From 0115c27c589423052de216eb8bacadaebc7c4b58 Mon Sep 17 00:00:00 2001 From: hedonglong Date: Fri, 21 Oct 2022 11:02:16 +0800 Subject: [PATCH 1/3] Add the processed data and checkpoint download link, refine readme document --- .github/LIT-PCBA_result.png | Bin 0 -> 323751 bytes .github/optimus_framework3.png | Bin 0 -> 738919 bytes .github/pcqm4mv2_result.png | Bin 0 -> 257407 bytes .../ChemRL/GEM-2/README.md | 35 +++++++- .../ChemRL/GEM-2/scripts/inference.sh | 33 ++++++++ .../ChemRL/GEM-2/scripts/train.sh | 2 +- .../ChemRL/GEM-2/src/config.py | 10 +-- .../ChemRL/GEM-2/src/optimus.py | 77 ++++++++++-------- .../ChemRL/GEM-2/train_gem2.py | 31 ++++--- 9 files changed, 135 insertions(+), 53 deletions(-) create mode 100644 .github/LIT-PCBA_result.png create mode 100644 .github/optimus_framework3.png create mode 100644 .github/pcqm4mv2_result.png create mode 100644 apps/pretrained_compound/ChemRL/GEM-2/scripts/inference.sh diff --git a/.github/LIT-PCBA_result.png 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z1|zD-th}&H3VU(KG!DP;h8cx@tk8@r-y2WRJIfZLf$`__IYiABk+cTaUB?UKSq7r2r8NujK2MCoOr1l?Y;kE!hK*&mK&sK{0x^%=FbJi zpXuQTI9qtO`FFF4|MO)3_mK$y`JVHFWQ4c8Ey2nEe}(zK$!!1meeg5j1yHi-Ci}CZ z~vkzeo4~`OyBaKf3+jiDhSUk(Q&|Sl~xlK|{Vm I)->dQ0Yqyt{r~^~ literal 0 HcmV?d00001 diff --git a/apps/pretrained_compound/ChemRL/GEM-2/README.md b/apps/pretrained_compound/ChemRL/GEM-2/README.md index 7daad974..d818069b 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/README.md +++ b/apps/pretrained_compound/ChemRL/GEM-2/README.md @@ -1,8 +1,24 @@ # GEM-2: Next Generation Molecular Property Prediction Network with Many-body and Full-range Interaction Modeling -Molecular property prediction is a fundamental task in the drug and material industries. Physically, the properties of a molecule are determined by its own electronic structure, which can be exactly described by the Schrödinger equation. However, solving the Schrödinger equation for most molecules is extremely challenging due to long-range interactions in the behavior of a quantum many-body system. While deep learning methods have proven to be effective in molecular property prediction, we design a novel method, namely GEM-2, which comprehensively considers both the long-range and many-body interactions in molecules. GEM-2 consists of two interacted tracks: an atom-level track modeling both the local and global correlation between any two atoms, and a pair-level track modeling the correlation between all atom pairs, which embed information between any 3 or 4 atoms. Extensive experiments demonstrated the superiority of GEM-2 over multiple baseline methods in quantum chemistry and drug discovery tasks. - +GEM-2 is a molecular modeling framework which comprehensively considers full-range many-body interactions in molecules. Multiple tracks are utilized to model the full-range interactions between the many-bodies with different orders, and a novel axial attention mechanism is designed to approximate the full-range interaction modeling with much lower computational cost. A preprint version of our work can be found [here](https://arxiv.org/abs/2208.05863). +# Framework +

+ +

+ +The overall framework of GEM-2. First, a molecule is described by the representations of many-bodies of multiple orders. Then, Optimus blocks are designed to update the representations. Each Optimus block contains $M$ tracks, and the $m$-th track contains a stack of many-body axial attentions to model the full-range interactions between the $m$-bodies. The many-body axial attentions and the Low2High module also play the roles of exchanging messages across the tracks. Finally, the molecular property prediction is made by pooling over the $1$-body representations. +# Result +## PCQM4Mv2 +

