diff --git a/Makefile b/Makefile index 5f06b17..e635cb5 100644 --- a/Makefile +++ b/Makefile @@ -7,7 +7,7 @@ CASUAL_CONV_VERSION = 1.4.0 .DEFAULT_GOAL := all .PHONY: all -all: format lint check_types test +all: format lint check_types test .PHONY: .pre-commit .pre-commit: @@ -17,9 +17,9 @@ all: format lint check_types test .PHONY: .install_common .install_common: @echo "Installing common Python dependencies" - uv sync --inexact --frozen + uv sync --all-extras --frozen -.PHONY: install_common_ci +.PHONY: install_common_ci install_common_ci: @echo "[CI] Installing common Python dependencies" uv sync --inexact --frozen --no-cache --quiet @@ -28,37 +28,37 @@ install_common_ci: install_cpu: .install_common .pre-commit @echo "Overriding/installing PyTorch (CPU)" uv pip install --reinstall \ - torch==${TORCH_VERSION} + "torch>=${TORCH_VERSION}" .PHONY: install_cuda install_cuda: .install_common .pre-commit @echo "Overriding/installing PyTorch (GPU)" uv pip install --reinstall \ - torch==${TORCH_VERSION} --index-url ${CUDA_INDEX_URL} + "torch>=${TORCH_VERSION}" --index-url ${CUDA_INDEX_URL} .PHONY: install_ci install_ci: install_common_ci @echo "[CI] Overriding/installing PyTorch (CPU)" uv pip install --reinstall --no-cache --quiet \ - torch==${TORCH_VERSION} + "torch>=${TORCH_VERSION}" .PHONY: install_mamba install_mamba: uv pip install --no-build-isolation \ - mamba-ssm==${MAMBA_VERSION} \ - causal-conv1d==${CASUAL_CONV_VERSION} + "mamba-ssm>=${MAMBA_VERSION}" \ + "causal-conv1d>=${CASUAL_CONV_VERSION}" .PHONY: check_types check_types: - uv run mypy src tests + uv run mypy src tests scripts .PHONY: lint lint: - uv run ruff check src tests + uv run ruff check src tests scripts .PHONY: format format: - uv run ruff format src tests + uv run ruff format src tests scripts .PHONY: test test: test_unit test_integration test_e2e @@ -69,7 +69,17 @@ test_unit: .PHONY: test_integration test_integration: - uv run pytest tests/integration + $(MAKE) test_integration_install + $(MAKE) test_integration_config + +.PHONY: test_integration_install +test_integration_install: + uv run --project tests/integration/install --reinstall-package mblm --quiet \ + pytest tests/integration/install + +.PHONY: test_integration_config +test_integration_config: + uv run pytest tests/integration/config E2E_RUN_TORCH = OMP_NUM_THREADS=1 \ uv run torchrun --nproc_per_node=2 \ @@ -86,8 +96,8 @@ test_e2e: .PHONY: test_e2e_grad_acc test_e2e_grad_acc: - TEST_ID=grad_acc_1 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-grad-acc-1.yml - TEST_ID=grad_acc_2 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-grad-acc-2.yml + TEST_ID=grad_acc_1 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-grad-acc-1.yaml + TEST_ID=grad_acc_2 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-grad-acc-2.yaml $(E2E_RUN_VALIDATE) \ --check-grad-acc-csv $(E2E_TEST_ROOT)/outputs/my-model_grad_acc_2/loss.csv \ --check-grad-acc-csv $(E2E_TEST_ROOT)/outputs/my-model_grad_acc_1/loss.csv @@ -96,15 +106,15 @@ test_e2e_grad_acc: test_e2e_trainer: # ---------------- Chain 3 runs of 1 epoch each ---------------- @echo "Running training for a single epoch" - TEST_ID=1 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-1-epoch.yml + TEST_ID=1 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-1-epoch.yaml $(E2E_RUN_VALIDATE) --check-output $(E2E_TEST_ROOT)/outputs/my-model_1 @echo "Chaining run 2 to run 1" - TEST_ID=2 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/outputs/my-model_1/config.yml + TEST_ID=2 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/outputs/my-model_1/config.yaml $(E2E_RUN_VALIDATE) --check-output $(E2E_TEST_ROOT)/outputs/my-model_2 @echo "Chaining run 3 to run 2" - TEST_ID=3 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/outputs/my-model_2/config.yml + TEST_ID=3 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/outputs/my-model_2/config.yaml $(E2E_RUN_VALIDATE) --check-output $(E2E_TEST_ROOT)/outputs/my-model_3 @echo "Asserting on chained training runs 1, 2, 3" @@ -112,15 +122,15 @@ test_e2e_trainer: --assert-equal-epochs \ --check-chained-csv $(E2E_TEST_ROOT)/outputs/my-model_1/loss.csv \ --check-chained-csv $(E2E_TEST_ROOT)/outputs/my-model_2/loss.csv \ - --check-chained-csv $(E2E_TEST_ROOT)/outputs/my-model_3/loss.csv + --check-chained-csv $(E2E_TEST_ROOT)/outputs/my-model_3/loss.csv # ------------ Chain 2 runs of more than 1 epoch each ------------ @echo "Running training for more than 1 epoch" - TEST_ID=4 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-1.5-epoch.yml + TEST_ID=4 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-1.5-epoch.yaml $(E2E_RUN_VALIDATE) --check-output $(E2E_TEST_ROOT)/outputs/my-model_4 @echo "Chaining run 5 to run 4" - TEST_ID=5 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/outputs/my-model_4/config.yml + TEST_ID=5 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/outputs/my-model_4/config.yaml $(E2E_RUN_VALIDATE) --check-output $(E2E_TEST_ROOT)/outputs/my-model_5 @echo "Asserting on chained training runs 4, 5" @@ -130,11 +140,11 @@ test_e2e_trainer: # ------------ Chain 2 runs of less than 1 epoch each ------------ @echo "Running training for less than 1 epoch" - TEST_ID=6 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-0.5-epoch.yml + TEST_ID=6 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/sample-config-0.5-epoch.yaml $(E2E_RUN_VALIDATE) --check-output $(E2E_TEST_ROOT)/outputs/my-model_6 @echo "Chaining run 7 to run 6" - TEST_ID=7 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/outputs/my-model_6/config.yml + TEST_ID=7 $(E2E_RUN_TORCH) -c $(E2E_TEST_ROOT)/outputs/my-model_6/config.yaml $(E2E_RUN_VALIDATE) --check-output $(E2E_TEST_ROOT)/outputs/my-model_7 @echo "Asserting on chained training runs 6, 7" diff --git a/README.md b/README.md index 87d1848..490690d 100644 --- a/README.md +++ b/README.md @@ -6,44 +6,254 @@ Multiscale Byte Language Model is a hierarchical byte-level sequence-to-sequence mblm-architecture

-## Quickstart +## Install -TBD. +Install from PyPI: -## Development setup +``` +pip install mblm +``` -We use `uv` for packaging and dependency management. Before proceeding, install a recent version (>= `0.4.17`) via the instructions on [the homepage](https://docs.astral.sh/uv/getting-started/installation/). +For `uv`: -### Install dependencies +``` +uv add mblm +``` -- With CUDA: `make install_cuda` -- CPU only (e.g., MacOS): `make install_cpu` +Available extras: + +- `'mblm[analysis]'`: Installs with a handful of packages used inside `mblm.analysis` -### Environment and Datasets +### Using Torch and Mamba -If you want to run the analysis notebooks and work with the datasets locally, you will need to create an `.env` file in the project root directory with the following entries: +You will need to **install a recent version PyTorch manually**. We use `>=2.4.1`. It is best to do this after installing the package since some sub-dependencies might install their own (CPU) PyTorch version. ``` -DATASET_PG19_DIR= # Path to the PG19 dataset on you system -DATASET_CLEVR_DIR= # Path to the Clevr dataset on you system +uv pip install 'torch>=2.4.1' --index-url https://download.pytorch.org/whl/cu124 ``` -Note that all paths to datasets must be _absolute_. The datasets can be downloaded here: +Finally, in order to use the efficient [Mamba-SSM](https://github.com/state-spaces/mamba), follow their instructions on the homepage. You'll need Linux and a GPU available during installation. + +``` +uv pip install mamba-ssm>=2.2.2 causal-conv1d>=1.4.0 --no-build-isolation +``` -- [PG19](https://github.com/google-deepmind/pg19) -- [Clevr](https://cs.stanford.edu/people/jcjohns/clevr/) v1.0 +If `mamba-ssm` is not available, we fall back to using `mambapy`, which is written in pure PyTorch. -## Running scripts +## Quickstart -- Project-related tasks (e.g., installing dependencies, running tests) are defined in the [Makefile](Makefile) +### Using a built-in stage block + +MBLM can be used with the default Transformer Decoder or Mamba block. The below model is a 2D MBLM with a global Mamba and local Transformer model. + +```py +import torch + +from mblm import MBLM, MambaBlockConfig, MBLMModelConfig, TransformerBlockConfig + +mblm = MBLM( + MBLMModelConfig( + num_tokens=257, + hidden_dims=[1024, 1024], + seq_lens=[1024, 8], + num_layers=[5, 5], + pad_token_id=256, + train_checkpoint_chunks=None, + block=[ + MambaBlockConfig( + d_state=128, + d_conv=4, + expand=2, + headdim=64, + pos_emb_type=None, + ), + TransformerBlockConfig( + attn_head_dims=64, + attn_num_heads=16, + attn_use_rot_embs=True, + use_flash_attn=True, + pos_emb_type="fixed", + ), + ], + ) +) + +x = torch.randint(0, 258, (1, 12)).long() +mblm.forward(x) +``` + +Alternatively, you can read configuration from a YAML string (or file): + +```py +import torch +import yaml + +from mblm import MBLM, MBLMModelConfig + +yml_model_config = """ +num_tokens: 257 +hidden_dims: [1024, 1024] +seq_lens: [1024, 8] +num_layers: [5, 5] +pad_token_id: 256 +train_checkpoint_chunks: null +block: + - d_state: 128 + d_conv: 4 + expand: 2 + headdim: 64 + pos_emb_type: null + - attn_head_dims: 64 + attn_num_heads: 16 + attn_use_rot_embs: true + use_flash_attn: true + pos_emb_type: fixed +""" + +parsed_config = yaml.safe_load(yml_model_config) +mblm = MBLM(MBLMModelConfig.model_validate(parsed_config)) +x = torch.randint(0, 258, (1, 12)).long() +mblm.forward(x) +``` + +### Custom stage blocks + +You can define custom stage blocks for MBLM as follows. A stageblock must provide a `block_type` field as well as a `to_model` function with the signature below that returns a `torch.nn.Module`. Other than that, specify whatever other parameters you might need. Note that the default blocks (Transformer and Mamba) are already registered. + +```py +import torch +from pydantic import BaseModel, Field + +from mblm import MBLM, MBLMModelConfig, StageBlock, TransformerBlockConfig + +# Define any custom model +class MyLSTM(torch.nn.Module): + def __init__(self, lstm: torch.nn.LSTM): + super().__init__() + self.lstm = lstm + + def forward(self, input_ids: torch.Tensor) -> torch.Tensor: + # Wrap the LSTM forward to extract the output + out, _ = self.lstm(input_ids) + return out + +# Add a block config and inherit from StageBlock and BaseModel +class LSTMBlockConfig(StageBlock, BaseModel): + block_type: str = Field(init=False, default="lstm") + + # Add whatever is needed + dropout: float + + def to_model(self, model_dim: int, num_layers: int) -> torch.nn.Module: + return MyLSTM( + torch.nn.LSTM( + input_size=model_dim, + hidden_size=model_dim, + batch_first=True, + dropout=self.dropout, + num_layers=num_layers, + ) + ) + +mblm = MBLM( + MBLMModelConfig( + num_tokens=257, + hidden_dims=[1024, 1024], + seq_lens=[1024, 8], + num_layers=[5, 5], + pad_token_id=256, + train_checkpoint_chunks=None, + block=[ + LSTMBlockConfig( + dropout=0.1, + pos_emb_type=None, + ), + TransformerBlockConfig( + attn_head_dims=64, + attn_num_heads=16, + attn_use_rot_embs=True, + use_flash_attn=True, + pos_emb_type="fixed", + ), + ], + ) +) + +x = torch.randint(0, 258, (1, 12)).long() +mblm.forward(x) +``` + +If you want to parse a YAML config to a custom block, **register the block** before creating the model: + +```py +import torch +import yaml +from pydantic import BaseModel, Field + +from mblm import MBLM, MBLMModelConfig, StageBlock, block_registry + +# Define any custom model +class MyLSTM(torch.nn.Module): + def __init__(self, lstm: torch.nn.LSTM): + super().__init__() + self.lstm = lstm + + def forward(self, input_ids: torch.Tensor) -> torch.Tensor: + # Wrap the LSTM forward to extract the output + out, _ = self.lstm(input_ids) + return out + +# Add a block config and inherit from StageBlock and BaseModel +class LSTMBlockConfig(StageBlock, BaseModel): + block_type: str = Field(init=False, default="lstm") + + # Add whatever is needed + dropout: float + + def to_model(self, model_dim: int, num_layers: int) -> torch.nn.Module: + return MyLSTM( + torch.nn.LSTM( + input_size=model_dim, + hidden_size=model_dim, + batch_first=True, + dropout=self.dropout, + num_layers=num_layers, + ) + ) + +yml_model_config = """ +num_tokens: 257 +hidden_dims: [1024, 1024] +seq_lens: [1024, 8] +num_layers: [5, 5] +pad_token_id: 256 +train_checkpoint_chunks: null +block: + - dropout: 0.1 + pos_emb_type: null + - attn_head_dims: 64 + attn_num_heads: 16 + attn_use_rot_embs: true + use_flash_attn: true + pos_emb_type: fixed +""" + +block_registry.register(LSTMBlockConfig) # Add this! + +parsed_config = yaml.safe_load(yml_model_config) +mblm = MBLM(MBLMModelConfig.model_validate(parsed_config)) +x = torch.randint(0, 258, (1, 12)).long() +mblm.forward(x) +``` -## Dependency management +## Local development setup -Most of the dependencies are installed via `uv`. However, some dependencies that require no build isolation, special index urls or a specific build environment are installed via `uv pip` directly, see the Makefile. +We use `uv` for packaging and dependency management. Before proceeding, install a recent version (>= `0.5`) via the instructions on [the homepage](https://docs.astral.sh/uv/getting-started/installation/). -- Whenever **Mamba/SSM dependencies** are upgraded (currently, `mamba-ssm` and `causal-conv1d`), set the versions in the Makefile +### Install dependencies -### Installing Mamba dependencies +- With CUDA: `make install_cuda` +- CPU only (e.g., MacOS): `make install_cpu` If you've noticed, there are two SSM/Mamba dependencies: @@ -58,6 +268,10 @@ make install_mamba For any experiments, we wish to use the new Mamba 2 block from `mamba-ssm`. If the import of this module fails, we fall back to a Mamba 1 block from `mambapy`, which is written in pure PyTorch. +## Running scripts + +- Project-related tasks (e.g., installing dependencies, running tests) are defined in the [Makefile](Makefile) + ## Pre-Commit Hooks Before every commit, we lint the _staged_ Python and Jupyter Notebook files and check if they are formatted correctly. Doing this locally speeds up development because one does not have to wait for the CI to catch issues. Errors of these checks are not fixed automatically, instead, you will have to fix the files yourself before committing. You may bypass hooks with `git commit -m --no-verify`. However, the CI will likely fail in this case. @@ -72,10 +286,6 @@ Note that: - The `lint` command is similar to the `make lint` command, but the `make` command operates on _all_ files in the project and not just the staged files - While `check-format` simply _checks_ the format, `make format` will _actually_ format the files -## Guidelines - -Don't forget to follow the guidelines decided here [here](https://github.ibm.com/AI4SD/ai4sd-misc/blob/main/markdown/coding_guidelines.md). - ## Citation TBD. diff --git a/assets/mblm-lg.png b/assets/mblm-lg.png new file mode 100644 index 0000000..6191f8c Binary files /dev/null and b/assets/mblm-lg.png differ diff --git a/config/clevr_7mi_360m_1d_s_pt1_ft.yaml b/config/clevr_7mi_360m_1d_s_pt1_ft.yaml index a896ded..dbe3f9d 100644 --- a/config/clevr_7mi_360m_1d_s_pt1_ft.yaml +++ b/config/clevr_7mi_360m_1d_s_pt1_ft.yaml @@ -35,7 +35,7 @@ params: expand: 2 headdim: 64 dropout: 0.1 - patch_pos_emb_type: null + pos_emb_type: null train: target_elements: 7_000_000 target_elements_strategy: batch diff --git a/config/pg19_30bb_360m_1d_t.yaml b/config/pg19_30bb_360m_1d_t.yaml index fdb92d2..7e412dd 100644 --- a/config/pg19_30bb_360m_1d_t.yaml +++ b/config/pg19_30bb_360m_1d_t.yaml @@ -20,7 +20,7 @@ params: attn_use_rot_embs: true attn_dropout: 0 use_flash_attn: true - patch_pos_emb_type: fixed + pos_emb_type: fixed train: target_elements: 30_000_000_000 target_elements_strategy: sequence diff --git a/config/pg19_30bb_360m_2d_ss.yaml b/config/pg19_30bb_360m_2d_ss.yaml index 075151f..cab2633 100644 --- a/config/pg19_30bb_360m_2d_ss.yaml +++ b/config/pg19_30bb_360m_2d_ss.yaml @@ -19,7 +19,7 @@ params: d_conv: 4 expand: 2 headdim: 64 - patch_pos_emb_type: null + pos_emb_type: null train: target_elements: 30_000_000_000 target_elements_strategy: sequence diff --git a/config/pg19_30bb_360m_2d_st.yaml b/config/pg19_30bb_360m_2d_st.yaml index 4fde520..bfc945c 100644 --- a/config/pg19_30bb_360m_2d_st.yaml +++ b/config/pg19_30bb_360m_2d_st.yaml @@ -19,13 +19,13 @@ params: d_conv: 4 expand: 2 headdim: 64 - patch_pos_emb_type: null + pos_emb_type: null - attn_head_dims: 64 attn_num_heads: 16 attn_use_rot_embs: true attn_dropout: 0 use_flash_attn: true - patch_pos_emb_type: fixed + pos_emb_type: fixed train: target_elements: 30_000_000_000 target_elements_strategy: sequence diff --git a/pyproject.toml b/pyproject.toml index 736e47a..b28ee26 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,33 +13,41 @@ dependencies = [ "filelock>=3.16.1", "mambapy>=1.2.0", "megabyte-pytorch>=0.3.5", + "numpy>=2.1.1", + "packaging>=24.2", "pillow>=11.0.0", - "polars-lts-cpu[plot]>=1.8.2 ; sys_platform == 'linux'", - "polars[plot]>=1.8.2 ; sys_platform != 'linux'", "pydantic>=2.9.2", "pyyaml>=6.0.2", - "tabulate>=0.9.0", "tqdm>=4.66.5", - "vegafusion[embed]>=1.6.9", ] -[tool.uv] -package = true -dev-dependencies = [ - "mypy>=1.11.2", +[dependency-groups] +dev = [ + "mypy>=1.14.0", "pytest>=8.3.3", "pytest-cov>=5.0.0", + "pytest-mock>=3.14.0", "python-dotenv>=1.0.1", - "ruff>=0.6.8", + "ruff>=0.8.4", "jupyter>=1.1.1", "types-pyyaml>=6.0.12.20240917", "nbqa>=1.9.0", "pre-commit>=3.8.0", - "pytest-mock>=3.14.0", - "types-tabulate>=0.9.0.20240106", + "polars-lts-cpu>=1.18.0 ; sys_platform == 'linux'", + "polars>=1.18.0 ; sys_platform != 'linux'", +] + +[project.optional-dependencies] +analysis = [ "rouge-score>=0.1.2", + "tabulate>=0.9.0", + "types-tabulate>=0.9.0.20240106", + "vegafusion[embed]<=2.0.0", + "polars-lts-cpu>=1.18.0 ; sys_platform == 'linux'", + "polars>=1.18.0 ; sys_platform != 'linux'", ] + [build-system] requires = ["hatchling"] build-backend = "hatchling.build" @@ -57,6 +65,7 @@ module = [ "mamba_ssm.