enable param group configuration in llm-foundry

llmfoundry/optim/lion8b.py

@@ -1,7 +1,7 @@
 # Copyright 2022 MosaicML LLM Foundry authors
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Callable, Dict, Iterable, Optional, Tuple
+from typing import Any, Callable, Dict, Iterable, Optional, Tuple, Union
 
 import torch
 
@@ -58,15 +58,17 @@ class DecoupledLionW_8bit(torch.optim.Optimizer):
             device, or b) step() is executed on a non-CUDA parameter.
     """
 
-    def __init__(self,
-                 params: Iterable[torch.Tensor],
-                 lr: float = 1e-3,
-                 betas: Tuple[float, float] = (0.9, 0.99),
-                 weight_decay: float = 0,
-                 quantize: bool = True,
-                 compress_state_dict: bool = False,
-                 error_correction: bool = False,
-                 _fused: bool = True):  # XXX this flag is mostly for testing...
+    def __init__(
+            self,
+            params: Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]],
+            lr: float = 1e-3,
+            betas: Tuple[float, float] = (0.9, 0.99),
+            weight_decay: float = 0,
+            quantize: bool = True,
+            compress_state_dict: bool = False,
+            error_correction: bool = False,
+            _fused: bool = True,  # XXX this flag is mostly for testing...
+    ):
 
         if lr < 0.0:
             raise ValueError('Invalid learning rate: {}'.format(lr))

llmfoundry/utils/builders.py

@@ -1,10 +1,13 @@
 # Copyright 2022 MosaicML LLM Foundry authors
 # SPDX-License-Identifier: Apache-2.0
 
+import functools
 import logging
 import os
+import re
 import warnings
-from typing import Any, Dict, List, Optional, Tuple, Union
+from collections import OrderedDict
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import torch
 from composer import algorithms
@@ -155,18 +158,118 @@ def build_algorithm(name: str, kwargs: Dict[str, Any]) -> Algorithm:
         raise ValueError(f'Not sure how to build algorithm: {name}')
 
 
+def _extract_param_groups(
+    model: torch.nn.Module,
+    optimizer_config: Dict[str, Any],
+) -> Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]]:
+    """Extracts parameter groups defined in the optimizer config.
+
+    The optimizer_config defines the optimizer args. It can additionally have key
+    `disable_grad` which is a string or list of strings. If a string matches a
+    parameter name, then that parameter will have `requires_grad=False`. This is
+    useful for freezing parameters. It can additionally have a key
+    `param_groups` which is a list of dicts. In this dict, key `param_str_match`
+    defines a string; if a parameter name contains this string, then it will be
+    in this parameter group. This is useful for grouping parameters together.
+    The dict can also contain any other key that is a valid optimizer arg.
+    Note: to handle name overlap conflicts, params are assigned to parameter
+    groups and added to `param_groups` in the order that `param_str_match` appear
+    in `param_groups`.
+
+    Usage
+    To disable gradient for all parameters that contain the string "norm" or "bias":
+    ```
+    optimizer_config: {
+        "name": "decoupled_lionw",
+        "lr": 1e-3,
+        "weight_decay": 1e-2,
+        "betas": [0.9, 0.999],
+        "eps": 1e-8,
+        "disable_grad": ["norm", "bias"]
+    }
+    ```
+
+    To create modify the optimizer parameters for all parameters that contain the
+    string "norm" and "bias" separately:
+    ```
+    optimizer_config: {
+        "name": "decoupled_lionw",
+        "lr": 1e-3,
+        "weight_decay": 1e-2,
+        "betas": [0.9, 0.999],
+        "eps": 1e-8,
+        "param_groups": [
+            {
+                "param_str_match": "norm",
+                "lr": 1e-4,
+                "weight_decay": 0.0,
+            },
+            {
+                "param_str_match": "bias",
+                "lr": 5e-4,
+                "weight_decay": 0.0,
+            },
+        ],
+    }
+    ```
+
+    Args:
+        model (torch.nn.Module): model to extract parameters from
+        optimizer_config (Dict[str, Any]): optimizer config
+
+    Returns:
+        Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]]: an iterable of
+            torch.Tensor's or dict's. Specifies what Tensors should be optimized.
+    """
+    if 'disable_grad' in optimizer_config.keys():
+        str_matches = optimizer_config.pop('disable_grad')
+        if isinstance(str_matches, str):
+            str_matches = [str_matches]
+        for str_match in str_matches:
+            for n, p in model.named_parameters():
+                if re.search(str_match, n):
+                    p.requires_grad = False
+                    log.debug(f'Setting `{n}.requires_grad = False`.')
+
+    if 'param_groups' in optimizer_config.keys():
+        params = []
+        param_dict = OrderedDict((n, p) for n, p in model.named_parameters())
+
+        for param_group_config in optimizer_config['param_groups']:
+            str_match = param_group_config.pop('param_str_match')
+            filter_fn = functools.partial(re.search, str_match)
+            param_names = [n for n in param_dict.keys() if filter_fn(n)]
+            group_params = {'params': [param_dict.pop(n) for n in param_names]}
+            group_params.update(param_group_config)
+
+            params.append(group_params)
+
+        optimizer_config.pop('param_groups')
+
+        params.insert(0, {'params': param_dict.values()})
+
+        log.debug(f'Optimizer param_groups: {params}.')
+
+        return params
+
+    return model.parameters()
+
+
 def build_optimizer(model: torch.nn.Module, name: str,
                     optimizer_config: Dict[str, Any]) -> Optimizer:
+
+    params = _extract_param_groups(model, optimizer_config)
+
     if name == 'decoupled_adamw':
-        return DecoupledAdamW(model.parameters(), **optimizer_config)
+        return DecoupledAdamW(params, **optimizer_config)
     elif name == 'decoupled_lionw':
-        return DecoupledLionW(model.parameters(), **optimizer_config)
+        return DecoupledLionW(params, **optimizer_config)
     elif name == 'clip_lion':
-        return DecoupledClipLion(model.parameters(), **optimizer_config)
+        return DecoupledClipLion(params, **optimizer_config)
     elif name == 'adalr_lion':
-        return DecoupledAdaLRLion(model.parameters(), **optimizer_config)
+        return DecoupledAdaLRLion(params, **optimizer_config)
     elif name == 'decoupled_lionw_8b':
-        return DecoupledLionW_8bit(model.parameters(), **optimizer_config)
+        return DecoupledLionW_8bit(params, **optimizer_config)
     else:
         raise ValueError(f'Not sure how to build optimizer: {name}')
 

