From 015aa68798248b064ed027c0649233cf01c434e9 Mon Sep 17 00:00:00 2001 From: Jai Dhyani Date: Mon, 12 Feb 2024 20:15:22 -0800 Subject: [PATCH 01/53] vscode now uses black from venv instead of bringing its own and causing conflicts (#28) --- .vscode/settings.json | 1 + 1 file changed, 1 insertion(+) diff --git a/.vscode/settings.json b/.vscode/settings.json index 934a143e..5a69a6b6 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -7,4 +7,5 @@ "source.organizeImports": "explicit" }, "python.analysis.typeCheckingMode": "basic", + "black-formatter.importStrategy": "fromEnvironment", } \ No newline at end of file From 737b0e1c25a89cc8ba139d944a88ae2db7bfedfd Mon Sep 17 00:00:00 2001 From: Jai Dhyani Date: Mon, 12 Feb 2024 20:19:40 -0800 Subject: [PATCH 02/53] Make isort use black profile to prevent conflicts (#29) --- .github/workflows/checks.yml | 2 +- .pre-commit-config.yaml | 3 ++- .vscode/settings.json | 6 ++++++ 3 files changed, 9 insertions(+), 2 deletions(-) diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index 397ae1dd..a1abbf67 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -29,6 +29,6 @@ jobs: - name: black run: black --check . - name: isort - run: isort --check . + run: isort --profile black --check . - name: pytest run: pytest \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 84fc89ad..a841252d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -8,4 +8,5 @@ repos: rev: 5.13.2 hooks: - id: isort - name: isort (python) \ No newline at end of file + name: isort (python) + args: ["--profile", "black"] diff --git a/.vscode/settings.json b/.vscode/settings.json index 5a69a6b6..e94f3e9b 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -7,5 +7,11 @@ "source.organizeImports": "explicit" }, "python.analysis.typeCheckingMode": "basic", + + "isort.args": [ + "--profile black" + ], + "black-formatter.importStrategy": "fromEnvironment", + } \ No newline at end of file From c72d4aa280f8bac4462f705eb6592bc51cb74923 Mon Sep 17 00:00:00 2001 From: Jai Dhyani Date: Tue, 13 Feb 2024 00:07:38 -0800 Subject: [PATCH 03/53] Add model/dataset cache to GH actions (#26) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add GH actions 🤗 cache * Add GH actions cache update instructions in README + README autoformatting * Add pip caching to GH actions --------- Co-authored-by: Jai --- .github/workflows/checks.yml | 7 +++++++ README.md | 36 ++++++++++++++++++++---------------- 2 files changed, 27 insertions(+), 16 deletions(-) diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index a1abbf67..e20ccd24 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -21,6 +21,13 @@ jobs: uses: actions/setup-python@v5 with: python-version: "3.10" + cache: "pip" + - name: cache models and datasets + uses: actions/cache@v3 + with: + path: | + ~/.cache/huggingface + key: ${{ runner.os }}-huggingface-cache-v1 # increment this key to invalidate the cache when new models/datasets are added - name: dependencies run: | python -m pip install --upgrade pip diff --git a/README.md b/README.md index c796115a..255a63d3 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,16 @@ # Delphi + Interpreting Small Language Models Across Time and Scale # setup + 1. make python 3.10 virtual env in `.venv` 2. install dependencies `pip install -r requirements.txt` 3. install the project in editable state `pip install -e .` 4. run tests `pytest` # formatting + We're using black & isort to format the code. To make sure your changes adhere to the rules: 1. follow setup instructions above @@ -19,21 +22,22 @@ When you save a file vscode should automatically format it. Otherwise, pre-commi # pull requests 1. make a branch - - if it relates to an existing issue - - go to the issue page and click *Create a branch* under *Development* - - if the default name is not very long, keep it; otherwise, make it shorter, but keep the issue number in the front - - otherwise pick a short but descriptive name, a few hyphen-separated-words + - if it relates to an existing issue + - go to the issue page and click _Create a branch_ under _Development_ + - if the default name is not very long, keep it; otherwise, make it shorter, but keep the issue number in the front + - otherwise pick a short but descriptive name, a few hyphen-separated-words 2. make your changes - - include unit tests - - update README if needed + - include unit tests + - update README if needed + - if new huggingface datasets/models are added to testing, increment the cache number in `.github/workflows/checks.yml` 3. make a pull request - - if it isn't ready for review yet, mark it as draft - - check if CI is passing - - if the change is big, try to keep the commit history clean using interactive rebase - - don't push more often than it's needed, we're running github actions on a free tier - - if there were any changes to the main branch, rebase on top of it - - explain the change - - provide short description; focus on things that were not mentioned in the relevant issue - - comment important sections of the code in *Files changed* tab - - when it's ready, add the relevant stakeholders as reviewers -4. after the comments are resolved and PR is approved, merge it using *Squash and merge* \ No newline at end of file + - if it isn't ready for review yet, mark it as draft + - check if CI is passing + - if the change is big, try to keep the commit history clean using interactive rebase + - don't push more often than it's needed, we're running github actions on a free tier + - if there were any changes to the main branch, rebase on top of it + - explain the change + - provide short description; focus on things that were not mentioned in the relevant issue + - comment important sections of the code in _Files changed_ tab + - when it's ready, add the relevant stakeholders as reviewers +4. after the comments are resolved and PR is approved, merge it using _Squash and merge_ From 4ed8b192f60148836618cb4a198054820373df79 Mon Sep 17 00:00:00 2001 From: Jai Dhyani Date: Tue, 13 Feb 2024 00:18:54 -0800 Subject: [PATCH 04/53] Add utility functions for text processing and visualization (#17) * Add utility functions for text processing and visualization * Add compare_models.py (w/ test) --- .github/workflows/checks.yml | 4 +- .vscode/settings.json | 3 - notebooks/vis_demo.ipynb | 148 ++++++++++++++++++++++++++++++ src/delphi/eval/compare_models.py | 91 ++++++++++++++++++ src/delphi/eval/utils.py | 53 ++++++++++- src/delphi/eval/vis.py | 140 ++++++++++++++++++++++++++++ tests/eval/test_compare_models.py | 23 +++++ 7 files changed, 453 insertions(+), 9 deletions(-) create mode 100644 notebooks/vis_demo.ipynb create mode 100644 src/delphi/eval/compare_models.py create mode 100644 src/delphi/eval/vis.py create mode 100644 tests/eval/test_compare_models.py diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index e20ccd24..89a889fa 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -6,7 +6,7 @@ on: - main pull_request: branches: - - '*' + - "*" permissions: actions: write @@ -38,4 +38,4 @@ jobs: - name: isort run: isort --profile black --check . - name: pytest - run: pytest \ No newline at end of file + run: pytest diff --git a/.vscode/settings.json b/.vscode/settings.json index e94f3e9b..b5f4dfd1 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -7,11 +7,8 @@ "source.organizeImports": "explicit" }, "python.analysis.typeCheckingMode": "basic", - "isort.args": [ "--profile black" ], - "black-formatter.importStrategy": "fromEnvironment", - } \ No newline at end of file diff --git a/notebooks/vis_demo.ipynb b/notebooks/vis_demo.ipynb new file mode 100644 index 00000000..842804d0 --- /dev/null +++ b/notebooks/vis_demo.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch; torch.set_grad_enabled(False)\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "\n", + "from delphi.eval.utils import tokenize, get_next_and_top_k_probs, load_validation_dataset\n", + "from delphi.eval.vis import vis_sample_prediction_probs\n", + "\n", + "model_name = \"roneneldan/TinyStories-1M\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForCausalLM.from_pretrained(model_name)\n", + "ds = load_validation_dataset(\"tinystories-v2-clean\")\n", + "ds_txt = ds[\"story\"][:100]\n", + "ds_tok = [tokenize(tokenizer, txt) for txt in ds_txt]\n", + "sample_tok = ds_tok[0]\n", + "\n", + "correct_probs, top_3_probs = get_next_and_top_k_probs(model, sample_tok, k=3)\n", + "_, top_5_probs = get_next_and_top_k_probs(model, sample_tok, k=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### collect top k predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "
<|endoftext|>
Once
 upon
 a
 time
,
 there
 was
 a
 kind
 girl
 named
 Lily
.
 Lily
 loved
 to
 mix
 things
.
 One
 day
,
 she
 found
 a
 big
 box
 full
 of
 colors
.
 Lily
 was
 very
 happy
.
\\n

L
ily
 took
 out
 a
 strip
 of
 red
 and
 a
 strip
 of
 blue
.
 She
 mixed
 them
 together
 and
 made
 a
 new
 color
,
 purple
!
 Lily
 was
 so
 excited
.
 She
 wanted
 to
 mix
 more
 colors
.
\\n

Next
,
 Lily
 took
 a
 strip
 of
 yellow
 and
 a
 strip
 of
 green
.
 She
 mixed
 them
 together
 and
 made
 a
 new
 color
,
 orange
!
 Lily
 was
 very
 proud
 of
 herself
.
 She
 showed
 her
 new
 colors
 to
 her
 mom
 and
 dad
,
 and
 they
 were
 proud
 of
 her
 too
.
 They
 all
 lived
 happily
 ever
 after
.
\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = vis_sample_prediction_probs(sample_tok, correct_probs, top_3_probs, tokenizer)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "
<|endoftext|>
Once
 upon
 a
 time
,
 there
 was
 a
 kind
 girl
 named
 Lily
.
 Lily
 loved
 to
 mix
 things
.
 One
 day
,
 she
 found
 a
 big
 box
 full
 of
 colors
.
 Lily
 was
 very
 happy
.
\\n

L
ily
 took
 out
 a
 strip
 of
 red
 and
 a
 strip
 of
 blue
.
 She
 mixed
 them
 together
 and
 made
 a
 new
 color
,
 purple
!
 Lily
 was
 so
 excited
.
 She
 wanted
 to
 mix
 more
 colors
.
\\n

Next
,
 Lily
 took
 a
 strip
 of
 yellow
 and
 a
 strip
 of
 green
.
 She
 mixed
 them
 together
 and
 made
 a
 new
 color
,
 orange
!
 Lily
 was
 very
 proud
 of
 herself
.
 She
 showed
 her
 new
 colors
 to
 her
 mom
 and
 dad
,
 and
 they
 were
 proud
 of
 her
 too
.
 They
 all
 lived
 happily
 ever
 after
.
