3603 |
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|
| 3604 |
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|
| 3605 |
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| 3606 |
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|
| 3607 |
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| 3608 |
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|
| 3609 |
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| 3610 |
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|
| 3611 |
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|
| 3612 |
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| 3613 |
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|
| 3614 |
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|
| 3615 |
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|
| 3616 |
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|
| 3617 |
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|
| 3618 |
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|
| 3619 |
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|
| 3620 |
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|
| 3621 |
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|
| 3622 |
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| 3623 |
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|
| 3624 |
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| 3625 |
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| 3626 |
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|
| 3627 |
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| 3628 |
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|
| 3629 |
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| 3630 |
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| 3631 |
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| 3632 |
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| 3633 |
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| 3634 |
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| 3635 |
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| 3636 |
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| 3637 |
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| 3638 |
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|
| 3639 |
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| 3640 |
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| 3641 |
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|
| 3642 |
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| 3643 |
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|
| 3644 |
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| 3645 |
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| 3646 |
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|
| 3647 |
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| 3648 |
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|
| 3649 |
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|
| 3650 |
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| 3651 |
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|
| 3652 |
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| 3653 |
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|
| 3654 |
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|
| 3655 |
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|
| 3656 |
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|
| 3657 |
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| 3658 |
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|
| 3659 |
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|
| 3660 |
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| 3661 |
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|
| 3662 |
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| 3663 |
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|
| 3664 |
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|
| 3665 |
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|
| 3666 |
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| 3667 |
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| 3668 |
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| 3669 |
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|
| 3670 |
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|
| 3671 |
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|
| 3672 |
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| 3673 |
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|
| 3674 |
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|
| 3675 |
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|
| 3676 |
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|
| 3677 |
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|
| 3678 |
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|
| 3679 |
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|
| 3680 |
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|
| 3681 |
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|
| 3682 |
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|
| 3683 |
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|
| 3684 |
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|
| 3685 |
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|
| 3686 |
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|
| 3687 |
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|
| 3688 |
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|
| 3689 |
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|
| 3690 |
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|
| 3691 |
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|
| 3692 |
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|
| 3693 |
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|
| 3694 |
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|
| 3695 |
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|
| 3696 |
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|
| 3697 |
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|
| 3698 |
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|
| 3699 |
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|
| 3700 |
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|
| 3701 |
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|
| 3702 |
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|
| 3703 |
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|
| 3704 |
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|
| 3705 |
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|
| 3706 |
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|
| 3707 |
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|
| 3708 |
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|
| 3709 |
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|
| 3710 |
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|
| 3711 |
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|
| 3712 |
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|
| 3713 |
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|
| 3714 |
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|
| 3715 |
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|
| 3716 |
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|
| 3717 |
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|
| 3718 |
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|
| 3719 |
+
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|
| 3720 |
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|
| 3721 |
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|
| 3722 |
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|
| 3723 |
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|
| 3724 |
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|
| 3725 |
+
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|
| 3726 |
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|
| 3727 |
+
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|
| 3728 |
+
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|
| 3729 |
+
"bits": 6,
|
| 3730 |
+
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|
| 3731 |
+
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|
| 3732 |
+
},
|
| 3733 |
+
"model.layers.52.mlp.shared_experts.down_proj": {
|
| 3734 |
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|
| 3735 |
+