+ +

+ +## LITPCBA +

+ +

+ # Installation guide ## Prerequisites @@ -32,6 +48,15 @@ You can download the PCQM4Mv2 dataset from ogb website: https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/pcqm4m-v2.zip +# Processed Data +You can download the processed PCQM4Mv2 dataset with rdkit generated 3d information from: + https://baidu-nlp.bj.bcebos.com/PaddleHelix/datasets/compound_datasets/pcqm4mv2_gem2.tgz +And then use tar to unzip the data. +```bash + mkdir -p ../data + tar xzf pcqm4mv2_gem2.tgz -C ../data +``` + # How to run ## Introduction to related configs You can adjsut the json files in the config folder to change the training settings. @@ -58,6 +83,12 @@ The models will be saved under `./model`. It will take around 60 mintues to finish one epoch on 16 A100 cards with total batch size of 512. +## Run inference +To reproduce the result from the ogb leaderboard, you can download the checkponit from: + https://baidu-nlp.bj.bcebos.com/PaddleHelix/models/molecular_modeling/gem2_l12_c256.pdparams +Then put it under the local `./model` folder and run the inference command: + sh scripts/inference.sh + ## Citing this work diff --git a/apps/pretrained_compound/ChemRL/GEM-2/scripts/inference.sh b/apps/pretrained_compound/ChemRL/GEM-2/scripts/inference.sh new file mode 100644 index 00000000..ef54c58a --- /dev/null +++ b/apps/pretrained_compound/ChemRL/GEM-2/scripts/inference.sh @@ -0,0 +1,33 @@ +#!/bin/bash +cd $(dirname $0) +cd .. + +train_pcqm(){ + mkdir -p log/$exp_name model/$exp_name + python3 train_gem2.py \ + --inference \ + --batch_size=$batch_size \ + --num_workers=10 \ + --max_epoch=150 \ + --dataset_config=$dataset_config \ + --data_cache_dir=$data_cache_dir \ + --model_config=$model_config \ + --encoder_config=$encoder_config \ + --train_config=$train_config \ + --init_model=$init_model \ + --start_step=$start_step \ + --model_dir=./model/$exp_name \ + --log_dir=./log/$exp_name +} + + +exp_name="" +batch_size=32 +dataset_config="configs/dataset_configs/pcqmv2.json" +data_cache_dir="../data/pcqm4m-v2-rdkit3d" +model_config="configs/model_configs/mol_regr-optimus-mae.json" +encoder_config="configs/model_configs/opt3d_l12_c256.json" +init_model="./model/gem2_l12_c256.pdparams" +train_config="configs/train_configs/lr4e-4-mid40.json" +start_step=0 +train_pcqm \ No newline at end of file diff --git a/apps/pretrained_compound/ChemRL/GEM-2/scripts/train.sh b/apps/pretrained_compound/ChemRL/GEM-2/scripts/train.sh index 83abde8d..fde9e302 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/scripts/train.sh +++ b/apps/pretrained_compound/ChemRL/GEM-2/scripts/train.sh @@ -22,7 +22,7 @@ train_pcqm(){ } - +exp_name="gem2_l12_c256_4e-4" echo "$exp_name" diff --git a/apps/pretrained_compound/ChemRL/GEM-2/src/config.py b/apps/pretrained_compound/ChemRL/GEM-2/src/config.py index 1131f609..ffa025ce 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/src/config.py +++ b/apps/pretrained_compound/ChemRL/GEM-2/src/config.py @@ -84,9 +84,9 @@ def make_updated_config(base_config, updated_dict): "optimus_block_num": 12, "optimus_block": { - "node_dropout_rate": 0.05, + "first_body_axial_attention_dropout": 0.05, "pair_dropout_rate": 0.05, - "node_attention": { + "first_body_axial_attention": { "use_pair_layer_norm": True, "num_head": 8, "dropout_rate": 0.05, @@ -96,15 +96,15 @@ def make_updated_config(base_config, updated_dict): "hidden_factor": 4, "dropout_rate": 0.1 }, - "outer_product": { + "low2high": { "inner_channel": 32 }, - "triangle_attention_start_node": { + "second_body_first_axis": { "num_head": 8, "dropout_rate": 0.05, "is_start": True }, - "triangle_attention_end_node": { + "second_body_second_axis": { "num_head": 8, "dropout_rate": 0.05, "is_start": False diff --git a/apps/pretrained_compound/ChemRL/GEM-2/src/optimus.py b/apps/pretrained_compound/ChemRL/GEM-2/src/optimus.py index a464a067..1ecbcabe 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/src/optimus.py +++ b/apps/pretrained_compound/ChemRL/GEM-2/src/optimus.py @@ -122,10 +122,10 @@ def forward(self, batch): return results -class NodeAttention(nn.Layer): +class FirstBodyAxialAttention(nn.Layer): """Compute self-attention over columns of a 2D input.""" def __init__(self, model_config, global_config): - super(NodeAttention, self).__init__() + super(FirstBodyAxialAttention, self).__init__() self.model_config = model_config node_channel = global_config.node_channel @@ -253,9 +253,9 @@ def forward(self, x): return x -class OuterProductMean(nn.Layer): +class Low2HighModule(nn.Layer): def __init__(self, model_config, global_config): - super(OuterProductMean, self).__init__() + super(Low2HighModule, self).__init__() node_channel = global_config.node_channel pair_channel = global_config.pair_channel inner_channel = model_config.inner_channel @@ -285,9 +285,9 @@ def forward(self, node_acts, node_mask): return act -class TriangleAttentionWithAngle(nn.Layer): +class SecondBodyAxialAttentionWithAngle(nn.Layer): def __init__(self, model_config, global_config): - super(TriangleAttentionWithAngle, self).__init__() + super(SecondBodyAxialAttentionWithAngle, self).__init__() pair_channel = global_config.pair_channel triple_channel = global_config.triple_channel self.num_head = model_config.num_head @@ -342,9 +342,9 @@ def forward(self, pair_acts, triple_acts, bias): return out -class TriangleAttentionWithAngleBias(nn.Layer): +class SecondBodyAxialAttentionWithAngleBias(nn.Layer): def __init__(self, model_config, global_config): - super(TriangleAttentionWithAngleBias, self).__init__() + super(SecondBodyAxialAttentionWithAngleBias, self).__init__() pair_channel = global_config.pair_channel triple_channel = global_config.triple_channel self.num_head = model_config.num_head @@ -395,15 +395,15 @@ def forward(self, pair_acts, triple_acts, bias): return out -class TriangleAttention(nn.Layer): +class SecondBodyAxialAttention(nn.Layer): def __init__(self, model_config, global_config): - super(TriangleAttention, self).__init__() + super(SecondBodyAxialAttention, self).__init__() self.is_start = model_config.is_start if model_config.get('angle_as_bias', False): - self.attn_mod = TriangleAttentionWithAngleBias(model_config, global_config) + self.attn_mod = SecondBodyAxialAttentionWithAngleBias(model_config, global_config) else: - self.attn_mod = TriangleAttentionWithAngle(model_config, global_config) + self.attn_mod = SecondBodyAxialAttentionWithAngle(model_config, global_config) def forward(self, pair_acts, triple_acts, triple_mask): """ @@ -431,29 +431,29 @@ def __init__(self, model_config, global_config): pair_channel = global_config.pair_channel ### node track - self.node_attn = NodeAttention( - model_config.node_attention, global_config) - self.node_attn_dropout = nn.Dropout(model_config.node_dropout_rate) + self.first_body_axial_attention = FirstBodyAxialAttention( + model_config.first_body_axial_attention, global_config) + self.first_body_axial_attention_dropout = nn.Dropout(model_config.first_body_axial_attention_dropout) self.node_ffn = FeedForwardNetwork( model_config.node_ffn, node_channel) - self.node_ffn_dropout = nn.Dropout(model_config.node_dropout_rate) + self.node_ffn_dropout = nn.Dropout(model_config.first_body_axial_attention_dropout) - ### outer - self.outer_product = OuterProductMean( - model_config.outer_product, global_config) - self.outer_product_dropout = nn.Dropout(model_config.pair_dropout_rate) + ### low2high + self.low2high = Low2HighModule( + model_config.low2high, global_config) + self.low2high_dropout = nn.Dropout(model_config.pair_dropout_rate) ### pair track self.pair_before_ln = nn.LayerNorm(pair_channel) - self.triangle_attn_start = TriangleAttention( - model_config.triangle_attention_start_node, global_config) - self.triangle_attn_start_dropout = nn.Dropout(model_config.pair_dropout_rate) + self.second_body_first_axis = SecondBodyAxialAttention( + model_config.second_body_first_axis, global_config) + self.second_body_first_axis_dropout = nn.Dropout(model_config.pair_dropout_rate) - self.triangle_attn_end = TriangleAttention( - model_config.triangle_attention_end_node, global_config) - self.triangle_attn_end_dropout = nn.Dropout(model_config.pair_dropout_rate) + self.second_body_second_axis = SecondBodyAxialAttention( + model_config.second_body_second_axis, global_config) + self.second_body_second_axis_dropout = nn.Dropout(model_config.pair_dropout_rate) self.pair_ffn = FeedForwardNetwork( model_config.pair_ffn, pair_channel) @@ -476,24 +476,24 @@ def forward(self, node_acts, pair_acts, triple_acts, mask_dict): triple_mask = mask_dict['triple'] # node track - residual = self.node_attn(node_acts, pair_acts, node_mask, pair_mask) - node_acts += self.node_attn_dropout(residual) + residual = self.first_body_axial_attention(node_acts, pair_acts, node_mask, pair_mask) + node_acts += self.first_body_axial_attention_dropout(residual) residual = self.node_ffn(node_acts) node_acts += self.node_ffn_dropout(residual) # outer - outer = self.outer_product(node_acts, node_mask) - pair_acts += self.outer_product_dropout(outer) + outer = self.low2high(node_acts, node_mask) + pair_acts += self.low2high_dropout(outer) # pair track pair_acts = self.pair_before_ln(pair_acts) - residual = self.triangle_attn_start(pair_acts, triple_acts, triple_mask) - pair_acts += self.triangle_attn_start_dropout(residual) + residual = self.second_body_first_axis(pair_acts, triple_acts, triple_mask) + pair_acts += self.second_body_first_axis_dropout(residual) - residual = self.triangle_attn_end(pair_acts, triple_acts, triple_mask) - pair_acts += self.triangle_attn_end_dropout(residual) + residual = self.second_body_second_axis(pair_acts, triple_acts, triple_mask) + pair_acts += self.second_body_second_axis_dropout(residual) residual = self.pair_ffn(pair_acts) pair_acts += self.pair_ffn_dropout(residual) @@ -534,6 +534,15 @@ def init_weights(self, layer): elif isinstance(layer, nn.LayerNorm): layer._epsilon = 1e-12 + def reduce_dropout(self): + """ + setting the model's dropout rate to 0 + """ + def reduce_p(layer): + if isinstance(layer, nn.Dropout): + layer.p = 0 + self.apply(reduce_p) + def _create_mask(self, batch): node_mask = batch["node_mask"] # (B, N) diff --git a/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py b/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py index 84bee268..fb9e878f 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py +++ b/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py @@ -75,15 +75,13 @@ def get_optimizer(args, train_config, model): return optimizer, scheduler -def get_train_steps_per_epoch(dataset_len): - if args.DEBUG: - return 20 - # add as