From 2691e8ff57dd4d997712668733473c8374510e8b Mon Sep 17 00:00:00 2001
From: David Chanin <chanindav@gmail.com>
Date: Thu, 16 Nov 2023 00:21:01 +0000
Subject: [PATCH] adding initial libs and classes

---
 .github/workflows/ci.yaml                     |   26 +
 .gitignore                                    |    8 +-
 README.md                                     |    0
 linear_relational/Lre.py                      |  213 +++
 linear_relational/Prompt.py                   |   12 +
 linear_relational/PromptValidator.py          |   95 +
 linear_relational/__init__.py                 |    0
 linear_relational/lib/TraceLayer.py           |  132 ++
 linear_relational/lib/TraceLayerDict.py       |   97 +
 .../lib/balance_grouped_items.py              |   52 +
 linear_relational/lib/constants.py            |    6 +
 .../lib/extract_token_activations.py          |  125 ++
 linear_relational/lib/layer_matching.py       |   61 +
 linear_relational/lib/logger.py               |   13 +
 linear_relational/lib/token_utils.py          |  278 +++
 linear_relational/lib/torch_utils.py          |   71 +
 linear_relational/lib/util.py                 |  108 ++
 .../lib/verify_answers_match_expected.py      |   71 +
 poetry.lock                                   | 1561 +++++++++++++++++
 pyproject.toml                                |   30 +
 setup.cfg                                     |   46 +
 tests/__init__.py                             |    0
 tests/conftest.py                             |   23 +
 tests/lib/test_balance_grouped_items.py       |   81 +
 tests/lib/test_token_utils.py                 |  168 ++
 tests/lib/test_torch_utils.py                 |   15 +
 tests/lib/test_util.py                        |   31 +
 .../lib/test_verify_answers_match_expected.py |   66 +
 tests/test_Lre.py                             |  120 ++
 tests/test_PromptValidator.py                 |    5 +
 30 files changed, 3513 insertions(+), 1 deletion(-)
 create mode 100644 .github/workflows/ci.yaml
 create mode 100644 README.md
 create mode 100644 linear_relational/Lre.py
 create mode 100644 linear_relational/Prompt.py
 create mode 100644 linear_relational/PromptValidator.py
 create mode 100644 linear_relational/__init__.py
 create mode 100644 linear_relational/lib/TraceLayer.py
 create mode 100644 linear_relational/lib/TraceLayerDict.py
 create mode 100644 linear_relational/lib/balance_grouped_items.py
 create mode 100644 linear_relational/lib/constants.py
 create mode 100644 linear_relational/lib/extract_token_activations.py
 create mode 100644 linear_relational/lib/layer_matching.py
 create mode 100644 linear_relational/lib/logger.py
 create mode 100644 linear_relational/lib/token_utils.py
 create mode 100644 linear_relational/lib/torch_utils.py
 create mode 100644 linear_relational/lib/util.py
 create mode 100644 linear_relational/lib/verify_answers_match_expected.py
 create mode 100644 poetry.lock
 create mode 100644 pyproject.toml
 create mode 100644 setup.cfg
 create mode 100644 tests/__init__.py
 create mode 100644 tests/conftest.py
 create mode 100644 tests/lib/test_balance_grouped_items.py
 create mode 100644 tests/lib/test_token_utils.py
 create mode 100644 tests/lib/test_torch_utils.py
 create mode 100644 tests/lib/test_util.py
 create mode 100644 tests/lib/test_verify_answers_match_expected.py
 create mode 100644 tests/test_Lre.py
 create mode 100644 tests/test_PromptValidator.py

diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
new file mode 100644
index 0000000..d5338d3
--- /dev/null
+++ b/.github/workflows/ci.yaml
@@ -0,0 +1,26 @@
+name: CI
+on: [push]
+jobs:
+  lint_test_and_build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v3
+        with:
+          python-version: "3.11"
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.4.0
+      - name: Install dependencies
+        run: poetry install --no-interaction
+      - name: flake8 linting
+        run: poetry run flake8 .
+      - name: black code formatting
+        run: poetry run black . --check
+      - name: mypy type checking
+        run: poetry run mypy .
+      - name: pytest
+        run: poetry run pytest
+      - name: build
+        run: poetry build
diff --git a/.gitignore b/.gitignore
index 68bc17f..3da3351 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,7 +14,7 @@ dist/
 downloads/
 eggs/
 .eggs/
-lib/
+# lib/
 lib64/
 parts/
 sdist/
@@ -158,3 +158,9 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+# misc
+.vscode/
+.DS_Store
+*.pt
+lightning_logs/
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/linear_relational/Lre.py b/linear_relational/Lre.py
new file mode 100644
index 0000000..263cf00
--- /dev/null
+++ b/linear_relational/Lre.py
@@ -0,0 +1,213 @@
+from typing import Any, Literal
+
+import torch
+from torch import nn
+
+
+class InvertedLre(nn.Module):
+    """Low-rank inverted LRE, used for calculating subject activations from object activations"""
+
+    relation: str
+    subject_layer: int
+    object_layer: int
+    # store u, v, s, and bias separately to avoid storing the full weight matrix
+    u: nn.Parameter
+    s: nn.Parameter
+    v: nn.Parameter
+    bias: nn.Parameter
+    object_aggregation: Literal["mean", "first_token"]
+    metadata: dict[str, Any] | None = None
+
+    def __init__(
+        self,
+        relation: str,
+        subject_layer: int,
+        object_layer: int,
+        object_aggregation: Literal["mean", "first_token"],
+        u: torch.Tensor,
+        s: torch.Tensor,
+        v: torch.Tensor,
+        bias: torch.Tensor,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__()
+        self.relation = relation
+        self.subject_layer = subject_layer
+        self.object_layer = object_layer
+        self.object_aggregation = object_aggregation
+        self.u = nn.Parameter(u, requires_grad=False)
+        self.s = nn.Parameter(s, requires_grad=False)
+        self.v = nn.Parameter(v, requires_grad=False)
+        self.bias = nn.Parameter(bias, requires_grad=False)
+        self.metadata = metadata
+
+    @property
+    def rank(self) -> int:
+        return self.s.shape[0]
+
+    def w_inv_times_vec(self, vec: torch.Tensor) -> torch.Tensor:
+        # group u.T @ vec to avoid calculating larger matrices than needed
+        return self.v @ torch.diag(1 / self.s) @ (self.u.T @ vec)
+
+    def forward(
+        self,
+        subject_activations: torch.Tensor,  # a tensor of shape (num_activations, hidden_activation_size)
+        normalize: bool = False,
+    ) -> torch.Tensor:
+        return self.calculate_object_activation(
+            subject_activations=subject_activations,
+            normalize=normalize,
+        )
+
+    def calculate_subject_activation(
+        self,
+        object_activations: torch.Tensor,  # a tensor of shape (num_activations, hidden_activation_size)
+        normalize: bool = False,
+    ) -> torch.Tensor:
+        # match precision of weight_inverse and bias
+        unbiased_acts = object_activations - self.bias.unsqueeze(0)
+        vec = self.w_inv_times_vec(unbiased_acts.T).mean(dim=1)
+
+        if normalize:
+            vec = vec / vec.norm()
+        return vec
+
+
+class LowRankLre(nn.Module):
+    """Low-rank approximation of a LRE"""
+
+    relation: str
+    subject_layer: int
+    object_layer: int
+    # store u, v, s, and bias separately to avoid storing the full weight matrix
+    u: nn.Parameter
+    s: nn.Parameter
+    v: nn.Parameter
+    bias: nn.Parameter
+    object_aggregation: Literal["mean", "first_token"]
+    metadata: dict[str, Any] | None = None
+
+    def __init__(
+        self,
+        relation: str,
+        subject_layer: int,
+        object_layer: int,
+        object_aggregation: Literal["mean", "first_token"],
+        u: torch.Tensor,
+        s: torch.Tensor,
+        v: torch.Tensor,
+        bias: torch.Tensor,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__()
+        self.relation = relation
+        self.subject_layer = subject_layer
+        self.object_layer = object_layer
+        self.object_aggregation = object_aggregation
+        self.u = nn.Parameter(u, requires_grad=False)
+        self.s = nn.Parameter(s, requires_grad=False)
+        self.v = nn.Parameter(v, requires_grad=False)
+        self.bias = nn.Parameter(bias, requires_grad=False)
+        self.metadata = metadata
+
+    @property
+    def rank(self) -> int:
+        return self.s.shape[0]
+
+    def w_times_vec(self, vec: torch.Tensor) -> torch.Tensor:
+        # group v.T @ vec to avoid calculating larger matrices than needed
+        return self.u @ torch.diag(self.s) @ (self.v.T @ vec)
+
+    def forward(
+        self,
+        subject_activations: torch.Tensor,  # a tensor of shape (num_activations, hidden_activation_size)
+        normalize: bool = False,
+    ) -> torch.Tensor:
+        return self.calculate_object_activation(
+            subject_activations=subject_activations,
+            normalize=normalize,
+        )
+
+    def calculate_object_activation(
+        self,
+        subject_activations: torch.Tensor,  # a tensor of shape (num_activations, hidden_activation_size)
+        normalize: bool = False,
+    ) -> torch.Tensor:
+        # match precision of weight_inverse and bias
+        ws = self.w_times_vec(subject_activations.T)
+        vec = (ws + self.bias.unsqueeze(-1)).mean(dim=1)
+        if normalize:
+            vec = vec / vec.norm()
+        return vec
+
+
+class Lre(nn.Module):
+    """Linear Relational Embedding"""
+
+    relation: str
+    subject_layer: int
+    object_layer: int
+    weight: nn.Parameter
+    bias: nn.Parameter
+    object_aggregation: Literal["mean", "first_token"]
+    metadata: dict[str, Any] | None = None
+
+    def __init__(
+        self,
+        relation: str,
+        subject_layer: int,
+        object_layer: int,