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|
| 3736 |
+
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|
| 3737 |
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|
| 3738 |
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|
| 3739 |
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|
| 3740 |
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|
| 3741 |
+
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|
| 3742 |
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|
| 3743 |
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|
| 3744 |
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|
| 3745 |
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|
| 3746 |
+
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|
| 3747 |
+
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|
| 3748 |
+
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|
| 3749 |
+
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|
| 3750 |
+
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|
| 3751 |
+
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|
| 3752 |
+
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|
| 3753 |
+
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|
| 3754 |
+
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|
| 3755 |
+
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|
| 3756 |
+
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|
| 3757 |
+
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|
| 3758 |
+
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|
| 3759 |
+
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|
| 3760 |
+
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|
| 3761 |
+
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|
| 3762 |
+
},
|
| 3763 |
+
"model.layers.52.self_attn.q_proj": {
|
| 3764 |
+
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|
| 3765 |
+
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|
| 3766 |
+
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|
| 3767 |
+
},
|
| 3768 |
+
"model.layers.52.self_attn.v_proj": {
|
| 3769 |
+
"bits": 6,
|
| 3770 |
+
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|
| 3771 |
+
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|
| 3772 |
+
},
|
| 3773 |
+
"model.layers.53.mlp.shared_experts.down_proj": {
|
| 3774 |
+
"bits": 8,
|
| 3775 |
+
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|
| 3776 |
+
"mode": "affine"
|
| 3777 |
+
},
|
| 3778 |
+
"model.layers.53.mlp.shared_experts.gate_proj": {
|
| 3779 |
+
"bits": 8,
|
| 3780 |
+
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|
| 3781 |
+
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|
| 3782 |
+
},
|
| 3783 |
+
"model.layers.53.mlp.shared_experts.up_proj": {
|
| 3784 |
+
"bits": 8,
|
| 3785 |
+
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|
| 3786 |
+
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|
| 3787 |
+
},
|
| 3788 |
+
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|
| 3789 |
+
"bits": 5,
|
| 3790 |
+
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|
| 3791 |
+
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|
| 3792 |
+
},
|
| 3793 |
+
"model.layers.53.self_attn.k_proj": {
|
| 3794 |
+
"bits": 6,
|
| 3795 |
+
"group_size": 64,
|
| 3796 |
+
"mode": "affine"
|
| 3797 |
+
},
|
| 3798 |
+
"model.layers.53.self_attn.o_proj": {
|
| 3799 |
+
"bits": 5,
|
| 3800 |
+
"group_size": 64,
|
| 3801 |
+
"mode": "affine"
|
| 3802 |
+
},
|
| 3803 |
+
"model.layers.53.self_attn.q_proj": {
|
| 3804 |
+
"bits": 6,
|
| 3805 |
+
"group_size": 64,
|
| 3806 |
+
"mode": "affine"
|
| 3807 |
+
},
|
| 3808 |
+
"model.layers.53.self_attn.v_proj": {
|
| 3809 |
+
"bits": 6,
|
| 3810 |
+
"group_size": 64,
|
| 3811 |
+
"mode": "affine"
|
| 3812 |
+
},
|
| 3813 |
+
"model.layers.54.mlp.shared_experts.down_proj": {
|
| 3814 |
+
"bits": 8,
|
| 3815 |
+
"group_size": 64,
|
| 3816 |
+
"mode": "affine"
|
| 3817 |
+
},
|
| 3818 |
+
"model.layers.54.mlp.shared_experts.gate_proj": {
|
| 3819 |
+
"bits": 8,
|
| 3820 |
+
"group_size": 64,
|
| 3821 |
+
"mode": "affine"
|
| 3822 |
+
},
|
| 3823 |
+
"model.layers.54.mlp.shared_experts.up_proj": {
|
| 3824 |
+
"bits": 8,
|
| 3825 |
+
"group_size": 64,
|
| 3826 |
+
"mode": "affine"
|
| 3827 |
+
},
|
| 3828 |
+
"model.layers.54.self_attn.gate_proj": {
|
| 3829 |
+
"bits": 5,
|
| 3830 |
+
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|
| 3831 |
+
"mode": "affine"
|
| 3832 |
+
},
|
| 3833 |
+
"model.layers.54.self_attn.k_proj": {
|
| 3834 |
+
"bits": 5,
|
| 3835 |
+
"group_size": 64,
|
| 3836 |
+
"mode": "affine"
|
| 3837 |
+
},
|
| 3838 |
+
"model.layers.54.self_attn.o_proj": {
|
| 3839 |
+
"bits": 5,
|
| 3840 |
+
"group_size": 64,
|
| 3841 |
+
"mode": "affine"
|
| 3842 |
+
},
|
| 3843 |
+
"model.layers.54.self_attn.q_proj": {
|
| 3844 |
+
"bits": 5,
|
| 3845 |
+
"group_size": 64,
|
| 3846 |
+
"mode": "affine"
|
| 3847 |
+
},
|
| 3848 |
+
"model.layers.54.self_attn.v_proj": {
|
| 3849 |
+
"bits": 5,
|
| 3850 |
+
"group_size": 64,
|
| 3851 |
+
"mode": "affine"
|
| 3852 |
+
},
|
| 3853 |
+
"model.layers.55.mlp.shared_experts.down_proj": {
|
| 3854 |
+
"bits": 8,
|
| 3855 |
+
"group_size": 64,
|
| 3856 |
+
"mode": "affine"
|
| 3857 |
+
},
|
| 3858 |
+
"model.layers.55.mlp.shared_experts.gate_proj": {
|
| 3859 |
+
"bits": 8,
|
| 3860 |
+
"group_size": 64,
|
| 3861 |
+
"mode": "affine"
|
| 3862 |
+
},
|
| 3863 |
+
"model.layers.55.mlp.shared_experts.up_proj": {
|
| 3864 |
+
"bits": 8,
|
| 3865 |
+
"group_size": 64,
|
| 3866 |
+
"mode": "affine"
|
| 3867 |
+
},
|
| 3868 |
+
"model.layers.55.self_attn.gate_proj": {
|
| 3869 |
+
"bits": 5,
|
| 3870 |
+
"group_size": 64,
|
| 3871 |
+
"mode": "affine"
|
| 3872 |
+
},
|
| 3873 |
+
"model.layers.55.self_attn.k_proj": {
|
| 3874 |
+
"bits": 5,
|
| 3875 |
+
"group_size": 64,
|
| 3876 |
+
"mode": "affine"
|
| 3877 |
+
},
|
| 3878 |
+
"model.layers.55.self_attn.o_proj": {
|
| 3879 |
+
"bits": 5,
|
| 3880 |
+
"group_size": 64,
|
| 3881 |
+
"mode": "affine"
|
| 3882 |
+
},
|
| 3883 |
+
"model.layers.55.self_attn.q_proj": {
|
| 3884 |
+
"bits": 5,
|
| 3885 |
+
"group_size": 64,
|
| 3886 |
+
"mode": "affine"
|
| 3887 |
+
},
|
| 3888 |
+
"model.layers.55.self_attn.v_proj": {
|
| 3889 |
+
"bits": 5,
|
| 3890 |
+
"group_size": 64,
|
| 3891 |
+
"mode": "affine"
|
| 3892 |
+
}
|
| 3893 |
+
}
|
| 3894 |
+
}
|
configuration_afmoe.py
ADDED
|
@@ -0,0 +1,133 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
+
# you may not use this file except in compliance with the License.
|
| 6 |
+
# You may obtain a copy of the License at
|
| 7 |
+
#
|
| 8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
+
#
|
| 10 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
+
# See the License for the specific language governing permissions and
|
| 14 |
+
# limitations under the License.
|
| 15 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 16 |
+
from transformers.modeling_rope_utils import rope_config_validation
|
| 17 |
+
from transformers.configuration_utils import layer_type_validation
|
| 18 |
+
from transformers.utils import logging
|
| 19 |
+
|
| 20 |
+
logger = logging.get_logger(__name__)
|
| 21 |
+
|
| 22 |
+
class AfmoeConfig(PretrainedConfig):
|
| 23 |
+
"""
|
| 24 |
+
n_group (`int`, *optional*, defaults to 1):
|
| 25 |
+
Number of groups for routed experts.
|
| 26 |
+
topk_group (`int`, *optional*, defaults to 1):
|
| 27 |
+
Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
|
| 28 |
+
"""
|
| 29 |
+
model_type = "afmoe"
|
| 30 |
+
base_model_pp_plan = {
|
| 31 |
+
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
|
| 32 |
+
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
|
| 33 |
+
"norm": (["hidden_states"], ["hidden_states"]),
|
| 34 |
+
}
|
| 35 |
+
|
| 36 |
+
def __init__(
|
| 37 |
+
self,
|
| 38 |
+
num_hidden_layers: int = 32,
|
| 39 |
+
vocab_size: int = 200192,
|
| 40 |
+
hidden_size: int = 2048,
|
| 41 |
+
intermediate_size: int = 6144,
|
| 42 |
+
moe_intermediate_size=1408,
|
| 43 |
+
num_dense_layers=1,
|
| 44 |
+
num_attention_heads=16,
|
| 45 |
+
num_key_value_heads=None,
|
| 46 |
+
head_dim=128,
|
| 47 |
+
hidden_act="silu",
|
| 48 |
+
max_position_embeddings=16384,
|
| 49 |
+
initializer_range=0.02,
|
| 50 |
+
rms_norm_eps=1e-5,
|
| 51 |
+
use_cache=True,
|
| 52 |
+
tie_word_embeddings=False,
|
| 53 |
+
rope_theta=10000.0,
|
