mirror of
https://github.com/wassname/discovering_latent_knowledge.git
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misc
This commit is contained in:
wassname
+6
-4
@@ -1784,8 +1784,10 @@ Ok so I got a center of mass intervention to the heads :)
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Now I just need to debug the dataloading script... nesxt week
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TODO: I need to split test train based on example_i, not row!
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TODO: the plotted and metric acc don't line up
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bugs?:
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- TODO: I need to split test train based on example_i, not row!
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- TODO: the plotted and metric acc don't line up
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maybe I need both head and mlp?
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or just residual stream?
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ideas:
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- maybe I need both head and mlp?
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- or just residual stream?
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File diff suppressed because one or more lines are too long
@@ -364,13 +364,13 @@ def create_intervention(ds_name, ds_tokens, model, layer_names, N=10):
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interventions = get_interventions_dict(activations, labels, layer_names, num_heads)
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return interventions
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def load_intervention(ds_name, cfg, model, tokenizer, model_name):
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def load_intervention(ds_name, cfg, model, tokenizer, model_name, N=30):
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num_heads = model.config.num_attention_heads
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intervention_f = root_folder / 'data' / 'interventions' / f'{model_name}.pkl'
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intervention_f.parent.mkdir(exist_ok=True, parents=True)
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if not intervention_f.exists():
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layer_names, layer_inds = ExtractHiddenStates(model, tokenizer, layer_stride=cfg.layer_stride, layer_padding=cfg.layer_padding).get_layer_names()
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ds_tokens = load_preproc_dataset(ds_name, cfg, tokenizer, N=10)
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ds_tokens = load_preproc_dataset(ds_name, cfg, tokenizer, N=N)
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interventions = create_intervention(ds_name, ds_tokens, model, layer_names)
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torch.save(interventions, intervention_f)
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else:
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@@ -408,7 +408,7 @@ if __name__ == "__main__":
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cfg = args.run
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print(cfg)
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BATCH_SIZE = 4 # None # None means auto # 6 gives 16Gb/25GB. where 10GB is the base model. so 6 is 6/15
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BATCH_SIZE = 2 # None # None means auto # 6 gives 16Gb/25GB. where 10GB is the base model. so 6 is 6/15
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ds_names = cfg.datasets
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Generated
+130
-1
@@ -1187,6 +1187,17 @@ MarkupSafe = ">=2.0"
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[package.extras]
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i18n = ["Babel (>=2.7)"]
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[[package]]
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name = "joblib"
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version = "1.3.2"
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description = "Lightweight pipelining with Python functions"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
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{file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
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]
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[[package]]
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name = "jupyter-client"
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version = "8.4.0"
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@@ -2442,6 +2453,24 @@ extra = ["hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.18.0)", "matplot
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strategies = ["deepspeed (>=0.8.2,<=0.9.3)"]
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test = ["cloudpickle (>=1.3)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0)", "onnxruntime (>=0.15.0)", "pandas (>1.0)", "psutil (<5.9.6)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "uvicorn"]
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[[package]]
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name = "pytorch-optimizer"
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version = "2.12.0"
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description = "optimizer & lr scheduler & objective function collections in PyTorch"
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optional = false
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python-versions = ">=3.7.2,<4.0.0"
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files = [
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{file = "pytorch_optimizer-2.12.0-py3-none-any.whl", hash = "sha256:1683f305265c9428b486365fc1286d792e6cacac97eb10395a725d460d441152"},
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{file = "pytorch_optimizer-2.12.0.tar.gz", hash = "sha256:81bd70988b564853f8a0e5ec3f787e385b727d205782a95d209145939dddec79"},
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]
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[package.dependencies]
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numpy = {version = "*", markers = "python_version >= \"3.8\""}
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torch = {version = ">=1.10", markers = "python_version >= \"3.8\""}
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[package.extras]