argument - min_data_len = paddle.to_tensor(dataset_len) - from paddle.distributed import ReduceOp - dist.all_reduce(min_data_len, ReduceOp.MIN) - dataset_len = min_data_len.numpy()[0] - logging.info(f'min dataset len: {dataset_len}') +def get_train_steps_per_epoch(dataset_len, args): + if args.distributed: + min_data_len = paddle.to_tensor(dataset_len) + from paddle.distributed import ReduceOp + dist.all_reduce(min_data_len, ReduceOp.MIN) + dataset_len = min_data_len.numpy()[0] + logging.info(f'min dataset len: {dataset_len}') return int(dataset_len / args.batch_size) - 5 @@ -135,7 +133,10 @@ def load_data(args, trainer_id, trainer_num, model_config, dataset_config, trans if args.DEBUG: train_npz_files = train_npz_files[:16] valid_npz_files = valid_npz_files[:8] - train_dataset = InMemoryDataset(npz_data_files=train_npz_files[trainer_id::trainer_num]) + if args.inference: + train_dataset = [] + else: + train_dataset = InMemoryDataset(npz_data_files=train_npz_files[trainer_id::trainer_num]) valid_dataset = InMemoryDataset(npz_data_files=valid_npz_files[trainer_id::trainer_num]) if model_config.data.get('post_transform', False): logging.info('post transform') @@ -284,6 +285,10 @@ def _read_json(path): ema_start_step = 0 if args.DEBUG else 30 optimizer, scheduler = get_optimizer(args, train_config, model) + if args.inference: + mean_mae = evaluate(args, 0, model, valid_dataset, collate_fn) + print(f"mean mae : {mean_mae}") + exit(0) ### start train data_writer = None @@ -292,7 +297,7 @@ def _read_json(path): data_writer = SummaryWriter(f'{args.log_dir}/tensorboard_log_dir', max_queue=0) except Exception as ex: print(f'Create data_writer failed: {ex}') - train_steps = get_train_steps_per_epoch(len(train_dataset)) + train_steps = get_train_steps_per_epoch(len(train_dataset), args) print("train_steps per epoch : ", train_steps) mean_mae_list = [] for _ in range(args.start_step): @@ -301,6 +306,9 @@ def _read_json(path): ## ema register if epoch_id >= ema_start_step and not ema.is_registered: ema.register() + + if epoch_id == 69: + model.encoder.reduce_dropout() ## train s_time = time.time() @@ -362,6 +370,7 @@ def _read_json(path): parser.add_argument("--model_config", type=str) parser.add_argument("--encoder_config", type=str) parser.add_argument("--train_config", type=str) + parser.add_argument("--inference", action='store_true', default=False) parser.add_argument("--init_model", type=str) parser.add_argument("--start_step", type=int) parser.add_argument("--model_dir", type=str, default="./debug_models") From 54c1e3d5127746bd58715a410bce436dd31be63d Mon Sep 17 00:00:00 2001 From: hedonglong Date: Fri, 21 Oct 2022 11:02:16 +0800 Subject: [PATCH 2/3] Add the processed data and checkpoint download link, refine readme document --- .github/LIT-PCBA_result.png | Bin 0 -> 323751 bytes .github/optimus_framework3.png | Bin 0 -> 738919 bytes .github/pcqm4mv2_result.png | Bin 0 -> 257407 bytes .../ChemRL/GEM-2/README.md | 39 ++++++++- .../ChemRL/GEM-2/scripts/inference.sh | 33 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z1|zD-th}&H3VU(KG!DP;h8cx@tk8@r-y2WRJIfZLf$`__IYiABk+cTaUB?UKSq7r2r8NujK2MCoOr1l?Y;kE!hK*&mK&sK{0x^%=FbJi zpXuQTI9qtO`FFF4|MO)3_mK$y`JVHFWQ4c8Ey2nEe}(zK$!!1meeg5j1yHi-Ci}CZ z~vkzeo4~`OyBaKf3+jiDhSUk(Q&|Sl~xlK|{Vm I)->dQ0Yqyt{r~^~ literal 0 HcmV?d00001 diff --git a/apps/pretrained_compound/ChemRL/GEM-2/README.md b/apps/pretrained_compound/ChemRL/GEM-2/README.md index 7daad974..c7ad9475 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/README.md +++ b/apps/pretrained_compound/ChemRL/GEM-2/README.md @@ -1,8 +1,27 @@ -# GEM-2: Next Generation Molecular Property Prediction Network with Many-body and Full-range Interaction Modeling -Molecular property prediction is a fundamental task in the drug and material industries. Physically, the properties of a molecule are determined by its own electronic structure, which can be exactly described by the Schrödinger equation. However, solving the Schrödinger equation for most molecules is extremely challenging due to long-range interactions in the behavior of a quantum many-body system. While deep learning methods have proven to be effective in molecular property prediction, we design a novel method, namely GEM-2, which comprehensively considers both the long-range and many-body interactions in molecules. GEM-2 consists of two interacted tracks: an atom-level track modeling both the local and global correlation between any two atoms, and a pair-level track modeling the correlation between all atom pairs, which embed information between any 3 or 4 atoms. Extensive experiments demonstrated the superiority of GEM-2 over multiple baseline methods in quantum chemistry and drug discovery tasks. - +# GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions +GEM-2 is a molecular modeling framework which comprehensively considers full-range many-body interactions in molecules. Multiple tracks are utilized to model the full-range interactions between the many-bodies with different orders, and a novel axial attention mechanism is designed to approximate the full-range interaction modeling with much lower computational cost. A preprint version of our work can be found [here](https://arxiv.org/abs/2208.05863). +# Framework +

+ +

+ +The overall framework of GEM-2. First, a molecule is described by the representations of many-bodies of multiple orders. Then, Optimus blocks are designed to update the representations. Each Optimus block contains $M$ tracks, and the $m$-th track contains a stack of many-body axial attentions to model the full-range interactions between the $m$-bodies. The many-body axial attentions and the Low2High module also play the roles of exchanging messages across the tracks. Finally, the molecular property prediction is made by pooling over the $1$-body representations. +# Result +## PCQM4Mv2 +PCQM4Mv2 is a large-scale quantum chemistry dataset containing the DFT-calculated HOMO-LUMO +energy gaps. The OGB leaderboard for PCQM4Mv2 can be found [here](https://ogb.stanford.edu/docs/lsc/leaderboards/#pcqm4mv2). +

+ +

+ +## LITPCBA +LIT-PCBA is a virtual screening dataset containing protein targets with their corresponding active and inactive compounds selected from high-confidence PubChem Bioassay data +

+ +

+ # Installation guide ## Prerequisites @@ -32,6 +51,14 @@ You can download the PCQM4Mv2 dataset from ogb website: https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/pcqm4m-v2.zip +# Processed Data +You can download the processed PCQM4Mv2 dataset with rdkit generated 3d information from [here]( https://baidu-nlp.bj.bcebos.com/PaddleHelix/datasets/compound_datasets/pcqm4mv2_gem2.tgz). +And then use tar to unzip the data. +```bash + mkdir -p ../data + tar xzf pcqm4mv2_gem2.tgz -C ../data +``` + # How to run ## Introduction to related configs You can adjsut the json files in the config folder to change the training settings. @@ -58,6 +85,12 @@ The models will be saved under `./model`. It will take around 60 mintues to finish one epoch on 16 A100 cards with total batch size of 512. +## Run inference +To reproduce the result from the ogb leaderboard, you can download the checkponit from [here](https://baidu-nlp.bj.bcebos.com/PaddleHelix/models/molecular_modeling/gem2_l12_c256.pdparams). +Then put it under the local `./model` folder and run the inference command: + + sh scripts/inference.sh + ## Citing this work diff --git a/apps/pretrained_compound/ChemRL/GEM-2/scripts/inference.sh b/apps/pretrained_compound/ChemRL/GEM-2/scripts/inference.sh new file mode 100644 index 00000000..ef54c58a --- /dev/null +++ b/apps/pretrained_compound/ChemRL/GEM-2/scripts/inference.sh @@ -0,0 +1,33 @@ +#!