+        object_aggregation: Literal["mean", "first_token"],
+        weight: torch.Tensor,
+        bias: torch.Tensor,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__()
+        self.relation = relation
+        self.subject_layer = subject_layer
+        self.object_layer = object_layer
+        self.object_aggregation = object_aggregation
+        self.weight = nn.Parameter(weight, requires_grad=False)
+        self.bias = nn.Parameter(bias, requires_grad=False)
+        self.metadata = metadata
+
+    def invert(self, rank: int) -> InvertedLre:
+        """Invert this LRE using a low-rank approximation"""
+        u, s, v = self._low_rank_svd(rank)
+        return InvertedLre(
+            relation=self.relation,
+            subject_layer=self.subject_layer,
+            object_layer=self.object_layer,
+            object_aggregation=self.object_aggregation,
+            u=u.detach().clone(),
+            s=s.detach().clone(),
+            v=v.detach().clone(),
+            bias=self.bias.detach().clone(),
+            metadata=self.metadata,
+        )
+
+    def to_low_rank(self, rank: int) -> LowRankLre:
+        """Create a low-rank approximation of this LRE"""
+        u, s, v = self._low_rank_svd(rank)
+        return LowRankLre(
+            relation=self.relation,
+            subject_layer=self.subject_layer,
+            object_layer=self.object_layer,
+            object_aggregation=self.object_aggregation,
+            u=u.detach().clone(),
+            s=s.detach().clone(),
+            v=v.detach().clone(),
+            bias=self.bias.detach().clone(),
+            metadata=self.metadata,
+        )
+
+    @torch.no_grad()
+    def _low_rank_svd(
+        self, rank: int
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        # use a float for the svd, then convert back to the original dtype
+        u, s, v = torch.svd(self.weight.float())
+        low_rank_u: torch.Tensor = u[:, :rank].to(self.weight.dtype)
+        low_rank_v: torch.Tensor = v[:, :rank].to(self.weight.dtype)
+        low_rank_s: torch.Tensor = s[:rank].to(self.weight.dtype)
+        return low_rank_u, low_rank_s, low_rank_v
diff --git a/linear_relational/Prompt.py b/linear_relational/Prompt.py
new file mode 100644
index 0000000..47dbff1
--- /dev/null
+++ b/linear_relational/Prompt.py
@@ -0,0 +1,12 @@
+from dataclasses import dataclass
+from typing import Optional
+
+
+@dataclass(frozen=True, slots=True)
+class Prompt:
+    text: str
+    answer: str
+    subject: str
+    subject_name: Optional[str] = None
+    object_name: Optional[str] = None
+    relation_name: Optional[str] = None
diff --git a/linear_relational/PromptValidator.py b/linear_relational/PromptValidator.py
new file mode 100644
index 0000000..3b7715a
--- /dev/null
+++ b/linear_relational/PromptValidator.py
@@ -0,0 +1,95 @@
+import hashlib
+import os
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable, Optional
+
+from dataclasses_json import DataClassJsonMixin
+from tokenizers import Tokenizer
+from torch import nn
+
+from linear_relational.lib.torch_utils import guess_model_name
+from linear_relational.lib.verify_answers_match_expected import (
+    verify_answers_match_expected,
+)
+from linear_relational.Prompt import Prompt
+
+
+@dataclass
+class MatchingPromptsCache(DataClassJsonMixin):
+    cache: dict[str, bool]
+    model_name: str
+
+
+class PromptValidator:
+    """
+    Helper class to filter prompts that match a given list of tokens.
+    This class handles caching results to avoid repeating work between runs.
+    """
+
+    _cache: MatchingPromptsCache
+    cache_file: str | Path | None
+    model: nn.Module
+    tokenizer: Tokenizer
+
+    def __init__(
+        self,
+        model: nn.Module,
+        tokenizer: Tokenizer,
+        cache_file: Optional[str | Path] = None,
+        load_saved_cache: bool = True,
+    ) -> None:
+        self.model = model
+        self.tokenizer = tokenizer
+        model_name = guess_model_name(model)
+        self.cache_file = cache_file
+        if cache_file and load_saved_cache and os.path.exists(cache_file):
+            with open(cache_file, "r") as f:
+                self._cache = MatchingPromptsCache.from_json(f.read())
+                if self._cache.model_name != model_name:
+                    raise ValueError(
+                        f"Cache file {cache_file} was generated by a different model"
+                    )
+        else:
+            self._cache = MatchingPromptsCache(cache={}, model_name=model_name)
+
+    def write_cache(self, cache_file: Optional[str | Path] = None) -> None:
+        _cache_file = cache_file or self.cache_file
+        if _cache_file is None:
+            raise ValueError("No cache file was provided")
+        with open(_cache_file, "w") as f:
+            f.write(self._cache.to_json())
+
+    def _is_cached(self, prompt: Prompt) -> bool:
+        key = cache_key(prompt.text, prompt.answer)
+        return key in self._cache.cache
+
+    def _prompt_matches(self, prompt: Prompt) -> bool:
+        key = cache_key(prompt.text, prompt.answer)
+        return self._cache.cache[key]
+
+    def filter_prompts(
+        self,
+        prompts: Iterable[Prompt],
+        batch_size: int = 8,
+        show_progress: bool = False,
+    ) -> list[Prompt]:
+        uncached_prompts = [prompt for prompt in prompts if not self._is_cached(prompt)]
+        if len(uncached_prompts) > 0:
+            answer_match_results = verify_answers_match_expected(
+                model=self.model,
+                tokenizer=self.tokenizer,
+                prompts=[prompt.text for prompt in uncached_prompts],
+                expected_answers=[prompt.answer for prompt in uncached_prompts],
+                batch_size=batch_size,
+                show_progress=show_progress,
+            )
+            for prompt, match_result in zip(uncached_prompts, answer_match_results):
+                key = cache_key(prompt.text, prompt.answer)
+                self._cache.cache[key] = match_result.answer_matches_expected
+        return [prompt for prompt in prompts if self._prompt_matches(prompt)]
+
+
+def cache_key(prompt_text: str, answer: str) -> str:
+    # return a md5 hash of the prompt text and answer
+    return hashlib.md5((prompt_text + answer).encode("utf-8")).hexdigest()[:15]
diff --git a/linear_relational/__init__.py b/linear_relational/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/linear_relational/lib/TraceLayer.py b/linear_relational/lib/TraceLayer.py
new file mode 100644
index 0000000..65d6157
--- /dev/null
+++ b/linear_relational/lib/TraceLayer.py
@@ -0,0 +1,132 @@
+"""
+Utilities for instrumenting a torch model.
+
+Trace will hook one layer at a time.
+TraceDict will hook multiple layers at once.
+subsequence slices intervals from Sequential modules.
+get_module, replace_module, get_parameter resolve dotted names.
+set_requires_grad recursively sets requires_grad in module parameters.
+
+Copied from https://github.com/kmeng01/rome/blob/main/util/nethook.py
+"""
+
+from __future__ import annotations
+
+import contextlib
+from typing import Any, Callable, Optional
+
+import torch
+from torch import nn
+
+from .torch_utils import get_module, recursive_tensor_copy
+
+
+class TraceLayer(contextlib.AbstractContextManager["TraceLayer"]):
+    """
+    To retain the output of the named layer during the computation of
+    the given network:
+
+        with Trace(net, 'layer.name') as ret:
+            _ = net(inp)
+            representation = ret.output
+
+    A layer module can be passed directly without a layer name, and
+    its output will be retained.  By default, a direct reference to
+    the output object is returned, but options can control this:
+
+        clone=True  - retains a copy of the output, which can be
+            useful if you want to see the output before it might
+            be modified by the network in-place later.
+        detach=True - retains a detached reference or copy.  (By
+            default the value would be left attached to the graph.)
+        retain_grad=True - request gradient to be retained on the
+            output.  After backward(), ret.output.grad is populated.
+
+        retain_input=True - also retains the input.
+        retain_output=False - can disable retaining the output.
+        edit_output=fn - calls the function to modify the output
+            of the layer before passing it the rest of the model.
+            fn can optionally accept (output, layer) arguments
+            for the original output and the layer name.
+        stop=True - throws a StopForward exception after the layer
+            is run, which allows running just a portion of a model.
+    """
+
+    input: Optional[torch.Tensor] = None
+    output: Optional[torch.Tensor] = None
+
+    def __init__(
+        self,
+        module: nn.Module,
+        layer: str,
+        retain_output: bool = True,
+        retain_input: bool = False,
+        clone: bool = False,
+        detach: bool = False,
+        retain_grad: bool = False,
+        edit_output: Optional[Callable[[Any, str], Any]] = None,
+        stop: bool = False,
+    ):
+        """
+        Method to replace a forward method with a closure that
+        intercepts the call, and tracks the hook so that it can be reverted.
+        """
+        retainer = self
+        self.layer = layer
+        layer_module = get_module(module, layer)
+
+        def retain_hook(_m: Any, inputs: Any, output: Any) -> Any:
+            if retain_input:
+                retainer.input = recursive_tensor_copy(
+                    inputs[0] if len(inputs) == 1 else inputs,
+                    clone=clone,
+                    detach=detach,
+                    retain_grad=False,
+                )  # retain_grad applies to output only.