|
||||
bitsandbytes = ["bitsandbytes (>=0.41,<0.42)"]
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[[package]]
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name = "pytz"
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version = "2023.3.post1"
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@@ -2909,6 +2938,95 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
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testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
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torch = ["safetensors[numpy]", "torch (>=1.10)"]
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||||
[[package]]
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name = "scikit-learn"
|
||||
version = "1.3.1"
|
||||
description = "A set of python modules for machine learning and data mining"
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optional = false
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||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "scikit-learn-1.3.1.tar.gz", hash = "sha256:1a231cced3ee3fa04756b4a7ab532dc9417acd581a330adff5f2c01ac2831fcf"},
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{file = "scikit_learn-1.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3153612ff8d36fa4e35ef8b897167119213698ea78f3fd130b4068e6f8d2da5a"},
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{file = "scikit_learn-1.3.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:6bb9490fdb8e7e00f1354621689187bef3cab289c9b869688f805bf724434755"},
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{file = "scikit_learn-1.3.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a7135a03af71138669f19bc96e7d0cc8081aed4b3565cc3b131135d65fc642ba"},
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{file = "scikit_learn-1.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7d8dee8c1f40eeba49a85fe378bdf70a07bb64aba1a08fda1e0f48d27edfc3e6"},
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{file = "scikit_learn-1.3.1-cp310-cp310-win_amd64.whl", hash = "sha256:4d379f2b34096105a96bd857b88601dffe7389bd55750f6f29aaa37bc6272eb5"},
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{file = "scikit_learn-1.3.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:58b0c2490eff8355dc26e884487bf8edaccf2ba48d09b194fb2f3a026dd64f9d"},
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{file = "scikit_learn-1.3.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ef540e09873e31569bc8b02c8a9f745ee04d8e1263255a15c9969f6f5caa627f"},
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{file = "scikit_learn-1.3.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:9147a3a4df4d401e618713880be023e36109c85d8569b3bf5377e6cd3fecdeac"},
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{file = "scikit_learn-1.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:0e1aa8f206d0de814b81b41d60c1ce31f7f2c7354597af38fae46d9c47c45122"},
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{file = "scikit_learn-1.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ce9233cdf0cdcf0858a5849d306490bf6de71fa7603a3835124e386e62f2311"},
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
joblib = ">=1.1.1"
|
||||
numpy = ">=1.17.3,<2.0"
|
||||
scipy = ">=1.5.0"
|
||||
threadpoolctl = ">=2.0.0"
|
||||
|
||||
[package.extras]
|
||||
benchmark = ["matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"]
|
||||
docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-gallery (>=0.10.1)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
|
||||
examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"]
|
||||
tests = ["black (>=23.3.0)", "matplotlib (>=3.1.3)", "mypy (>=1.3)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.0.272)", "scikit-image (>=0.16.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
version = "1.11.3"
|
||||
description = "Fundamental algorithms for scientific computing in Python"
|
||||
optional = false
|
||||
python-versions = "<3.13,>=3.9"
|
||||
files = [
|
||||
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||||
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.21.6,<1.28.0"
|
||||
|
||||
[package.extras]
|
||||
dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
|
||||
doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
|
||||
test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
|
||||
[[package]]
|
||||
name = "sentencepiece"
|
||||
version = "0.1.99"
|
||||
@@ -3090,6 +3208,17 @@ files = [
|
||||
[package.extras]
|
||||
doc = ["reno", "sphinx", "tornado (>=4.5)"]
|
||||
|
||||
[[package]]
|
||||
name = "threadpoolctl"
|
||||
version = "3.2.0"
|
||||
description = "threadpoolctl"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"},
|
||||
{file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tokenizers"
|
||||
version = "0.14.1"
|
||||
@@ -3739,4 +3868,4 @@ multidict = ">=4.0"
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.13"
|
||||
content-hash = "3c2b3c2e82ad5527db4b9bb5e6df127c62ab439e6440d005f026cc43904c51ef"
|
||||
content-hash = "6068bb1c5ec5042e14fe98ec08112adb751f51829405378a393df028baf8c881"
|
||||
|
||||
@@ -24,6 +24,8 @@ loguru = "^0.7.2"
|
||||
einops = "^0.7.0"
|
||||
baukit = {git = "https://github.com/davidbau/baukit.git"}
|
||||
eleuther-elk = "0.1.1"
|
||||
scikit-learn = "^1.3.1"
|
||||
pytorch-optimizer = "^2.12.0"
|
||||
|
||||
[[tool.poetry.source]]
|
||||
name = "pytorch"
|
||||
|
||||
@@ -18,7 +18,7 @@ class ExtractConfig(Serializable):
|
||||
# int4: bool = True
|
||||
# """Whether to perform inference in mixed int8 precision with `bitsandbytes`."""
|
||||
|
||||
max_examples: tuple[int, int] = (400, 400)
|
||||
max_examples: tuple[int, int] = (800, 800)
|
||||
"""Maximum number of examples to use from each split of the dataset."""
|
||||
|
||||
num_shots: int = 1
|
||||
@@ -41,11 +41,11 @@ class ExtractConfig(Serializable):
|
||||
seed: int = 42
|
||||
"""Seed to use for prompt randomization. Defaults to 42."""
|
||||
|
||||
token_loc: Literal["first", "last", "mean"] = "last"
|
||||
"""The location of the token to extract hidden states from."""
|
||||
# token_loc: Literal["first", "last", "mean"] = "last"
|
||||
# """The location of the token to extract hidden states from."""
|
||||
|
||||
template_path: str | None = None
|
||||
"""Path to pass into `DatasetTemplates`. By default we use the dataset name."""
|
||||
|
||||
max_length: int | None = 1000
|
||||
max_length: int | None = 555
|
||||
"""Maximum length of the input sequence passed to the tokenize encoder function"""
|
||||
|
||||
Reference in New Issue
Block a user