| 54 |
+
rope_scaling=None,
|
| 55 |
+
num_experts=64,
|
| 56 |
+
num_experts_per_tok=6,
|
| 57 |
+
num_shared_experts=2,
|
| 58 |
+
num_expert_groups=1,
|
| 59 |
+
num_limited_groups=1,
|
| 60 |
+
score_func="sigmoid",
|
| 61 |
+
route_norm=True,
|
| 62 |
+
route_scale=1.0,
|
| 63 |
+
global_attn_every_n_layers=4,
|
| 64 |
+
sliding_window=1024,
|
| 65 |
+
mup_enabled=False,
|
| 66 |
+
layer_types=None,
|
| 67 |
+
attention_dropout: float = 0.0,
|
| 68 |
+
n_group: int = 1,
|
| 69 |
+
topk_group: int = 1,
|
| 70 |
+
**kwargs,
|
| 71 |
+
):
|
| 72 |
+
self.vocab_size = vocab_size
|
| 73 |
+
self.max_position_embeddings = max_position_embeddings
|
| 74 |
+
self.hidden_size = hidden_size
|
| 75 |
+
self.intermediate_size = intermediate_size
|
| 76 |
+
self.num_hidden_layers = num_hidden_layers
|
| 77 |
+
self.num_dense_layers = num_dense_layers
|
| 78 |
+
self.num_attention_heads = num_attention_heads
|
| 79 |
+
self.head_dim = head_dim
|
| 80 |
+
self.hidden_act = hidden_act
|
| 81 |
+
self.initializer_range = initializer_range
|
| 82 |
+
self.rms_norm_eps = rms_norm_eps
|
| 83 |
+
self.use_cache = use_cache
|
| 84 |
+
self.rope_theta = rope_theta
|
| 85 |
+
self.rope_scaling = rope_scaling
|
| 86 |
+
|
| 87 |
+
|
| 88 |
+
# MoE specific
|
| 89 |
+
self.moe_intermediate_size = moe_intermediate_size
|
| 90 |
+
self.num_experts_per_tok = num_experts_per_tok
|
| 91 |
+
self.n_group = n_group
|
| 92 |
+
self.topk_group = topk_group
|
| 93 |
+
self.num_experts = num_experts
|
| 94 |
+
self.num_shared_experts = num_shared_experts
|
| 95 |
+
self.num_expert_groups = num_expert_groups
|
| 96 |
+
self.num_limited_groups = num_limited_groups
|
| 97 |
+
self.score_func = score_func
|
| 98 |
+
self.route_norm = route_norm
|
| 99 |
+
self.route_scale = route_scale
|
| 100 |
+
|
| 101 |
+
|
| 102 |
+
# Attention specific
|
| 103 |
+
self.attention_dropout = attention_dropout
|
| 104 |
+
self.global_attn_every_n_layers = global_attn_every_n_layers
|
| 105 |
+
self.sliding_window = sliding_window
|
| 106 |
+
self.layer_types = layer_types
|
| 107 |
+
if self.layer_types is None:
|
| 108 |
+
self.layer_types = [
|
| 109 |
+
"sliding_attention" if bool((i + 1) % global_attn_every_n_layers) else "full_attention" for i in range(self.num_hidden_layers)
|
| 110 |
+
]
|
| 111 |
+
layer_type_validation(self.layer_types)
|
| 112 |
+
|
| 113 |
+
# muP specific
|
| 114 |
+
self.mup_enabled = mup_enabled
|
| 115 |
+
|
| 116 |
+
if num_key_value_heads is None:
|
| 117 |
+
num_key_value_heads = num_attention_heads
|
| 118 |
+
|
| 119 |
+
self.num_key_value_heads = num_key_value_heads
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
# Validate rope configs
|
| 123 |
+
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
| 124 |
+
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
| 125 |
+
rope_config_validation(self)
|
| 126 |
+
|
| 127 |
+
super().__init__(
|
| 128 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 129 |
+
**kwargs,
|
| 130 |
+
)
|
| 131 |
+
|
| 132 |
+
|
| 133 |
+
__all__ = ["AfmoeConfig"]
|
generation_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"transformers_version": "4.57.3"
|
| 4 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d4d9a215b9c3db385776c0ab462e010861812871bb8238eb6b5115a1db14cfab
|
| 3 |
+
size 3606303096
|
modeling_afmoe.py
ADDED
|
@@ -0,0 +1,680 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
from typing import Callable, Optional, Tuple, Union
|
| 2 |
+
|
| 3 |
+
import torch
|
| 4 |
+
import torch.nn.functional as F
|
| 5 |
+
from torch import nn
|
| 6 |
+
|
| 7 |
+
from transformers.activations import ACT2FN
|
| 8 |
+
from transformers.generation import GenerationMixin
|
| 9 |
+
from transformers.modeling_outputs import (
|
| 10 |
+
MoeCausalLMOutputWithPast,
|
| 11 |
+
MoeModelOutputWithPast,
|
| 12 |
+
)
|
| 13 |
+
from transformers.modeling_utils import PreTrainedModel, ALL_ATTENTION_FUNCTIONS
|
| 14 |
+
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
| 15 |
+
from transformers.masking_utils import (
|
| 16 |
+
create_causal_mask,
|
| 17 |
+
create_sliding_window_causal_mask,
|
| 18 |
+
)
|
| 19 |
+
from transformers.modeling_layers import GradientCheckpointingLayer
|
| 20 |
+
from transformers.processing_utils import Unpack
|
| 21 |
+
from transformers.utils import TransformersKwargs
|
| 22 |
+
from transformers.cache_utils import Cache, DynamicCache
|
| 23 |
+
from transformers.integrations import use_kernel_forward_from_hub
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
try:
|
| 27 |
+
from .configuration_afmoe import AfmoeConfig
|
| 28 |
+
except:
|
| 29 |
+
from configuration_afmoe import AfmoeConfig
|
| 30 |
+
|
| 31 |
+
class AfmoeRotaryEmbedding(nn.Module):
|
| 32 |
+
|
| 33 |
+
def __init__(self, config: AfmoeConfig, device=None):
|
| 34 |
+
super().__init__()
|
| 35 |
+
# BC: "rope_type" was originally "type"
|
| 36 |
+
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
|
| 37 |
+
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
| 38 |
+
else:
|
| 39 |
+
self.rope_type = "default"
|
| 40 |
+
self.max_seq_len_cached = config.max_position_embeddings
|
| 41 |
+
self.original_max_seq_len = config.max_position_embeddings
|
| 42 |
+
|
| 43 |
+
self.config = config
|
| 44 |
+
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
| 45 |
+
|
| 46 |
+
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
| 47 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 48 |
+
self.original_inv_freq = self.inv_freq
|
| 49 |
+
|
| 50 |
+
def _dynamic_frequency_update(self, position_ids, device):
|
| 51 |
+
"""
|
| 52 |
+
dynamic RoPE layers should recompute `inv_freq` in the following situations:
|
| 53 |
+
1 - growing beyond the cached sequence length (allow scaling)
|
| 54 |
+
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
|
| 55 |
+
"""
|
| 56 |
+
seq_len = torch.max(position_ids) + 1
|
| 57 |
+
if seq_len > self.max_seq_len_cached: # growth
|
| 58 |
+
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
|
| 59 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
|
| 60 |
+
self.max_seq_len_cached = seq_len
|
| 61 |
+
|
| 62 |
+
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
|
| 63 |
+
# This .to() is needed if the model has been moved to a device after being initialized (because
|
| 64 |
+
# the buffer is automatically moved, but not the original copy)
|
| 65 |
+
self.original_inv_freq = self.original_inv_freq.to(device)
|
| 66 |
+
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
|
| 67 |
+
self.max_seq_len_cached = self.original_max_seq_len
|
| 68 |
+
|
| 69 |
+
@torch.no_grad()
|
| 70 |
+
def forward(self, x, position_ids):
|
| 71 |
+
if "dynamic" in self.rope_type:
|
| 72 |
+
self._dynamic_frequency_update(position_ids, device=x.device)
|
| 73 |
+
|
| 74 |
+
# Core RoPE block
|
| 75 |
+
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
| 76 |
+
position_ids_expanded = position_ids[:, None, :].float()
|
| 77 |
+
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
|
| 78 |
+
device_type = x.device.type
|
| 79 |
+
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
| 80 |
+
with torch.autocast(device_type=device_type, enabled=False):
|
| 81 |
+
freqs = (inv_freq_expanded.float().to(x.device) @ position_ids_expanded.float()).transpose(1, 2)
|
| 82 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 83 |
+
cos = emb.cos()
|
| 84 |
+
sin = emb.sin()
|
| 85 |
+
|
| 86 |
+
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
|
| 87 |
+
cos = cos * self.attention_scaling
|
| 88 |
+
sin = sin * self.attention_scaling
|
| 89 |
+
|
| 90 |
+
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
def rotate_half(x):
|
| 94 |
+
"""Rotates half the hidden dims of the input."""
|
| 95 |
+
x1 = x[..., : x.shape[-1] // 2]
|
| 96 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
| 97 |
+
return torch.cat((-x2, x1), dim=-1)
|
| 98 |
+
|
| 99 |
+
|
| 100 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
| 101 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
| 102 |
+
|
| 103 |
+
Args:
|
| 104 |
+
q (`torch.Tensor`): The query tensor.
|
| 105 |
+
k (`torch.Tensor`): The key tensor.
|
| 106 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
| 107 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
| 108 |
+
position_ids (`torch.Tensor`, *optional*):
|
| 109 |
+
Deprecated and unused.