\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = vis_sample_prediction_probs(sample_tok, correct_probs, top_5_probs, tokenizer)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/delphi/eval/compare_models.py b/src/delphi/eval/compare_models.py new file mode 100644 index 00000000..e03b300c --- /dev/null +++ b/src/delphi/eval/compare_models.py @@ -0,0 +1,91 @@ +from dataclasses import dataclass + +import torch +from jaxtyping import Int +from transformers import PreTrainedModel + +from delphi.eval.utils import get_all_and_next_logprobs_single + + +def identify_model(model: PreTrainedModel) -> str: + return model.config.name_or_path + + +@dataclass +class TokenPrediction: + token: int + base_model_prob: float + lift_model_prob: float + + +@dataclass +class NextTokenStats: + base_model: str + lift_model: str + next_prediction: TokenPrediction + topk: list[TokenPrediction] + + +def compare_models( + model_a: PreTrainedModel, + model_b: PreTrainedModel, + sample_tok: Int[torch.Tensor, "seq"], + top_k: int = 3, +) -> list[NextTokenStats | None]: + """ + Compare the probabilities of the next token for two models and get the top k token predictions according to model B. + Args: + - model_a: The first model (assumed to be the base model) + - model_b: The second model (assumed to be the improved model) + - sample_tok: The tokenized prompt + - top_k: The number of top token predictions to retrieve (default is 5) + Returns: + A list of NextTokenStats objects, one for each token in the prompt. + Tensors are aligned to the token they are predicting (by prepending a -1 to the start of the tensor) + """ + assert ( + model_a.device == model_b.device + ), "Both models must be on the same device for comparison." + + device = model_a.device + sample_tok = sample_tok.to(device) + + logprobs_a, next_probs_a = get_all_and_next_logprobs_single(model_a, sample_tok) + logprobs_b, next_probs_b = get_all_and_next_logprobs_single(model_b, sample_tok) + + probs_a = torch.exp(logprobs_a) + probs_b = torch.exp(logprobs_b) + + top_k_b = torch.topk(probs_b, top_k, dim=-1) + top_k_a_probs = torch.gather(probs_a, 1, top_k_b.indices) + + top_k_b_tokens = top_k_b.indices + top_k_b_probs = top_k_b.values + + comparisons = [] + # ignore first token when evaluating predictions + comparisons.append(None) + + for next_p_a, next_p_b, top_toks_b, top_probs_a, top_probs_b in zip( + next_probs_a, next_probs_b, top_k_b_tokens, top_k_a_probs, top_k_b_probs + ): + nts = NextTokenStats( + base_model=identify_model(model_a), + lift_model=identify_model(model_b), + next_prediction=TokenPrediction( + token=int(next_p_a.item()), + base_model_prob=next_p_a.item(), + lift_model_prob=next_p_b.item(), + ), + topk=[ + TokenPrediction( + token=int(top_toks_b[i].item()), + base_model_prob=top_probs_a[i].item(), + lift_model_prob=top_probs_b[i].item(), + ) + for i in range(top_k) + ], + ) + comparisons.append(nts) + + return comparisons diff --git a/src/delphi/eval/utils.py b/src/delphi/eval/utils.py index ee9893d9..16b26e4c 100644 --- a/src/delphi/eval/utils.py +++ b/src/delphi/eval/utils.py @@ -4,6 +4,7 @@ import torch from datasets import Dataset, load_dataset from jaxtyping import Float, Int +from transformers import PreTrainedModel, PreTrainedTokenizerBase def get_all_logprobs( @@ -14,6 +15,13 @@ def get_all_logprobs( return torch.log_softmax(logits, dim=-1) +# convenience wrapper for calling on a single sample +def get_single_logprobs( + model: Callable, input_ids: Int[torch.Tensor, "seq"] +) -> Float[torch.Tensor, "seq vocab"]: + return get_all_logprobs(model, input_ids.unsqueeze(0))[0] + + def gather_logprobs( logprobs: Float[torch.Tensor, "batch seq vocab"], tokens: Int[torch.Tensor, "batch seq"], @@ -21,12 +29,39 @@ def gather_logprobs( return torch.gather(logprobs, -1, tokens.unsqueeze(-1)).squeeze(-1) -def get_next_logprobs( - model: Callable, input_ids: Int[torch.Tensor, "batch seq"] -) -> Float[torch.Tensor, "batch shorter_seq"]: +def get_all_and_next_logprobs( + model: Callable, + input_ids: Int[torch.Tensor, "batch seq"], +) -> tuple[ + Float[torch.Tensor, "batch shorter_seq vocab"], + Float[torch.Tensor, "batch shorter_seq"], +]: logprobs = get_all_logprobs(model, input_ids[:, :-1]) next_tokens = input_ids[:, 1:] - return gather_logprobs(logprobs, next_tokens) + return logprobs, gather_logprobs(logprobs, next_tokens) + + +def get_all_and_next_logprobs_single( + model: Callable, + input_ids: Int[torch.Tensor, "seq"], +) -> tuple[ + Float[torch.Tensor, "shorter_seq vocab"], + Float[torch.Tensor, "shorter_seq"], +]: + all_logprobs, next_logprobs = get_all_and_next_logprobs( + model, input_ids.unsqueeze(0) + ) + return all_logprobs[0], next_logprobs[0] + + +def get_next_and_top_k_probs( + model: PreTrainedModel, input_ids: Int[torch.Tensor, "seq"], k: int = 3 +) -> tuple[Float[torch.Tensor, "shorter_seq"], torch.return_types.topk,]: + all_logprobs, next_logprobs = get_all_and_next_logprobs_single(model, input_ids) + all_probs = torch.exp(all_logprobs) + next_probs = torch.exp(next_logprobs) + top_k = torch.topk(all_probs, k, dim=-1) + return next_probs, top_k def load_validation_dataset(dataset_name: str) -> Dataset: @@ -42,3 +77,13 @@ def load_validation_dataset(dataset_name: str) -> Dataset: split="train", ) return cast(Dataset, dataset) + + +def tokenize( + tokenizer: PreTrainedTokenizerBase, sample_txt: str +) -> Int[torch.Tensor, "seq"]: + # supposedly this can be different than prepending the bos token id + return cast( + Int[torch.Tensor, "seq"], + tokenizer.encode(tokenizer.bos_token + sample_txt, return_tensors="pt")[0], + ) diff --git a/src/delphi/eval/vis.py b/src/delphi/eval/vis.py new file mode 100644 index 00000000..5dd4fdb2 --- /dev/null +++ b/src/delphi/eval/vis.py @@ -0,0 +1,140 @@ +import uuid +from typing import cast + +import torch +from IPython.core.display import HTML +from IPython.core.display_functions import display +from jaxtyping import Float, Int +from transformers import PreTrainedTokenizerBase + + +def probs_to_colors(probs: Float[torch.Tensor, "next_pos"]) -> list[str]: + # for the endoftext token + # no prediction, no color + colors = ["white"] + for p in probs.tolist(): + red_gap = 150 # the higher it is, the less red the tokens will be + green_blue_val = red_gap + int((255 - red_gap) * (1 - p)) + colors.append(f"rgb(255, {green_blue_val}, {green_blue_val})") + return colors + + +def to_tok_prob_str(tok: int, prob: float, tokenizer: PreTrainedTokenizerBase) -> str: + tok_str = tokenizer.decode(tok).replace(" ", " ").replace("\n", r"\n") + prob_str = f"{prob:.2%}" + return f"{prob_str:>6} |{tok_str}|" + + +def token_to_html( + token: int, + tokenizer: PreTrainedTokenizerBase, + bg_color: str, + data: dict, +) -> str: + data = data or {} # equivalent to if not data: data = {} + # non-breakable space, w/o it leading spaces wouldn't be displayed + str_token = tokenizer.decode(token).replace(" ", " ") + + # background or user-select (for \n) goes here + specific_styles = {} + # for now just adds line break or doesn't + br = "" + + if bg_color: + specific_styles["background-color"] = bg_color + if str_token == "\n": + # replace new line character with two characters: \ and n + str_token = r"\n" + # add line break in html + br += "
" + # this is so we can copy the prompt without "\n"s + specific_styles["user-select"] = "none" + + style_str = data_str = "" + # converting style dict into the style attribute + if specific_styles: + inside_style_str = "; ".join(f"{k}: {v}" for k, v in specific_styles.items()) + style_str = f" style='{inside_style_str}'" + if data: + data_str = "".join( + f" data-{k}='{v.replace(' ', ' ')}'" for k, v in data.items() + ) + return f"
{str_token}
{br}" + + +_token_style = { + "border": "1px solid #888", + "display": "inline-block", + # each character of the same width, so we can easily spot a space + "font-family": "monospace", + "font-size": "14px", + "color": "black", + "background-color": "white", + "margin": "1px 0px 1px 1px", + "padding": "0px 1px 1px 1px", +} +_token_style_str = " ".join([f"{k}: {v};" for k, v in _token_style.items()]) + + +def vis_sample_prediction_probs( + sample_tok: Int[torch.Tensor, "pos"], + correct_probs: Float[torch.Tensor, "pos"], + top_k_probs: torch.return_types.topk, + tokenizer: PreTrainedTokenizerBase, +) -> str: + colors = probs_to_colors(correct_probs) + token_htmls = [] + + # Generate a unique ID for this instance (so we can have multiple instances on the same page) + unique_id = str(uuid.uuid4()) + + token_class = f"token_{unique_id}" + hover_div_id = f"hover_info_{unique_id}" + + for i in range(sample_tok.shape[0]): + tok = cast(int, sample_tok[i].item()) + data = {} + if i > 0: + correct_prob = correct_probs[i - 1].item() + data["next"] = to_tok_prob_str(tok, correct_prob, tokenizer) + top_k_probs_tokens = top_k_probs.indices[i - 1] + top_k_probs_values = top_k_probs.values[i - 1] + for j in range(top_k_probs_tokens.shape[0]): + top_tok = top_k_probs_tokens[j].item() + top_tok = cast(int, top_tok) + top_prob = top_k_probs_values[j].item() + data[f"top{j}"] = to_tok_prob_str(top_tok, top_prob, tokenizer) + + token_htmls.append( + token_to_html(tok, tokenizer, bg_color=colors[i], data=data).replace( + "class='token'", f"class='{token_class}'" + ) + ) + + html_str = f""" + + {"".join(token_htmls)}