+            if edit_output:
+                output = edit_output(output, self.layer)
+            if retain_output:
+                retainer.output = recursive_tensor_copy(
+                    output, clone=clone, detach=detach, retain_grad=retain_grad
+                )
+                # When retain_grad is set, also insert a trivial
+                # copy operation.  That allows in-place operations
+                # to follow without error.
+                if retain_grad and retainer.output is not None:
+                    output = recursive_tensor_copy(
+                        retainer.output, clone=True, detach=False
+                    )
+            if stop:
+                raise StopForward()
+            return output
+
+        self.registered_hook = layer_module.register_forward_hook(retain_hook)
+        self.stop = stop
+
+    def __enter__(self) -> "TraceLayer":
+        return self
+
+    def __exit__(self, type: Any, _value: Any, _traceback: Any) -> bool | None:
+        self.close()
+        if self.stop and issubclass(type, StopForward):
+            return True
+        return None
+
+    def close(self) -> None:
+        self.registered_hook.remove()
+
+
+class StopForward(Exception):
+    """
+    If the only output needed from running a network is the retained
+    submodule then Trace(submodule, stop=True) will stop execution
+    immediately after the retained submodule by raising the StopForward()
+    exception.  When Trace is used as context manager, it catches that
+    exception and can be used as follows:
+
+    with Trace(net, layername, stop=True) as tr:
+        net(inp) # Only runs the network up to layername
+    print(tr.output)
+    """
+
+    pass
diff --git a/linear_relational/lib/TraceLayerDict.py b/linear_relational/lib/TraceLayerDict.py
new file mode 100644
index 0000000..2c3aca1
--- /dev/null
+++ b/linear_relational/lib/TraceLayerDict.py
@@ -0,0 +1,97 @@
+"""
+Utilities for instrumenting a torch model.
+
+Trace will hook one layer at a time.
+TraceDict will hook multiple layers at once.
+subsequence slices intervals from Sequential modules.
+get_module, replace_module, get_parameter resolve dotted names.
+set_requires_grad recursively sets requires_grad in module parameters.
+
+Copied from https://github.com/kmeng01/rome/blob/main/util/nethook.py
+"""
+
+from __future__ import annotations
+
+import contextlib
+from collections import OrderedDict
+from typing import Any, Callable, Iterable, Optional
+
+from torch import nn
+
+from .TraceLayer import StopForward, TraceLayer
+
+
+class TraceLayerDict(
+    OrderedDict[str, TraceLayer],
+    contextlib.AbstractContextManager["TraceLayerDict"],
+):
+    """
+    To retain the output of multiple named layers during the computation
+    of the given network:
+
+        with TraceDict(net, ['layer1.name1', 'layer2.name2']) as ret:
+            _ = net(inp)
+            representation = ret['layer1.name1'].output
+
+    If edit_output is provided, it should be a function that takes
+    two arguments: output, and the layer name; and then it returns the
+    modified output.
+
+    Other arguments are the same as Trace.  If stop is True, then the
+    execution of the network will be stopped after the last layer
+    listed (even if it would not have been the last to be executed).
+    """
+
+    def __init__(
+        self,
+        module: nn.Module,
+        layers: Optional[Iterable[str]] = None,
+        retain_output: bool = True,
+        retain_input: bool = False,
+        clone: bool = False,
+        detach: bool = False,
+        retain_grad: bool = False,
+        edit_output: Optional[Callable[[Any, str], Any]] = None,
+        stop: bool = False,
+    ):
+        self.stop = stop
+
+        def flag_last_unseen(it: Any) -> Any:
+            try:
+                it = iter(it)
+                prev = next(it)
+                seen = set([prev])
+            except StopIteration:
+                return
+            for item in it:
+                if item not in seen:
+                    yield False, prev
+                    seen.add(item)
+                    prev = item
+            yield True, prev
+
+        for is_last, layer in flag_last_unseen(layers):
+            self[layer] = TraceLayer(
+                module=module,
+                layer=layer,
+                retain_output=retain_output,
+                retain_input=retain_input,
+                clone=clone,
+                detach=detach,
+                retain_grad=retain_grad,
+                edit_output=edit_output,
+                stop=stop and is_last,
+            )
+
+    def __enter__(self) -> "TraceLayerDict":
+        return self
+
+    def __exit__(self, type: Any, _value: Any, _traceback: Any) -> bool | None:
+        self.close()
+        if self.stop and issubclass(type, StopForward):
+            return True
+        return None
+
+    def close(self) -> None:
+        for _layer, trace in reversed(self.items()):
+            trace.close()
diff --git a/linear_relational/lib/balance_grouped_items.py b/linear_relational/lib/balance_grouped_items.py
new file mode 100644
index 0000000..d690781
--- /dev/null
+++ b/linear_relational/lib/balance_grouped_items.py
@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import Optional, TypeVar
+
+from linear_relational.lib.util import stable_shuffle
+
+T = TypeVar("T")
+
+
+def balance_grouped_items(
+    items_by_group: dict[str, list[T]],
+    max_per_group: Optional[int] = None,
+    max_total: Optional[int] = None,
+    seed: int | float | str = 42,
+) -> list[T]:
+    """
+    Helper to pick items in a round-robin fashion from each of the possible groups
+    Tries to balance the amount of items that come from each group as much as possible
+    `items_by_group` is a dict of group name to list of items
+    """
+    requests: list[T] = []
+    concept_names = stable_shuffle(list(items_by_group.keys()), seed=seed)
+    shuffled_reqs_by_concept = {
+        concept: stable_shuffle(reqs, seed=f"{seed}{concept}")
+        for concept, reqs in items_by_group.items()
+    }
+    prompts_per_concept: dict[str, int] = defaultdict(int)
+    total_prompts = 0
+    for reqs in items_by_group.values():
+        num_reqs_from_concept = len(reqs)
+        if max_per_group is not None and num_reqs_from_concept > max_per_group:
+            num_reqs_from_concept = max_per_group
+        total_prompts += num_reqs_from_concept
+    if max_total is not None:
+        total_prompts = min(total_prompts, max_total)
+
+    concept_index = 0
+    while len(requests) < total_prompts:
+        concept_name = concept_names[concept_index]
+        reqs = shuffled_reqs_by_concept[concept_name]
+        concept_index = (concept_index + 1) % len(concept_names)
+        if (
+            max_per_group is not None
+            and prompts_per_concept[concept_name] >= max_per_group
+        ):
+            continue
+        if prompts_per_concept[concept_name] >= len(reqs):
+            continue
+        requests.append(reqs[prompts_per_concept[concept_name]])
+        prompts_per_concept[concept_name] += 1
+    return requests
diff --git a/linear_relational/lib/constants.py b/linear_relational/lib/constants.py
new file mode 100644
index 0000000..ec44298
--- /dev/null
+++ b/linear_relational/lib/constants.py
@@ -0,0 +1,6 @@
+from pathlib import Path
+
+import torch
+
+DATA_DIR = Path(__file__).parent.parent.parent / "data"
+DEFAULT_DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/linear_relational/lib/extract_token_activations.py b/linear_relational/lib/extract_token_activations.py
new file mode 100644
index 0000000..a0e455c
--- /dev/null
+++ b/linear_relational/lib/extract_token_activations.py
@@ -0,0 +1,125 @@
+from collections import OrderedDict
+from typing import Iterable, Sequence
+
+import torch
+from tokenizers import Tokenizer
+from torch import nn
+
+from .constants import DEFAULT_DEVICE
+from .token_utils import find_final_word_token_index, make_inputs
+from .torch_utils import untuple_tensor
+from .TraceLayerDict import TraceLayerDict
+from .util import batchify, tuplify
+
+
+def extract_token_activations(
+    model: nn.Module,
+    tokenizer: Tokenizer,
+    layers: Iterable[str],
+    texts: Sequence[str],
+    token_indices: Sequence[tuple[int, ...] | int],
+    device: torch.device = DEFAULT_DEVICE,
+    move_results_to_cpu: bool = True,
+    batch_size: int = 32,
+    show_progress: bool = False,
+) -> list[OrderedDict[str, list[torch.Tensor]]]:
+    if len(texts) != len(token_indices):
+        raise ValueError(
+            f"Expected {len(texts)} texts to match {len(token_indices)} subject token indices"
+        )
+    results: list[OrderedDict[str, list[torch.Tensor]]] = []
+    for batch in batchify(
+        # need to turn the zip into a list or mypy complains
+        list(zip(texts, token_indices)),
+        batch_size=batch_size,
+        show_progress=show_progress,
+    ):
+        batch_texts = [t for t, _ in batch]
+        batch_subject_token_indices = [tuplify(indices) for _, indices in batch]
+        batch_subj_token_activations = _extract_token_activations_batch(
+            model=model,
+            tokenizer=tokenizer,
+            layers=layers,
+            texts=batch_texts,
+            token_indices=batch_subject_token_indices,
+            device=device,
+            move_results_to_cpu=move_results_to_cpu,
+        )
+        results.extend(batch_subj_token_activations)
+    return results
+
+
+def _extract_token_activations_batch(
+    model: nn.Module,
+    tokenizer: Tokenizer,
+    layers: Iterable[str],
+    texts: list[str],
+    token_indices: list[tuple[int, ...]],
