support training, fix some inplace func of nn (#118)

Gy-Lu · Shenggan · web-flow · commit ceee81d71f82 · 2022-12-12T11:11:23.000+08:00
* support training, fix some inplace func of nn

* fix some merge issue

Co-authored-by: shenggan &lt;csg19971016@gmail.com&gt;
diff --git a/fastfold/model/fastnn/template.py b/fastfold/model/fastnn/template.py
@@ -391,7 +391,7 @@ def forward(
             for i in range(0, t.shape[0]):
                 t[i] = self.layer_norm(t[i])
         else:
-            t = self.layer_norm(t[i])
+            t = self.layer_norm(t)
         return t
     
     def inplace(
diff --git a/fastfold/model/hub/__init__.py b/fastfold/model/hub/__init__.py
@@ -1,3 +1,5 @@
 from .alphafold import AlphaFold
+from .lr_scheduler import AlphaFoldLRScheduler
+from .loss import AlphaFoldLoss
 
-__all__ = ["AlphaFold"]
+__all__ = ["AlphaFold", "AlphaFoldLRScheduler", "AlphaFoldLoss"]
diff --git a/fastfold/model/hub/loss.py b/fastfold/model/hub/loss.py
diff --git a/fastfold/model/hub/lr_scheduler.py b/fastfold/model/hub/lr_scheduler.py
@@ -0,0 +1,97 @@
+# Copyright 2022 HPC-AI Tech Inc
+# Copyright 2021 AlQuraishi Laboratory
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+
+
+class AlphaFoldLRScheduler(torch.optim.lr_scheduler._LRScheduler):
+    """ Implements the learning rate schedule defined in the AlphaFold 2
+        supplement. A linear warmup is followed by a plateau at the maximum
+        learning rate and then exponential decay.
+         
+        Note that the initial learning rate of the optimizer in question is 
+        ignored; use this class' base_lr parameter to specify the starting 
+        point of the warmup.
+    """
+    def __init__(self, 
+        optimizer, 
+        last_epoch: int = -1, 
+        verbose: bool = False,
+        base_lr: float = 0.,
+        max_lr: float = 0.001,
+        warmup_no_steps: int = 1000,
+        start_decay_after_n_steps: int = 50000,
+        decay_every_n_steps: int = 50000,
+        decay_factor: float = 0.95,
+    ):
+        step_counts = {
+            "warmup_no_steps": warmup_no_steps,
+            "start_decay_after_n_steps": start_decay_after_n_steps,
+        }
+
+        for k,v in step_counts.items():
+            if(v < 0):
+                raise ValueError(f"{k} must be nonnegative")
+
+        if(warmup_no_steps > start_decay_after_n_steps):
+            raise ValueError(
+                "warmup_no_steps must not exceed start_decay_after_n_steps"
+            )
+
+        self.optimizer = optimizer
+        self.last_epoch = last_epoch
+        self.verbose = verbose
+        self.base_lr = base_lr
+        self.max_lr = max_lr
+        self.warmup_no_steps = warmup_no_steps
+        self.start_decay_after_n_steps = start_decay_after_n_steps
+        self.decay_every_n_steps = decay_every_n_steps
+        self.decay_factor = decay_factor
+
+        super(AlphaFoldLRScheduler, self).__init__(
+            optimizer,
+            last_epoch=last_epoch, 
+            verbose=verbose,
+        )
+
+    def state_dict(self):
+        state_dict = {
+            k:v for k,v in self.__dict__.items() if k not in ["optimizer"]
+        }
+
+        return state_dict
+
+    def load_state_dict(self, state_dict):
+        self.__dict__.update(state_dict)
+
+    def get_lr(self):
+        if(not self._get_lr_called_within_step):
+            raise RuntimeError(
+                "To get the last learning rate computed by the scheduler, use "
+                "get_last_lr()"
+            )
+
+        step_no = self.last_epoch
+
+        if(step_no <= self.warmup_no_steps):
+            lr = self.base_lr + (step_no / self.warmup_no_steps) * self.max_lr
+        elif(step_no > self.start_decay_after_n_steps):
+            steps_since_decay = step_no - self.start_decay_after_n_steps
+            exp = (steps_since_decay // self.decay_every_n_steps) + 1
+            lr = self.max_lr * (self.decay_factor ** exp)
+        else: # plateau
+            lr = self.max_lr
+
+        return [lr for group in self.optimizer.param_groups]
diff --git a/fastfold/model/nn/dropout.py b/fastfold/model/nn/dropout.py
@@ -56,7 +56,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
                 shape[bd] = 1
         mask = x.new_ones(shape)
         mask = self.dropout(mask)
-        x *= mask
+        x = x * mask
         return x
 