*", "MEGABYTE_pytorch.*", "rouge_score.*", + "tabulate.*", ] ignore_missing_imports = true diff --git a/src/mblm/scripts/clevr_generation.py b/scripts/clevr_generation.py similarity index 95% rename from src/mblm/scripts/clevr_generation.py rename to scripts/clevr_generation.py index 6aa703d..8d8a789 100644 --- a/src/mblm/scripts/clevr_generation.py +++ b/scripts/clevr_generation.py @@ -25,12 +25,11 @@ from pathlib import Path import torch -from pydantic import BaseModel from tqdm import tqdm from mblm import MBLM, MBLMReturnType from mblm.analysis.utils import load_model -from mblm.data.dataset.clevr import Clevr, ClevrOptionalArgs +from mblm.data.dataset.clevr import Clevr, ClevrModelGeneration, ClevrOptionalArgs from mblm.data.types import ModelMode from mblm.utils.seed import seed_everything @@ -40,17 +39,6 @@ DATASET_MODE = ModelMode.VALID -class ClevrModelGeneration(BaseModel): - id_model: str - sample_idx: int - question: str - question_type: str - answer_gen: list[int] - answer_truth: list[int] - ce: float - timestamp: str - - def sample_clevr_by_question_type(clevr: Clevr, items_per_question: int) -> list[tuple[str, int]]: """ Sample clevr indices by question type and return a list of (question_type, diff --git a/scripts/clevr_generation.sh b/scripts/clevr_generation.sh new file mode 100644 index 0000000..ddde033 --- /dev/null +++ b/scripts/clevr_generation.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +uv run src/mblm/scripts/clevr_generation.py \ + --model-dir ../../disk/inference/mmb_clevr \ + --clevr-dir ../../disk/data/clevr \ + --out-file misc/clevr_qa_pt3.jsonl \ + -m 8k_2mi_1b_2d_s_pt3_disc \ + -m 8k_2mi_1b_2d_s_pt3_jpeg \ + -m 8k_2mi_1b_2d_s_pt3_nodisc \ + -n 300 \ No newline at end of file diff --git a/src/mblm/scripts/cuda_check.py b/scripts/cuda_check.py similarity index 100% rename from src/mblm/scripts/cuda_check.py rename to scripts/cuda_check.py diff --git a/src/mblm/scripts/model_overview.py b/scripts/model_overview.py similarity index 96% rename from src/mblm/scripts/model_overview.py rename to scripts/model_overview.py index e0b6f0e..7a6eda7 100644 --- a/src/mblm/scripts/model_overview.py +++ b/scripts/model_overview.py @@ -29,7 +29,7 @@ from tabulate import tabulate from mblm import MBLM -from mblm.scripts.train_mblm import TrainEntryConfig +from mblm.trainer.mblm import TrainEntryConfig from mblm.utils.io import load_yml from mblm.utils.misc import count_params @@ -60,8 +60,6 @@ def print_model_sizes( key=lambda pc: (pc[1].params.input_seq_len, pc[1].params.seq_lens), ) for path, conf in configs: - if conf.io.dataset_id != "pg19": - continue inp_len = conf.params.input_seq_len seg_lens = conf.params.seq_lens layers = str(conf.params.num_layers) diff --git a/src/mblm/scripts/pg19_generation.py b/scripts/pg19_generation.py similarity index 96% rename from src/mblm/scripts/pg19_generation.py rename to scripts/pg19_generation.py index 405908b..7f61509 100644 --- a/src/mblm/scripts/pg19_generation.py +++ b/scripts/pg19_generation.py @@ -26,11 +26,10 @@ from typing import cast import torch -from pydantic import BaseModel from mblm import MBLM, MBLMReturnType from mblm.analysis.utils import load_model -from mblm.data.dataset.pg19 import PG19 +from mblm.data.dataset.pg19 import PG19, PG19ModelGeneration from mblm.data.types import ModelMode from mblm.data.utils import Bytes from mblm.utils.io import NDJSONWriter @@ -42,18 +41,6 @@ START_OF_SENTENCE_RE = re.compile(r"\.\s[A-Z][a-z]{1,}\s[a-z]{1,}") -class PG19ModelGeneration(BaseModel): - id_model: str - book_id: str - book_txt_offset: int - ctx_len: int - generated: list[int] - truth: list[int] - ce: float - generation_time: float - timestamp: str - - def seek_to_start_of_sentence(text: str) -> int | None: start_of_sentence = START_OF_SENTENCE_RE.search(text) if start_of_sentence: diff --git a/src/mblm/scripts/pg19_generation.sh b/scripts/pg19_generation.sh similarity index 100% rename from src/mblm/scripts/pg19_generation.sh rename to scripts/pg19_generation.sh diff --git a/scripts/train_mblm.py b/scripts/train_mblm.py new file mode 100644 index 0000000..bc662f8 --- /dev/null +++ b/scripts/train_mblm.py @@ -0,0 +1,42 @@ +__copyright__ = """MIT License + +Copyright (c) 2024 - IBM Research + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE.""" + +from pathlib import Path + +from mblm.trainer.mblm import TrainEntryConfig, train_mblm +from mblm.utils.io import load_yml + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "-c", + dest="config_path", + required=True, + type=Path, + help="Path to the experiment yaml config file", + ) + + args = parser.parse_args() + train_cfg = load_yml(args.config_path, parse_to=TrainEntryConfig) + train_mblm(train_cfg) diff --git a/src/mblm/scripts/train_launch.sh b/scripts/train_mblm.sh similarity index 90% rename from src/mblm/scripts/train_launch.sh rename to scripts/train_mblm.sh index 65d6cde..d40d77d 100644 --- a/src/mblm/scripts/train_launch.sh +++ b/scripts/train_mblm.sh @@ -16,5 +16,5 @@ export DISPLAY_PROGRESS=1 OMP_NUM_THREADS=1 uv run torchrun \ --standalone \ --nproc_per_node=gpu \ - src/mblm/scripts/train_mblm.py \ + scripts/train_mblm.py \ -c $1 \ No newline at end of file diff --git a/src/mblm/__init__.py b/src/mblm/__init__.py index a470b91..5f89da6 100644 --- a/src/mblm/__init__.py +++ b/src/mblm/__init__.py @@ -10,7 +10,19 @@ __version__ = "0.0.1" +from mblm.model.block import StageBlock from mblm.model.config import MBLMModelConfig, MBLMReturnType +from mblm.model.mamba import MambaBlockConfig from mblm.model.mblm import MBLM +from mblm.model.transformer import TransformerBlockConfig +from mblm.registry import block_registry -__all__ = ["MBLM", "MBLMModelConfig", "MBLMReturnType"] +__all__ = [ + "MBLM", + "MBLMModelConfig", + "MBLMReturnType", + "TransformerBlockConfig", + "MambaBlockConfig", + "StageBlock", + "block_registry", +] diff --git a/src/mblm/analysis/aggregate.py b/src/mblm/analysis/aggregate.py index 25850f7..c129f07 100644 --- a/src/mblm/analysis/aggregate.py +++ b/src/mblm/analysis/aggregate.py @@ -31,8 +31,7 @@ from pydantic import ValidationError from mblm.data.types import ModelMode -from mblm.model.block import BlockType -from mblm.scripts.train_mblm import TrainOutputConfig +from mblm.trainer.mblm import TrainOutputConfig from mblm.utils.io import load_yml ModelType = Literal["SSM", "Transformer", "Mixed"] @@ -124,10 +123,10 @@ def _df_with_common_props( ) def _determine_model_type(self, config: TrainOutputConfig) -> ModelType: - block_types: list[BlockType] = [block.block_type for block in config.params.blocks()] - if all([block_type == "mamba2" for block_type in block_types]): + block_ids = [block.block_type for block in config.params.stage_blocks] + if all([block_id == "mamba2" for block_id in block_ids]): return "SSM" - if all([block_type == "transformer" for block_type in block_types]): + if all([block_id == "transformer" for block_id in block_ids]): return "Transformer" return "Mixed" @@ -141,7 +140,7 @@ def _read_experiments( for folder, exp_name in folders: path = Path(folder) try: - config = load_yml(path / "config.yml", parse_to=TrainOutputConfig) + config = load_yml(path / "config.yaml", TrainOutputConfig, try_yaml_suffixes=True) grad_acc_every = config.train.gradient_accumulate_every model_type = self._determine_model_type(config) @@ -158,14 +157,14 @@ def _read_experiments( num_tokens=pl.lit(config.params.num_tokens), checkpoints=pl.lit(config.params.train_checkpoint_chunks), patch_pos_emb=pl.lit( - [b.patch_pos_emb_type or "none" for b in config.params.blocks()] + [b.pos_emb_type or "none" for b in config.params.stage_blocks] ), lr=pl.lit(config.train.learning_rate), batch_size=pl.lit(config.train.batch_size), grad_step=pl.lit(grad_acc_every * config.train.batch_size), grad_clip=pl.lit(config.train.gradient_clipping), num_gpus=pl.lit(config.summary.num_workers), - dataset_args=pl.lit(config.io.dataset_args, allow_object=True), + # dataset_args=pl.lit(config.io.dataset_args), comment=pl.lit(config.io.description), training_finished=pl.lit(bool(config.summary.training_end)), error=pl.lit(config.summary.error), diff --git a/src/mblm/analysis/metrics.py b/src/mblm/analysis/metrics.py index 9798692..8144385 100644 --- a/src/mblm/analysis/metrics.py +++ b/src/mblm/analysis/metrics.py @@ -26,10 +26,10 @@ from mblm.data.utils.bytes import Bytes -T = TypeVar("T") +_T = TypeVar("_T") -def token_accuracy(a: Sequence[T], b: Sequence[T], allow_var_lens: bool) -> float: +def token_accuracy(a: Sequence[_T], b: Sequence[_T], allow_var_lens: bool) -> float: if len(a) != len(b) and not allow_var_lens: raise ValueError("Sequences must have the same length") if len(a) == 0 or len(b) == 0: @@ -41,7 +41,7 @@ def token_accuracy(a: Sequence[T], b: Sequence[T], allow_var_lens: bool) -> floa return correct / len(a) -rouge_scorers = dict( +_rouge_scorers = dict( rouge1=rouge_scorer.RougeScorer(["rouge1"]), rougeL=rouge_scorer.RougeScorer(["rougeL"]) ) @@ -52,7 +52,7 @@ def rouge_score_from_bytes( try: t = Bytes.byte_list_to_str(target) p = Bytes.byte_list_to_str(predicted) - score: scoring.Score = rouge_scorers[which].score(t, p)[which] + score: scoring.Score = _rouge_scorers[which].score(t, p)[which] return score.fmeasure except Exception: return -1 diff --git a/src/mblm/analysis/utils.py b/src/mblm/analysis/utils.py index bc61c61..bf4441d 100644 --- a/src/mblm/analysis/utils.py +++ b/src/mblm/analysis/utils.py @@ -24,9 +24,12 @@ from pathlib import Path from typing import TypeAlias +import polars as pl +from tabulate import tabulate + from mblm import MBLM, MBLMModelConfig from mblm.data.utils import Bytes -from mblm.scripts.train_mblm import TrainOutputConfig +from mblm.trainer.mblm import TrainOutputConfig from mblm.utils.io import load_model_state, load_yml ExpCollection: TypeAlias = list[tuple[str, str]] @@ -46,6 +49,13 @@ def strip_line_breaks(text: str) -> str: return LINE_BREAK_TABS_RE.sub(" ", text) +def dataframe_to_md_table(df: pl.DataFrame, output_file: str | Path) -> None: + as_str = tabulate(df.to_dict(), headers=df.columns, tablefmt="github") + with Path(output_file).open("w") as f: + f.write(as_str) + return None + + def load_model( model_id: str, model_dir: Path, diff --git a/src/mblm/data/dataset/clevr.py b/src/mblm/data/dataset/clevr.py index 8eb8a5b..8ba78ea 100644 --- a/src/mblm/data/dataset/clevr.py +++ b/src/mblm/data/dataset/clevr.py @@ -57,6 +57,17 @@ class ClevrQuestion(TypedDict): program: list[ClevrFunction] +class ClevrModelGeneration(BaseModel): + id_model: str + sample_idx: int + question: str + question_type: str + answer_gen: list[int] + answer_truth: list[int] + ce: float + timestamp: str + + class ClevrOptionalArgs(BaseModel): model_config = ConfigDict(extra="forbid") diff --git a/src/mblm/data/dataset/pg19.py b/src/mblm/data/dataset/pg19.py index 886adea..bb48f12 100644 --- a/src/mblm/data/dataset/pg19.py +++ b/src/mblm/data/dataset/pg19.py @@ -27,6 +27,7 @@ import torch import tqdm +from pydantic import BaseModel from typing_extensions import Unpack from mblm.data.datasets import BatchWithLossMask, DistributedDataset, DistributedDatasetConfig @@ -34,6 +35,18 @@ from mblm.data.utils import Bytes +class PG19ModelGeneration(BaseModel): + id_model: str + book_id: str + book_txt_offset: int + ctx_len: int + generated: list[int] + truth: list[int] + ce: float + generation_time: float + timestamp: str + + class PG19(DistributedDataset[BatchWithLossMask]): """ https://github.com/google-deepmind/pg19 diff --git a/src/mblm/model/block.py b/src/mblm/model/block.py index d6698a8..37c51d3 100644 --- a/src/mblm/model/block.py +++ b/src/mblm/model/block.py @@ -20,19 +20,18 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" + from abc import ABC, abstractmethod -from typing import Literal, TypeAlias +from typing import Literal import torch from pydantic import BaseModel -BlockType: TypeAlias = Literal["mamba1", "mamba2", "transformer"] - class StageBlock(ABC, BaseModel): - block_type: BlockType + block_type: str - patch_pos_emb_type: Literal["fixed", "rope"] | None + pos_emb_type: Literal["fixed", "rope"] | None @abstractmethod def to_model( diff --git a/src/mblm/model/config.py b/src/mblm/model/config.py index dde96c0..afaf770 100644 --- a/src/mblm/model/config.py +++ b/src/mblm/model/config.py @@ -22,12 +22,12 @@ from enum import Enum, auto from itertools import repeat -from typing import Sequence +from typing import Any, Sequence -from pydantic import BaseModel +from pydantic import BaseModel, computed_field, field_validator, model_validator -from mblm.model.mamba import MambaBlockConfig -from mblm.model.transformer import TransformerBlockConfig +from mblm.model.block import StageBlock +from mblm.registry import block_registry class MBLMReturnType(str, Enum): @@ -47,7 +47,7 @@ class MBLMModelConfig(BaseModel): hidden_dims: The model's hidden dimensions at each stage num_layers: The number of layers at each stage seq_lens: The sequence length at each stage - block: Either a Transformer or Mamba block configuration + block: A Transformer or Mamba block """ num_tokens: int @@ -56,14 +56,44 @@ class MBLMModelConfig(BaseModel): num_layers: Sequence[int] seq_lens: Sequence[int] train_checkpoint_chunks: list[int] | None - block: ( - TransformerBlockConfig | MambaBlockConfig | list[TransformerBlockConfig | MambaBlockConfig] - ) - def blocks(self) -> list[TransformerBlockConfig | MambaBlockConfig]: + block: StageBlock | list[StageBlock] + + @field_validator("block", mode="before") + @classmethod + def parse_block(cls, value: Any) -> Any: + """ + Try to parse the block to any registered stage block + """ + given_as_list = isinstance(value, list) + value = value if given_as_list else [value] + parsed = [] + for block_data in value: + if isinstance(block_data, StageBlock): + parsed.append(block_data) + continue + parsed_block = block_registry.try_parse( + block_data, lambda model, data: model.model_validate(data) + ) + parsed.append(parsed_block) + return parsed if given_as_list else parsed[0] + + @model_validator(mode="after") + def validate_stages(self): + is_equal_num_stages = len(self.hidden_dims) == len(self.num_layers) == len(self.seq_lens) + if not is_equal_num_stages: + raise ValueError("hidden_dims, num_layers and seq_lens are not of the same length") + + if isinstance(self.block, list) and len(self.block) != len(self.hidden_dims): + raise ValueError( + "When given as a list of blocks, must have the appropriate number of stages" + ) + return self + + # https://docs.pydantic.dev/2.0/usage/computed_fields/ + @computed_field # type: ignore[misc] + @property + def stage_blocks(self) -> list[StageBlock]: if isinstance(self.block, list): - assert len(self.block) == len( - self.hidden_dims - ), "If blocks are given as list, lengths must match" return self.block return list(repeat(self.block, len(self.hidden_dims))) diff --git a/src/mblm/model/mamba.py b/src/mblm/model/mamba.py index c6a91ae..39d4af6 100644 --- a/src/mblm/model/mamba.py +++ b/src/mblm/model/mamba.py @@ -20,10 +20,12 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" + from pydantic import BaseModel, Field -from mblm.model.block import BlockType, StageBlock +from mblm.model.block import StageBlock from mblm.model.mamba_shim import Mamba1, Mamba1Config, Mamba2Mixer +from mblm.registry import block_registry class MambaBlockConfig(StageBlock, BaseModel): @@ -52,10 +54,7 @@ class MambaBlockConfig(StageBlock, BaseModel): headdim: int # Mamba2 only dropout: float = 0.0 # Mamba2 only. Default for backwards compatibility - block_type: BlockType = Field( - init=False, - default_factory=lambda: "mamba1" if Mamba2Mixer is None else "mamba2", - ) + block_type: str = Field(init=False, default="mamba1" if Mamba2Mixer is None else "mamba2") def to_model(self, model_dim, num_layers): if Mamba2Mixer is not None: @@ -81,3 +80,6 @@ def to_model(self, model_dim, num_layers): raise RuntimeError( "Failed to import any Mamba version - this should never happen", ) + + +block_registry.register(MambaBlockConfig) diff --git a/src/mblm/model/mblm.py b/src/mblm/model/mblm.py index 1cec06a..a5774ef 100644 --- a/src/mblm/model/mblm.py +++ b/src/mblm/model/mblm.py @@ -20,8 +20,9 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" +import logging import math -from typing import Literal, cast, overload +from typing import Iterable, Literal, Sequence, cast, overload import torch import torch.nn.functional as F # noqa: N812 @@ -31,6 +32,7 @@ from torch.utils.checkpoint import checkpoint from tqdm import tqdm +from mblm.model.block import StageBlock from mblm.model.config import MBLMModelConfig, MBLMReturnType from mblm.model.utils import ByteToUtf8Streamer, RoPE, gumbel_sample, top_k @@ -62,14 +64,12 @@ class MBLM(nn.Module): def __init__(self, cfg: MBLMModelConfig): super().