+    device: torch.device = DEFAULT_DEVICE,
+    move_results_to_cpu: bool = True,
+) -> list[OrderedDict[str, list[torch.Tensor]]]:
+    if len(texts) != len(token_indices):
+        raise ValueError(
+            f"Expected {len(texts)} texts to match {len(token_indices)} subject token indices"
+        )
+    inputs = make_inputs(
+        tokenizer=tokenizer,
+        prompts=texts,
+        device=device,
+    )
+    batch_token_activations: list[OrderedDict[str, list[torch.Tensor]]] = [
+        OrderedDict() for _ in texts
+    ]
+    with TraceLayerDict(
+        model,
+        layers=layers,
+        retain_output=True,
+    ) as td:
+        model(**inputs)
+        for layer_name, layer_trace in td.items():
+            assert layer_trace.output is not None
+            raw_output = untuple_tensor(layer_trace.output).detach()
+            for i, toks in enumerate(token_indices):
+                activations = []
+                for tok in toks:
+                    activation = raw_output[i, tok].clone().detach().type(torch.float32)
+                    if move_results_to_cpu:
+                        activation = activation.cpu()
+                    activations.append(activation)
+                batch_token_activations[i][layer_name] = activations
+    return batch_token_activations
+
+
+def extract_final_token_activations(
+    model: nn.Module,
+    tokenizer: Tokenizer,
+    texts: Sequence[str],
+    layers: Iterable[str],
+    device: torch.device = DEFAULT_DEVICE,
+    move_results_to_cpu: bool = True,
+    batch_size: int = 32,
+    show_progress: bool = False,
+) -> list[OrderedDict[str, torch.Tensor]]:
+    raw_activations = extract_token_activations(
+        model,
+        tokenizer,
+        layers=layers,
+        texts=texts,
+        token_indices=[
+            find_final_word_token_index(tokenizer, text, text) for text in texts
+        ],
+        device=device,
+        batch_size=batch_size,
+        show_progress=show_progress,
+        move_results_to_cpu=move_results_to_cpu,
+    )
+    return [_pick_first_activation(activations) for activations in raw_activations]
+
+
+def _pick_first_activation(
+    token_activations: OrderedDict[str, list[torch.Tensor]]
+) -> OrderedDict[str, torch.Tensor]:
+    return OrderedDict(
+        (layer_name, layer_activations[0])
+        for layer_name, layer_activations in token_activations.items()
+    )
diff --git a/linear_relational/lib/layer_matching.py b/linear_relational/lib/layer_matching.py
new file mode 100644
index 0000000..99058a1
--- /dev/null
+++ b/linear_relational/lib/layer_matching.py
@@ -0,0 +1,61 @@
+from typing import Callable, Union
+
+from torch import nn
+
+LayerMatcher = Union[str, Callable[[nn.Module, int], str]]
+
+
+def collect_matching_layers(model: nn.Module, layer_matcher: LayerMatcher) -> list[str]:
+    """
+    Find all layers in the model that match the layer_matcher, in order by layer_num.
+    layer_matcher can be a string formatted like "transformer.h.{num}.mlp" or a callable
+    If layer_matcher is a callable, it should take in a model and layer_num and return
+    a string representing the layer name corresponding to that layer number.
+    If layer_matcher is a string, it's considered a template and MUST contain a "{num}" portion
+    """
+    matcher_callable = _layer_matcher_to_callable(layer_matcher)
+    all_layer_names = dict(model.named_modules()).keys()
+    matching_layers = []
+    for layer_num, layer in enumerate(model.modules()):
+        layer_name = matcher_callable(model, layer_num)
+        if layer_name in all_layer_names:
+            matching_layers.append(layer_name)
+        else:
+            break
+    return matching_layers
+
+
+def get_layer_name(
+    model: nn.Module, layer_matcher: LayerMatcher, layer_num: int
+) -> str:
+    matcher_callable = _layer_matcher_to_callable(layer_matcher)
+    layer_num = fix_neg_layer_num(model, layer_matcher, layer_num)
+    return matcher_callable(model, layer_num)
+
+
+def fix_neg_layer_num(
+    model: nn.Module, layer_matcher: LayerMatcher, layer_num: int
+) -> int:
+    """Helper to handle negative layer nums. If layer_num is negative, return len(layers) + layer_num"""
+    if layer_num >= 0:
+        return layer_num
+    matching_layers = collect_matching_layers(model, layer_matcher)
+    return len(matching_layers) + layer_num
+
+
+def get_layer_by_name(model: nn.Module, layer_name: str) -> nn.Module:
+    return dict(model.named_modules())[layer_name]
+
+
+def _layer_matcher_to_callable(
+    layer_matcher: LayerMatcher,
+) -> Callable[[nn.Module, int], str]:
+    if isinstance(layer_matcher, str):
+        if "{num}" not in layer_matcher:
+            raise ValueError(
+                "layer_matcher must be a callable or a string containing {num}"
+            )
+        matcher_callable = lambda _model, layer_num: layer_matcher.format(num=layer_num)
+        # for some reason mypy doesn't like directly returning the lambda without assigning to a var first
+        return matcher_callable
+    return layer_matcher
diff --git a/linear_relational/lib/logger.py b/linear_relational/lib/logger.py
new file mode 100644
index 0000000..5a2cd65
--- /dev/null
+++ b/linear_relational/lib/logger.py
@@ -0,0 +1,13 @@
+import logging
+from typing import Any
+
+LOGGER_NAME = "linear_relational"
+
+logger = logging.getLogger(LOGGER_NAME)
+
+
+def log_or_print(msg: Any, verbose: bool, level: int = logging.INFO) -> None:
+    if verbose:
+        print(msg)
+    else:
+        logger.log(level, msg)
diff --git a/linear_relational/lib/token_utils.py b/linear_relational/lib/token_utils.py
new file mode 100644
index 0000000..c66680a
--- /dev/null
+++ b/linear_relational/lib/token_utils.py
@@ -0,0 +1,278 @@
+from dataclasses import dataclass
+from typing import Iterable, Sequence
+
+import torch
+from tokenizers import Tokenizer
+from torch import nn
+
+from linear_relational.lib.util import find_all_substring_indices
+
+from .constants import DEFAULT_DEVICE
+
+
+def make_inputs(
+    tokenizer: Tokenizer,
+    prompts: Sequence[str],
+    device: torch.device = DEFAULT_DEVICE,
+    add_pad_token: bool = True,
+) -> dict[str, torch.Tensor]:
+    ensure_tokenizer_has_pad_token(tokenizer, add_pad_token=add_pad_token)
+    return tokenizer(prompts, padding=True, return_tensors="pt").to(device)
+
+
+def ensure_tokenizer_has_pad_token(
+    tokenizer: Tokenizer, add_pad_token: bool = True
+) -> None:
+    # from https://github.com/huggingface/transformers/issues/12594#issuecomment-877358955
+    if not tokenizer.pad_token:
+        if add_pad_token:
+            tokenizer.pad_token = tokenizer.eos_token
+        else:
+            raise ValueError("Tokenizer must have a pad token")
+
+
+def predict_from_input(
+    model: nn.Module,
+    inp: dict[str, torch.Tensor],
+    answer_id_overrides: list[tuple[int, int]] = [],
+) -> tuple[torch.Tensor, torch.Tensor]:
+    probs = predict_probs_from_input(model, inp)
+    prob, pred = torch.max(probs, dim=1)
+    for i, j in answer_id_overrides:
+        pred[i] = j
+        prob[i] = probs[i, j]
+    return pred, prob
+
+
+def predict_probs_from_input(
+    model: nn.Module,
+    inp: dict[str, torch.Tensor],
+) -> torch.Tensor:
+    logits = predict_logits_from_input(model, inp)
+    return torch.softmax(logits, dim=-1)
+
+
+def predict_logits_from_input(
+    model: nn.Module,
+    inp: dict[str, torch.Tensor],
+) -> torch.Tensor:
+    all_logits = model(**inp)["logits"]
+    final_token_positions = find_final_attention_positions(inp["attention_mask"])
+    batch_indices = torch.arange(all_logits.size(0))
+    return all_logits[batch_indices, final_token_positions]
+
+
+def predict_next_tokens_greedy(
+    model: nn.Module,
+    tokenizer: Tokenizer,
+    prompts: list[str],
+    num_tokens: int = 1,
+    device: torch.device = DEFAULT_DEVICE,
+) -> list[list[int]]:
+    """
+    Greedily predict the next N tokens for each prompt in the list.
+    Should correctly handle right-padding.
+    """
+    next_prompts = [*prompts]  # copy to avoid modifying the original
+    results: list[list[int]] = []
+    for _i in range(num_tokens):
+        # decoding and then re-encoding in a loop is wasteful, but it's the easiest way to handle
+        # batches of different lengths, since model.generate() doesn't work with right-padding
+        inputs = make_inputs(
+            tokenizer,
+            next_prompts,
+            device=device,
+        )
+        pred_res = predict_from_input(model, inputs)
+        for j, pred in enumerate(pred_res[0].detach().cpu()):
+            if j >= len(results):
+                results.append([])
+            results[j].append(pred.item())
+            next_prompts[j] += tokenizer.decode(pred)
+    return results
+
+
+def find_final_attention_positions(attention_mask: torch.Tensor) -> torch.Tensor:
+    # convoluted, and generated by ChatGPT, but seems to work
+    indices = torch.arange(attention_mask.size(1)).to(attention_mask.device)
+    # use broadcasting to expand indices to the shape of attention_mask
+    indices = indices[None, :].expand_as(attention_mask)
+    # set indices where attention_mask is 0 to -1
+    indices = torch.where(attention_mask == 1, indices, -1)
+    # find the max indices
+    max_indices = indices.max(dim=1).values
+    return max_indices
+
+
+def get_answer_token_ids(
+    tokenizer: Tokenizer,
+    answer: str,
+    ensure_space_prefix: bool = True,
+    strip_start_token: bool = True,
+    strip_blank_start_token: bool = True,
+) -> list[int]:
+    """
+    Helper to find the token ids for the given answer as if it were a continuation of the prompt.