 
diff --git a/fastfold/model/nn/evoformer.py b/fastfold/model/nn/evoformer.py
@@ -263,12 +263,7 @@ def __init__(self,
             inf=inf,
             eps=eps,
         )
-        
-        self.outer_product_mean = OuterProductMean(
-            c_m,
-            c_z,
-            c_hidden_opm,
-        )
+ 
         self.is_multimer = is_multimer
 
     def forward(self,
diff --git a/fastfold/model/nn/heads.py b/fastfold/model/nn/heads.py
@@ -17,7 +17,7 @@
 import torch.nn as nn
 
 from fastfold.model.nn.primitives import Linear, LayerNorm
-from fastfold.model.loss import (
+from fastfold.model.hub.loss import (
     compute_plddt,
     compute_tm,
     compute_predicted_aligned_error,
diff --git a/train.py b/train.py
@@ -0,0 +1,216 @@
+import random
+import torch
+import numpy as np
+import colossalai
+from colossalai.logging import disable_existing_loggers, get_dist_logger
+from colossalai.core import global_context as gpc
+from colossalai.nn.optimizer import HybridAdam
+
+from tqdm import tqdm
+
+from fastfold.config import model_config
+from fastfold.model.hub import AlphaFold, AlphaFoldLRScheduler, AlphaFoldLoss
+from fastfold.utils.inject_fastnn import inject_fastnn
+from fastfold.data.data_modules import SetupTrainDataset, TrainDataLoader
+from fastfold.utils.tensor_utils import tensor_tree_map
+
+import logging
+logging.disable(logging.WARNING)
+import torch.multiprocessing
+torch.multiprocessing.set_sharing_strategy('file_system')
+
+def main():
+    parser = colossalai.get_default_parser()
+    parser.add_argument('--from_torch', default=False, action='store_true')
+    parser.add_argument(
+        "--template_mmcif_dir", type=str,
+        help="Directory containing mmCIF files to search for templates"
+    )
+    parser.add_argument(
+        "--max_template_date", type=str,
+        help='''Cutoff for all templates. In training mode, templates are also 
+                filtered by the release date of the target'''
+    )
+    parser.add_argument(
+        "--train_data_dir", type=str,
+        help="Directory containing training mmCIF files"
+    )
+    parser.add_argument(
+        "--train_alignment_dir", type=str,
+        help="Directory containing precomputed training alignments"
+    )
+    parser.add_argument(
+        "--train_chain_data_cache_path", type=str, default=None,
+    )
+    parser.add_argument(
+        "--distillation_data_dir", type=str, default=None,
+        help="Directory containing training PDB files"
+    )
+    parser.add_argument(
+        "--distillation_alignment_dir", type=str, default=None,
+        help="Directory containing precomputed distillation alignments"
+    )
+    parser.add_argument(
+        "--distillation_chain_data_cache_path", type=str, default=None,
+    )
+    parser.add_argument(
+        "--val_data_dir", type=str, default=None,
+        help="Directory containing validation mmCIF files"
+    )
+    parser.add_argument(
+        "--val_alignment_dir", type=str, default=None,
+        help="Directory containing precomputed validation alignments"
+    )
+    parser.add_argument(
+        "--kalign_binary_path", type=str, default='/usr/bin/kalign',
+        help="Path to the kalign binary"
+    )
+    parser.add_argument(
+        "--train_filter_path", type=str, default=None,
+        help='''Optional path to a text file containing names of training
+                examples to include, one per line. Used to filter the training 
+                set'''
+    )
+    parser.add_argument(
+        "--distillation_filter_path", type=str, default=None,
+        help="""See --train_filter_path"""
+    )