__init__() - assert len(cfg.hidden_dims) == len(cfg.num_layers) == len(cfg.seq_lens) - - self.num_stages = len(cfg.hidden_dims) + assert len(cfg.hidden_dims) == len(cfg.num_layers) == len(cfg.seq_lens) if cfg.train_checkpoint_chunks: - assert self.num_stages - 1 == len(cfg.train_checkpoint_chunks) + assert len(cfg.hidden_dims) - 1 == len(cfg.train_checkpoint_chunks) - self.model_dims = cfg.hidden_dims + self.num_stages = len(cfg.hidden_dims) self.seq_lens = cfg.seq_lens self.pad_token_id = cfg.pad_token_id self.checkpoint_chunks: list[int | None] = ( @@ -79,18 +79,41 @@ def __init__(self, cfg: MBLMModelConfig): ) self.start_tokens = nn.ParameterList( - [nn.Parameter(torch.randn(model_dim)) for model_dim in self.model_dims] + [nn.Parameter(torch.randn(model_dim)) for model_dim in cfg.hidden_dims] + ) + + self.pos_embs = self._init_positional_embeddings( + cfg.hidden_dims, cfg.seq_lens, cfg.stage_blocks + ) + + self.token_embs_rev = self._init_token_embeddings( + cfg.hidden_dims, cfg.seq_lens, cfg.num_tokens, cfg.pad_token_id ) - self.patch_pos_embs = nn.ModuleList() + self.stage_models, self.to_next_stage_proj = self._init_models_at_stages( + cfg.hidden_dims, cfg.seq_lens, cfg.num_layers, cfg.stage_blocks + ) + + self.to_logits = nn.Linear(cfg.hidden_dims[-1], cfg.num_tokens) - for model_dim, seq_len, block in zip(self.model_dims, cfg.seq_lens, cfg.blocks()): - if not block.patch_pos_emb_type: - self.patch_pos_embs.append(nn.Identity()) - elif block.patch_pos_emb_type == "fixed": - self.patch_pos_embs.append(nn.Embedding(seq_len, model_dim)) - elif block.patch_pos_emb_type == "rope": - self.patch_pos_embs.append( + @classmethod + def _init_positional_embeddings( + cls, + model_dims: Iterable[int], + seq_lens: Iterable[int], + blocks: Iterable[StageBlock], + ) -> nn.ModuleList: + """ + Create positional embeddings for each patch. + """ + modules = nn.ModuleList() + for model_dim, seq_len, block in zip(model_dims, seq_lens, blocks): + if not block.pos_emb_type: + modules.append(nn.Identity()) + elif block.pos_emb_type == "fixed": + modules.append(nn.Embedding(seq_len, model_dim)) + elif block.pos_emb_type == "rope": + modules.append( RoPE( model_dim, # only used for caching, will not fail if we're feeding @@ -99,43 +122,65 @@ def __init__(self, cfg: MBLMModelConfig): max_seq_len=seq_len, ) ) - - local_dim = self.model_dims[-1] # D_n - - # token embeddings are created in reverse order from local to global: - # [(V, D_n), ..., (V, D_1)] - self.token_embs_rev = nn.ModuleList( - [nn.Embedding(cfg.num_tokens, local_dim, padding_idx=self.pad_token_id)] + return modules + + @classmethod + def _init_token_embeddings( + cls, + model_dims: Sequence[int], + seq_lens: Sequence[int], + vocab_size: int, + pad_token_id: int, + ) -> nn.ModuleList: + """ + Embed the tokens for each stage (in reverse order). + """ + local_dim = model_dims[-1] + token_embs_rev = nn.ModuleList( + [nn.Embedding(vocab_size, local_dim, padding_idx=pad_token_id)] ) - patch_size = 1 - for dim_out, seq_len in zip( + for model_dim, seq_len in zip( # all except the local model - reversed(self.model_dims[:-1]), # (D_n-1, ..., D_1) - reversed(self.seq_lens[1:]), # (P_2, ..., P_n) + reversed(model_dims[:-1]), # (D_n-1, ..., D_1) + reversed(seq_lens[1:]), # (P_2, ..., P_n) ): + # for the global models, fuse the embedding and patch projection + # step patch_size *= seq_len - self.token_embs_rev.append( + token_embs_rev.append( nn.Sequential( - nn.Embedding(cfg.num_tokens, local_dim, padding_idx=self.pad_token_id), + nn.Embedding(vocab_size, local_dim, padding_idx=pad_token_id), Rearrange("... r d -> ... (r d)"), nn.LayerNorm(patch_size * local_dim), - nn.Linear(patch_size * local_dim, dim_out), - nn.LayerNorm(dim_out), + nn.Linear(patch_size * local_dim, model_dim), + nn.LayerNorm(model_dim), ) ) - - self.stage_models = nn.ModuleList([]) - self.to_next_stage_proj = nn.ModuleList([]) + return token_embs_rev + + @classmethod + def _init_models_at_stages( + cls, + model_dims: Sequence[int], + seq_lens: Sequence[int], + num_stage_layers: Iterable[int], + blocks: Iterable[StageBlock], + ) -> tuple[nn.ModuleList, nn.ModuleList]: + """ + Initialize the models and next-stage projections for each stage. + """ + stage_models = nn.ModuleList([]) + to_next_stage_proj = nn.ModuleList([]) for block, model_dim, num_layers, next_model_dim, next_seq_len in zip( - cfg.blocks(), - self.model_dims, - cfg.num_layers, - tuple(self.model_dims[1:]) + (None,), - tuple(self.seq_lens[1:]) + (None,), + blocks, + model_dims, + num_stage_layers, + tuple(model_dims[1:]) + (None,), + tuple(seq_lens[1:]) + (None,), ): - self.stage_models.append(block.to_model(model_dim=model_dim, num_layers=num_layers)) + stage_models.append(block.to_model(model_dim=model_dim, num_layers=num_layers)) proj: torch.nn.Module = nn.Identity() @@ -146,71 +191,14 @@ def __init__(self, cfg: MBLMModelConfig): Rearrange("b m (n d) -> (b m) n d", n=next_seq_len), ) - self.to_next_stage_proj.append(proj) + to_next_stage_proj.append(proj) + return stage_models, to_next_stage_proj - self.to_logits = nn.Linear(local_dim, cfg.num_tokens) - - @torch.inference_mode() - def generate( - self, - prime: torch.Tensor | None = None, - stream: ByteToUtf8Streamer | None = None, - num_tokens_to_generate: int | None = None, - end_of_gen_token_id: int = -1, - temperature: float = 1.0, - filter_thres: float = 1.0, - enable_progress: bool = True, - ) -> torch.Tensor: + def forward_empty(self, batch_size: int) -> torch.Tensor: """ - Autoregressively generate tokens. - - Args: - prime: The prompt as a 1D tensor of type torch.long. Batches are - not supported for now - stream: Instead of generating and then returning the sequene, stream - the intermediate results. Only possible when the bytes represent - UTF-8 encoded text - num_tokens_to_generate: If set, ignore the context window and - generate an exact amount of tokens. If `None`, generate up to - the maximum possible sequence length. - temperature: A float in the range (0, 1] that determines the - temperature. 1 means deterministic, 0.1 very random. Has - no effect if `filter_thres` is set to 1 - filter_thres: A float in the range [0, 1] to determine the - threshold for the top k logits. 1 means the token with - the maximum logit is sampled. + Take care of special case where you sample from input of length 0 (start + token only). """ - if prime is not None: - assert prime.ndim == 1, "Batch mode not supported" - prime = prime.unsqueeze(0) - else: - device = next(self.parameters()).device - prime = torch.empty((1, 0), dtype=torch.long, device=device) - - total_iters = num_tokens_to_generate or math.prod(self.seq_lens) - prime.shape[-1] - - iter_rng = range(total_iters) - iterator = tqdm(iter_rng, leave=False) if (stream is None and enable_progress) else iter_rng - - sequence = prime - for _ in iterator: - logits = self.forward(sequence, return_type=MBLMReturnType.LOGITS)[:, -1] - logits = top_k(logits, thres=filter_thres) - sampled = gumbel_sample(logits, dim=-1, temperature=temperature) - sequence = torch.cat((sequence, rearrange(sampled, "b -> b 1")), dim=-1) - - newest_token = int(sampled.item()) - - if stream is not None: - stream.write(newest_token) - - if newest_token == end_of_gen_token_id: - break - return sequence.squeeze() - - def forward_empty(self, batch_size: int) -> torch.Tensor: - # take care of special case where you sample from input of 0 (start - # token only) prev_stage_tokens_repr: torch.Tensor | None = None tokens: torch.Tensor = torch.empty(0) @@ -298,18 +286,18 @@ def forward( # in the 2nd example, L = prod(seq_lens) = 5 * 4 * 3 = 60 = P_1 = P_1' if flattened_dims: # pad/fill up all inner sequence lengths (all except most global) - inner_seq_lens = self.seq_lens[1:] - multiple_of = math.prod(inner_seq_lens) + local_seq_lens = self.seq_lens[1:] + multiple_of = math.prod(local_seq_lens) # use the complement of modulo - the difference to the next multiple # of multiple_of - to infer the right padding length padding = -flat_seq_len % multiple_of input_ids = F.pad(input_ids, (0, padding), value=self.pad_token_id) # reshape and infer the P_1' dimension - input_ids = input_ids.reshape(batch_size, -1, *inner_seq_lens) + input_ids = input_ids.reshape(batch_size, -1, *local_seq_lens) # make sure the above condition holds, i.e., P_1' <= P_1 _P_1_prime, _P_1 = input_ids.shape[1], self.seq_lens[0] # noqa: N806 - fixed_global_patch_encoding = isinstance(self.patch_pos_embs[0], nn.Embedding) + fixed_global_patch_encoding = isinstance(self.pos_embs[0], nn.Embedding) if fixed_global_patch_encoding: assert _P_1_prime <= _P_1, ( f"Because you are using a fixed global patch embedding, " @@ -329,7 +317,7 @@ def forward( # [2]: (B, P_1', P_2, P_3, D_3) for stage_idx, pos_emb, token_emb in zip( range(self.num_stages - 1, -1, -1), - reversed(self.patch_pos_embs), + reversed(self.pos_embs), self.token_embs_rev, ): stage_token_embs: torch.Tensor = token_emb(input_ids) @@ -393,11 +381,12 @@ def forward( (0, 0, 1, 0), value=0, ) + _start_token = stage_tokens[..., 0, :] stage_tokens = stage_tokens + prev_stage_tokens_repr + assert _start_token.equal(stage_tokens[..., 0, :]) # stage_tokens is now [B, P_n, D_n]. for the first stage, B is the # actual batch size whereas for the other stages, the batch size - # corresponds to the sequence length of the previous hierarchy - # stage + # corresponds to the sequence length of the previous hierarchy stage # skip checkpointing for the first global stage if checkpoint_chunk is None: @@ -447,16 +436,17 @@ def forward( # the most local sequences still contain the preprended start tokens # (i.e., the logits) - extract this token once and later prepend it to # the flattened sequence - first_start_token_index = (slice(None), *((0,) * (logits.ndim - 2)), slice(None)) + first_start_token_logits_idx = (slice(None), *((0,) * (logits.ndim - 2)), slice(None)) # extract it - start_token = logits[first_start_token_index] # type: ignore + start_token_logits = logits[first_start_token_logits_idx] # type: ignore + # drop ALL start tokens and combine inner dimensions into one... logits = rearrange(logits[..., 1:, :], "b ... v -> b (...) v") # spread across batches - start_token = rearrange(start_token, "b v -> b 1 v") + start_token_logits = rearrange(start_token_logits, "b v -> b 1 v") # right-shift by one by appending the start token to the flat sequence - logits = torch.cat((start_token, logits), dim=-2) + logits = torch.cat((start_token_logits, logits), dim=-2) # drop the last item to match lengths logits = logits[..., :-1, :] @@ -493,9 +483,67 @@ def forward( if torch.isnan(loss): # special case when the loss is zero across target elements - should # theoretically never happen - print("Edge case detected, loss is nan") - loss = torch.zeros_like(loss) + logging.fatal("Edge case detected, loss is nan") + loss = torch.zeros_like(loss, requires_grad=True) if return_type == MBLMReturnType.LOSS: return loss return loss, logits + + @torch.inference_mode() + def generate( + self, + prime: torch.Tensor | None = None, + stream: ByteToUtf8Streamer | None = None, + num_tokens_to_generate: int | None = None, + end_of_gen_token_id: int = -1, + temperature: float = 1.0, + filter_thres: float = 1.0, + enable_progress: bool = True, + ) -> torch.Tensor: + """ + Autoregressively generate tokens. + + Args: + prime: The prompt as a 1D tensor of type torch.long. Batches are + not supported for now + stream: Instead of generating and then returning the sequene, stream + the intermediate results. Only possible when the bytes represent + UTF-8 encoded text + num_tokens_to_generate: If set, ignore the context window and + generate an exact amount of tokens. If `None`, generate up to + the maximum possible sequence length. + temperature: A float in the range (0, 1] that determines the + temperature. 1 means deterministic, 0.1 very random. Has + no effect if `filter_thres` is set to 1 + filter_thres: A float in the range [0, 1] to determine the + threshold for the top k logits. 1 means the token with + the maximum logit is sampled. + """ + if prime is not None: + assert prime.ndim == 1, "Batch mode not supported" + prime = prime.unsqueeze(0) + else: + device = next(self.parameters()).device + prime = torch.empty((1, 0), dtype=torch.long, device=device) + + total_iters = num_tokens_to_generate or math.prod(self.seq_lens) - prime.shape[-1] + + iter_rng = range(total_iters) + iterator = tqdm(iter_rng, leave=False) if (stream is None and enable_progress) else iter_rng + + sequence = prime + for _ in iterator: + logits = self.forward(sequence, return_type=MBLMReturnType.LOGITS)[:, -1] + logits = top_k(logits, thres=filter_thres) + sampled = gumbel_sample(logits, dim=-1, temperature=temperature) + sequence = torch.cat((sequence, rearrange(sampled, "b -> b 1")), dim=-1) + + newest_token = int(sampled.item()) + + if stream is not None: + stream.write(newest_token) + + if newest_token == end_of_gen_token_id: + break + return sequence.squeeze() diff --git a/src/mblm/model/transformer.py b/src/mblm/model/transformer.py index a238208..ad8a97f 100644 --- a/src/mblm/model/transformer.py +++ b/src/mblm/model/transformer.py @@ -20,18 +20,20 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" + from typing import Callable, Iterable, cast import torch from MEGABYTE_pytorch.megabyte import Attention, FeedForward, RMSNorm, RotaryEmbedding, token_shift from pydantic import BaseModel, Field -from mblm.model.block import BlockType, StageBlock +from mblm.model.block import StageBlock +from mblm.registry import block_registry class TransformerBlockConfig(StageBlock, BaseModel): """ - General config for creating a Transformer block inside MBLM. + General config for creating a Transformer Decocer block inside MBLM. """ attn_head_dims: int @@ -42,7 +44,7 @@ class TransformerBlockConfig(StageBlock, BaseModel): ff_dropout: float = 0.0 use_flash_attn: bool = False - block_type: BlockType = Field(init=False, default="transformer") + block_type: str = Field(init=False, default="transformer") def to_model(self, model_dim, num_layers): return TransformerDecoder( @@ -104,3 +106,6 @@ def forward(self, input_ids: torch.Tensor) -> torch.Tensor: input_ids = ff(token_shift(input_ids)) + input_ids return self.norm(input_ids) + + +block_registry.register(TransformerBlockConfig) diff --git a/src/mblm/registry.py b/src/mblm/registry.py new file mode 100644 index 0000000..be39b57 --- /dev/null +++ b/src/mblm/registry.py @@ -0,0 +1,51 @@ +__copyright__ = """MIT License + +Copyright (c) 2024 - IBM Research + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE.""" + +from typing import Any, Callable, Generic, TypeVar + +from mblm.model.block import StageBlock + +_T = TypeVar("_T") + + +class ClassRegistry(Generic[_T]): + __registry = set[type[_T]]() + + def __init__(self, name: str): + self.__name = name + + def register(self, klass: type[_T]) -> None: + if klass in self.__registry: + raise ValueError(f"{self.__name} already has {klass}") + self.__registry.add(klass) + + def try_parse(self, data: Any, parse_func: Callable[[type[_T], Any], _T]): + for klass in self.__registry: + try: + return parse_func(klass, data) + except Exception: + pass + + raise ValueError(f"Coult not parse data to any of {self.__registry}") + + +block_registry = ClassRegistry[StageBlock]("stage_block") diff --git a/src/mblm/scripts/clevr_generation.sh b/src/mblm/scripts/clevr_generation.sh deleted file mode 100644 index dc73bb9..0000000 --- a/src/mblm/scripts/clevr_generation.