/bin/bash +cd $(dirname $0) +cd .. + +train_pcqm(){ + mkdir -p log/$exp_name model/$exp_name + python3 train_gem2.py \ + --inference \ + --batch_size=$batch_size \ + --num_workers=10 \ + --max_epoch=150 \ + --dataset_config=$dataset_config \ + --data_cache_dir=$data_cache_dir \ + --model_config=$model_config \ + --encoder_config=$encoder_config \ + --train_config=$train_config \ + --init_model=$init_model \ + --start_step=$start_step \ + --model_dir=./model/$exp_name \ + --log_dir=./log/$exp_name +} + + +exp_name="" +batch_size=32 +dataset_config="configs/dataset_configs/pcqmv2.json" +data_cache_dir="../data/pcqm4m-v2-rdkit3d" +model_config="configs/model_configs/mol_regr-optimus-mae.json" +encoder_config="configs/model_configs/opt3d_l12_c256.json" +init_model="./model/gem2_l12_c256.pdparams" +train_config="configs/train_configs/lr4e-4-mid40.json" +start_step=0 +train_pcqm \ No newline at end of file diff --git a/apps/pretrained_compound/ChemRL/GEM-2/scripts/train.sh b/apps/pretrained_compound/ChemRL/GEM-2/scripts/train.sh index 83abde8d..fde9e302 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/scripts/train.sh +++ b/apps/pretrained_compound/ChemRL/GEM-2/scripts/train.sh @@ -22,7 +22,7 @@ train_pcqm(){ } - +exp_name="gem2_l12_c256_4e-4" echo "$exp_name" diff --git a/apps/pretrained_compound/ChemRL/GEM-2/src/config.py b/apps/pretrained_compound/ChemRL/GEM-2/src/config.py index 1131f609..ffa025ce 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/src/config.py +++ b/apps/pretrained_compound/ChemRL/GEM-2/src/config.py @@ -84,9 +84,9 @@ def make_updated_config(base_config, updated_dict): "optimus_block_num": 12, "optimus_block": { - "node_dropout_rate": 0.05, + "first_body_axial_attention_dropout": 0.05, "pair_dropout_rate": 0.05, - "node_attention": { + "first_body_axial_attention": { "use_pair_layer_norm": True, "num_head": 8, "dropout_rate": 0.05, @@ -96,15 +96,15 @@ def make_updated_config(base_config, updated_dict): "hidden_factor": 4, "dropout_rate": 0.1 }, - "outer_product": { + "low2high": { "inner_channel": 32 }, - "triangle_attention_start_node": { + "second_body_first_axis": { "num_head": 8, "dropout_rate": 0.05, "is_start": True }, - "triangle_attention_end_node": { + "second_body_second_axis": { "num_head": 8, "dropout_rate": 0.05, "is_start": False diff --git a/apps/pretrained_compound/ChemRL/GEM-2/src/optimus.py b/apps/pretrained_compound/ChemRL/GEM-2/src/optimus.py index a464a067..1ecbcabe 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/src/optimus.py +++ b/apps/pretrained_compound/ChemRL/GEM-2/src/optimus.py @@ -122,10 +122,10 @@ def forward(self, batch): return results -class NodeAttention(nn.Layer): +class FirstBodyAxialAttention(nn.Layer): """Compute self-attention over columns of a 2D input.""" def __init__(self, model_config, global_config): - super(NodeAttention, self).__init__() + super(FirstBodyAxialAttention, self).__init__() self.model_config = model_config node_channel = global_config.node_channel @@ -253,9 +253,9 @@ def forward(self, x): return x -class OuterProductMean(nn.Layer): +class Low2HighModule(nn.Layer): def __init__(self, model_config, global_config): - super(OuterProductMean, self).