+    parser.add_argument(
+        "--obsolete_pdbs_file_path", type=str, default=None,
+        help="""Path to obsolete.dat file containing list of obsolete PDBs and 
+             their replacements."""
+    )
+    parser.add_argument(
+        "--template_release_dates_cache_path", type=str, default=None,
+        help="""Output of scripts/generate_mmcif_cache.py run on template mmCIF
+                files."""
+    )
+    parser.add_argument(
+        "--train_epoch_len", type=int, default=10000,
+        help=(
+            "The virtual length of each training epoch. Stochastic filtering "
+            "of training data means that training datasets have no "
+            "well-defined length. This virtual length affects frequency of "
+            "validation & checkpointing (by default, one of each per epoch)."
+        )
+    )
+    parser.add_argument(
+        "--_alignment_index_path", type=str, default=None,
+        help="Training alignment index. See the README for instructions."
+    )
+    parser.add_argument(
+        "--config_preset", type=str, default="initial_training",
+        help=(
+            'Config setting. Choose e.g. "initial_training", "finetuning", '
+            '"model_1", etc. By default, the actual values in the config are '
+            'used.'
+        )
+    )
+    parser.add_argument(
+        "--_distillation_structure_index_path", type=str, default=None,
+    )
+    parser.add_argument(
+        "--distillation_alignment_index_path", type=str, default=None,
+        help="Distillation alignment index. See the README for instructions."
+    )
+    parser.add_argument(
+        "--seed", type=int, default=42,
+        help="Random seed"
+    )
+
+    args = parser.parse_args()
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+    if args.from_torch:
+        colossalai.launch_from_torch(config=dict(torch_ddp=dict(static_graph=True)))
+    disable_existing_loggers()
+    logger = get_dist_logger()
+
+    config = model_config(args.config_preset, train=True)
+    config.globals.inplace = False
+    model = AlphaFold(config)
+    model = inject_fastnn(model)
+
+
+    train_dataset, test_dataset = SetupTrainDataset(
+        config=config.data,
+        template_mmcif_dir=args.template_mmcif_dir,
+        max_template_date=args.max_template_date,
+        train_data_dir=args.train_data_dir,
+        train_alignment_dir=args.train_alignment_dir,
+        train_chain_data_cache_path=args.train_chain_data_cache_path,
+        distillation_data_dir=args.distillation_data_dir,
+        distillation_alignment_dir=args.distillation_alignment_dir,
+        distillation_chain_data_cache_path=args.distillation_chain_data_cache_path,
+        val_data_dir=args.val_data_dir,
+        val_alignment_dir=args.val_alignment_dir,
+        kalign_binary_path=args.kalign_binary_path,
+        # train_mapping_path=args.train_mapping_path,
+        # distillation_mapping_path=args.distillation_mapping_path,
+        obsolete_pdbs_file_path=args.obsolete_pdbs_file_path,
+        template_release_dates_cache_path=args.template_release_dates_cache_path,
+        train_epoch_len=args.train_epoch_len, 
+        _alignment_index_path=args._alignment_index_path,
+    )
+
+    train_dataloader, test_dataloader = TrainDataLoader(
+        config=config.data,
+        train_dataset=train_dataset,
+        test_dataset=test_dataset,
+        batch_seed=args.seed,
+        )
+
+
+    criterion = AlphaFoldLoss(config.loss)
+
+    optimizer = HybridAdam(model.parameters(), lr=1e-3, eps=1e-8)
+
+    lr_scheduler = AlphaFoldLRScheduler(optimizer)
+    
+
