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[Minor] Fix the pyvene101 with correct examples #130

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Mar 14, 2024
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[Minor] Fix the pyvene101 with correct examples #130

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aed0a23
[Minor] Fix the pyvene101 with correct examples
frankaging Mar 14, 2024
d36a3d5
remove extra debugging code
frankaging Mar 14, 2024
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34 changes: 22 additions & 12 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -38,31 +38,41 @@ pip install pyvene
```

## _Wrap_ , _Intervene_ and _Share_
You can intervene with supported models as,
You can intervene with any HuggingFace model as,
```python
import torch
import pyvene as pv
from transformers import AutoTokenizer, AutoModelForCausalLM

_, tokenizer, gpt2 = pv.create_gpt2()
model_name = "meta-llama/Llama-2-7b-hf" # your HF model name.
model = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype=torch.bfloat16, device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name)

def zeroout_intervention_fn(b, s):
b[:,3] = 0. # 3rd position
return b

pv_gpt2 = pv.IntervenableModel({
"layer": 0, "component": "block_output",
"source_representation": torch.zeros(gpt2.config.n_embd)
}, model=gpt2)
pv_model = pv.IntervenableModel({
"component": "model.layers[15].mlp.output", # string access
"intervention": zeroout_intervention_fn}, model=model)

orig_outputs, intervened_outputs = pv_gpt2(
base = tokenizer("The capital of Spain is", return_tensors="pt"),
unit_locations={"base": 3}
# run the intervened forward pass
orig_outputs, intervened_outputs = pv_model(
tokenizer("The capital of Spain is", return_tensors="pt").to('cuda'),
output_original_output=True
)
print(intervened_outputs.last_hidden_state - orig_outputs.last_hidden_state)
print(intervened_outputs.logits - orig_outputs.logits)
```
which returns,
```
tensor([[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[ 0.0483, -0.1212, -0.2816, ..., 0.1958, 0.0830, 0.0784],
[ 0.0519, 0.2547, -0.1631, ..., 0.0050, -0.0453, -0.1624]]])
[ 0.4375, 1.0625, 0.3750, ..., -0.1562, 0.4844, 0.2969],
[ 0.0938, 0.1250, 0.1875, ..., 0.2031, 0.0625, 0.2188],
[ 0.0000, -0.0625, -0.0312, ..., 0.0000, 0.0000, -0.0156]]],
device='cuda:0')
```

## _IntervenableModel_ Loaded from HuggingFace Directly
Expand Down
80 changes: 13 additions & 67 deletions pyvene_101.ipynb
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Expand Up @@ -126,7 +126,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 2,
"id": "17c7f2f6-b0d3-4fe2-8e4f-c044b93f3ef0",
"metadata": {},
"outputs": [],
Expand All @@ -135,27 +135,19 @@
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"\n",
"model_name = \"gpt2\"\n",
"model = AutoModelForCausalLM.from_pretrained(model_name)\n",
"gpt2 = AutoModelForCausalLM.from_pretrained(model_name)\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"\n",
"# create a dict-based intervention config\n",
"pv_config = pv.IntervenableConfig({\n",
" \"component\": \"transformer.h[0].mlp.output\"},\n",
" intervention_types=pv.VanillaIntervention\n",
")\n",
"# wrap your model with the config\n",
"pv_gpt2 = pv.IntervenableModel(pv_config, model=model)\n",
"pv_gpt2 = pv.IntervenableModel({\n",
" \"layer\": 10,\n",
" \"component\": \"attention_weight\",\n",
" \"intervention_type\": pv.CollectIntervention}, model=gpt2)\n",
"\n",
"# run an interchange intervention (activation swap between two examples)\n",
"intervened_outputs = pv_gpt2(\n",
" # the base input\n",
" base=tokenizer(\"The capital of Spain is\", return_tensors = \"pt\"), \n",
" # the source input\n",
" sources=tokenizer(\"The capital of Italy is\", return_tensors = \"pt\"), \n",
" # the location to intervene at (3rd token)\n",
" unit_locations={\"sources->base\": 3},\n",
" output_original_output=True # False then the first element in the tuple is None\n",
")"
"base = \"When John and Mary went to the shops, Mary gave the bag to\"\n",
"collected_attn_w = pv_gpt2(\n",
" base = tokenizer(base, return_tensors=\"pt\"\n",
" ), unit_locations={\"base\": [h for h in range(12)]}\n",
")[0][-1][0]"
]
},
{
Expand All @@ -166,53 +158,6 @@
"#### Get Attention Weights with Direct Access String"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "1ef4a1db-5187-4457-9878-f1dc03e9859b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GPT2LMHeadModel(\n",
" (transformer): GPT2Model(\n",
" (wte): Embedding(50257, 768)\n",
" (wpe): Embedding(1024, 768)\n",
" (drop): Dropout(p=0.1, inplace=False)\n",
" (h): ModuleList(\n",
" (0-11): 12 x GPT2Block(\n",
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
")"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 19,
Expand All @@ -231,6 +176,7 @@
"import torch\n",
"import pyvene as pv\n",
"\n",
"# gpt2 helper loading model from HuggingFace\n",
"_, tokenizer, gpt2 = pv.create_gpt2()\n",
"\n",
"pv_gpt2 = pv.IntervenableModel({\n",
Expand Down Expand Up @@ -724,7 +670,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 2,
"id": "7f058ecd",
"metadata": {},
"outputs": [
Expand Down
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