|
| 110 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
| 111 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
| 112 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
| 113 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
| 114 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
| 115 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
| 116 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
| 117 |
+
Returns:
|
| 118 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
| 119 |
+
"""
|
| 120 |
+
cos = cos.unsqueeze(unsqueeze_dim)
|
| 121 |
+
sin = sin.unsqueeze(unsqueeze_dim)
|
| 122 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
| 123 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
| 124 |
+
return q_embed, k_embed
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 128 |
+
"""
|
| 129 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
| 130 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
| 131 |
+
"""
|
| 132 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
| 133 |
+
if n_rep == 1:
|
| 134 |
+
return hidden_states
|
| 135 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(
|
| 136 |
+
batch, num_key_value_heads, n_rep, slen, head_dim
|
| 137 |
+
)
|
| 138 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
| 139 |
+
|
| 140 |
+
@use_kernel_forward_from_hub("RMSNorm")
|
| 141 |
+
class AfmoeRMSNorm(nn.Module):
|
| 142 |
+
def __init__(self, hidden_size: int, eps: float):
|
| 143 |
+
"""
|
| 144 |
+
AfmoeRMSNorm is equivalent to T5LayerNorm
|
| 145 |
+
"""
|
| 146 |
+
super().__init__()
|
| 147 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
| 148 |
+
self.variance_epsilon = eps
|
| 149 |
+
|
| 150 |
+
def forward(self, hidden_states):
|
| 151 |
+
input_dtype = hidden_states.dtype
|
| 152 |
+
hidden_states = hidden_states.to(torch.float32)
|
| 153 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
| 154 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
| 155 |
+
return self.weight * hidden_states.to(input_dtype)
|
| 156 |
+
|
| 157 |
+
def extra_repr(self):
|
| 158 |
+
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
|
| 162 |
+
def eager_attention_forward(
|
| 163 |
+
module: nn.Module,
|
| 164 |
+
query: torch.Tensor,
|
| 165 |
+
key: torch.Tensor,
|
| 166 |
+
value: torch.Tensor,
|
| 167 |
+
attention_mask: Optional[torch.Tensor],
|
| 168 |
+
scaling: float,
|
| 169 |
+
dropout: float = 0.0,
|
| 170 |
+
**kwargs,
|
| 171 |
+
):
|
| 172 |
+
key_states = repeat_kv(key, module.num_key_value_groups)
|
| 173 |
+
value_states = repeat_kv(value, module.num_key_value_groups)
|
| 174 |
+
|
| 175 |
+
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
|
| 176 |
+
if attention_mask is not None:
|
| 177 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
| 178 |
+
attn_weights = attn_weights + causal_mask
|
| 179 |
+
|
| 180 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
|
| 181 |
+
query.dtype
|
| 182 |
+
)
|
| 183 |
+
attn_weights = nn.functional.dropout(
|
| 184 |
+
attn_weights, p=dropout, training=module.training
|
| 185 |
+
)
|
| 186 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
| 187 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 188 |
+
|
| 189 |
+
return attn_output, attn_weights
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
class AfmoeMLP(nn.Module):
|
| 193 |
+
def __init__(self, config, intermediate_size=None):
|
| 194 |
+
super().__init__()
|
| 195 |
+
self.config = config
|
| 196 |
+
self.hidden_size = config.hidden_size
|
| 197 |
+
self.intermediate_size = intermediate_size or config.intermediate_size
|
| 198 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
| 199 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
| 200 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
| 201 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
| 202 |
+
|
| 203 |
+
def forward(self, x):
|
| 204 |
+
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
| 205 |
+
|
| 206 |
+
|
| 207 |
+
class AfmoeTokenChoiceRouter(nn.Module):
|
| 208 |
+
"""Token-choice top-K router for MoE routing."""
|
| 209 |
+
|
| 210 |
+
def __init__(self, config):
|
| 211 |
+
super().__init__()
|
| 212 |
+
self.config = config
|
| 213 |
+
self.top_k = config.num_experts_per_tok
|
| 214 |
+
self.num_experts = config.num_experts
|
| 215 |
+
self.score_func = config.score_func
|
| 216 |
+
self.route_norm = config.route_norm
|
| 217 |
+
self.route_scale = config.route_scale
|
| 218 |
+
self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
|
| 219 |
+
|
| 220 |
+
def forward(self, hidden_states, expert_bias: torch.Tensor | None):
|
| 221 |
+
_, _, hidden_dim = hidden_states.shape
|
| 222 |
+
hidden_states = hidden_states.view(-1, hidden_dim)
|
| 223 |
+
|
| 224 |
+
scores = self.gate(hidden_states)
|
| 225 |
+
|
| 226 |
+
# Apply scoring function in float32 for stability
|
| 227 |
+
if self.score_func == "sigmoid":
|
| 228 |
+
scores = torch.sigmoid(scores.to(torch.float32))
|
| 229 |
+
else:
|
| 230 |
+
scores = F.softmax(scores.to(torch.float32), dim=-1)
|
| 231 |
+
|
| 232 |
+
if expert_bias is not None:
|
| 233 |
+
_, selected_experts = torch.topk(scores + expert_bias, k=self.top_k, dim=1)
|
| 234 |
+
top_scores = scores.gather(dim=1, index=selected_experts)
|
| 235 |
+
else:
|
| 236 |
+
top_scores, selected_experts = torch.topk(scores, k=self.top_k, dim=1)
|
| 237 |
+
|
| 238 |
+
# Normalize weights if using sigmoid
|
| 239 |
+
if self.score_func == "sigmoid" and self.route_norm:
|
| 240 |
+
denominator = top_scores.sum(dim=-1, keepdim=True) + 1e-20
|
| 241 |
+
top_scores = top_scores / denominator
|
| 242 |
+
|
| 243 |
+
top_scores = top_scores * self.route_scale
|
| 244 |
+
return top_scores, selected_experts
|
| 245 |
+
|
| 246 |
+
class AfmoeMoE(nn.Module):
|
| 247 |
+
def __init__(self, config):
|
| 248 |
+
super().__init__()
|
| 249 |
+
self.config = config
|
| 250 |
+
self.router = AfmoeTokenChoiceRouter(config)
|
| 251 |
+
|
| 252 |
+
self.shared_experts = None
|
| 253 |
+
if config.num_shared_experts > 0:
|
| 254 |
+
self.shared_experts = AfmoeMLP(
|
| 255 |
+
config, config.moe_intermediate_size * config.num_shared_experts
|
| 256 |
+
)
|
| 257 |
+
self.experts = nn.ModuleList(
|
| 258 |
+
[AfmoeMLP(
|
| 259 |
+
config, intermediate_size=config.moe_intermediate_size
|
| 260 |
+
) for _ in range(config.num_experts)]
|
| 261 |
+
)
|
| 262 |
+
self.expert_bias = nn.Parameter(torch.zeros(config.num_experts, dtype=torch.float32), requires_grad=False)
|
| 263 |
+
|
| 264 |
+
|
| 265 |
+
def forward(self, hidden_states):
|
| 266 |
+
batch_size, seq_len, hidden_dim = hidden_states.shape
|
| 267 |
+
hidden_states_flat = hidden_states.view(-1, hidden_dim)
|
| 268 |
+
|
| 269 |
+
# Get routing decisions
|
| 270 |
+
top_scores, selected_experts = self.router(hidden_states, self.expert_bias)
|
| 271 |
+
|
| 272 |
+
# Process through shared experts
|
| 273 |
+
if self.shared_experts is not None:
|
| 274 |
+
shared_output = self.shared_experts(hidden_states_flat)
|
| 275 |
+
else:
|
| 276 |
+
shared_output = torch.zeros_like(hidden_states_flat)
|
| 277 |
+
|
| 278 |
+
# Reorder tokens by expert for efficient processing
|
| 279 |
+
token_indices_sorted = torch.argsort(selected_experts.view(-1), stable=True)
|
| 280 |
+
top_scores_sorted = top_scores.view(-1)[token_indices_sorted]
|
| 281 |
+
token_to_expert = selected_experts.view(-1)[token_indices_sorted]
|
| 282 |
+
token_indices_sorted = token_indices_sorted // self.config.num_experts_per_tok
|
| 283 |
+
|
| 284 |
+
# Gather input tokens
|
| 285 |
+
token_indices_expanded = token_indices_sorted.unsqueeze(-1).expand(
|
| 286 |
+
-1, hidden_dim
|
| 287 |
+
)
|
| 288 |
+
routed_input = torch.gather(
|
| 289 |
+
hidden_states_flat, dim=0, index=token_indices_expanded
|
| 290 |
+
)
|
| 291 |
+
|
| 292 |
+
routed_output = torch.zeros_like(routed_input)
|
| 293 |
+
for expert_id in range(self.config.num_experts):
|
| 294 |
+
mask = token_to_expert == expert_id
|
| 295 |
+
if mask.any():
|
| 296 |
+
expert_input = routed_input[mask]
|
| 297 |
+
expert_out = self.experts[expert_id](expert_input)
|
| 298 |
+
routed_output[mask] = expert_out
|
| 299 |
+
|
| 300 |
+
routed_output = (
|
| 301 |
+
routed_output.to(torch.float32) * top_scores_sorted.unsqueeze(-1)
|
| 302 |
+
).to(hidden_states.dtype)
|
| 303 |
+
|
| 304 |
+
# Scatter back to original positions
|
| 305 |
+
output = shared_output.scatter_add(
|
| 306 |
+
dim=0, index=token_indices_expanded, src=routed_output
|
| 307 |
+
)
|
| 308 |
+
|
| 309 |
+
return output.view(batch_size, seq_len, hidden_dim)
|
| 310 |
+
|
| 311 |
+
|
| 312 |
+
class AfmoeAttention(nn.Module):
|
| 313 |
+
"""Multi-headed attention with local/global pattern and gating."""