+    engine, train_dataloader, test_dataloader, lr_scheduler = colossalai.initialize(
+                                                                model=model,
+                                                                optimizer=optimizer,
+                                                                criterion=criterion,
+                                                                lr_scheduler=lr_scheduler,
+                                                                train_dataloader=train_dataloader,
+                                                                test_dataloader=test_dataloader,
+                                                                )
+    
+    for epoch in range(200):
+        engine.train()
+        if gpc.get_global_rank() == 0:
+            train_dataloader = tqdm(train_dataloader)
+        for batch in train_dataloader:
+            batch = {k: torch.as_tensor(v).cuda() for k, v in batch.items()}
+            engine.zero_grad()
+            output = engine(batch)
+            batch = tensor_tree_map(lambda t: t[..., -1], batch)
+            loss, loss_breakdown = engine.criterion(
+                    output, batch, _return_breakdown=True)
+            if gpc.get_global_rank() == 0:
+                train_dataloader.set_postfix(loss=float(loss))
+            engine.backward(loss)
+            engine.step()
+        lr_scheduler.step()
+        
+        if test_dataloader is not None:
+            engine.eval()
+            if gpc.get_global_rank() == 0:
+                train_dataloader = tqdm(train_dataloader)
+            for batch in test_dataloader:
+                batch = {k: torch.as_tensor(v).cuda() for k, v in batch.items()}
+                with torch.no_grad():
+                    output = engine(batch)
+                    batch = tensor_tree_map(lambda t: t[..., -1], batch)
+                    _, loss_breakdown = engine.criterion(
+                            output, batch, _return_breakdown=True)
+                    if gpc.get_global_rank() == 0:
+                        train_dataloader.set_postfix(loss=float(loss))
+        
+
+
+if __name__ == "__main__":
+    main()
diff --git a/train.sh b/train.sh
@@ -0,0 +1,30 @@
+DATA_DIR=/path/to/data
+PROJECT_DIR=/path/to/project
+
+gpus_per_node=2
+nnodes=1
+
+max_template_date=2021-10-10
+
+train_data_dir=${DATA_DIR}/mmcif_dir  # specify the dir contains *.cif or *.pdb
+train_alignment_dir=${DATA_DIR}/alignment_dir  # a dir to save template and features.pkl of training sequence
+mkdir -p ${train_alignment_dir}
+
+# val_data_dir=${PROJECT_DIR}/dataset/val_pdb
+# val_alignment_dir=${PROJECT_DIR}/dataset/alignment_val_pdb  # a dir to save template and features.pkl of vld sequence
+
+template_mmcif_dir=${DATA_DIR}/data/pdb_mmcif/mmcif_files
+template_release_dates_cache_path=${DATA_DIR}/mmcif_cache.json  # a cache used to pre-filter templates
+train_chain_data_cache_path=${DATA_DIR}/chain_data_cache.json  # a separate chain-level cache with data used for training-time data filtering
+
+train_epoch_len=10000  # virtual length of each training epoch, which affects frequency of validation & checkpointing
+
+torchrun --standalone --nproc_per_node ${gpus_per_node} --nnodes ${nnodes} train.py \
+    --from_torch \
+    --template_mmcif_dir=${template_mmcif_dir} \
+    --max_template_date=${max_template_date} \
+    --train_data_dir=${train_data_dir} \
+    --train_alignment_dir=${train_alignment_dir} \
+    --train_chain_data_cache_path=${train_chain_data_cache_path} \
+    --template_release_dates_cache_path=${template_release_dates_cache_path} \
+    --train_epoch_len=${train_epoch_len} \