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash - -uv run src/mblm/scripts/clevr_generation.py \ - --model-dir ../../disk/inference/mmb_clevr \ - --clevr-dir ../../disk/data/clevr \ - --out-file misc/clevr_qa_pt1.jsonl \ - -m 8k_7mi_360m_1d_s_pt1_ft \ - -m 8k_7mi_360m_1d_s_pt1_nft \ - -m 8k_7mi_360m_1d_t_pt1_ft \ - -m 8k_7mi_360m_1d_t_pt1_nft \ - -n 300 -# uv run src/mblm/scripts/clevr_generation.py \ -# --model-dir ../../disk/inference/mmb_clevr \ -# --clevr-dir ../../disk/data/clevr \ -# --out-file misc/clevr_qa_pt2.jsonl \ -# -m 8k_2mi_360m_1d_s_pt2_disc \ -# -m 8k_2mi_360m_1d_s_pt2_nodisc \ -# -m 8k_2mi_360m_1d_s_pt2_jpeg \ -# -m 8k_2mi_360m_1d_t_pt2_disc \ -# -m 8k_2mi_360m_1d_t_pt2_nodisc \ -# -m 8k_2mi_360m_1d_t_pt2_jpeg \ -# -n 300 \ No newline at end of file diff --git a/src/mblm/trainer/config.py b/src/mblm/trainer/config.py index 6bf7a13..12b22a7 100644 --- a/src/mblm/trainer/config.py +++ b/src/mblm/trainer/config.py @@ -100,7 +100,9 @@ class ResumeConfig(BaseModel): default=False, description="Migrate an existing smaller number of embeddings" ) rename_modules: bool = Field( - default=False, description="Rename modules from existing models to new names" + default=False, + description="Rename modules from existing models to new names", + deprecated="Not used anymore. All modules use the same name again and there is no need to rename", ) resumed_from: str | None = Field( default=None, diff --git a/src/mblm/trainer/core.py b/src/mblm/trainer/core.py index c90c28f..37ca0f0 100644 --- a/src/mblm/trainer/core.py +++ b/src/mblm/trainer/core.py @@ -68,7 +68,7 @@ @dataclass class CoreTrainerOptions: - config_file_name: str = "config.yml" + config_file_name: str = "config.yaml" loss_file_name: str = "loss.csv" timemem_file_name: str = "timemem.csv" max_train_restarts: int = 0 diff --git a/src/mblm/scripts/train_mblm.py b/src/mblm/trainer/mblm.py similarity index 91% rename from src/mblm/scripts/train_mblm.py rename to src/mblm/trainer/mblm.py index 56970c5..619e7c8 100644 --- a/src/mblm/scripts/train_mblm.py +++ b/src/mblm/trainer/mblm.py @@ -22,7 +22,6 @@ import math import os -from pathlib import Path from typing import Any, Iterator, Literal import torch @@ -45,7 +44,6 @@ ) from mblm.trainer.core import CoreTrainer from mblm.utils.distributed import process_group -from mblm.utils.io import load_yml from mblm.utils.logging import create_logger, shutdown_log_handlers from mblm.utils.misc import count_params @@ -164,14 +162,9 @@ def migrate_embeddings_if_enabled(self): # enabled via yaml config return MBLM_TOKEN_EMB_MIGRATION - def rename_modules_if_enabled(self): - # we have renamed pos_embs to patch_pos_embs in newer versions. enabled - # via yaml config - return (("pos_embs", "patch_pos_embs"),) - @record -def main(config: TrainEntryConfig) -> None: +def train_mblm(config: TrainEntryConfig) -> None: log = create_logger(__name__, log_dir=config.io.output_dir) try: @@ -201,20 +194,3 @@ def main(config: TrainEntryConfig) -> None: except Exception as error: log.fatal(error, exc_info=True) shutdown_log_handlers() - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument( - "-c", - dest="config_path", - required=True, - type=Path, - help="Path to the experiment yaml config file", - ) - - args = parser.parse_args() - train_cfg = load_yml(args.config_path, parse_to=TrainEntryConfig) - main(train_cfg) diff --git a/src/mblm/utils/io.py b/src/mblm/utils/io.py index 7c3503b..c6c8318 100644 --- a/src/mblm/utils/io.py +++ b/src/mblm/utils/io.py @@ -26,12 +26,10 @@ from pathlib import Path from typing import Any, Callable, Generic, Iterable, NamedTuple, TypeAlias, TypeVar -import polars as pl import torch import yaml from filelock import FileLock from pydantic import BaseModel -from tabulate import tabulate from torch import load as torch_load from torch import save as torch_save from torch.nn import Module as TorchModule @@ -50,25 +48,36 @@ def _to_path(path: str | Path) -> Path: def load_yml( - path: str | Path, - parse_to: type[_TBaseModel], + path: str | Path, parse_to: type[_TBaseModel], try_yaml_suffixes: bool = False ) -> _TBaseModel: """ - Load any configuration from a (nested) yml file. + Load any configuration from a (nested) yaml file. Args: - path (str | Path): The path to the yml file + path (str | Path): The path to the yaml file parse_to: An instance of a Pydantic model + check_yaml_suffixes (bool=False): If `True`, try + loading the file with either a `yaml` or + `yml` extension Returns: A populated instance of the Pydantic model """ - - with Path.open(_to_path(path), "r") as file: - yaml_data = yaml.safe_load(file) - return parse_to.model_validate(yaml_data) + path = _to_path(path) + yaml_suffixes = {".yaml", ".yml"} + assert path.suffix in yaml_suffixes, f"{path} is not a yaml file" + try: + with Path.open(path, "r") as file: + yaml_data = yaml.safe_load(file) + return parse_to.model_validate(yaml_data) + except FileNotFoundError as exception: + if try_yaml_suffixes: + yaml_suffixes.remove(path.suffix) + new_suffix = next(iter(yaml_suffixes)) + return load_yml(path.with_suffix(new_suffix), parse_to, try_yaml_suffixes=False) + raise exception def dump_yml(path: str | Path, data: BaseModel) -> Path: - path = _to_path(path).with_suffix(".yml") + path = _to_path(path).with_suffix(".yaml") with Path.open(path, "w") as file: yaml.safe_dump(data.model_dump(), file) return path @@ -149,13 +158,6 @@ def read_jsonl( return [parse_lines_to(**json.loads(line)) for line in f.readlines()] -def dataframe_to_md_table(df: pl.DataFrame, output_file: str | Path) -> None: - as_str = tabulate(df.to_dict(), headers=df.columns, tablefmt="github") - with _to_path(output_file).open("w") as f: - f.write(as_str) - return None - - StateDict: TypeAlias = dict[str, Any] diff --git a/tests/e2e/trainer/sample-config-0.5-epoch.yml b/tests/e2e/trainer/sample-config-0.5-epoch.yaml similarity index 100% rename from tests/e2e/trainer/sample-config-0.5-epoch.yml rename to tests/e2e/trainer/sample-config-0.5-epoch.yaml diff --git a/tests/e2e/trainer/sample-config-1-epoch.yml b/tests/e2e/trainer/sample-config-1-epoch.yaml similarity index 100% rename from tests/e2e/trainer/sample-config-1-epoch.yml rename to tests/e2e/trainer/sample-config-1-epoch.yaml diff --git a/tests/e2e/trainer/sample-config-1.5-epoch.yml b/tests/e2e/trainer/sample-config-1.5-epoch.yaml similarity index 100% rename from tests/e2e/trainer/sample-config-1.5-epoch.yml rename to tests/e2e/trainer/sample-config-1.5-epoch.yaml diff --git a/tests/e2e/trainer/sample-config-grad-acc-1.yml b/tests/e2e/trainer/sample-config-grad-acc-1.yaml similarity index 100% rename from tests/e2e/trainer/sample-config-grad-acc-1.yml rename to tests/e2e/trainer/sample-config-grad-acc-1.yaml diff --git a/tests/e2e/trainer/sample-config-grad-acc-2.yml b/tests/e2e/trainer/sample-config-grad-acc-2.yaml similarity index 100% rename from tests/e2e/trainer/sample-config-grad-acc-2.yml rename to tests/e2e/trainer/sample-config-grad-acc-2.yaml diff --git a/tests/e2e/trainer/validate.py b/tests/e2e/trainer/validate.py index 9e64896..fd895d9 100644 --- a/tests/e2e/trainer/validate.py +++ b/tests/e2e/trainer/validate.py @@ -23,11 +23,11 @@ def ensure_no_error_logs(log_file: Path): def assert_on_model_run_output(output_dir: Path) -> None: checkpoints = list(output_dir.rglob("*.pth")) csv_loss_file = output_dir / "loss.csv" - yml_config_file = output_dir / "config.yml" + yml_config_file = output_dir / "config.yaml" log_file = output_dir / "train.log" assert csv_loss_file.exists(), "Expected a CSV loss file" - assert yml_config_file.exists(), "Expected a YML config file" + assert yml_config_file.exists(), "Expected a YAML config file" assert log_file.exists(), "Expected a log file" ensure_no_error_logs(log_file) diff --git a/tests/integration/model_config/test_config_to_model.py b/tests/integration/config/test_sample_config_to_model.py similarity index 95% rename from tests/integration/model_config/test_config_to_model.py rename to tests/integration/config/test_sample_config_to_model.py index 1978465..6791bc9 100644 --- a/tests/integration/model_config/test_config_to_model.py +++ b/tests/integration/config/test_sample_config_to_model.py @@ -7,10 +7,9 @@ from mblm.data.dataset.clevr import ClevrOptionalArgs from mblm.model.mamba import MambaBlockConfig from mblm.model.transformer import TransformerBlockConfig -from mblm.scripts.train_mblm import TrainEntryConfig +from mblm.trainer.mblm import TrainEntryConfig from mblm.utils.io import load_yml -# TODO CONFIG_FILES_DIR = "config" CONFIG_FILES = [(config,) for config in Path(CONFIG_FILES_DIR).glob("*.yaml")] @@ -31,7 +30,7 @@ def ensure_dataset_args_are_valid(self, config: TrainEntryConfig) -> None: return None def ensure_model_is_created(self, config: TrainEntryConfig) -> None: - for b in config.params.blocks(): + for b in config.params.stage_blocks: assert isinstance(b, (TransformerBlockConfig, MambaBlockConfig)) if isinstance(b, TransformerBlockConfig): assert b.block_type == "transformer" diff --git a/tests/integration/install/README.md b/tests/integration/install/README.md new file mode 100644 index 0000000..1056a42 --- /dev/null +++ b/tests/integration/install/README.md @@ -0,0 +1 @@ +Making sure things can be installed. These files also serve to test the main project readme. diff --git a/tests/integration/install/pyproject.toml b/tests/integration/install/pyproject.toml new file mode 100644 index 0000000..6636e7d --- /dev/null +++ b/tests/integration/install/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "mblm-test" +version = "0.0.1" +description = "Multiscale Byte Language Model - Test project" +authors = [ + { name = "Eric Egli", email = "eric.christian.egli@ibm.com" }, + { name = "Jannis Born", email = "jab@zurich.ibm.com" }, +] +readme = "README.md" +requires-python = ">=3.10" +dependencies = ["mblm", "torch>=2.5.1"] + +[tool.uv.sources] +mblm = { path = "../../../" } + +[dependency-groups] +dev = ["pytest>=8.3.3"] + +[tool.pytest.ini_options] +testpaths = ["."] +addopts = ["--import-mode=importlib"] diff --git a/tests/integration/install/test_custom.py b/tests/integration/install/test_custom.py new file mode 100644 index 0000000..ed96fed --- /dev/null +++ b/tests/integration/install/test_custom.py @@ -0,0 +1,127 @@ +from mblm.utils.seed import seed_everything + +seed_everything(8) + + +def test_from_config(): + import torch + from pydantic import BaseModel, Field + + from mblm import MBLM, MBLMModelConfig, StageBlock, TransformerBlockConfig + + # Define any custom model + class MyLSTM(torch.nn.Module): + def __init__(self, lstm: torch.nn.LSTM): + super().