|
| 314 |
+
|
| 315 |
+
def __init__(self, config: AfmoeConfig, layer_idx: int):
|
| 316 |
+
super().__init__()
|
| 317 |
+
self.config = config
|
| 318 |
+
self.layer_idx = layer_idx
|
| 319 |
+
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
| 320 |
+
self.num_heads = config.num_attention_heads
|
| 321 |
+
self.num_key_value_heads = config.num_key_value_heads
|
| 322 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
| 323 |
+
|
| 324 |
+
self.scaling = self.head_dim**-0.5
|
| 325 |
+
self.attention_dropout = config.attention_dropout
|
| 326 |
+
self.is_local_attention = config.layer_types[layer_idx] == "sliding_attention"
|
| 327 |
+
self.sliding_window = config.sliding_window if self.is_local_attention else None
|
| 328 |
+
|
| 329 |
+
self.q_proj = nn.Linear(
|
| 330 |
+
config.hidden_size, self.num_heads * self.head_dim, bias=False
|
| 331 |
+
)
|
| 332 |
+
self.k_proj = nn.Linear(
|
| 333 |
+
config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
|
| 334 |
+
)
|
| 335 |
+
self.v_proj = nn.Linear(
|
| 336 |
+
config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
|
| 337 |
+
)
|
| 338 |
+
self.o_proj = nn.Linear(
|
| 339 |
+
self.num_heads * self.head_dim, config.hidden_size, bias=False
|
| 340 |
+
)
|
| 341 |
+
|
| 342 |
+
self.q_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
| 343 |
+
self.k_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
| 344 |
+
|
| 345 |
+
self.gate_proj = nn.Linear(
|
| 346 |
+
config.hidden_size, self.num_heads * self.head_dim, bias=False
|
| 347 |
+
)
|
| 348 |
+
|
| 349 |
+
def forward(
|
| 350 |
+
self,
|
| 351 |
+
hidden_states: torch.Tensor,
|
| 352 |
+
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
| 353 |
+
attention_mask: Optional[torch.Tensor],
|
| 354 |
+
past_key_value: Optional[Cache] = None,
|
| 355 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 356 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 357 |
+
) -> torch.Tensor:
|
| 358 |
+
|
| 359 |
+
input_shape = hidden_states.shape[:-1]
|
| 360 |
+
hidden_shape = (*input_shape, -1, self.head_dim)
|
| 361 |
+
|
| 362 |
+
query_states = self.q_proj(hidden_states).view(hidden_shape)
|
| 363 |
+
key_states = self.k_proj(hidden_states).view(hidden_shape)
|
| 364 |
+
value_states = self.v_proj(hidden_states).view(hidden_shape)
|
| 365 |
+
gate_states = self.gate_proj(hidden_states)
|
| 366 |
+
|
| 367 |
+
query_states = self.q_norm(query_states)
|
| 368 |
+
key_states = self.k_norm(key_states)
|
| 369 |
+
|
| 370 |
+
query_states = query_states.transpose(1, 2)
|
| 371 |
+
key_states = key_states.transpose(1, 2)
|
| 372 |
+
value_states = value_states.transpose(1, 2)
|
| 373 |
+
|
| 374 |
+
if self.is_local_attention:
|
| 375 |
+
cos, sin = position_embeddings
|
| 376 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
| 377 |
+
|
| 378 |
+
if past_key_value is not None:
|
| 379 |
+
cache_kwargs = {"cache_position": cache_position}
|
| 380 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
| 381 |
+
|
| 382 |
+
attention_interface: Callable = eager_attention_forward
|
| 383 |
+
if self.config._attn_implementation != "eager":
|
| 384 |
+
attention_interface = ALL_ATTENTION_FUNCTIONS[
|
| 385 |
+
self.config._attn_implementation
|
| 386 |
+
]
|
| 387 |
+
|
| 388 |
+
output, _ = attention_interface(
|
| 389 |
+
self,
|
| 390 |
+
query_states,
|
| 391 |
+
key_states,
|
| 392 |
+
value_states,
|
| 393 |
+
attention_mask=attention_mask,
|
| 394 |
+
dropout=0.0 if not self.training else self.attention_dropout,
|
| 395 |
+
scaling=self.scaling,
|
| 396 |
+
sliding_window=self.sliding_window,
|
| 397 |
+
**kwargs,
|
| 398 |
+
)
|
| 399 |
+
|
| 400 |
+
output = output.view(*input_shape, -1).contiguous()
|
| 401 |
+
output = output * F.sigmoid(gate_states)
|
| 402 |
+
return self.o_proj(output)
|
| 403 |
+
|
| 404 |
+
|
| 405 |
+
class AfmoeDecoderLayer(GradientCheckpointingLayer):
|
| 406 |
+
def __init__(self, config: AfmoeConfig, layer_idx: int):
|
| 407 |
+
super().__init__()
|
| 408 |
+
self.hidden_size = config.hidden_size
|
| 409 |
+
self.layer_idx = layer_idx
|
| 410 |
+
|
| 411 |
+
self.self_attn = AfmoeAttention(config=config, layer_idx=layer_idx)
|
| 412 |
+
self.attention_type = config.layer_types[layer_idx]
|
| 413 |
+
|
| 414 |
+
# Dual normalization for attention
|
| 415 |
+
self.input_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 416 |
+
self.post_attention_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 417 |
+
|
| 418 |
+
# Dual normalization for FFN
|
| 419 |
+
self.pre_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 420 |
+
self.post_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 421 |
+
|
| 422 |
+
# MoE or dense FFN
|
| 423 |
+
self.moe_enabled = layer_idx >= config.num_dense_layers
|
| 424 |
+
if self.moe_enabled:
|
| 425 |
+
self.mlp = AfmoeMoE(config)
|
| 426 |
+
else:
|
| 427 |
+
self.mlp = AfmoeMLP(config)
|
| 428 |
+
|
| 429 |
+
def forward(
|
| 430 |
+
self,
|
| 431 |
+
hidden_states: torch.Tensor,
|
| 432 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 433 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 434 |
+
past_key_value: Optional[Cache] = None,
|
| 435 |
+
use_cache: Optional[bool] = None,
|
| 436 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 437 |
+
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
|
| 438 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 439 |
+
) -> torch.FloatTensor:
|
| 440 |
+
residual = hidden_states
|
| 441 |
+
|
| 442 |
+
# Self Attention with dual normalization
|
| 443 |
+
hidden_states = self.input_layernorm(hidden_states)
|
| 444 |
+
hidden_states = self.self_attn(
|
| 445 |
+
hidden_states=hidden_states,
|
| 446 |
+