+ + """ + display(HTML(html_str)) + return html_str diff --git a/tests/eval/test_compare_models.py b/tests/eval/test_compare_models.py new file mode 100644 index 00000000..0521b0cb --- /dev/null +++ b/tests/eval/test_compare_models.py @@ -0,0 +1,23 @@ +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from delphi.eval.compare_models import NextTokenStats, compare_models +from delphi.eval.utils import load_validation_dataset, tokenize + + +def test_compare_models(): + with torch.set_grad_enabled(False): + model = AutoModelForCausalLM.from_pretrained("roneneldan/TinyStories-1M") + model_instruct = AutoModelForCausalLM.from_pretrained( + "roneneldan/TinyStories-Instruct-1M" + ) + ds_txt = load_validation_dataset("tinystories-v2-clean")["story"] + tokenizer = AutoTokenizer.from_pretrained("roneneldan/TinyStories-1M") + sample_tok = tokenize(tokenizer, ds_txt[0]) + K = 3 + model_comparison = compare_models(model, model_instruct, sample_tok, top_k=K) + # ignore the first element comparison + assert model_comparison[0] is None + assert isinstance(model_comparison[1], NextTokenStats) + assert len(model_comparison) == sample_tok.shape[0] + assert len(model_comparison[1].topk) == K From b8d0d8ce09a59fc35c42a2e2370bf1c5e68b5071 Mon Sep 17 00:00:00 2001 From: Rai <62800649+menamerai@users.noreply.github.com> Date: Wed, 14 Feb 2024 14:15:25 -0500 Subject: [PATCH 05/53] Ported token mapping function (#22) * add token mapping * use cast for typing * moved token_map function to library * added test case for token_map * added test cases for token_map * review changes * add load_hf_dataset function * shorten docstring * revert pickling changes * added token mapping script --- scripts/map_tokens.py | 22 +++++++++++++++++ src/delphi/eval/token_map.py | 18 ++++++++++++++ tests/eval/test_token_map.py | 48 ++++++++++++++++++++++++++++++++++++ 3 files changed, 88 insertions(+) create mode 100644 scripts/map_tokens.py create mode 100644 src/delphi/eval/token_map.py create mode 100644 tests/eval/test_token_map.py diff --git a/scripts/map_tokens.py b/scripts/map_tokens.py new file mode 100644 index 00000000..327f4651 --- /dev/null +++ b/scripts/map_tokens.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 + +import argparse +import pickle + +from delphi.eval.token_map import token_map +from delphi.eval.utils import load_validation_dataset + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="") + parser.add_argument( + "dataset_name", help="Dataset from huggingface to run token_map on" + ) + parser.add_argument("--output", help="Output file name", default="token_map.pkl") + args = parser.parse_args() + + dataset = load_validation_dataset(args.dataset_name) + + mapping = token_map(dataset) + + with open(f"data/{args.output}", "wb") as f: + pickle.dump(mapping, file=f) diff --git a/src/delphi/eval/token_map.py b/src/delphi/eval/token_map.py new file mode 100644 index 00000000..17e8e2ca --- /dev/null +++ b/src/delphi/eval/token_map.py @@ -0,0 +1,18 @@ +import os +from typing import cast + +from datasets import Dataset + + +def token_map( + tokenized_dataset: Dataset, +) -> dict[int, list[tuple[int, int]]]: + """Return a mapping of tokens to their (prompt_idx, token_idx) locations in the tokenized_dataset.""" + + mapping = {} + for prompt_idx, prompt in enumerate(tokenized_dataset): + prompt = cast(dict, prompt) + for token_idx, token in enumerate(prompt["tokens"]): + mapping.setdefault(token, []).append((prompt_idx, token_idx)) + + return mapping diff --git a/tests/eval/test_token_map.py b/tests/eval/test_token_map.py new file mode 100644 index 00000000..5a276080 --- /dev/null +++ b/tests/eval/test_token_map.py @@ -0,0 +1,48 @@ +import pytest +from datasets import Dataset + +from delphi.eval.token_map import token_map + + +def test_token_map(): + tokenized_dataset = Dataset.from_dict( + { + "tokens": [ + [0, 1, 2, 3, 4, 5, 0, 6, 7], + [0, 1, 2, 3, 4, 5, 0, 6, 7], + [0, 1, 2, 3, 4, 5, 0, 6, 7], + ] + } + ) + mapping = token_map(tokenized_dataset) + assert mapping == { + 0: [(0, 0), (0, 6), (1, 0), (1, 6), (2, 0), (2, 6)], + 1: [(0, 1), (1, 1), (2, 1)], + 2: [(0, 2), (1, 2), (2, 2)], + 3: [(0, 3), (1, 3), (2, 3)], + 4: [(0, 4), (1, 4), (2, 4)], + 5: [(0, 5), (1, 5), (2, 5)], + 6: [(0, 7), (1, 7), (2, 7)], + 7: [(0, 8), (1, 8), (2, 8)], + } + + # fmt: off + tokenized_dataset = Dataset.from_dict( + { # one really long prompt + "tokens": [ + [0, 1, 2, 3, 4, 5, 0, 6, 7, 0, 1, 2, 3, 4, 5, 0, 6, 7, 0, 1, 2, 3, 4, 5, 0, 6, 7] + ] + } + ) + # fmt: on + mapping = token_map(tokenized_dataset) + assert mapping == { + 0: [(0, 0), (0, 6), (0, 9), (0, 15), (0, 18), (0, 24)], + 1: [(0, 1), (0, 10), (0, 19)], + 2: [(0, 2), (0, 11), (0, 20)], + 3: [(0, 3), (0, 12), (0, 21)], + 4: [(0, 4), (0, 13), (0, 22)], + 5: [(0, 5), (0, 14), (0, 23)], + 6: [(0, 7), (0, 16), (0, 25)], + 7: [(0, 8), (0, 17), (0, 26)], + } From bd4a88b3c1c92a0d89dfe68fbfaf2830a19009de Mon Sep 17 00:00:00 2001 From: Joshua We <80349780+joshuawe@users.noreply.github.com> Date: Sat, 17 Feb 2024 03:56:28 +0100 Subject: [PATCH 06/53] add spacy token labelling (#21) * add token labelling * add explanation function * add notebook * test * swtich off dependency labels + add spacy to requirements * small improvements * improve notebook explanation * fix errors * add notebook * test * swtich off dependency labels + add spacy to requirements * small improvements * improve notebook explanation * fix errors * complete UPOS tags for token labels * add tests * update requirements for delphi tokenizer * added token label script * add the files containing token information/labels * small enhancements suggested for PR * rebasing * improve optional downloading of spacy language model * bugfix: handle tokens empty string '' * add argparse for label_all_tokens.py script * add tokenized dicts * update notebook * undo __init__ * change spacy model from "trf" to "sm" * bug fix --- notebooks/token_labelling.ipynb | 435 ++++++++++++++++++++ requirements.txt | 6 +- scripts/label_all_tokens.py | 110 +++++ src/delphi/eval/all_tokens_list.txt | Bin 0 -> 45121 bytes src/delphi/eval/labelled_token_ids_dict.pkl | Bin 0 -> 274517 bytes src/delphi/eval/token_labelling.py | 210 ++++++++++ tests/eval/test_token_labelling.py | 114 +++++ 7 files changed, 874 insertions(+), 1 deletion(-) create mode 100644 notebooks/token_labelling.ipynb create mode 100644 scripts/label_all_tokens.py create mode 100644 src/delphi/eval/all_tokens_list.txt create mode 100644 src/delphi/eval/labelled_token_ids_dict.pkl create mode 100644 src/delphi/eval/token_labelling.py create mode 100644 tests/eval/test_token_labelling.py diff --git a/notebooks/token_labelling.ipynb b/notebooks/token_labelling.ipynb new file mode 100644 index 00000000..45423d8c --- /dev/null +++ b/notebooks/token_labelling.ipynb @@ -0,0 +1,435 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Giving tokens a label - How to categorize tokens\n", + "\n", + "\n", + "The first part of this Notebook contains elements that explain how to label tokens and how the functions work.\n", + "\n", + "The second part shows how all tokens are labelled that are used for our delphi language models.3\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# autoreload\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from pprint import pprint \n", + "\n", + "import spacy\n", + "from tqdm.auto import tqdm\n", + "\n", + "import delphi\n", + "\n", + "from delphi.eval import token_labelling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1) How to use the token labelling functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We analyze a simple sentence and receive the respective tokens with their analyzed attributes. \n", + "The grammatical/linguistic analysis is done by a model provided by spaCy for the English language." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Peter \t PROPN \t nsubj \t PERSON\n", + "is \t AUX \t ROOT \t \n", + "a \t DET \t det \t \n", + "person \t NOUN \t attr \t \n" + ] + } + ], + "source": [ + "# Load the english model\n", + "nlp = spacy.load(\"en_core_web_sm\")\n", + "\n", + "# Create a Doc object from a given text\n", + "doc = nlp(\"Peter is a person\")\n", + "\n", + "token = doc[0]\n", + "for tok in doc:\n", + " print(tok,\"\\t\", tok.pos_, \"\\t\", tok.dep_, \"\\t\", tok.ent_type_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get the label for our custom token that we just printed." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Capitalized': True,\n", + " 'Is Adjective': False,\n", + " 'Is Adposition': False,\n", + " 'Is Adverb': False,\n", + " 'Is Auxiliary': False,\n", + " 'Is Coordinating conjuction': False,\n", + " 'Is Determiner': False,\n", + " 'Is Interjunction': False,\n", + " 'Is Named Entity': True,\n", + " 'Is Noun': False,\n", + " 'Is Numeral': False,\n", + " 'Is Other': False,\n", + " 'Is Particle': False,\n", + " 'Is Pronoun': False,\n", + " 'Is Proper Noun': True,\n", + " 'Is Punctuation': False,\n", + " 'Is Subordinating conjuction': False,\n", + " 'Is Symbol': False,\n", + " 'Is Verb': False,\n", + " 'Starts with space': False}\n" + ] + } + ], + "source": [ + "from delphi.eval import token_labelling\n", + "\n", + "label = token_labelling.label_single_token(token)\n", + "pprint(label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get an understanding of what the labels acutally mean.\n", + "Use this function to receive an explanation for a single token." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------- Explanation of token labels --------\n", + "Token text: Peter\n", + "Token dependency: nominal subject\n", + "Token POS: proper noun\n", + "---------------- Token labels ---------------\n", + " 0 Starts with space False\n", + " 1 Capitalized True\n", + " 2 Is Adjective False\n", + " 3 Is Adposition False\n", + " 4 Is Adverb False\n", + " 5 Is Auxiliary False\n", + " 6 Is Coordinating conjuction False\n", + " 7 Is Determiner False\n", + " 8 Is Interjunction False\n", + " 9 Is Noun False\n", + " 10 Is Numeral False\n", + " 11 Is Particle False\n", + " 12 Is Pronoun False\n", + " 13 Is Proper Noun True\n", + " 14 Is Punctuation False\n", + " 15 Is Subordinating conjuction False\n", + " 16 Is Symbol False\n", + " 17 Is Verb False\n", + " 18 Is Other False\n", + " 19 Is Named Entity True\n" + ] + } + ], + "source": [ + "token_labelling.explain_token_labels(token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are interested in all the possible labels a token can have, that spaCy is capable of assigning, then call the same function but without any argument:\n", + "```Python\n", + ">>> token_labelling.explain_token_labels()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Batched token labelling\n", + "Next, let us analyze a batch of sentences and have them labelled.\n", + "> In the example below the input sentences are not yet tokenized, so spaCy uses its internal tokenizer." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Token: Peter\n", + "Starts with space | Capitalized | Is Adjective | Is Adposition | Is Adverb | Is Auxiliary | Is Coordinating conjuction | Is Determiner | Is Interjunction | Is Noun | Is Numeral | Is Particle | Is Pronoun | Is Proper Noun | Is Punctuation | Is Subordinating conjuction | Is Symbol | Is Verb | Is Other | Is Named Entity\n", + "False | True | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | True \n", + "---\n", + "Token: is\n", + "Starts with space | Capitalized | Is Adjective | Is Adposition | Is Adverb | Is Auxiliary | Is Coordinating conjuction | Is Determiner | Is Interjunction | Is Noun | Is Numeral | Is Particle | Is Pronoun | Is Proper Noun | Is Punctuation | Is Subordinating conjuction | Is Symbol | Is Verb | Is Other | Is Named Entity\n", + "False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False \n", + "---\n", + "Token: a\n", + "Starts with space | Capitalized | Is Adjective | Is Adposition | Is Adverb | Is Auxiliary | Is Coordinating conjuction | Is Determiner | Is Interjunction | Is Noun | Is Numeral | Is Particle | Is Pronoun | Is Proper Noun | Is Punctuation | Is Subordinating conjuction | Is Symbol | Is Verb | Is Other | Is Named Entity\n", + "False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False \n", + "---\n", + "Token: person\n", + "Starts with space | Capitalized | Is Adjective | Is Adposition | Is Adverb | Is Auxiliary | Is Coordinating conjuction | Is Determiner | Is Interjunction | Is Noun | Is Numeral | Is Particle | Is Pronoun | Is Proper Noun | Is Punctuation | Is Subordinating conjuction | Is Symbol | Is Verb | Is Other | Is Named Entity\n", + "False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False \n", + "---\n", + "Token: .\n", + "Starts with space | Capitalized | Is Adjective | Is Adposition | Is Adverb | Is Auxiliary | Is Coordinating conjuction | Is Determiner | Is Interjunction | Is Noun | Is Numeral | Is Particle | Is Pronoun | Is Proper Noun | Is Punctuation | Is Subordinating conjuction | Is Symbol | Is Verb | Is Other | Is Named Entity\n", + "False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False \n", + "---\n", + "\n", + "\n", + "5\n", + "[{'Starts with space': False, 'Capitalized': True, 'Is Adjective': False, 'Is Adposition': False, 'Is Adverb': False, 'Is Auxiliary': False, 'Is Coordinating conjuction': False, 'Is Determiner': False, 'Is Interjunction': False, 'Is Noun': False, 'Is Numeral': False, 'Is Particle': False, 'Is Pronoun': False, 'Is Proper Noun': True, 'Is Punctuation': False, 'Is Subordinating conjuction': False, 'Is Symbol': False, 'Is Verb': False, 'Is Other': False, 'Is Named Entity': True}, {'Starts with space': False, 'Capitalized': False, 'Is Adjective': False, 'Is Adposition': False, 'Is Adverb': False, 'Is Auxiliary': True, 'Is Coordinating conjuction': False, 'Is Determiner': False, 'Is Interjunction': False, 'Is Noun': False, 'Is Numeral': False, 'Is Particle': False, 'Is Pronoun': False, 'Is Proper Noun': False, 'Is Punctuation': False, 'Is Subordinating conjuction': False, 'Is Symbol': False, 'Is Verb': False, 'Is Other': False, 'Is Named Entity': False}, {'Starts with space': False, 'Capitalized': False, 'Is Adjective': False, 'Is Adposition': False, 'Is Adverb': False, 'Is Auxiliary': False, 'Is Coordinating conjuction': False, 'Is Determiner': True, 'Is Interjunction': False, 'Is Noun': False, 'Is Numeral': False, 'Is Particle': False, 'Is Pronoun': False, 'Is Proper Noun': False, 'Is Punctuation': False, 'Is Subordinating conjuction': False, 'Is Symbol': False, 'Is Verb': False, 'Is Other': False, 'Is Named Entity': False}, {'Starts with space': False, 'Capitalized': False, 'Is Adjective': False, 'Is Adposition': False, 'Is Adverb': False, 'Is Auxiliary': False, 'Is Coordinating conjuction': False, 'Is Determiner': False, 'Is Interjunction': False, 'Is Noun': True, 'Is Numeral': False, 'Is Particle': False, 'Is Pronoun': False, 'Is Proper Noun': False, 'Is Punctuation': False, 'Is Subordinating conjuction': False, 'Is Symbol': False, 'Is Verb': False, 'Is Other': False, 'Is Named Entity': False}, {'Starts with space': False, 'Capitalized': False, 'Is Adjective': False, 'Is Adposition': False, 'Is Adverb': False, 'Is Auxiliary': False, 'Is Coordinating conjuction': False, 'Is Determiner': False, 'Is Interjunction': False, 'Is Noun': False, 'Is Numeral': False, 'Is Particle': False, 'Is Pronoun': False, 'Is Proper Noun': False, 'Is Punctuation': True, 'Is Subordinating conjuction': False, 'Is Symbol': False, 'Is Verb': False, 'Is Other': False, 'Is Named Entity': False}]\n" + ] + } + ], + "source": [ + "sentences = [\n", + " \"Peter is a person.\"\n", + "]\n", + "labels = token_labelling.label_batch_sentences(sentences, tokenized=False, verbose=True)\n", + "\n", + "print(len(labels[0]))\n", + "print(labels[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now with our own tokenization. E.g. the one from our TinyStories models." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "[{'Starts with space': False, 'Capitalized': True, 'Is Noun': True, 'Is Pronoun': False, 'Is Adjective': False, 'Is Verb': False, 'Is Adverb': False, 'Is Preposition': False, 'Is Conjunction': False, 'Is Interjunction': False, 'Is Named Entity': False}, {'Starts with space': False, 'Capitalized': False, 'Is Noun': False, 'Is Pronoun': False, 'Is Adjective': False, 'Is Verb': False, 'Is Adverb': True, 'Is Preposition': False, 'Is Conjunction': False, 'Is Interjunction': False, 'Is Named Entity': False}, {'Starts with space': False, 'Capitalized': False, 'Is Noun': False, 'Is Pronoun': False, 'Is Adjective': True, 'Is Verb': False, 'Is Adverb': False, 'Is Preposition': False, 'Is Conjunction': False, 'Is Interjunction': False, 'Is Named Entity': False}, {'Starts with space': False, 'Capitalized': False, 'Is Noun': True, 'Is Pronoun': False, 'Is Adjective': False, 'Is Verb': False, 'Is Adverb': False, 'Is Preposition': False, 'Is Conjunction': False, 'Is Interjunction': False, 'Is Named Entity': False}, {'Starts with space': False, 'Capitalized': False, 'Is Noun': False, 'Is Pronoun': False, 'Is Adjective': False, 'Is Verb': False, 'Is Adverb': False, 'Is Preposition': False, 'Is Conjunction': False, 'Is Interjunction': False, 'Is Named Entity': False}]\n" + ] + } + ], + "source": [ + "sentences = [\n", + " [\"This \", \"is \", \"a \", \"sentence\", \".\"]\n", + "]\n", + "labelled_sentences = token_labelling.label_batch_sentences(sentences, tokenized=True, verbose=False)\n", + "\n", + "print(len(labelled_sentences[0]))\n", + "print(labelled_sentences[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2) Labelling all tokens in the dataset\n", + "\n", + "Now we want to label all the tokens that our tokenizer knows - its entire vocabulary.\n", + "\n", + "Using thy script in `scripts/label_all_tokens.py` we get the files:\n", + "- `src\\delphi\\eval\\all_tokens_list.txt`\n", + "- `src\\delphi\\eval\\labelled_token_ids_dict.pkl`\n", + "\n", + "Let's load the tokenizer so that we can look at the labelled tokens.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\joshu\\anaconda3\\envs\\delphi2\\lib\\site-packages\\transformers\\utils\\generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n", + " _torch_pytree._register_pytree_node(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The vocab size is: 4096\n" + ] + } + ], + "source": [ + "# Get all the tokens of the tokenizer\n", + "from transformers import AutoTokenizer, PreTrainedTokenizer\n", + "\n", + "\n", + "# Decode a sentence\n", + "def decode(tokenizer: PreTrainedTokenizer, token_ids: list[int]) -> str:\n", + " return tokenizer.decode(token_ids, skip_special_tokens=True)\n", + "\n", + "model = \"delphi-suite/delphi-llama2-100k\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model)\n", + "vocab_size = tokenizer.vocab_size\n", + "print(\"The vocab size is:\", vocab_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the pickle." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "path = \"../src/delphi/eval/labelled_token_ids_dict.pkl\"\n", + "# load \n", + "with open(path, \"rb\") as f:\n", + " labelled_token_ids_dict = pickle.load(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Look at some random tokens and their labels" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The token id is: 1143\n", + "The decoded token is: has\n", + "The label is:\n", + "{'Capitalized': False,\n", + " 'Is Adjective': False,\n", + " 'Is Adposition': False,\n", + " 'Is Adverb': False,\n", + " 'Is Auxiliary': False,\n", + " 'Is Coordinating conjuction': False,\n", + " 'Is Determiner': False,\n", + " 'Is Interjunction': True,\n", + " 'Is Named Entity': False,\n", + " 'Is Noun': False,\n", + " 'Is Numeral': False,\n", + " 'Is Other': False,\n", + " 'Is Particle': False,\n", + " 'Is Pronoun': False,\n", + " 'Is Proper Noun': False,\n", + " 'Is Punctuation': False,\n", + " 'Is Subordinating conjuction': False,\n", + " 'Is Symbol': False,\n", + " 'Is Verb': False,\n", + " 'Starts with space': False}\n" + ] + } + ], + "source": [ + "import random\n", + "from pprint import pprint\n", + "# Get a random token id between 0 and 4000\n", + "token_id = random.randint(0, 4000)\n", + "# decode the token id\n", + "decoded_token = decode(tokenizer, [token_id])\n", + "# get the corresponding label\n", + "label = labelled_token_ids_dict[token_id]\n", + "# print the results\n", + "print(\"The token id is:\", token_id)\n", + "print(\"The decoded token is:\", decoded_token)\n", + "print(\"The label is:\")\n", + "pprint(label)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv_tinyevals", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt index 65b457a4..5fdc84c0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,8 @@ black==23.12.1 jaxtyping==0.2.25 beartype==0.16.4 pre-commit==3.6.0 -isort==5.13.2 \ No newline at end of file +isort==5.13.2 +spacy==3.7.2 +chardet==5.2.0 +sentencepiece==0.1.99 +protobuf==4.25.2 \ No newline at end of file diff --git a/scripts/label_all_tokens.py b/scripts/label_all_tokens.py new file mode 100644 index 00000000..01bf4cf1 --- /dev/null +++ b/scripts/label_all_tokens.py @@ -0,0 +1,110 @@ +import argparse +import pickle +from pathlib import Path + +from tqdm.auto import tqdm +from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast + +from delphi.eval import token_labelling + + +def tokenize( + tokenizer: PreTrainedTokenizer | PreTrainedTokenizerFast, sample_txt: str +) -> int: + # supposedly this can be different than prepending the bos token id + return tokenizer.encode(tokenizer.bos_token + sample_txt, return_tensors="pt")[0] + + +# Decode a sentence +def decode( + tokenizer: PreTrainedTokenizer | PreTrainedTokenizerFast, token_ids: int | list[int] +) -> str: + return tokenizer.decode(token_ids, skip_special_tokens=True) + + +def main(): + # Setup argparse + parser = argparse.ArgumentParser(description="Tokenization and labeling utility.") + parser.add_argument( + "--model_name", + type=str, + help="Name of the model to use for tokenization and labeling.", + default="delphi-suite/delphi-llama2-100k", + required=False, + ) + args = parser.parse_args() + + # Access command-line arguments + # Directory to save the results + SAVE_DIR = Path("src/delphi/eval/") + model_name = args.model_name + + print("\n", " LABEL ALL TOKENS ".center(50, "="), "\n") + print(f"You chose the model: {model_name}\n") + print( + f"The language model will be loaded from Huggingface and its tokenizer used to do two things:\n\t1) Create a list of all tokens in the tokenizer's vocabulary.