tests/test_builders.py

@@ -1,17 +1,22 @@
 # Copyright 2022 MosaicML LLM Foundry authors
 # SPDX-License-Identifier: Apache-2.0
 
+import re
 import unittest.mock as mock
-from typing import Union
+from copy import deepcopy
+from typing import Any, Dict, Union
 
 import pytest
+import torch
+import torch.nn as nn
 from composer.callbacks import Generate
 from omegaconf import OmegaConf as om
 from transformers import PreTrainedTokenizerBase
 
 from llmfoundry.callbacks import HuggingFaceCheckpointer
 from llmfoundry.tokenizers.tiktoken import TiktokenTokenizerWrapper
-from llmfoundry.utils.builders import build_callback, build_tokenizer
+from llmfoundry.utils.builders import (build_callback, build_optimizer,
+                                       build_tokenizer)
 
 
 @pytest.mark.parametrize('tokenizer_name,tokenizer_kwargs', [
@@ -110,3 +115,76 @@ def test_build_hf_checkpointer_callback():
         assert isinstance(kwargs['mlflow_logging_config'], dict)
         assert isinstance(kwargs['mlflow_logging_config']['metadata'], dict)
         assert kwargs['mlflow_logging_config'] == mlflow_logging_config_dict
+
+
+class _DummyModule(nn.Module):
+
+    def __init__(self, device: str = 'cpu', dtype: torch.dtype = torch.float32):
+        super().__init__()
+        self.linear0 = nn.Linear(4, 3, device=device, dtype=dtype)
+        self.norm0 = nn.LayerNorm(3, device=device, dtype=dtype)
+        self.linear1 = nn.Linear(3, 5, device=device, dtype=dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:  # type:ignore
+        return self.linear1(self.norm0(self.linear0(x)))
+
+
+@pytest.mark.parametrize('name, optimizer_config', [
+    ('decoupled_adamw', {}),
+    ('decoupled_lionw', {}),
+    ('clip_lion', {}),
+    ('adalr_lion', {}),
+    pytest.param('decoupled_lionw_8b', {}, marks=pytest.mark.gpu),
+])
+@pytest.mark.parametrize('opt_additional_config', [
+    {
+        'disable_grad': 'norm'
+    },
+    {
+        'disable_grad': ['norm', 'bias']
+    },
+    {
+        'param_groups': [{
+            'param_str_match': 'norm',
+            'lr': 1e-9,
+            'weight_decay': 0.0,
+        },]
+    },
+    {
+        'param_groups': [{
+            'param_str_match': 'norm',
+            'lr': 1e-4,
+            'weight_decay': 0.0,
+        },],
+        'disable_grad': ['bias'],
+    },
+])
+def test_build_optimizer(name: str, optimizer_config: Dict[str, Any],
+                         opt_additional_config: Dict[str, Any]):
+    model = _DummyModule()
+    optimizer_config.update(deepcopy(opt_additional_config))
+    optimizer = build_optimizer(model, name, optimizer_config)
+
+    if 'disable_grad' in opt_additional_config.keys():
+        disable_grad = opt_additional_config['disable_grad']
+        if isinstance(disable_grad, str):
+            disable_grad = [disable_grad]
+        for n, p in model.named_parameters():
+            for k in disable_grad:
+                if re.search(k, n):
+                    assert not p.requires_grad
+
+    if 'param_groups' in opt_additional_config.keys():
+        for param_group_config, param_group in zip(
+                opt_additional_config['param_groups'],
+                optimizer.param_groups[1:]):
+            param_group_config = deepcopy(param_group_config)
+            param_str_match = param_group_config.pop('param_str_match')
+
+            for k, v in param_group_config.items():
+                assert param_group[k] == v
+
+            param_ids = [id(p) for p in param_group['params']]
+            for n, p in model.named_parameters():
+                if re.search(param_str_match, n):
+                    assert id(p) in param_ids