attention_mask=attention_mask,
|
| 447 |
+
position_ids=position_ids,
|
| 448 |
+
past_key_value=past_key_value,
|
| 449 |
+
use_cache=use_cache,
|
| 450 |
+
cache_position=cache_position,
|
| 451 |
+
position_embeddings=position_embeddings,
|
| 452 |
+
**kwargs,
|
| 453 |
+
)
|
| 454 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
| 455 |
+
hidden_states = residual + hidden_states
|
| 456 |
+
|
| 457 |
+
# FFN with dual normalization
|
| 458 |
+
residual = hidden_states
|
| 459 |
+
hidden_states = self.pre_mlp_layernorm(hidden_states)
|
| 460 |
+
|
| 461 |
+
if self.moe_enabled:
|
| 462 |
+
hidden_states = self.mlp(hidden_states)
|
| 463 |
+
else:
|
| 464 |
+
hidden_states = self.mlp(hidden_states)
|
| 465 |
+
|
| 466 |
+
hidden_states = self.post_mlp_layernorm(hidden_states)
|
| 467 |
+
hidden_states = residual + hidden_states
|
| 468 |
+
return hidden_states
|
| 469 |
+
|
| 470 |
+
|
| 471 |
+
class AfmoePreTrainedModel(PreTrainedModel):
|
| 472 |
+
config_class = AfmoeConfig
|
| 473 |
+
base_model_prefix = "model"
|
| 474 |
+
_no_split_modules = ["AfmoeDecoderLayer"]
|
| 475 |
+
_skip_keys_device_placement = ["past_key_values"]
|
| 476 |
+
_keep_in_fp32_modules = [
|
| 477 |
+
"input_layernorm",
|
| 478 |
+
"post_attention_layernorm",
|
| 479 |
+
"pre_mlp_layernorm",
|
| 480 |
+
"post_mlp_layernorm",
|
| 481 |
+
"q_norm",
|
| 482 |
+
"k_norm",
|
| 483 |
+
"norm",
|
| 484 |
+
]
|
| 485 |
+
_supports_sdpa = True
|
| 486 |
+
_supports_attention_backend = True
|
| 487 |
+
supports_gradient_checkpointing = True
|
| 488 |
+
|
| 489 |
+
|
| 490 |
+
class AfmoeModel(AfmoePreTrainedModel):
|
| 491 |
+
_no_split_modules = ["AfmoeDecoderLayer"]
|
| 492 |
+
|
| 493 |
+
def __init__(self, config: AfmoeConfig):
|
| 494 |
+
super().__init__(config)
|
| 495 |
+
self.padding_idx = config.pad_token_id
|
| 496 |
+
self.vocab_size = config.vocab_size
|
| 497 |
+
|
| 498 |
+
self.embed_tokens = nn.Embedding(
|
| 499 |
+
config.vocab_size, config.hidden_size, self.padding_idx
|
| 500 |
+
)
|
| 501 |
+
self.layers = nn.ModuleList(
|
| 502 |
+
[
|
| 503 |
+
AfmoeDecoderLayer(config, layer_idx)
|
| 504 |
+
for layer_idx in range(config.num_hidden_layers)
|
| 505 |
+
]
|
| 506 |
+
)
|
| 507 |
+
self.norm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 508 |
+
self.rotary_emb = AfmoeRotaryEmbedding(config=config)
|
| 509 |
+
self.gradient_checkpointing = False
|
| 510 |
+
|
| 511 |
+
self.post_init()
|
| 512 |
+
|
| 513 |
+
def get_input_embeddings(self):
|
| 514 |
+
return self.embed_tokens
|
| 515 |
+
|
| 516 |
+
def set_input_embeddings(self, value):
|
| 517 |
+
self.embed_tokens = value
|
| 518 |
+
|
| 519 |
+
|
| 520 |
+
def forward(
|
| 521 |
+
self,
|
| 522 |
+
input_ids: torch.LongTensor,
|
| 523 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 524 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 525 |
+
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
| 526 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 527 |
+
use_cache: Optional[bool] = None,
|
| 528 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 529 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 530 |
+
) -> MoeModelOutputWithPast:
|
| 531 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
| 532 |
+
raise ValueError(
|
| 533 |
+
"You must specify exactly one of input_ids or inputs_embeds"
|
| 534 |
+
)
|
| 535 |
+
|
| 536 |
+
if use_cache and past_key_values is None:
|
| 537 |
+
past_key_values = DynamicCache()
|
| 538 |
+
|
| 539 |
+
if inputs_embeds is None:
|
| 540 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
| 541 |
+
|
| 542 |
+
if cache_position is None:
|
| 543 |
+
past_seen_tokens = (
|
| 544 |
+
past_key_values.get_seq_length() if past_key_values is not None else 0
|
| 545 |
+
)
|
| 546 |
+
cache_position = torch.arange(
|
| 547 |
+
past_seen_tokens,
|
| 548 |
+
past_seen_tokens + inputs_embeds.shape[1],
|
| 549 |
+
device=inputs_embeds.device,
|
| 550 |
+
)
|
| 551 |
+
if position_ids is None:
|
| 552 |
+
position_ids = cache_position.unsqueeze(0)
|
| 553 |
+
|
| 554 |
+
# It may already have been prepared by e.g. `generate`
|
| 555 |
+
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
| 556 |
+
mask_kwargs = {
|
| 557 |
+
"config": self.config,
|
| 558 |
+
"input_embeds": inputs_embeds,
|
| 559 |
+
"attention_mask": attention_mask,
|
| 560 |
+
"cache_position": cache_position,
|
| 561 |
+
"past_key_values": past_key_values,
|
| 562 |
+
}
|
| 563 |
+
causal_mask_mapping = {
|
| 564 |
+
"full_attention": create_causal_mask(**mask_kwargs),
|
| 565 |
+
"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
|
| 566 |
+
}
|
| 567 |
+
|
| 568 |
+
hidden_states = inputs_embeds
|
| 569 |
+
|
| 570 |
+
# Apply muP input scaling if enabled
|
| 571 |
+
if self.config.mup_enabled:
|
| 572 |
+
hidden_states = hidden_states * (self.config.hidden_size**0.5)
|
| 573 |
+
|
| 574 |
+
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
| 575 |
+
|
| 576 |
+
for decoder_layer in self.layers:
|
| 577 |
+
hidden_states = decoder_layer(
|
| 578 |
+
hidden_states,
|
| 579 |
+
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
| 580 |
+
position_ids=position_ids,
|
| 581 |
+
past_key_value=past_key_values,
|
| 582 |
+
use_cache=use_cache,
|
| 583 |
+
cache_position=cache_position,
|
| 584 |
+
position_embeddings=position_embeddings,
|
| 585 |
+
**kwargs,
|
| 586 |
+
)
|
| 587 |
+
|
| 588 |
+
hidden_states = self.norm(hidden_states)
|
| 589 |
+
return MoeModelOutputWithPast(
|
| 590 |
+
last_hidden_state=hidden_states,
|
| 591 |
+
past_key_values=past_key_values,
|
| 592 |
+
)
|
| 593 |
+
|
| 594 |
+
|
| 595 |
+
class AfmoeForCausalLM(AfmoePreTrainedModel, GenerationMixin):