\n\t2) Label each token with its part of speech, dependency, and named entity recognition tags.\nThe respective results will be saved to files located at: '{SAVE_DIR}'\n" + ) + + # ================ (1) ================= + print("(1) Create a list of all tokens in the tokenizer's vocabulary ...") + + # Load the tokenizer from Huggingface + tokenizer = AutoTokenizer.from_pretrained(model_name) + vocab_size = tokenizer.vocab_size + print("Loaded the tokenizer.\nThe vocab size is:", vocab_size) + + # Create a list of all tokens in the tokenizer's vocabulary + tokens_str = "" # will hold all tokens and their ids + for i in range(tokenizer.vocab_size): + tokens_str += f"{i},{decode(tokenizer, i)}\n" + + # Save the list of all tokens to a file + filename = "all_tokens_list.txt" + filepath = SAVE_DIR / filename + with open(filepath, "w", encoding="utf-8") as f: + f.write(tokens_str) + + print(f"Saved the list of all tokens to:\n\t{filepath}\n") + + # ================ (2) ================= + print("(2) Label each token ...") + + # let's label each token + labelled_token_ids_dict: dict[int, dict[str, bool]] = {} # token_id: labels + max_token_id = tokenizer.vocab_size # stop at which token id, vocab size + # we iterate over all token_ids individually + for token_id in tqdm(range(0, max_token_id), desc="Labelling tokens"): + # decode the token_ids to get a list of tokens, a 'sentence' + tokens = decode(tokenizer, token_id) # list of tokens == sentence + # put the sentence into a list, to make it a batch of sentences + sentences = [tokens] + # label the batch of sentences + labels = token_labelling.label_batch_sentences( + sentences, tokenized=True, verbose=False + ) + # create a dict with the token_ids and their labels + # update the labelled_token_ids_dict with the new dict + labelled_token_ids_dict[token_id] = labels[0][0] + + # Save the labelled tokens to a file + filename = "labelled_token_ids_dict.pkl" + filepath = SAVE_DIR / filename + with open(filepath, "wb") as f: + pickle.dump(labelled_token_ids_dict, f) + + print(f"Saved the labelled tokens to:\n\t{filepath}\n") + + # sanity check that The pickled and the original dict are the same + print("Sanity check ...", end="") + # load pickle + with open(filepath, "rb") as f: + pickled = pickle.load(f) + # compare + assert labelled_token_ids_dict == pickled + print(" completed.") + + print(" END ".center(50, "=")) + + +if __name__ == "__main__": + main() diff --git a/src/delphi/eval/all_tokens_list.txt b/src/delphi/eval/all_tokens_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..438dddae5a703d7c5bd4efc3b840708916eb9dc7 GIT binary patch literal 45121 zcmZ9V>3Snanw`BbvgLhWpBaxmUUJ$+Y>Aw)-Bz{KtyW7dO_ydg-CjWuBvAzdXn-iH zz1Je&6#&vh?@|k%2_65pmA9#EIaG?!ynR_?z%I?f%$@AI!Qx{^1An?oWL9 z!J_+na`p7E8OEn_gDG6>i!y^lkTtcIqm)ipR?|7@;UGR7N3jmZ}ZuAe}~Uy 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Download only starts if not already installed. +SPACY_MODEL = "en_core_web_sm" # small: "en_core_web_sm", large: "en_core_web_trf" +NLP = None # global var to hold the language model +if not is_package(SPACY_MODEL): + spacy.cli.download(SPACY_MODEL, False, False) + + +TOKEN_LABELS: dict[str, Callable] = { + # --- custom categories --- + "Starts with space": (lambda token: token.text.startswith(" ")), # bool + "Capitalized": (lambda token: token.text[0].isupper()), # bool + # --- POS (part-of-speech) categories --- + # They include the Universal POS tags (https://universaldependencies.org/u/pos/) + # -> "POS Tag": (lambda token: token.pos_), # 'NOUN', 'VB', .. + "Is Adjective": (lambda token: token.pos_ == "ADJ"), + "Is Adposition": (lambda token: token.pos_ == "ADP"), + "Is Adverb": (lambda token: token.pos_ == "ADV"), + "Is Auxiliary": (lambda token: token.pos_ == "AUX"), + "Is Coordinating conjuction": (lambda token: token.pos_ == "CCONJ"), + "Is Determiner": (lambda token: token.pos_ == "DET"), + "Is Interjunction": (lambda token: token.pos_ == "INTJ"), + "Is Noun": (lambda token: token.pos_ == "NOUN"), + "Is Numeral": (lambda token: token.pos_ == "NUM"), + "Is Particle": (lambda token: token.pos_ == "PART"), + "Is Pronoun": (lambda token: token.pos_ == "PRON"), + "Is Proper Noun": (lambda token: token.pos_ == "PROPN"), + "Is Punctuation": (lambda token: token.pos_ == "PUNCT"), + "Is Subordinating conjuction": (lambda token: token.pos_ == "SCONJ"), + "Is Symbol": (lambda token: token.pos_ == "SYM"), + "Is Verb": (lambda token: token.pos_ == "VERB"), + "Is Other": (lambda token: token.pos_ == "X"), + # --- dependency categories --- + # -> "Dependency": (lambda token: token.dep_), # 'nsubj', 'ROOT', 'dobj', .. + # "Is Subject": (lambda token: token.dep_ == "nsubj"), + # "Is Object": (lambda token: token.dep_ == "dobj"), + # "Is Root": ( + # lambda token: token.dep_ == "ROOT" + # ), # root of the sentence (often a verb) + # "Is auxiliary": (lambda token: token.dep_ == "aux"), + # --- Named entity recognition (NER) categories --- + # "Named Entity Type": (lambda token: token.ent_type_), # '', 'PERSON', 'ORG', 'GPE', .. + "Is Named Entity": (lambda token: token.ent_type_ != ""), +} + + +def explain_token_labels(token: Optional[Token] = None) -> None: + """ + Prints the explanation of a specific token's labels or of ALL + possible labels (POS, dependency, NER, ...), if no token is provided. + + Parameters + ---------- + token : Optional[Token], optional + The token, whose labels should be explained. If None, all labels + possible labels are explained, by default None. + """ + if token is not None: + # get token labels + labels = label_single_token(token) + print(" Explanation of token labels ".center(45, "-")) + print("Token text:".ljust(20), token.text) + print("Token dependency:".ljust(20), spacy.glossary.explain(token.dep_)) + print("Token POS:".ljust(20), spacy.glossary.explain(token.pos_)) + print(" Token labels ".center(45, "-")) + for i, (label_name, value) in enumerate(labels.items()): + print(f" {i:2} ", label_name.ljust(20), value) + + else: + glossary = spacy.glossary.GLOSSARY + print( + f"Explanation of all {len(glossary.keys())} token labels (POS, dependency, NER, ...):" + ) + for label, key in glossary.items(): + print(" ", label.ljust(10), key) + + +def label_single_token(token: Token | None) -> dict[str, bool]: + """ + Labels a single token. A token, that has been analyzed by the spaCy + library. + + Parameters + ---------- + token : Token | None + The token to be labelled. + + Returns + ------- + dict[str, bool] + Returns a dictionary with the token's labels as keys and their + corresponding boolean values. + """ + labels = dict() # The dict holding labels of a single token + # if token is None, then it is a '' empty strong token or similar + if token is None: + for label_name, category_check in TOKEN_LABELS.items(): + labels[label_name] = False + labels["Is Other"] = True + return labels + # all other cases / normal tokens + for label_name, category_check in TOKEN_LABELS.items(): + labels[label_name] = category_check(token) + return labels + + +def label_sentence(tokens: Doc | list[Token]) -> list[dict[str, bool]]: + """ + Labels spaCy Tokens in a sentence. Takes the context of the token into account + for dependency labels (e.g. subject, object, ...), IF dependency labels are turned on. + + Parameters + ---------- + tokens : list[Token] + A list of tokens. + + Returns + ------- + list[dict[str, bool]] + Returns a list of the tokens' labels. + """ + labelled_tokens = list() # list holding labels for all tokens of sentence + # if the list is empty it is because token is '' empty string or similar + if len(tokens) == 0: + labels = label_single_token(None) + labelled_tokens.append(labels) + return labelled_tokens + # in all other cases + for token in tokens: + labels = label_single_token(token) + labelled_tokens.append(labels) + return labelled_tokens + + +def label_batch_sentences( + sentences: list[str] | list[list[str]], + tokenized: bool = True, + verbose: bool = False, +) -> list[list[dict[str, bool]]]: + """ + Labels tokens in a sentence batchwise. Takes the context of the token into + account for dependency labels (e.g. subject, object, ...). + + Parameters + ---------- + sentences : list + A batch/list of sentences, each being a list of tokens. + tokenized : bool, optional + Whether the sentences are already tokenized, by default True. If the sentences + are full strings and not lists of tokens, then set to False. If true then `sentences` must be list[list[str]]. + verbose : bool, optional + Whether to print the tokens and their labels to the console, by default False. + + Returns + ------- + list[list[dict[str, bool]] + Returns a list of sentences. Each sentence contains a list of its + corresponding token length where each entry provides the labels/categories + for the token. Sentence -> Token -> Labels + """ + global NLP, SPACY_MODEL + + if NLP is None: + # Load english