+    """
+    processed_answer = answer
+    if ensure_space_prefix and not processed_answer.startswith(" "):
+        processed_answer = " " + processed_answer
+    tokens = tokenizer.encode(processed_answer)
+    if strip_start_token and tokens[0] == tokenizer.bos_token_id:
+        tokens = tokens[1:]
+    # llama only includes an explicit space token at the start of the string if it's the first token
+    if strip_blank_start_token and tokenizer.decode([tokens[0]]) == "":
+        tokens = tokens[1:]
+    return tokens
+
+
+def any_answer_matches_expected(
+    answers: Iterable[str], expected_answers: Iterable[str], exact_match: bool = True
+) -> bool:
+    """
+    Check if any of the given answers match any of the expected answers. Handles case and whitespace.
+    """
+    for answer in answers:
+        if answer_matches_expected(answer, expected_answers, exact_match=exact_match):
+            return True
+    return False
+
+
+def answer_matches_expected(
+    answer: str, expected_answers: Iterable[str], exact_match: bool = True
+) -> bool:
+    """
+    Check if the given answer matches any of the expected answers. Handles case and whitespace.
+    """
+    processed_answer = process_answer(answer, exact_match)
+    return processed_answer in {
+        process_answer(a, exact_match) for a in expected_answers
+    }
+
+
+def process_answer(answer: str, exact_match: bool = True) -> str:
+    """
+    Process the given answer to make it easier to compare to other answers by removing case and trimming.
+    """
+    processed_answer = answer.strip()
+    if not exact_match:
+        processed_answer = processed_answer.lower()
+    return processed_answer
+
+
+@dataclass
+class PromptAnswerData:
+    answer_start_index: int
+    answer: str
+    answer_tokens: list[int]
+    base_prompt: str
+    full_prompt: str
+
+    @property
+    def num_answer_tokens(self) -> int:
+        return len(self.answer_tokens)
+
+    @property
+    def output_answer_token_indices(self) -> tuple[int, ...]:
+        # everything is shifted 1 earlier for output tokens
+        output_start_index = self.answer_start_index - 1
+        return tuple(
+            range(output_start_index, output_start_index + len(self.answer_tokens))
+        )
+
+
+def find_prompt_answer_data(
+    tokenizer: Tokenizer, base_prompt: str, answer: str
+) -> PromptAnswerData:
+    """
+    Find the number of tokens in the given answer, after it's appended to the prompt.
+    This assumes that the answer immediately follows the prompt
+    NOTE: the prompt SHOULD NOT include the answer
+    """
+    base_prompt_stripped = base_prompt.strip()
+    answer_stripped = answer.strip()
+    full_prompt = base_prompt_stripped + " " + answer_stripped
+    base_prompt_tokens = tokenizer.encode(base_prompt_stripped)
+    full_prompt_tokens = tokenizer.encode(full_prompt)
+    return PromptAnswerData(
+        answer=answer_stripped,
+        answer_start_index=len(base_prompt_tokens),
+        answer_tokens=full_prompt_tokens[len(base_prompt_tokens) :],
+        base_prompt=base_prompt_stripped,
+        full_prompt=full_prompt,
+    )
+
+
+def find_num_answer_tokens(tokenizer: Tokenizer, base_prompt: str, answer: str) -> int:
+    return find_prompt_answer_data(tokenizer, base_prompt, answer).num_answer_tokens
+
+
+def decode_tokens(
+    tokenizer: Tokenizer, token_array: list[int] | torch.Tensor | list[torch.Tensor]
+) -> list[str]:
+    return [tokenizer.decode([t]) for t in token_array]
+
+
+def find_token_range(
+    tokenizer: Tokenizer,
+    token_array: list[int] | torch.Tensor,
+    substring: str,
+    find_last_match: bool = True,
+) -> tuple[int, int]:
+    # sometimes the tokenizer messes with non-alphanumeric characters
+    # so make sure the substring goes through an encoding/decoding cycle as well
+    substr_toks = decode_tokens(tokenizer, tokenizer(substring)["input_ids"])
+    # we want to remove the start of sentence token if the tokenizer adds it
+    if tokenizer.bos_token and substr_toks[0] == tokenizer.bos_token:
+        substr_toks = substr_toks[1:]
+    recoded_substr = "".join(substr_toks)
+    toks = decode_tokens(tokenizer, token_array)
+    whole_string = "".join(toks)
+    char_locs = find_all_substring_indices(whole_string, recoded_substr)
+    if len(char_locs) == 0:
+        # sometimes adding a space in front causes different tokenization which works
+        if substring[0] != " ":
+            return find_token_range(tokenizer, token_array, " " + substring)
+        raise ValueError(f"Could not find substring {recoded_substr} in {whole_string}")
+    token_ranges: list[tuple[int, int]] = []
+    for char_loc in char_locs:
+        loc = 0
+        tok_start, tok_end = None, None
+        for i, t in enumerate(toks):
+            loc += len(t)
+            if tok_start is None and loc > char_loc:
+                tok_start = i
+            if tok_end is None and loc >= char_loc + len(recoded_substr):
+                tok_end = i + 1
+                break
+        if tok_start is not None and tok_end is not None:
+            token_ranges.append((tok_start, tok_end))
+    if len(token_ranges) == 0:
+        raise ValueError(f"Could not find substring {recoded_substr} in {toks}")
+    return token_ranges[-1] if find_last_match else token_ranges[0]
+
+
+def find_final_word_token_index(tokenizer: Tokenizer, prompt: str, word: str) -> int:
+    tokens = tokenizer.encode(prompt)
+    _start, end = find_token_range(tokenizer, tokens, word)
+    return end - 1
+
+
+def predict_all_token_probs_from_input(
+    model: nn.Module,
+    inp: dict[str, torch.Tensor],
+    move_to_cpu: bool = True,
+) -> list[torch.Tensor]:
+    """
+    Returns logits for each item in the batch as a list.
+    This will handle batches of different lengths correctly.
+    """
+    all_logits = model(**inp)["logits"]
+    all_probs = torch.softmax(all_logits, dim=-1)
+    final_token_positions = find_final_attention_positions(inp["attention_mask"])
+    if move_to_cpu:
+        all_probs = all_probs.cpu()
+    return [
+        all_probs[batch_idx, : final_token + 1]
+        for batch_idx, final_token in enumerate(final_token_positions.tolist())
+    ]
diff --git a/linear_relational/lib/torch_utils.py b/linear_relational/lib/torch_utils.py
new file mode 100644
index 0000000..7a9ca86
--- /dev/null
+++ b/linear_relational/lib/torch_utils.py
@@ -0,0 +1,71 @@
+from typing import Any, Optional, TypeVar, cast
+
+import torch
+from torch import nn
+
+
+def untuple_tensor(x: torch.Tensor | tuple[torch.Tensor, ...]) -> torch.Tensor:
+    return x[0] if isinstance(x, tuple) else x
+
+
+def get_module(model: nn.Module, name: str) -> nn.Module:
+    """
+    Finds the named module within the given model.
+    """
+    for n, m in model.named_modules():
+        if n == name:
+            return m
+    raise LookupError(name)
+
+
+def get_device(model: nn.Module) -> torch.device:
+    """
+    Returns the device on which the model is running.
+    """
+    if isinstance(model.device, torch.device):
+        return model.device
+    return next(model.parameters()).device
+
+
+T = TypeVar("T", torch.Tensor, dict[Any, Any], list[Any], tuple[Any, ...])
+
+
+def recursive_tensor_copy(
+    x: T,
+    clone: Optional[bool] = None,
+    detach: Optional[bool] = None,
+    retain_grad: Optional[bool] = None,
+) -> T:
+    """
+    Copies a reference to a tensor, or an object that contains tensors,
+    optionally detaching and cloning the tensor(s).  If retain_grad is
+    true, the original tensors are marked to have grads retained.
+    """
+    if not clone and not detach and not retain_grad:
+        return x
+    if isinstance(x, torch.Tensor):
+        if retain_grad:
+            if not x.requires_grad:
+                x.requires_grad = True
+            x.retain_grad()
+        elif detach:
+            x = x.detach()
+        if clone:
+            x = x.clone()
+        return x
+    # Only dicts, lists, and tuples (and subclasses) can be copied.
+    if isinstance(x, dict):
+        return type(x)({k: recursive_tensor_copy(v) for k, v in x.items()})
+    elif isinstance(x, (list, tuple)):
+        return type(x)([recursive_tensor_copy(v) for v in x])
+    else:
+        assert False, f"Unknown type {type(x)} cannot be broken into tensors."
+
+
+def guess_model_name(model: nn.Module) -> str:
+    """
+    Guesses the model name from the model's config.