|
| 596 |
+
_tied_weights_keys = ["lm_head.weight"]
|
| 597 |
+
_tp_plan = {"lm_head": "colwise_rep"}
|
| 598 |
+
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
| 599 |
+
|
| 600 |
+
def __init__(self, config):
|
| 601 |
+
super().__init__(config)
|
| 602 |
+
self.model = AfmoeModel(config)
|
| 603 |
+
self.vocab_size = config.vocab_size
|
| 604 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 605 |
+
|
| 606 |
+
# Initialize weights and apply final processing
|
| 607 |
+
self.post_init()
|
| 608 |
+
|
| 609 |
+
def get_input_embeddings(self):
|
| 610 |
+
return self.model.embed_tokens
|
| 611 |
+
|
| 612 |
+
def set_input_embeddings(self, value):
|
| 613 |
+
self.model.embed_tokens = value
|
| 614 |
+
|
| 615 |
+
def get_output_embeddings(self):
|
| 616 |
+
return self.lm_head
|
| 617 |
+
|
| 618 |
+
def set_output_embeddings(self, new_embeddings):
|
| 619 |
+
self.lm_head = new_embeddings
|
| 620 |
+
|
| 621 |
+
def set_decoder(self, decoder):
|
| 622 |
+
self.model = decoder
|
| 623 |
+
|
| 624 |
+
def get_decoder(self):
|
| 625 |
+
return self.model
|
| 626 |
+
|
| 627 |
+
def forward(
|
| 628 |
+
self,
|
| 629 |
+
input_ids: torch.LongTensor,
|
| 630 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 631 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 632 |
+
past_key_values: Optional[Cache] = None,
|
| 633 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 634 |
+
labels: Optional[torch.LongTensor] = None,
|
| 635 |
+
use_cache: Optional[bool] = None,
|
| 636 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 637 |
+
logits_to_keep: Union[int, torch.Tensor] = 0,
|
| 638 |
+
token_type_ids: Optional[torch.Tensor] = None, # will be ignored
|
| 639 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 640 |
+
) -> Union[Tuple, MoeCausalLMOutputWithPast]:
|
| 641 |
+
outputs: MoeModelOutputWithPast = self.model(
|
| 642 |
+
input_ids=input_ids,
|
| 643 |
+
attention_mask=attention_mask,
|
| 644 |
+
position_ids=position_ids,
|
| 645 |
+
past_key_values=past_key_values,
|
| 646 |
+
inputs_embeds=inputs_embeds,
|
| 647 |
+
use_cache=use_cache,
|
| 648 |
+
cache_position=cache_position,
|
| 649 |
+
**kwargs,
|
| 650 |
+
)
|
| 651 |
+
|
| 652 |
+
hidden_states = outputs.last_hidden_state
|
| 653 |
+
# Only compute necessary logits
|
| 654 |
+
slice_indices = (
|
| 655 |
+
slice(-logits_to_keep, None)
|
| 656 |
+
if isinstance(logits_to_keep, int)
|
| 657 |
+
else logits_to_keep
|
| 658 |
+
)
|
| 659 |
+
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
| 660 |
+
|
| 661 |
+
loss = None
|
| 662 |
+
if labels is not None:
|
| 663 |
+
loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
|
| 664 |
+
|
| 665 |
+
|
| 666 |
+
return MoeCausalLMOutputWithPast(
|
| 667 |
+
loss=loss,
|
| 668 |
+
logits=logits,
|
| 669 |
+
past_key_values=outputs.past_key_values,
|
| 670 |
+
hidden_states=outputs.hidden_states,
|
| 671 |
+
attentions=outputs.attentions,
|
| 672 |
+
router_logits=outputs.router_logits,
|
| 673 |
+
)
|
| 674 |
+
|
| 675 |
+
|
| 676 |
+
__all__ = [
|
| 677 |
+
"AfmoeForCausalLM",
|
| 678 |
+
"AfmoeModel",
|
| 679 |
+
"AfmoePreTrainedModel",
|
| 680 |
+
]
|
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": "<|im_end|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "<|pad|>",
|
| 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 sha256:606b0740c5d8940dbee3268e05beaa3e34f4e43bc7df153681122f8fcc78e134
|
| 3 |
+
size 14614897
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,271 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": false,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"add_prefix_space": null,
|
| 5 |
+
"added_tokens_decoder": {
|
| 6 |
+
"0": {
|
| 7 |
+
"content": "<|begin_of_text|>",
|
| 8 |
+
"lstrip": false,
|
| 9 |
+
"normalized": false,
|
| 10 |
+
"rstrip": false,
|
| 11 |
+
"single_word": false,
|
| 12 |
+
"special": true
|
| 13 |
+
},
|
| 14 |
+
"1": {
|
| 15 |
+
"content": "<|end_of_text|>",
|
| 16 |
+
"lstrip": false,
|
| 17 |
+
"normalized": false,
|
| 18 |
+
"rstrip": false,
|
| 19 |
+
"single_word": false,
|
| 20 |
+
"special": true
|
| 21 |
+
},
|
| 22 |
+
"2": {
|
| 23 |
+
"content": "<|im_start|>",
|
| 24 |
+
"lstrip": false,
|
| 25 |
+
"normalized": false,
|
| 26 |
+
"rstrip": false,
|
| 27 |
+
"single_word": false,
|
| 28 |
+
"special": true
|
| 29 |
+
},
|
| 30 |
+
"3": {
|
| 31 |
+
"content": "<|im_end|>",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false,
|
| 36 |
+
"special": true
|
| 37 |
+
},
|
| 38 |
+
"4": {
|
| 39 |
+
"content": "<|eot_id|>",
|
| 40 |
+
"lstrip": false,
|
| 41 |
+
"normalized": false,
|
| 42 |
+
"rstrip": false,
|
| 43 |
+
"single_word": false,
|
| 44 |
+
"special": true
|
| 45 |
+
},
|
| 46 |
+
"5": {
|
| 47 |
+
"content": "<|start|>",
|
| 48 |
+
"lstrip": false,
|
| 49 |
+
"normalized": false,
|
| 50 |
+
"rstrip": false,
|
| 51 |
+
"single_word": false,
|
| 52 |
+
"special": true
|
| 53 |
+
},
|
| 54 |
+
"6": {
|
| 55 |
+
"content": "<|channel|>",
|
| 56 |
+
"lstrip": false,
|
| 57 |
+
"normalized": false,
|
| 58 |
+
"rstrip": false,
|
| 59 |
+
"single_word": false,
|
| 60 |
+
"special": true
|
| 61 |
+
},
|
| 62 |
+
"7": {
|
| 63 |
+
"content": "<|message|>",
|
| 64 |
+
"lstrip": false,
|
| 65 |
+
"normalized": false,
|
| 66 |
+
"rstrip": false,
|
| 67 |
+
"single_word": false,
|
| 68 |
+
"special": true
|
| 69 |
+
},
|
| 70 |
+
"8": {
|
| 71 |
+
"content": "<|end|>",
|
| 72 |
+
"lstrip": false,
|
| 73 |
+
"normalized": false,
|
| 74 |
+
"rstrip": false,
|
| 75 |
+
"single_word": false,
|
| 76 |
+
"special": true
|
| 77 |
+
},
|
| 78 |
+
"9": {
|
| 79 |
+
"content": "<|fitm_start|>",
|
| 80 |
+
"lstrip": false,
|
| 81 |
+
"normalized": false,