language model + NLP = spacy.load(SPACY_MODEL) + # labelled tokens, list holding sentences holding tokens holding corresponding token labels + labelled_sentences: list[list[dict[str, bool]]] = list() + + # go through each sentence in the batch + for sentence in sentences: + if tokenized: + # sentence is a list of tokens + doc = Doc(NLP.vocab, words=sentence) # type: ignore + # Apply the spaCy pipeline, except for the tokenizer + for name, proc in NLP.pipeline: + if name != "tokenizer": + doc = proc(doc) + else: + # sentence is a single string + doc = NLP(sentence) # type: ignore + + labelled_tokens = list() # list holding labels for all tokens of sentence + labelled_tokens = label_sentence(doc) + + # print the token and its labels to console + if verbose is True: + # go through each token in the sentence + for token, labelled_token in zip(doc, labelled_tokens): + print(f"Token: {token}") + print(" | ".join(list(TOKEN_LABELS.keys()))) + printable = [ + str(l).ljust(len(name)) for name, l in labelled_token.items() + ] + printable = " | ".join(printable) + print(printable) + print("---") + # add current sentence's tokens' labels to the list + labelled_sentences.append(labelled_tokens) + + if verbose is True: + print("\n") + + return labelled_sentences diff --git a/tests/eval/test_token_labelling.py b/tests/eval/test_token_labelling.py new file mode 100644 index 00000000..a727ddc0 --- /dev/null +++ b/tests/eval/test_token_labelling.py @@ -0,0 +1,114 @@ +import pytest +import spacy +from spacy.language import Language +from spacy.tokens import Doc + +import delphi.eval.token_labelling as tl + + +@pytest.fixture +def dummy_doc() -> tuple[str, Doc, dict[str, bool]]: + """ + Create a dummy Doc (list of Tokens) with specific attributes for testing purposes. + """ + nlp_dummy = Language() + + # Assume we're creating a dummy token with specific attributes + words = ["Peter", "is", "a", "person"] + spaces = [True, True, True, True] # No space after "dummy_token" + pos_tags = ["PROPN", "AUX", "DET", "NOUN"] # Part-of-speech tag + dep_tags = ["nsubj", "ROOT", "det", "attr"] # Dependency tag + ner_tags = ["PERSON", "", "", ""] # Named entity tag + + # Ensure the length of pos_tags and dep_tags matches the length of words + assert len(words) == len(pos_tags) == len(dep_tags) == len(ner_tags) + + # Create a Doc with one dummy token + doc = Doc(nlp_dummy.vocab, words=words, spaces=spaces) + + # Manually set POS, dependency and NER tags + for token, pos, dep, ner_tag in zip(doc, pos_tags, dep_tags, ner_tags): + token.pos_, token.dep_, token.ent_type_ = pos, dep, ner_tag + + # Token labels for "Peter" in the dummy doc + PETER_TOKEN_LABEL = { + "Starts with space": False, + "Capitalized": True, + "Is Adjective": False, + "Is Adposition": False, + "Is Adverb": False, + "Is Auxiliary": False, + "Is Coordinating conjuction": False, + "Is Determiner": False, + "Is Interjunction": False, + "Is Noun": False, + "Is Numeral": False, + "Is Particle": False, + "Is Pronoun": False, + "Is Proper Noun": True, + "Is Punctuation": False, + "Is Subordinating conjuction": False, + "Is Symbol": False, + "Is Verb": False, + "Is Other": False, + "Is Named Entity": True, + } + text = " ".join(words) + return text, doc, PETER_TOKEN_LABEL + + +def test_explain_token_labels(dummy_doc): + """ + Test the explain_token_labels function. + """ + # explain all labels + tl.explain_token_labels() + # print explanations for the first token in doc + text, doc, PETER_TOKEN_LABEL = dummy_doc + tl.explain_token_labels(doc[0]) + + +def test_label_single_token(dummy_doc): + """ + Test the label_single_token function. + """ + # create a dummy token + text, doc, PETER_TOKEN_LABEL = dummy_doc + token = doc[0] + # label the token + labels = tl.label_single_token(token) + # check if the labels are correct + assert labels == PETER_TOKEN_LABEL + + +def test_label_sentence(dummy_doc): + """ + Test the label_sentence function. + """ + text, doc, PETER_TOKEN_LABEL = dummy_doc + # label the sentence + labels = tl.label_sentence(doc) + # assert the first token is labeled correctly + assert labels[0] == PETER_TOKEN_LABEL + # iterate through tokens in doc + for token, label in zip(doc, labels): + assert label == tl.label_single_token(token) + + +def test_label_batch_sentences(dummy_doc): + """ + Test the label_batch_sentences function. + """ + # create a batch of sentences + text, doc, PETER_TOKEN_LABEL = dummy_doc + text = text.split(" ") + batch = [text, text, text] + # label the batch + labels = tl.label_batch_sentences(batch, tokenized=True) + # assert the first token is labeled correctly + assert labels[0][0] == PETER_TOKEN_LABEL + assert labels[1][0] == PETER_TOKEN_LABEL + assert labels[2][0] == PETER_TOKEN_LABEL + # iterate through tokens in doc + for token, label in zip(doc, labels[0]): + assert label == tl.label_single_token(token) From 369bdcafdf9ba7ea1d5322cf81bc7918f0577629 Mon Sep 17 00:00:00 2001 From: menamerai Date: Wed, 14 Feb 2024 16:48:37 -0500 Subject: [PATCH 07/53] added mock data --- data/mock-per-token-performance.py | 47 ++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 data/mock-per-token-performance.py diff --git a/data/mock-per-token-performance.py b/data/mock-per-token-performance.py new file mode 100644 index 00000000..312a113f --- /dev/null +++ b/data/mock-per-token-performance.py @@ -0,0 +1,47 @@ +from math import log2 +from random import random, seed + +seed(0) + +performance_data = { + "llama2-100k": { + "nouns": [log2(random()) for _ in range(200)], + "verbs": [log2(random()) for _ in range(100)], + "adjectives": [log2(random()) for _ in range(250)], + "adverbs": [log2(random()) for _ in range(40)], + "pronouns": [log2(random()) for _ in range(55)], + "prepositions": [log2(random()) for _ in range(234)], + }, + "llama2-1m": { + "nouns": [log2(random()) for _ in range(200)], + "verbs": [log2(random()) for _ in range(100)], + "adjectives": [log2(random()) for _ in range(250)], + "adverbs": [log2(random()) for _ in range(40)], + "pronouns": [log2(random()) for _ in range(55)], + "prepositions": [log2(random()) for _ in range(234)], + }, + "llama2-10m": { + "nouns": [log2(random()) for _ in range(200)], + "verbs": [log2(random()) for _ in range(100)], + "adjectives": [log2(random()) for _ in range(250)], + "adverbs": [log2(random()) for _ in range(40)], + "pronouns": [log2(random()) for _ in range(55)], + "prepositions": [log2(random()) for _ in range(234)], + }, + "llama2-100m": { + "nouns": [log2(random()) for _ in range(200)], + "verbs": [log2(random()) for _ in range(100)], + "adjectives": [log2(random()) for _ in range(250)], + "adverbs": [log2(random()) for _ in range(40)], + "pronouns": [log2(random()) for _ in range(55)], + "prepositions": [log2(random()) for _ in range(234)], + }, + "llama2-1b": { + "nouns": [log2(random()) for _ in range(200)], + "verbs": [log2(random()) for _ in range(100)], + "adjectives": [log2(random()) for _ in range(250)], + "adverbs": [log2(random()) for _ in range(40)], + "pronouns": [log2(random()) for _ in range(55)], + "prepositions": [log2(random()) for _ in range(234)], + }, +} From 128b626a368b66d576002c13bbd21077cd13f888 Mon Sep 17 00:00:00 2001 From: Siwei Li Date: Wed, 14 Feb 2024 17:03:25 -0800 Subject: [PATCH 08/53] Add visualization function for per token model comparison --- .../dataset/mock_per_token_performance.py | 0 src/delphi/eval/vis_per_token_model.py | 37 +++++++++++++++++++ 2 files changed, 37 insertions(+) rename data/mock-per-token-performance.py => src/delphi/dataset/mock_per_token_performance.py (100%) create mode 100644 src/delphi/eval/vis_per_token_model.py diff --git a/data/mock-per-token-performance.py b/src/delphi/dataset/mock_per_token_performance.py similarity index 100% rename from data/mock-per-token-performance.py rename to src/delphi/dataset/mock_per_token_performance.py diff --git a/src/delphi/eval/vis_per_token_model.py b/src/delphi/eval/vis_per_token_model.py new file mode 100644 index 00000000..81897078 --- /dev/null +++ b/src/delphi/eval/vis_per_token_model.py @@ -0,0 +1,37 @@ +import ipywidgets as widgets +import matplotlib.pyplot as plt +import numpy as np +from ipywidgets import interact + + +def visualize_per_token_category(input): + model_names = list(input.keys()) + categories = list(list(input.values())[0].keys()) + + def _f(category): + x = np.array([input[name][category] for name in model_names]).T + means = np.mean(x, axis=0) + median = np.median(x, axis=0) + q1 = np.quantile(x, 0.25, axis=0) + q3 = np.quantile(x, 0.75, axis=0) + + ax = plt.gca() + ax.set_ylim([-5, 5]) # TODO + + plt.plot(model_names, means) + plt.errorbar(model_names, median, yerr=[median - q1, q3 - median], fmt="o") + + interact( + _f, + category=widgets.Dropdown( + options=categories, + placeholder="", + description="Token Category:", + disabled=False, + ), + ) + + +# Usage: +# from dataset.mock_per_token_performance import performance_datas +# visualize_per_token_category(performance_data) From 11411e28f915d946ed0f7b36a8e229652347f315 Mon Sep 17 00:00:00 2001 From: Siwei Li Date: Sun, 18 Feb 2024 20:54:11 -0800 Subject: [PATCH 09/53] Update plotting by using Plotly --- notebooks/per_token_plot.ipynb | 75 +++++++++++++++++++ .../dataset/mock_per_token_performance.py | 47 ------------ src/delphi/eval/vis_per_token_model.py | 67 ++++++++++------- 3 files changed, 115 insertions(+), 74 deletions(-) create mode 100644 notebooks/per_token_plot.ipynb delete mode 100644 src/delphi/dataset/mock_per_token_performance.py diff --git a/notebooks/per_token_plot.ipynb b/notebooks/per_token_plot.ipynb new file mode 100644 index 00000000..ae00c68c --- /dev/null +++ b/notebooks/per_token_plot.ipynb @@ -0,0 +1,75 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6825ba6543ba45438b18cf0949576020", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Dropdown(description='Token Category:', options=('nouns', 'verbs', 'prepositions', 'adjectives'…" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import defaultdict\n", + "import math\n", + "import random\n", + "import numpy as np\n", + "\n", + "from delphi.eval.vis_per_token_model import