__init__() + self.lstm = lstm + + def forward(self, input_ids: torch.Tensor) -> torch.Tensor: + # Wrap the LSTM forward to extract the output + out, _ = self.lstm(input_ids) + return out + + # Add a block config and inherit from StageBlock and BaseModel + class LSTMBlockConfig(StageBlock, BaseModel): + block_type: str = Field(init=False, default="lstm") + + # Add whatever is needed + dropout: float + + def to_model(self, model_dim: int, num_layers: int) -> torch.nn.Module: + return MyLSTM( + torch.nn.LSTM( + input_size=model_dim, + hidden_size=model_dim, + batch_first=True, + dropout=self.dropout, + num_layers=num_layers, + ) + ) + + mblm = MBLM( + MBLMModelConfig( + num_tokens=257, + hidden_dims=[1024, 1024], + seq_lens=[1024, 8], + num_layers=[5, 5], + pad_token_id=256, + train_checkpoint_chunks=None, + block=[ + LSTMBlockConfig( + dropout=0.1, + pos_emb_type=None, + ), + TransformerBlockConfig( + attn_head_dims=64, + attn_num_heads=16, + attn_use_rot_embs=True, + use_flash_attn=True, + pos_emb_type="fixed", + ), + ], + ) + ) + + x = torch.randint(0, 258, (1, 12)).long() + mblm.forward(x) + + +def test_from_yaml(): + import torch + import yaml + from pydantic import BaseModel, Field + + from mblm import MBLM, MBLMModelConfig, StageBlock, block_registry + + # Define any custom model + class MyLSTM(torch.nn.Module): + def __init__(self, lstm: torch.nn.LSTM): + super().__init__() + self.lstm = lstm + + def forward(self, input_ids: torch.Tensor) -> torch.Tensor: + # Wrap the LSTM forward to extract the output + out, _ = self.lstm(input_ids) + return out + + # Add a block config and inherit from StageBlock and BaseModel + class LSTMBlockConfig(StageBlock, BaseModel): + block_type: str = Field(init=False, default="lstm") + + # Add whatever is needed + dropout: float + + def to_model(self, model_dim: int, num_layers: int) -> torch.nn.Module: + return MyLSTM( + torch.nn.LSTM( + input_size=model_dim, + hidden_size=model_dim, + batch_first=True, + dropout=self.dropout, + num_layers=num_layers, + ) + ) + + yml_model_config = """ + num_tokens: 257 + hidden_dims: [1024, 1024] + seq_lens: [1024, 8] + num_layers: [5, 5] + pad_token_id: 256 + train_checkpoint_chunks: null + block: + - dropout: 0.1 + pos_emb_type: null + - attn_head_dims: 64 + attn_num_heads: 16 + attn_use_rot_embs: true + use_flash_attn: true + pos_emb_type: fixed + """ + + block_registry.register(LSTMBlockConfig) # Add this! + + parsed_config = yaml.safe_load(yml_model_config) + mblm = MBLM(MBLMModelConfig.model_validate(parsed_config)) + x = torch.randint(0, 258, (1, 12)).long() + mblm.forward(x) diff --git a/tests/integration/install/test_defaults.py b/tests/integration/install/test_defaults.py new file mode 100644 index 0000000..c6935b0 --- /dev/null +++ b/tests/integration/install/test_defaults.py @@ -0,0 +1,71 @@ +from mblm.utils.seed import seed_everything + +seed_everything(8) + + +def test_from_config(): + import torch + + from mblm import MBLM, MambaBlockConfig, MBLMModelConfig, TransformerBlockConfig + + mblm = MBLM( + MBLMModelConfig( + num_tokens=257, + hidden_dims=[1024, 1024], + seq_lens=[1024, 8], + num_layers=[5, 5], + pad_token_id=256, + train_checkpoint_chunks=None, + block=[ + MambaBlockConfig( + d_state=128, + d_conv=4, + expand=2, + headdim=64, + pos_emb_type=None, + ), + TransformerBlockConfig( + attn_head_dims=64, + attn_num_heads=16, + attn_use_rot_embs=True, + use_flash_attn=True, + pos_emb_type="fixed", + ), + ], + ) + ) + + x = torch.randint(0, 258, (1, 12)).long() + mblm.forward(x) + + +def test_from_yaml(): + import torch + import yaml + + from mblm import MBLM, MBLMModelConfig + + yml_model_config = """ + num_tokens: 257 + hidden_dims: [1024, 1024] + seq_lens: [1024, 8] + num_layers: [5, 5] + pad_token_id: 256 + train_checkpoint_chunks: null + block: + - d_state: 128 + d_conv: 4 + expand: 2 + headdim: 64 + pos_emb_type: null + - attn_head_dims: 64 + attn_num_heads: 16 + attn_use_rot_embs: true + use_flash_attn: true + pos_emb_type: fixed + """ + + parsed_config = yaml.safe_load(yml_model_config) + mblm = MBLM(MBLMModelConfig.model_validate(parsed_config)) + x = torch.randint(0, 258, (1, 12)).long() + mblm.forward(x) diff --git a/tests/integration/install/uv.lock b/tests/integration/install/uv.lock new file mode 100644 index 0000000..f10f096 --- /dev/null +++ b/tests/integration/install/uv.lock @@ -0,0 +1,822 @@ +version = 1 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.12'", + "python_full_version < '3.12'", +] + +[[package]] +name = "annotated-types" +version = "0.7.0" 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import_clevr_from_experiment(exp_config: str | TrainEntryConfig) -> Clevr: - """ - General helper to load the Clevr dataset from a yml experiment config - """ - exp_conf = ( - exp_config - if isinstance(exp_config, TrainEntryConfig) - else import_experiment_config(exp_config) - ) - # adjust the dataset dir to point to the fixture - exp_conf.io.dataset_dir = str(CLEVR_FIXTURE_PATH) - # we can be sure that this is clevr - return cast(Clevr, exp_conf.import_dataset(ModelMode.VALID, worker_id=0, num_workers=1)) - - def import_clevr_from_fixture(pad_token_id: int, optional_args: ClevrOptionalArgs): """ General helper to directly import the Clevr dataset from the fixture diff --git a/tests/unit/model/test_mblm.py b/tests/unit/model/test_mblm.py index dffa3a6..ab23b46 100644 --- a/tests/unit/model/test_mblm.py +++ b/tests/unit/model/test_mblm.py @@ -40,7 +40,7 @@ def test_masked_loss( attn_dropout=self.dropout, ff_multiplier=self.ff_mult, ff_dropout=self.dropout, - patch_pos_emb_type="fixed", + pos_emb_type="fixed", attn_use_rot_embs=self.use_rot_emb, use_flash_attn=self.use_flash_attn, ), diff --git a/tests/unit/model/test_megabyte_diff.py b/tests/unit/model/test_megabyte_diff.py index c14cdaf..9379366 100644 --- a/tests/unit/model/test_megabyte_diff.py +++ b/tests/unit/model/test_megabyte_diff.py @@ -29,7 +29,7 @@ def boostrap_models( self, model_dims: tuple[int, ...], seq_lens: tuple[int, ...], - use_fixed_patch_use_encoding: bool = False, + use_fixed_pos_encoding: bool = False, ): num_layers = (1,) * len(model_dims) @@ -46,7 +46,7 @@ def boostrap_models( ff_mult=self.ff_mult, ff_dropout=self.dropout, rel_pos=self.use_rot_emb, - pos_emb=use_fixed_patch_use_encoding, + pos_emb=use_fixed_pos_encoding, flash_attn=self.use_flash_attn, ) # generating random numbers changes the stage of the random number @@ -67,7 +67,7 @@ def boostrap_models( attn_dropout=self.dropout, ff_multiplier=self.ff_mult, ff_dropout=self.dropout, - patch_pos_emb_type="fixed" if use_fixed_patch_use_encoding else None, + pos_emb_type="fixed" if use_fixed_pos_encoding else None, attn_use_rot_embs=self.use_rot_emb, use_flash_attn=self.use_flash_attn, ), @@ -112,9 +112,7 @@ def test_transformer_seq_len_overflow( model_dims: tuple[int, ...], seq_lens: tuple[int, ...], ): - original, patched = self.boostrap_models( - model_dims, seq_lens, use_fixed_patch_use_encoding=True - ) + original, patched = self.boostrap_models(model_dims, seq_lens, use_fixed_pos_encoding=True) input_seq_len = math.prod(seq_lens) # max sequence length possible input_tensor = self.make_input_tensor(input_seq_len) input_tensor_too_large = self.make_input_tensor(input_seq_len + 1) diff --git a/tests/unit/utils/test_io.py b/tests/unit/utils/test_io.py index 6035609..4e15f72 100644 --- a/tests/unit/utils/test_io.py +++ b/tests/unit/utils/test_io.py @@ -162,7 +162,7 @@ def create_model(num_tokens: int): attn_head_dims=64, attn_num_heads=8, attn_use_rot_embs=True, - patch_pos_emb_type=None, + 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