|
| 82 |
+
"rstrip": false,
|
| 83 |
+
"single_word": false,
|
| 84 |
+
"special": true
|
| 85 |
+
},
|
| 86 |
+
"10": {
|
| 87 |
+
"content": "<|fitm_end|>",
|
| 88 |
+
"lstrip": false,
|
| 89 |
+
"normalized": false,
|
| 90 |
+
"rstrip": false,
|
| 91 |
+
"single_word": false,
|
| 92 |
+
"special": true
|
| 93 |
+
},
|
| 94 |
+
"11": {
|
| 95 |
+
"content": "<|fitm_hole|>",
|
| 96 |
+
"lstrip": false,
|
| 97 |
+
"normalized": false,
|
| 98 |
+
"rstrip": false,
|
| 99 |
+
"single_word": false,
|
| 100 |
+
"special": true
|
| 101 |
+
},
|
| 102 |
+
"12": {
|
| 103 |
+
"content": "<|pad|>",
|
| 104 |
+
"lstrip": false,
|
| 105 |
+
"normalized": false,
|
| 106 |
+
"rstrip": false,
|
| 107 |
+
"single_word": false,
|
| 108 |
+
"special": true
|
| 109 |
+
},
|
| 110 |
+
"13": {
|
| 111 |
+
"content": "<|reserved_special_0|>",
|
| 112 |
+
"lstrip": false,
|
| 113 |
+
"normalized": false,
|
| 114 |
+
"rstrip": false,
|
| 115 |
+
"single_word": false,
|
| 116 |
+
"special": true
|
| 117 |
+
},
|
| 118 |
+
"14": {
|
| 119 |
+
"content": "<|reserved_special_1|>",
|
| 120 |
+
"lstrip": false,
|
| 121 |
+
"normalized": false,
|
| 122 |
+
"rstrip": false,
|
| 123 |
+
"single_word": false,
|
| 124 |
+
"special": true
|
| 125 |
+
},
|
| 126 |
+
"15": {
|
| 127 |
+
"content": "<|reserved_special_2|>",
|
| 128 |
+
"lstrip": false,
|
| 129 |
+
"normalized": false,
|
| 130 |
+
"rstrip": false,
|
| 131 |
+
"single_word": false,
|
| 132 |
+
"special": true
|
| 133 |
+
},
|
| 134 |
+
"16": {
|
| 135 |
+
"content": "<|reserved_special_3|>",
|
| 136 |
+
"lstrip": false,
|
| 137 |
+
"normalized": false,
|
| 138 |
+
"rstrip": false,
|
| 139 |
+
"single_word": false,
|
| 140 |
+
"special": true
|
| 141 |
+
},
|
| 142 |
+
"17": {
|
| 143 |
+
"content": "<|reserved_special_4|>",
|
| 144 |
+
"lstrip": false,
|
| 145 |
+
"normalized": false,
|
| 146 |
+
"rstrip": false,
|
| 147 |
+
"single_word": false,
|
| 148 |
+
"special": true
|
| 149 |
+
},
|
| 150 |
+
"18": {
|
| 151 |
+
"content": "<|reserved_special_5|>",
|
| 152 |
+
"lstrip": false,
|
| 153 |
+
"normalized": false,
|
| 154 |
+
"rstrip": false,
|
| 155 |
+
"single_word": false,
|
| 156 |
+
"special": true
|
| 157 |
+
},
|
| 158 |
+
"19": {
|
| 159 |
+
"content": "<|reserved_special_6|>",
|
| 160 |
+
"lstrip": false,
|
| 161 |
+
"normalized": false,
|
| 162 |
+
"rstrip": false,
|
| 163 |
+
"single_word": false,
|
| 164 |
+
"special": true
|
| 165 |
+
},
|
| 166 |
+
"20": {
|
| 167 |
+
"content": "<|reserved_special_7|>",
|
| 168 |
+
"lstrip": false,
|
| 169 |
+
"normalized": false,
|
| 170 |
+
"rstrip": false,
|
| 171 |
+
"single_word": false,
|
| 172 |
+
"special": true
|
| 173 |
+
},
|
| 174 |
+
"21": {
|
| 175 |
+
"content": "<|reserved_special_8|>",
|
| 176 |
+
"lstrip": false,
|
| 177 |
+
"normalized": false,
|
| 178 |
+
"rstrip": false,
|
| 179 |
+
"single_word": false,
|
| 180 |
+
"special": true
|
| 181 |
+
},
|
| 182 |
+
"22": {
|
| 183 |
+
"content": "<|reserved_special_9|>",
|
| 184 |
+
"lstrip": false,
|
| 185 |
+
"normalized": false,
|
| 186 |
+
"rstrip": false,
|
| 187 |
+
"single_word": false,
|
| 188 |
+
"special": true
|
| 189 |
+
},
|
| 190 |
+
"23": {
|
| 191 |
+
"content": "<|reserved_special_10|>",
|
| 192 |
+
"lstrip": false,
|
| 193 |
+
"normalized": false,
|
| 194 |
+
"rstrip": false,
|
| 195 |
+
"single_word": false,
|
| 196 |
+
"special": true
|
| 197 |
+
},
|
| 198 |
+
"24": {
|
| 199 |
+
"content": "<|reserved_special_11|>",
|
| 200 |
+
"lstrip": false,
|
| 201 |
+
"normalized": false,
|
| 202 |
+
"rstrip": false,
|
| 203 |
+
"single_word": false,
|
| 204 |
+
"special": true
|
| 205 |
+
},
|
| 206 |
+
"25": {
|
| 207 |
+
"content": "<|reserved_special_12|>",
|
| 208 |
+
"lstrip": false,
|
| 209 |
+
"normalized": false,
|
| 210 |
+
"rstrip": false,
|
| 211 |
+
"single_word": false,
|
| 212 |
+
"special": true
|
| 213 |
+
},
|
| 214 |
+
"26": {
|
| 215 |
+
"content": "<|reserved_special_13|>",
|
| 216 |
+
"lstrip": false,
|
| 217 |
+
"normalized": false,
|
| 218 |
+
"rstrip": false,
|
| 219 |
+
"single_word": false,
|
| 220 |
+
"special": true
|
| 221 |
+
},
|
| 222 |
+
"27": {
|
| 223 |
+
"content": "<|reserved_special_14|>",
|
| 224 |
+
"lstrip": false,
|
| 225 |
+
"normalized": false,
|
| 226 |
+
"rstrip": false,
|
| 227 |
+
"single_word": false,
|
| 228 |
+
"special": true
|
| 229 |
+
},
|
| 230 |
+
"28": {
|
| 231 |
+
"content": "<|reserved_special_15|>",
|
| 232 |
+
"lstrip": false,
|
| 233 |
+
"normalized": false,
|
| 234 |
+
"rstrip": false,
|
| 235 |
+
"single_word": false,
|
| 236 |
+
"special": true
|
| 237 |
+
},
|
| 238 |
+
"29": {
|
| 239 |
+
"content": "<|reserved_special_16|>",
|
| 240 |
+
"lstrip": false,
|
| 241 |
+
"normalized": false,
|
| 242 |
+
"rstrip": false,
|
| 243 |
+
"single_word": false,
|
| 244 |
+
"special": true
|
| 245 |
+
},
|
| 246 |
+
"30": {
|
| 247 |
+
"content": "<|reserved_special_17|>",
|
| 248 |
+
"lstrip": false,
|
| 249 |
+
"normalized": false,
|
| 250 |
+
"rstrip": false,
|
| 251 |
+
"single_word": false,
|
| 252 |
+
"special": true
|
| 253 |
+
},
|
| 254 |
+
"31": {
|
| 255 |
+
"content": "<|reserved_special_18|>",
|
| 256 |
+
"lstrip": false,
|
| 257 |
+
"normalized": false,
|
| 258 |
+
"rstrip": false,
|
| 259 |
+
"single_word": false,
|
| 260 |
+
"special": true
|
| 261 |
+
}
|
| 262 |
+
},
|
| 263 |
+
"bos_token": "<|begin_of_text|>",
|
| 264 |
+
"clean_up_tokenization_spaces": false,
|
| 265 |
+
"eos_token": "<|im_end|>",
|
| 266 |
+
"extra_special_tokens": {},
|
| 267 |
+
"model_max_length": 65536,
|
| 268 |
+
"pad_token": "<|pad|>",
|
| 269 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
| 270 |
+
"use_default_system_prompt": false
|
| 271 |
+
}
|