visualize_per_token_category\n", + "\n", + "\n", + "random.seed(0)\n", + "\n", + "# generate mock data\n", + "model_names = ['llama2-100k', 'llama2-200k', 'llama2-1m', 'llama2-10m']\n", + "categories = ['nouns', 'verbs', 'prepositions', 'adjectives']\n", + "entries = [200, 100, 150, 300]\n", + "performance_data = defaultdict()\n", + "for i, model in enumerate(model_names):\n", + " performance_data[model] = defaultdict()\n", + " for cat in categories:\n", + " x = [math.log2(random.random()) for _ in range(entries[i])]\n", + " means = np.mean(x)\n", + " err_low = means - np.percentile(x, 25)\n", + " err_hi = np.percentile(x, 75) - means\n", + " performance_data[model][cat] = (-means, err_low, err_hi)\n", + "\n", + "\n", + "visualize_per_token_category(performance_data)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/delphi/dataset/mock_per_token_performance.py b/src/delphi/dataset/mock_per_token_performance.py deleted file mode 100644 index 312a113f..00000000 --- a/src/delphi/dataset/mock_per_token_performance.py +++ /dev/null @@ -1,47 +0,0 @@ -from math import log2 -from random import random, seed - -seed(0) - -performance_data = { - "llama2-100k": { - "nouns": [log2(random()) for _ in range(200)], - "verbs": [log2(random()) for _ in range(100)], - "adjectives": [log2(random()) for _ in range(250)], - "adverbs": [log2(random()) for _ in range(40)], - "pronouns": [log2(random()) for _ in range(55)], - "prepositions": [log2(random()) for _ in range(234)], - }, - "llama2-1m": { - "nouns": [log2(random()) for _ in range(200)], - "verbs": [log2(random()) for _ in range(100)], - "adjectives": [log2(random()) for _ in range(250)], - "adverbs": [log2(random()) for _ in range(40)], - "pronouns": [log2(random()) for _ in range(55)], - "prepositions": [log2(random()) for _ in range(234)], - }, - "llama2-10m": { - "nouns": [log2(random()) for _ in range(200)], - "verbs": [log2(random()) for _ in range(100)], - "adjectives": [log2(random()) for _ in range(250)], - "adverbs": [log2(random()) for _ in range(40)], - "pronouns": [log2(random()) for _ in range(55)], - "prepositions": [log2(random()) for _ in range(234)], - }, - "llama2-100m": { - "nouns": [log2(random()) for _ in range(200)], - "verbs": [log2(random()) for _ in range(100)], - "adjectives": [log2(random()) for _ in range(250)], - "adverbs": [log2(random()) for _ in range(40)], - "pronouns": [log2(random()) for _ in range(55)], - "prepositions": [log2(random()) for _ in range(234)], - }, - "llama2-1b": { - "nouns": [log2(random()) for _ in range(200)], - "verbs": [log2(random()) for _ in range(100)], - "adjectives": [log2(random()) for _ in range(250)], - "adverbs": [log2(random()) for _ in range(40)], - "pronouns": [log2(random()) for _ in range(55)], - "prepositions": [log2(random()) for _ in range(234)], - }, -} diff --git a/src/delphi/eval/vis_per_token_model.py b/src/delphi/eval/vis_per_token_model.py index 81897078..70856902 100644 --- a/src/delphi/eval/vis_per_token_model.py +++ b/src/delphi/eval/vis_per_token_model.py @@ -1,37 +1,50 @@ -import ipywidgets as widgets -import matplotlib.pyplot as plt +import ipywidgets import numpy as np -from ipywidgets import interact +import plotly.graph_objects as go +from beartype.typing import Dict -def visualize_per_token_category(input): +def visualize_per_token_category(input: Dict[str, Dict[str, tuple]]) -> ipywidgets.VBox: model_names = list(input.keys()) - categories = list(list(input.values())[0].keys()) + categories = list(input[model_names[0]].keys()) + category = categories[0] - def _f(category): + def get_plot_values(category: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]: x = np.array([input[name][category] for name in model_names]).T - means = np.mean(x, axis=0) - median = np.median(x, axis=0) - q1 = np.quantile(x, 0.25, axis=0) - q3 = np.quantile(x, 0.75, axis=0) - - ax = plt.gca() - ax.set_ylim([-5, 5]) # TODO - - plt.plot(model_names, means) - plt.errorbar(model_names, median, yerr=[median - q1, q3 - median], fmt="o") - - interact( - _f, - category=widgets.Dropdown( - options=categories, - placeholder="", - description="Token Category:", - disabled=False, + means, err_lo, err_hi = x[0], x[1], x[2] + return means, err_lo, err_hi + + means, err_low, err_hi = get_plot_values(category) + g = go.FigureWidget( + data=go.Scatter( + x=model_names, + y=means, + error_y=dict( + type="data", + symmetric=False, + array=err_hi, + arrayminus=err_low, + color="purple", + ), ), + layout=go.Layout(yaxis=dict(title="Loss")), ) + selected_category = ipywidgets.Dropdown( + options=categories, + placeholder="", + description="Token Category:", + disabled=False, + ) + + def response(change): + if selected_category.value: + means, err_lo, err_hi = get_plot_values(selected_category.value) + with g.batch_update(): + g.data[0].y = means + g.data[0].error_y["array"] = err_hi + g.data[0].error_y["arrayminus"] = err_lo + + selected_category.observe(response, names="value") -# Usage: -# from dataset.mock_per_token_performance import performance_datas -# visualize_per_token_category(performance_data) + return ipywidgets.VBox([selected_category, g]) From 2d73bfeb60d9e247fc3b059287352da269a133ca Mon Sep 17 00:00:00 2001 From: Siwei Li Date: Tue, 20 Feb 2024 19:10:30 -0800 Subject: [PATCH 10/53] Update the arguments for visualization styling --- notebooks/per_token_plot.ipynb | 33 ++++++++++++++++++++++---- src/delphi/eval/vis_per_token_model.py | 31 ++++++++++++++++-------- 2 files changed, 50 insertions(+), 14 deletions(-) diff --git a/notebooks/per_token_plot.ipynb b/notebooks/per_token_plot.ipynb index ae00c68c..198057c7 100644 --- a/notebooks/per_token_plot.ipynb +++ b/notebooks/per_token_plot.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6825ba6543ba45438b18cf0949576020", + "model_id": "696575431f65420e9dc22c3b3476bfbb", "version_major": 2, "version_minor": 0 }, @@ -16,7 +16,7 @@ "VBox(children=(Dropdown(description='Token Category:', options=('nouns', 'verbs', 'prepositions', 'adjectives'…" ] }, - "execution_count": 1, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -47,7 +47,32 @@ " performance_data[model][cat] = (-means, err_low, err_hi)\n", "\n", "\n", - "visualize_per_token_category(performance_data)" + "visualize_per_token_category(performance_data, log_scale=True, bg_color='LightGrey', line_color=\"Red\", marker_color='Orange', bar_color='Green')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cb3af5248a4a40118c36a527c927289d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Dropdown(description='Token Category:', options=('nouns', 'verbs', 'prepositions', 'adjectives'…" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualize_per_token_category(performance_data, log_scale=False)" ] } ], diff --git a/src/delphi/eval/vis_per_token_model.py b/src/delphi/eval/vis_per_token_model.py index 70856902..a8e269fe 100644 --- a/src/delphi/eval/vis_per_token_model.py +++ b/src/delphi/eval/vis_per_token_model.py @@ -1,10 +1,11 @@ import ipywidgets import numpy as np import plotly.graph_objects as go -from beartype.typing import Dict -def visualize_per_token_category(input: Dict[str, Dict[str, tuple]]) -> ipywidgets.VBox: +def visualize_per_token_category( + input: dict[str, dict[str, tuple]], log_scale=False, **kwargs: str +) -> ipywidgets.VBox: model_names = list(input.keys()) categories = list(input[model_names[0]].keys()) category = categories[0] @@ -24,10 +25,21 @@ def get_plot_values(category: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]: symmetric=False, array=err_hi, arrayminus=err_low, - color="purple", + color=kwargs.get("bar_color", "purple"), ), + marker=dict( + color=kwargs.get("marker_color", "SkyBlue"), + size=15, + line=dict(color=kwargs.get("line_color", "MediumPurple"), width=2), + ), + ), + layout=go.Layout( + yaxis=dict( + title="Loss", + type="log" if log_scale else "linear", + ), + plot_bgcolor=kwargs.get("bg_color", "AliceBlue"), ), - layout=go.Layout(yaxis=dict(title="Loss")), ) selected_category = ipywidgets.Dropdown( @@ -38,12 +50,11 @@ def get_plot_values(category: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]: ) def response(change): - if selected_category.value: - means, err_lo, err_hi = get_plot_values(selected_category.value) - with g.batch_update(): - g.data[0].y = means - g.data[0].error_y["array"] = err_hi - g.data[0].error_y["arrayminus"] = err_lo + means, err_lo, err_hi = get_plot_values(selected_category.value) + with g.batch_update(): + g.data[0].y = means + g.data[0].error_y["array"] = err_hi + g.data[0].error_y["arrayminus"] = err_lo selected_category.observe(response, names="value") From 75e68aaddb846d384ca2d96fa78dd4f999ede4f3 Mon Sep 17 00:00:00 2001 From: transcendingvictor <92531256+transcendingvictor@users.noreply.github.com> Date: Wed, 21 Feb 2024 18:02:23 +0100 Subject: [PATCH 11/53] inference script (#16) * script to get correct logprobs of llama 100k * python script to get logprobs and shell script that iterates over the models * moved the scripts to the Scripts folder * new files * other changes * corrected version after comments * prepend NaN values to logprobs and clean up * comments reloved and functional test * batch processing and other changes --- scripts/generate_logprobs.sh | 28 +++++ scripts/inference.py | 119 ++++++++++++++++++ .../functional_test_generate_logprobs.sh | 22 ++++ 3 files changed, 169 insertions(+) create mode 100644 scripts/generate_logprobs.sh create mode 100644 scripts/inference.py create mode 100644 tests/scripts/functional_test_generate_logprobs.sh diff --git a/scripts/generate_logprobs.sh b/scripts/generate_logprobs.sh new file mode 100644 index 00000000..770ca7ad --- /dev/null +++ b/scripts/generate_logprobs.