+    """
+    if hasattr(model, "config") and hasattr(model.config, "_name_or_path"):
+        return cast(str, model.config._name_or_path)
+    return model.__class__.__name__
diff --git a/linear_relational/lib/util.py b/linear_relational/lib/util.py
new file mode 100644
index 0000000..c94c1c2
--- /dev/null
+++ b/linear_relational/lib/util.py
@@ -0,0 +1,108 @@
+import random
+from collections import defaultdict
+from typing import Callable, Generator, Iterable, Mapping, Sequence, TypeVar
+
+from tqdm import tqdm
+
+T = TypeVar("T")
+
+
+# based on https://stackoverflow.com/a/480227/245362
+def dedupe_stable(items: list[T]) -> list[T]:
+    seen: set[T] = set()
+    seen_add = seen.add
+    return [item for item in items if not (item in seen or seen_add(item))]
+
+
+def shallow_flatten(items: Iterable[Iterable[T]]) -> list[T]:
+    return [item for sublist in items for item in sublist]
+
+
+def batchify(
+    data: Sequence[T], batch_size: int, show_progress: bool = False
+) -> Generator[Sequence[T], None, None]:
+    """Generate batches from data. If show_progress is True, display a progress bar."""
+
+    for i in tqdm(
+        range(0, len(data), batch_size),
+        total=(len(data) // batch_size + (len(data) % batch_size != 0)),
+        disable=not show_progress,
+    ):
+        yield data[i : i + batch_size]
+
+
+def tuplify(item: T | tuple[T, ...]) -> tuple[T, ...]:
+    return item if isinstance(item, tuple) else (item,)
+
+
+def stable_shuffle(items: list[T], seed: int | float | str = 42) -> list[T]:
+    """
+    Shuffle a list in a stable way
+    """
+    generator = random.Random(seed)
+    # copy items to avoid modifying original
+    results = [*items]
+    generator.shuffle(results)
+    return results
+
+
+def stable_sample(items: list[T], k: int, seed: int | float | str = 42) -> list[T]:
+    """
+    Sample from a list in a stable way
+    """
+    generator = random.Random(seed)
+    return generator.sample(items, k)
+
+
+def find_all_substring_indices(
+    string: str, substring: str, start: int = 0, end: int | None = None
+) -> list[int]:
+    """
+    Find all indices of a substring in a string
+    """
+    indices = []
+    while True:
+        index = string.find(substring, start, end)
+        if index == -1:
+            break
+        indices.append(index)
+        start = index + len(substring)
+    return indices
+
+
+def sample_or_all(items: list[T], k: int, seed: int | float | str = 42) -> list[T]:
+    """
+    same as random.sample, but if k >= len(items), return items unmodified
+    """
+    generator = random.Random(seed)
+    if k >= len(items):
+        return items
+    return generator.sample(items, k=k)
+
+
+def mean(items: Sequence[float]) -> float:
+    """
+    Compute the mean of a list of numbers
+    """
+    return sum(items) / len(items)
+
+
+def mean_values(items: Sequence[Mapping[T, float]]) -> dict[T, float]:
+    """
+    Compute the mean of a list of dicts of numbers
+    """
+    return {
+        key: mean([item[key] for item in items if key in item])
+        for key in set(key for item in items for key in item.keys())
+    }
+
+
+def group_items(items: Iterable[T], group_fn: Callable[[T], str]) -> dict[str, list[T]]:
+    """
+    Group items by the result of a function
+    """
+    grouped_items: dict[str, list[T]] = defaultdict(list)
+    for item in items:
+        group = group_fn(item)
+        grouped_items[group].append(item)
+    return grouped_items
diff --git a/linear_relational/lib/verify_answers_match_expected.py b/linear_relational/lib/verify_answers_match_expected.py
new file mode 100644
index 0000000..5dcc083
--- /dev/null
+++ b/linear_relational/lib/verify_answers_match_expected.py
@@ -0,0 +1,71 @@
+from dataclasses import dataclass
+from typing import Sequence
+
+from tokenizers import Tokenizer
+from torch import nn
+
+from .token_utils import (
+    any_answer_matches_expected,
+    get_answer_token_ids,
+    predict_next_tokens_greedy,
+)
+from .util import batchify, shallow_flatten, tuplify
+
+
+@dataclass
+class AnswerMatchResult:
+    prompt: str
+    expected_answers: tuple[str, ...]
+    potential_answers: set[str]
+    answer_matches_expected: bool
+
+
+def verify_answers_match_expected(
+    model: nn.Module,
+    tokenizer: Tokenizer,
+    prompts: Sequence[str],
+    expected_answers: Sequence[tuple[str, ...] | str],
+    batch_size: int = 8,
+    show_progress: bool = True,
+    exact_match: bool = True,
+) -> list[AnswerMatchResult]:
+    if len(prompts) != len(expected_answers):
+        raise ValueError(
+            f"Expected {len(prompts)} prompts to match {len(expected_answers)} expected answers"
+        )
+    results: list[AnswerMatchResult] = []
+    for batch in batchify(
+        list(zip(prompts, expected_answers)), batch_size, show_progress
+    ):
+        batch_prompts = [prompt for prompt, _ in batch]
+        batch_expected_answers = [tuplify(answers) for _, answers in batch]
+
+        all_expected_answers = shallow_flatten(batch_expected_answers)
+        tokenized_answers = [
+            get_answer_token_ids(tokenizer, answer) for answer in all_expected_answers
+        ]
+        max_answer_length = max(len(tokens) for tokens in tokenized_answers)
+        batch_next_tokens = predict_next_tokens_greedy(
+            model,
+            tokenizer,
+            batch_prompts,
+            num_tokens=max_answer_length,
+        )
+        for next_tokens, cur_expected_answers, cur_prompt in zip(
+            batch_next_tokens, batch_expected_answers, batch_prompts
+        ):
+            potential_answers: set[str] = set()
+            for index in range(len(next_tokens)):
+                potential_answers.add(tokenizer.decode(next_tokens[: index + 1]))
+            answer_matches = any_answer_matches_expected(
+                potential_answers, cur_expected_answers, exact_match=exact_match
+            )
+            results.append(
+                AnswerMatchResult(
+                    prompt=cur_prompt,
+                    expected_answers=cur_expected_answers,
+                    potential_answers=potential_answers,
+                    answer_matches_expected=answer_matches,
+                )
+            )
+    return results
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..f78d4c8
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1561 @@
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+
+[[package]]
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+optional = false
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..36b544b
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,30 @@
+[tool.poetry]
+name = "linear-relational"
+version = "0.1.0"
+description = "A Python library for working with Linear Relational Embeddings (LREs) and Linear Relational Concepts (LRCs) for LLMs"
+authors = ["David Chanin <chanindav@gmail.com>"]
+readme = "README.md"
+
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+
+[build-system]
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+
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diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..6d5ff2e
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,46 @@
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+
+[mypy-tests.*]
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+[mypy-pytest.*]
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+
+[mypy-tqdm.*]
+ignore_missing_imports = True
+
+[mypy-datasets.*]
+ignore_missing_imports = True
+
+[mypy-transformers.*]
+ignore_missing_imports = True
+
+[mypy-tokenizers.*]
+ignore_missing_imports = True
+
+[mypy-matplotlib.*]
+ignore_missing_imports = True
+
+[mypy-lightning_fabric.*]
+ignore_missing_imports = True
+
+[mypy-sklearn.*]
+ignore_missing_imports = True
+
+[mypy-seaborn.*]
+ignore_missing_imports = True
+[mypy-scipy.*]
+ignore_missing_imports = True
\ No newline at end of file
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..3faeb0c
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,23 @@
+import pytest
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast, LlamaTokenizer
+
+# loading in advance so it won't reload on every test
+# just need to make sure not to edit these models in tests...