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +# Define the batch size +BATCH_SIZE=80 # This worked well in my CPU, but 200 was too much +DATASET_NAME="delphi-suite/tinystories-v2-clean-tokenized" +USERNAME="transcendingvictor" # your Hugging Face username +TOKEN="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" # your Hugging Face API token + + +# List of models +declare -a MODEL_NAMES=("delphi-suite/delphi-llama2-100k" + "delphi-suite/delphi-llama2-200k" + "delphi-suite/delphi-llama2-400k" + "delphi-suite/delphi-llama2-800k" + "delphi-suite/delphi-llama2-1.6m" + "delphi-suite/delphi-llama2-3.2m" + "delphi-suite/delphi-llama2-6.4m" + "delphi-suite/delphi-llama2-12.8m" + "delphi-suite/delphi-llama2-25.6m") + +# Loop through each model and generate log probabilities +for MODEL_NAME in "${MODEL_NAMES[@]}" +do + echo "Processing $MODEL_NAME" + python scripts/inference.py "$MODEL_NAME" --batch-size "$BATCH_SIZE" --dataset-name "$DATASET_NAME" --username "$USERNAME" --token "$TOKEN" +done + +echo "All models processed." diff --git a/scripts/inference.py b/scripts/inference.py new file mode 100644 index 00000000..bb0c346c --- /dev/null +++ b/scripts/inference.py @@ -0,0 +1,119 @@ +import argparse +import os + +import pandas as pd +import torch +from datasets import Dataset, load_dataset +from jaxtyping import Int +from tqdm.auto import tqdm +from transformers import AutoModelForCausalLM + +from delphi.eval.utils import get_all_and_next_logprobs, load_validation_dataset + +torch.set_grad_enabled(False) + + +def main( + model_name: str, + batch_size: Int, + dataset_name: str, + username: str, + token: str, + funct_test: bool = False, +): + """ + Outputs the log probabilities of the next token for each token in the validation dataset. + And uploads the resulting dataset to huggingface. + Args: + - model_name: The name of the model to use for inference + - batch_size: The batch size for processing. 80 worked well in CPU. + - dataset_name: The name of the dataset from which validation set will be loaded + - username: Hugging Face API username + - token: Hugging Face API token + """ + val_ds = load_validation_dataset(dataset_name) + + model = AutoModelForCausalLM.from_pretrained(model_name) + + logprobs_list = [] + total_sequences = ( + len(val_ds) if not funct_test else 320 + ) # Use only 320 sequences if funct_test is True + + for i in tqdm(range(0, total_sequences, batch_size)): + batch_end = min(i + batch_size, total_sequences) + batch_sequences = [val_ds[j]["tokens"] for j in range(i, batch_end)] + batch_sequences_tensor = torch.tensor(batch_sequences) + + _, next_logprobs = get_all_and_next_logprobs(model, batch_sequences_tensor) + logprobs_list.append(next_logprobs) + + accumulated_logprobs = torch.cat(logprobs_list, dim=0) + + nan_tensor = torch.full((accumulated_logprobs.size(0), 1), float("nan")) + extended_next_logprobs = torch.cat( + [nan_tensor, accumulated_logprobs], dim=1 + ) # 513 tokens + + df_dataset = pd.DataFrame({"logprobs": extended_next_logprobs.tolist()}) + hf_dataset = Dataset.from_pandas(df_dataset) + + # change the repo_id to your hf username in generate_logprobs.sh + # change the yout hf token in generate_logprobs.sh + + repo_id = f"{username}/{model_name.rsplit('/', 1)[-1]}-validation-logprobs" + if funct_test: + repo_id += "-funct-test" + hf_dataset.push_to_hub( + repo_id=repo_id, + split="validation", + private=False, + token=token, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Run inference and generate log probabilities." + ) + parser.add_argument( + "model_name", type=str, help="Model name with or without delphi-suite/ prefix" + ) + parser.add_argument( + "--batch-size", + type=int, + default=80, + help="Batch size for processing (default: 80)", + ) + parser.add_argument( + "--dataset-name", + type=str, + help="Dataset name with or without delphi-suite/ prefix", + ) + parser.add_argument( + "--username", + type=str, + help="Hugging Face API username", + ) + parser.add_argument( + "--token", + type=str, + help="Hugging Face API token", + ) + parser.add_argument( + "--test-funct", action="store_true", help="Enable test function mode" + ) + + args = parser.parse_args() + + if "/" not in args.model_name: + args.model_name = "delphi-suite/" + args.model_name + + main( + args.model_name, + args.batch_size, + args.dataset_name, + args.username, + args.token, + args.test_funct, + ) diff --git a/tests/scripts/functional_test_generate_logprobs.sh b/tests/scripts/functional_test_generate_logprobs.sh new file mode 100644 index 00000000..9f95190c --- /dev/null +++ b/tests/scripts/functional_test_generate_logprobs.sh @@ -0,0 +1,22 @@ +#!/bin/bash +#test to check if whether inference.py uploads log probabilities to Hugging Face. +#similar to generate_logprobs.sh, much smaller. + +BATCH_SIZE=80 +DATASET_NAME="delphi-suite/tinystories-v2-clean-tokenized" +USERNAME="transcendingvictor" # Your Hugging Face username +TOKEN="hf_aaaaaaaaaaaaaaaaaaaaaaaaaaaaa" # Your Hugging Face API token + +# List of models +declare -a MODEL_NAMES=("delphi-suite/delphi-llama2-100k" + "delphi-suite/delphi-llama2-200k" + ) + +# Loop through each model and generate log probabilities +for MODEL_NAME in "${MODEL_NAMES[@]}" +do + echo "Processing $MODEL_NAME" + python scripts/inference.py "$MODEL_NAME" --batch-size "$BATCH_SIZE" --dataset-name "$DATASET_NAME" --username "$USERNAME" --token "$TOKEN" --test-funct +done + +echo "All models processed." \ No newline at end of file From 4409a9e914d0a4f4e502f99d31b9fb5b06d320f8 Mon Sep 17 00:00:00 2001 From: Rai <62800649+menamerai@users.noreply.github.com> Date: Fri, 23 Feb 2024 12:10:02 -0500 Subject: [PATCH 12/53] Added static file folder (#41) * added static file folder * added constants file --- scripts/label_all_tokens.py | 17 ++++++++--------- scripts/map_tokens.py | 3 ++- setup.py | 1 + src/delphi/constants.py | 3 +++ src/delphi/eval/labelled_token_ids_dict.pkl | Bin 274517 -> 0 bytes src/delphi/static/__init__.py | 0 .../{eval => static}/all_tokens_list.txt | Bin src/delphi/static/labelled_token_ids_dict.pkl | Bin 0 -> 274517 bytes src/delphi/static/token_map.pkl | Bin 0 -> 42168448 bytes 9 files changed, 14 insertions(+), 10 deletions(-) create mode 100644 src/delphi/constants.py delete mode 100644 src/delphi/eval/labelled_token_ids_dict.pkl create mode 100644 src/delphi/static/__init__.py rename src/delphi/{eval => static}/all_tokens_list.txt (100%) create mode 100644 src/delphi/static/labelled_token_ids_dict.pkl create mode 100644 src/delphi/static/token_map.pkl diff --git a/scripts/label_all_tokens.py b/scripts/label_all_tokens.py index 01bf4cf1..6519eaca 100644 --- a/scripts/label_all_tokens.py +++ b/scripts/label_all_tokens.py @@ -1,10 +1,10 @@ import argparse import pickle -from pathlib import Path from tqdm.auto import tqdm from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast +from delphi.constants import STATIC_ASSETS_DIR from delphi.eval import token_labelling @@ -35,14 +35,13 @@ def main(): args = parser.parse_args() # Access command-line arguments - # Directory to save the results - SAVE_DIR = Path("src/delphi/eval/") + model_name = args.model_name print("\n", " LABEL ALL TOKENS ".center(50, "="), "\n") print(f"You chose the model: {model_name}\n") print( - f"The language model will be loaded from Huggingface and its tokenizer used to do two things:\n\t1) Create a list of all tokens in the tokenizer's vocabulary.\n\t2) Label each token with its part of speech, dependency, and named entity recognition tags.\nThe respective results will be saved to files located at: '{SAVE_DIR}'\n" + f"The language model will be loaded from Huggingface and its tokenizer used to do two things:\n\t1) Create a list of all tokens in the tokenizer's vocabulary.\n\t2) Label each token with its part of speech, dependency, and named entity recognition tags.\nThe respective results will be saved to files located at: '{STATIC_ASSETS_DIR}'\n" ) # ================ (1) ================= @@ -60,8 +59,8 @@ def main(): # Save the list of all tokens to a file filename = "all_tokens_list.txt" - filepath = SAVE_DIR / filename - with open(filepath, "w", encoding="utf-8") as f: + filepath = STATIC_ASSETS_DIR.joinpath(filename) + with open(f"{filepath}", "w", encoding="utf-8") as f: f.write(tokens_str) print(f"Saved the list of all tokens to:\n\t{filepath}\n") @@ -88,8 +87,8 @@ def main(): # Save the labelled tokens to a file filename = "labelled_token_ids_dict.pkl" - filepath = SAVE_DIR / filename - with open(filepath, "wb") as f: + filepath = STATIC_ASSETS_DIR.joinpath(filename) + with open(f"{filepath}", "wb") as f: pickle.dump(labelled_token_ids_dict, f) print(f"Saved the labelled tokens to:\n\t{filepath}\n") @@ -97,7 +96,7 @@ def main(): # sanity check that The pickled and the original dict are the same print("Sanity check ...", end="") # load pickle - with open(filepath, "rb") as f: + with open(f"{filepath}", "rb") as f: pickled = pickle.load(f) # compare assert labelled_token_ids_dict == pickled diff --git a/scripts/map_tokens.py b/scripts/map_tokens.py index 327f4651..2acea2da 100644 --- a/scripts/map_tokens.py +++ b/scripts/map_tokens.py @@ -3,6 +3,7 @@ import argparse import pickle +from delphi.constants import STATIC_ASSETS_DIR from delphi.eval.token_map import token_map from delphi.eval.utils import load_validation_dataset @@ -18,5 +19,5 @@ mapping = token_map(dataset) - with open(f"data/{args.output}", "wb") as f: + with open(f"{STATIC_ASSETS_DIR}/{args.output}", "wb") as f: pickle.dump(mapping, file=f) diff --git a/setup.py b/setup.py index 80059fc2..a4156702 100644 --- a/setup.py +++ b/setup.py @@ -5,4 +5,5 @@ version="0.1", packages=find_packages(where="src"), package_dir={"": "src"}, + package_data={"delphi.static": ["*"]}, ) diff --git a/src/delphi/constants.py b/src/delphi/constants.py new file mode 100644 index 00000000..5216566c --- /dev/null +++ b/src/delphi/constants.py @@ -0,0 +1,3 @@ +from importlib.resources import files + +STATIC_ASSETS_DIR = files("delphi.static") diff --git a/src/delphi/eval/labelled_token_ids_dict.pkl b/src/delphi/eval/labelled_token_ids_dict.pkl deleted file mode 100644 index 5fe96a3904c3ef3c78e4e34cfa453e812f68eb99..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 274517 zcmbu|b97zX7Qo@uwr$(Ck=j<;lOk=?q&9PE+qP}nwrv|PZN{CW?=|Cc4&xA$)0>DAQ7)2mg|=Iy;&`}$jd{;$8Sk{&)D-t9cS zJiPt?dQ|^EDCgz>2d#a*{`pH`{Qs;{d*9#xsqnx5(YKw4chk21zl-4iyDI+e_iWzw zcf)@F%PQXOy#oGf9o>EqvJ-wMI}ht?6?=?LL;gmgMW zIUS*$jxbI~Sf?YL(-Ge3h~RWYbUGqA9g&@mC{9OIrz4uv5#8yC;dI3GbHwr&f@2}X ze-!-wYJ>An8$x0s6c$2bAq*D6Vj&zB!eb!<79##f;qRyph^RV3OScr;+X#Y_N zoSV`Ai{SVF6((RVV*D4uzeWXaLrg5h!a{5;#KA&bEX2b?d@LluLP9JgG6{aJ*Uf;a 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