+_model = GPT2LMHeadModel.from_pretrained("gpt2")
+_tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
+_vicuna_tokenizer = LlamaTokenizer.from_pretrained("lmsys/vicuna-7b-v1.3", legacy=False)
+
+
+@pytest.fixture
+def model() -> GPT2LMHeadModel:
+    return _model
+
+
+@pytest.fixture
+def tokenizer() -> GPT2TokenizerFast:
+    return _tokenizer
+
+
+@pytest.fixture
+def vicuna_tokenizer() -> LlamaTokenizer:
+    return _vicuna_tokenizer
diff --git a/tests/lib/test_balance_grouped_items.py b/tests/lib/test_balance_grouped_items.py
new file mode 100644
index 0000000..d765e33
--- /dev/null
+++ b/tests/lib/test_balance_grouped_items.py
@@ -0,0 +1,81 @@
+from collections import defaultdict
+from dataclasses import dataclass
+
+from linear_relational.lib.balance_grouped_items import balance_grouped_items
+
+
+@dataclass
+class FakeItem:
+    subject: str
+
+
+items_by_group = {
+    "London": [
+        FakeItem(subject="London"),
+        FakeItem(subject="London"),
+        FakeItem(subject="London"),
+        FakeItem(subject="London"),
+    ],
+    "Paris": [
+        FakeItem(subject="Paris"),
+        FakeItem(subject="Paris"),
+        FakeItem(subject="Paris"),
+        FakeItem(subject="Paris"),
+    ],
+    "Berlin": [
+        FakeItem(subject="Berlin"),
+        FakeItem(subject="Berlin"),
+    ],
+}
+
+
+def test_balance_grouped_items_includes_all_items_with_no_limits_specified() -> None:
+    items = balance_grouped_items(items_by_group)
+    assert len(items) == 10
+    counts_by_subj: dict[str, int] = defaultdict(int)
+    for item in items:
+        counts_by_subj[item.subject] += 1
+    assert counts_by_subj == {
+        "London": 4,
+        "Paris": 4,
+        "Berlin": 2,
+    }
+
+
+def test_balance_grouped_items_balances_all_items_until_the_max_is_hit() -> None:
+    items = balance_grouped_items(items_by_group, max_total=6)
+    assert len(items) == 6
+    counts_by_subj: dict[str, int] = defaultdict(int)
+    for item in items:
+        counts_by_subj[item.subject] += 1
+    assert counts_by_subj == {
+        "London": 2,
+        "Paris": 2,
+        "Berlin": 2,
+    }
+
+
+def test_balance_grouped_items_balances_limits_the_amount_per_group() -> None:
+    items = balance_grouped_items(items_by_group, max_per_group=3)
+    assert len(items) == 8
+    counts_by_subj: dict[str, int] = defaultdict(int)
+    for item in items:
+        counts_by_subj[item.subject] += 1
+    assert counts_by_subj == {
+        "London": 3,
+        "Paris": 3,
+        "Berlin": 2,
+    }
+
+
+def test_balance_grouped_items_with_total_and_per_group_limit() -> None:
+    items = balance_grouped_items(items_by_group, max_per_group=3, max_total=100)
+    assert len(items) == 8
+    counts_by_subj: dict[str, int] = defaultdict(int)
+    for item in items:
+        counts_by_subj[item.subject] += 1
+    assert counts_by_subj == {
+        "London": 3,
+        "Paris": 3,
+        "Berlin": 2,
+    }
diff --git a/tests/lib/test_token_utils.py b/tests/lib/test_token_utils.py
new file mode 100644
index 0000000..ca6facb
--- /dev/null
+++ b/tests/lib/test_token_utils.py
@@ -0,0 +1,168 @@
+import pytest
+import torch
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast, LlamaTokenizer
+
+from linear_relational.lib.token_utils import (
+    decode_tokens,
+    find_final_attention_positions,
+    find_final_word_token_index,
+    find_token_range,
+    get_answer_token_ids,
+    make_inputs,
+    predict_all_token_probs_from_input,
+    predict_next_tokens_greedy,
+)
+
+
+def test_decode_tokens(tokenizer: GPT2TokenizerFast) -> None:
+    tokens = tokenizer.encode("Hello, my dog is cute")
+    decoded = decode_tokens(tokenizer, tokens)
+    assert decoded == ["Hello", ",", " my", " dog", " is", " cute"]
+
+
+def test_find_token_range(tokenizer: GPT2TokenizerFast) -> None:
+    tokens = tokenizer.encode("Hello, my dog is cute")
+    assert find_token_range(tokenizer, tokens, "dog") == (3, 4)
+
+
+def test_find_token_range_returns_the_final_instance_of_the_token_by_default(
+    tokenizer: GPT2TokenizerFast,
+) -> None:
+    tokens = tokenizer.encode("Hello, my dog is a cute dog")
+    assert find_token_range(tokenizer, tokens, "dog") == (7, 8)
+
+
+def test_find_token_range_returns_the_first_instance_of_the_token_if_requested(
+    tokenizer: GPT2TokenizerFast,
+) -> None:
+    tokens = tokenizer.encode("Hello, my dog is a cute dog")
+    assert find_token_range(tokenizer, tokens, "dog", find_last_match=False) == (3, 4)
+
+
+def test_find_token_range_spanning_multiple_tokens(
+    tokenizer: GPT2TokenizerFast,
+) -> None:
+    tokens = tokenizer.encode("Hello, my dog is cute")
+    assert find_token_range(tokenizer, tokens, "dog is") == (3, 5)
+
+
+def test_find_token_range_non_alphanum(tokenizer: GPT2TokenizerFast) -> None:
+    tokens = tokenizer.encode("Jean Pierre Joseph d’Arcet has the profession of")
+    assert find_token_range(tokenizer, tokens, "Jean Pierre Joseph d’Arcet") == (0, 8)
+
+
+def test_find_token_range_accented(tokenizer: GPT2TokenizerFast) -> None:
+    tokens = tokenizer.encode("I think Örebro is in the continent of")
+    assert find_token_range(tokenizer, tokens, "Örebro") == (2, 5)
+
+
+def test_find_token_missing_token(tokenizer: GPT2TokenizerFast) -> None:
+    tokens = tokenizer.encode("Hello, my dog is cute")
+    with pytest.raises(ValueError):
+        find_token_range(tokenizer, tokens, "cat")
+
+
+def test_find_token_works_with_llama(vicuna_tokenizer: LlamaTokenizer) -> None:
+    tokens = vicuna_tokenizer.encode("I think Paris is located in the country of")
+    assert find_token_range(vicuna_tokenizer, tokens, "Paris") == (3, 4)
+
+
+def test_find_final_word_token_index(tokenizer: GPT2TokenizerFast) -> None:
+    assert (
+        find_final_word_token_index(tokenizer, "Bill Gates is the CEO of", "Bill Gates")
+        == 1
+    )
+    assert (
+        find_final_word_token_index(tokenizer, "Bill Gates is the CEO of", "Bill") == 0
+    )
+
+
+def test_predict_all_token_probs_from_input(
+    model: GPT2LMHeadModel, tokenizer: GPT2TokenizerFast
+) -> None:
+    prompts = [
+        "Steve Jobs is the CEO of",
+        "Bill Gates is the CEO of",
+        "Hello world",
+    ]
+    logits = predict_all_token_probs_from_input(model, make_inputs(tokenizer, prompts))
+    assert len(logits) == 3
+    assert logits[0].shape == (6, 50257)
+    assert logits[1].shape == (6, 50257)
+    assert logits[2].shape == (2, 50257)
+    assert torch.allclose(logits[0].sum(-1), torch.ones(6), 0.0001)
+    assert torch.allclose(logits[1].sum(-1), torch.ones(6), 0.0001)
+    assert torch.allclose(logits[2].sum(-1), torch.ones(2), 0.0001)
+
+
+def test_make_inputs(tokenizer: GPT2TokenizerFast) -> None:
+    prompts = [
+        "Steve Jobs is the CEO of",
+        "Bill Gates is the CEO of",
+        "Hello world",
+    ]
+    inputs = make_inputs(tokenizer, prompts)
+    assert inputs["input_ids"].shape == (3, 6)
+    assert inputs["attention_mask"].shape == (3, 6)
+    assert torch.allclose(
+        inputs["attention_mask"][0, :], torch.ones(6, dtype=torch.long)
+    )
+    assert torch.allclose(
+        inputs["attention_mask"][1, :], torch.ones(6, dtype=torch.long)
+    )
+    assert torch.allclose(
+        inputs["attention_mask"][2, :2], torch.ones(2, dtype=torch.long)
+    )
+    assert torch.allclose(
+        inputs["attention_mask"][2, 2:], torch.zeros(4, dtype=torch.long)
+    )
+
+
+def test_find_final_attention_positions_right_padding() -> None:
+    attention_mask = torch.tensor(
+        [
+            [1, 1, 1, 0, 0, 0],
+            [1, 1, 1, 1, 0, 0],
+        ]
+    )
+    assert find_final_attention_positions(attention_mask).tolist() == [2, 3]
+
+
+def test_find_final_attention_positions_left_padding() -> None:
+    attention_mask = torch.tensor(
+        [
+            [0, 0, 1, 1, 1, 1],
+            [0, 0, 0, 1, 1, 1],
+        ]
+    )
+    assert find_final_attention_positions(attention_mask).tolist() == [5, 5]
+
+
+def test_predict_next_tokens_greedy(
+    model: GPT2LMHeadModel, tokenizer: GPT2TokenizerFast
+) -> None:
+    prompts = [
+        "Steve Jobs is the CEO of",
+        "Bill Gates is the CEO of",
+        "Tokyo is located in the country of",
+    ]
+    next_tokens = predict_next_tokens_greedy(model, tokenizer, prompts, 4)
+    assert len(next_tokens) == 3
+    assert next_tokens[0] == [4196, 13, 679, 318]
+    assert next_tokens[1] == [5413, 11, 290, 339]
+    assert next_tokens[2] == [2869, 11, 290, 318]
+    assert tokenizer.decode(next_tokens[0][0]) == " Apple"
+    assert tokenizer.decode(next_tokens[1][0]) == " Microsoft"
+    assert tokenizer.decode(next_tokens[2][0]) == " Japan"
+
+
+def test_get_answer_token_ids_adds_a_space_prefix(tokenizer: GPT2TokenizerFast) -> None:
+    assert get_answer_token_ids(tokenizer, "Apple") == [4196]
+    assert get_answer_token_ids(tokenizer, " Apple") == [4196]
+
+
+def test_get_answer_token_ids_strips_start_tokens(
+    vicuna_tokenizer: LlamaTokenizer,
+) -> None:
+    assert get_answer_token_ids(vicuna_tokenizer, "Apple") == [12113]
+    assert get_answer_token_ids(vicuna_tokenizer, "OK Apple") == [9280, 12113]
diff --git a/tests/lib/test_torch_utils.py b/tests/lib/test_torch_utils.py
new file mode 100644
index 0000000..9b4825d
--- /dev/null
+++ b/tests/lib/test_torch_utils.py
@@ -0,0 +1,15 @@
+from transformers import GPT2LMHeadModel
+
+from linear_relational.lib.layer_matching import fix_neg_layer_num
+from linear_relational.lib.torch_utils import guess_model_name
+
+
+def test_guess_model_name(model: GPT2LMHeadModel) -> None:
+    assert guess_model_name(model) == "gpt2"
+
+
+def test_fix_neg_layer_num(model: GPT2LMHeadModel) -> None:
+    assert fix_neg_layer_num(model, "transformer.h.{num}", -1) == 11
+    assert fix_neg_layer_num(model, "transformer.h.{num}", -3) == 9
+    assert fix_neg_layer_num(model, "transformer.h.{num}", 11) == 11
+    assert fix_neg_layer_num(model, "transformer.h.{num}", 3) == 3
diff --git a/tests/lib/test_util.py b/tests/lib/test_util.py
new file mode 100644
index 0000000..33dbcde
--- /dev/null
+++ b/tests/lib/test_util.py
@@ -0,0 +1,31 @@
+from linear_relational.lib.util import (
+    dedupe_stable,
+    find_all_substring_indices,
+    stable_shuffle,
+)
+
+
+def test_dedule_stable() -> None:
+    items = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+    assert dedupe_stable(items) == items
+    assert dedupe_stable(items + items) == items
+    assert dedupe_stable(items + items + items) == items
+
+
+def test_stable_shuffle() -> None:
+    original = [1, 2, 3, 4, 5]
+    shuffled1 = stable_shuffle(original, seed=42)
+    shuffled2 = stable_shuffle(original, seed=42)
+    shuffled3 = stable_shuffle(original, seed=123)
+    assert original == [1, 2, 3, 4, 5]
+    assert shuffled1 != original
+    assert shuffled3 != original
+    assert shuffled1 == shuffled2
+    assert shuffled1 != shuffled3
+
+
+def test_find_all_substring_indices() -> None:
+    assert find_all_substring_indices("Hello, World!", "l") == [2, 3, 10]
+    assert find_all_substring_indices("Hello, World!", "l", 3) == [3, 10]
+    assert find_all_substring_indices("Hello, World!", "l", 3, 9) == [3]
+    assert find_all_substring_indices("Hello, World!", "Hello") == [0]
diff --git a/tests/lib/test_verify_answers_match_expected.py b/tests/lib/test_verify_answers_match_expected.py
new file mode 100644
index 0000000..ddf2255
--- /dev/null
+++ b/tests/lib/test_verify_answers_match_expected.py
@@ -0,0 +1,66 @@
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast
+
+from linear_relational.lib.verify_answers_match_expected import (
+    verify_answers_match_expected,
+)
+
+
+def test_verify_answers_match_expected(
+    model: GPT2LMHeadModel, tokenizer: GPT2TokenizerFast
+) -> None:
+    results = verify_answers_match_expected(
+        model,
+        tokenizer,
+        [
+            "Bill Gates is the CEO of",
+            "Tokyo is located in the country of",
+            "Steve Jobs is the CEO of",
+        ],
+        [(" Microsoft", " Microsoft Corporation"), (" Japan",), (" Orange",)],
+    )
+
+    assert [res.answer_matches_expected for res in results] == [True, True, False]
+    assert [res.prompt for res in results] == [
+        "Bill Gates is the CEO of",
+        "Tokyo is located in the country of",
+        "Steve Jobs is the CEO of",
+    ]
+    assert results[0].potential_answers == {" Microsoft", " Microsoft,"}
+    assert results[1].potential_answers == {" Japan", " Japan,"}
+    assert results[2].potential_answers == {" Apple", " Apple."}
+
+
+def test_verify_answers_match_expected_handles_space_normalization(
+    model: GPT2LMHeadModel, tokenizer: GPT2TokenizerFast
+) -> None:
+    results = verify_answers_match_expected(
+        model,
+        tokenizer,
+        [
+            "Bill Gates is the CEO of",
+            "Tokyo is located in the country of",
+            "Steve Jobs is the CEO of",
+        ],
+        [("Microsoft", "Microsoft Corporation"), ("Japan",), ("Orange",)],
+    )
+    assert [res.answer_matches_expected for res in results] == [True, True, False]
+
+
+def test_verify_answers_match_allows_passing_single_strings(
+    model: GPT2LMHeadModel, tokenizer: GPT2TokenizerFast
+) -> None:
+    results = verify_answers_match_expected(
+        model,
+        tokenizer,
+        [
+            "Bill Gates is the CEO of",
+            "Tokyo is located in the country of",
+            "Steve Jobs is the CEO of",
+        ],
+        [
+            ("Microsoft", "Microsoft Corporation"),
+            "Japan",
+            "Orange",
+        ],
+    )
+    assert [res.answer_matches_expected for res in results] == [True, True, False]
diff --git a/tests/test_Lre.py b/tests/test_Lre.py
new file mode 100644
index 0000000..79c16c5
--- /dev/null
+++ b/tests/test_Lre.py
@@ -0,0 +1,120 @@
+import torch
+
+from linear_relational.Lre import InvertedLre, LowRankLre, Lre
+
+
+def test_Lre_invert() -> None:
+    bias = torch.tensor([1.0, 0.0, 0.0])
+    lre = Lre(
+        relation="test",
+        subject_layer=5,
+        object_layer=10,
+        object_aggregation="mean",
+        bias=bias,
+        weight=torch.eye(3),
+    )
+    inv_lre = lre.invert(rank=2)
+    assert inv_lre.relation == "test"
+    assert inv_lre.subject_layer == 5
+    assert inv_lre.object_layer == 10
+    assert inv_lre.object_aggregation == "mean"
+    assert torch.allclose(inv_lre.bias, bias)
+    assert inv_lre.u.shape == (3, 2)
+    assert inv_lre.s.shape == (2,)
+    assert inv_lre.v.shape == (3, 2)
+    assert inv_lre.rank == 2
+
+
+def test_Lre_to_low_rank() -> None:
+    bias = torch.tensor([1.0, 0.0, 0.0])
+    lre = Lre(
+        relation="test",
+        subject_layer=5,
+        object_layer=10,
+        object_aggregation="mean",
+        bias=bias,
+        weight=torch.eye(3),
+    )
+    low_rank_lre = lre.to_low_rank(rank=2)
+    assert low_rank_lre.relation == "test"
+    assert low_rank_lre.subject_layer == 5
+    assert low_rank_lre.object_layer == 10
+    assert low_rank_lre.object_aggregation == "mean"
+    assert torch.allclose(low_rank_lre.bias, bias)
+    assert low_rank_lre.u.shape == (3, 2)
+    assert low_rank_lre.s.shape == (2,)
+    assert low_rank_lre.v.shape == (3, 2)
+    assert low_rank_lre.rank == 2
+
+
+def test_LowRankLre_calculate_object_activation_unnormalized() -> None:
+    acts = torch.stack(
+        [
+            torch.tensor([2.0, 1.0, 1.0]),
+            torch.tensor([1.0, 0.0, 0.0]),
+        ]
+    )
+    bias = torch.tensor([-1.0, 0.0, 0.0])
+    lre = LowRankLre(
+        relation="test",
+        subject_layer=0,
+        object_layer=0,
+        # this u,s,v makes W_inv the identity matrix
+        u=torch.eye(3),
+        s=torch.ones(3),
+        v=torch.eye(3),
+        object_aggregation="mean",
+        bias=bias,
+    )
+    vec = lre.calculate_object_activation(acts, normalize=False)
+    assert torch.allclose(vec, torch.tensor([0.5, 0.5, 0.5]))
+    assert lre.rank == 3
+
+
+def test_InvertedLre_calculate_subject_activation_unnormalized() -> None:
+    acts = torch.stack(
+        [
+            torch.tensor([2.0, 1.0, 1.0]),
+            torch.tensor([1.0, 0.0, 0.0]),
+        ]
+    )
+    bias = torch.tensor([1.0, 0.0, 0.0])
+    inv_lre = InvertedLre(
+        relation="test",
+        subject_layer=0,
+        object_layer=0,
+        # this u,s,v makes W_inv the identity matrix
+        u=torch.eye(3),
+        s=torch.ones(3),
+        v=torch.eye(3),
+        object_aggregation="mean",
+        bias=bias,
+    )
+    vec = inv_lre.calculate_subject_activation(acts, normalize=False)
+    assert torch.allclose(vec, torch.tensor([0.5, 0.5, 0.5]))
+    assert inv_lre.rank == 3
+
+
+def test_InvertedLre_calculate_subject_activation_normalized() -> None:
+    acts = torch.stack(
+        [
+            torch.tensor([2.0, 1.0, 1.0]),
+            torch.tensor([1.0, 0.0, 0.0]),
+        ]
+    )
+    bias = torch.tensor([1.0, 0.0, 0.0])
+    inv_lre = InvertedLre(
+        relation="test",
+        subject_layer=0,
+        object_layer=0,
+        # this u,s,v makes W_inv the identity matrix
+        u=torch.eye(3),
+        s=torch.ones(3),
+        v=torch.eye(3),
+        object_aggregation="mean",
+        bias=bias,
+    )
+    vec = inv_lre.calculate_subject_activation(acts, normalize=True)
+    raw_target = torch.tensor([0.5, 0.5, 0.5])
+    target = raw_target / raw_target.norm()
+    assert torch.allclose(vec, target)
diff --git a/tests/test_PromptValidator.py b/tests/test_PromptValidator.py
new file mode 100644
index 0000000..c63417f
--- /dev/null
+++ b/tests/test_PromptValidator.py
@@ -0,0 +1,5 @@
+from linear_relational.PromptValidator import cache_key
+
+
+def test_cache_key() -> None:
+    assert cache_key("blah this is a prompt", "answer") == "474096ceddcbb81"