__init__() + super(Low2HighModule, self).__init__() node_channel = global_config.node_channel pair_channel = global_config.pair_channel inner_channel = model_config.inner_channel @@ -285,9 +285,9 @@ def forward(self, node_acts, node_mask): return act -class TriangleAttentionWithAngle(nn.Layer): +class SecondBodyAxialAttentionWithAngle(nn.Layer): def __init__(self, model_config, global_config): - super(TriangleAttentionWithAngle, self).__init__() + super(SecondBodyAxialAttentionWithAngle, self).__init__() pair_channel = global_config.pair_channel triple_channel = global_config.triple_channel self.num_head = model_config.num_head @@ -342,9 +342,9 @@ def forward(self, pair_acts, triple_acts, bias): return out -class TriangleAttentionWithAngleBias(nn.Layer): +class SecondBodyAxialAttentionWithAngleBias(nn.Layer): def __init__(self, model_config, global_config): - super(TriangleAttentionWithAngleBias, self).__init__() + super(SecondBodyAxialAttentionWithAngleBias, self).__init__() pair_channel = global_config.pair_channel triple_channel = global_config.triple_channel self.num_head = model_config.num_head @@ -395,15 +395,15 @@ def forward(self, pair_acts, triple_acts, bias): return out -class TriangleAttention(nn.Layer): +class SecondBodyAxialAttention(nn.Layer): def __init__(self, model_config, global_config): - super(TriangleAttention, self).__init__() + super(SecondBodyAxialAttention, self).__init__() self.is_start = model_config.is_start if model_config.get('angle_as_bias', False): - self.attn_mod = TriangleAttentionWithAngleBias(model_config, global_config) + self.attn_mod = SecondBodyAxialAttentionWithAngleBias(model_config, global_config) else: - self.attn_mod = TriangleAttentionWithAngle(model_config, global_config) + self.attn_mod = SecondBodyAxialAttentionWithAngle(model_config, global_config) def forward(self, pair_acts, triple_acts, triple_mask): """ @@ -431,29 +431,29 @@ def __init__(self, model_config, global_config): pair_channel = global_config.pair_channel ### node track - self.node_attn = NodeAttention( - model_config.node_attention, global_config) - self.node_attn_dropout = nn.Dropout(model_config.node_dropout_rate) + self.first_body_axial_attention = FirstBodyAxialAttention( + model_config.first_body_axial_attention, global_config) + self.first_body_axial_attention_dropout = nn.Dropout(model_config.first_body_axial_attention_dropout) self.node_ffn = FeedForwardNetwork( model_config.node_ffn, node_channel) - self.node_ffn_dropout = nn.Dropout(model_config.node_dropout_rate) + self.node_ffn_dropout = nn.Dropout(model_config.first_body_axial_attention_dropout) - ### outer - self.outer_product = OuterProductMean( - model_config.outer_product, global_config) - self.outer_product_dropout = nn.Dropout(model_config.pair_dropout_rate) + ### low2high + self.low2high = Low2HighModule( + model_config.low2high, global_config) + self.low2high_dropout = nn.Dropout(model_config.pair_dropout_rate) ### pair track self.pair_before_ln = nn.LayerNorm(pair_channel) - self.triangle_attn_start = TriangleAttention( - model_config.triangle_attention_start_node, global_config) - self.triangle_attn_start_dropout = nn.Dropout(model_config.pair_dropout_rate) + self.second_body_first_axis = SecondBodyAxialAttention( + model_config.second_body_first_axis, global_config) + self.second_body_first_axis_dropout = nn.Dropout(model_config.pair_dropout_rate) - self.triangle_attn_end = TriangleAttention( - model_config.triangle_attention_end_node, global_config) - self.triangle_attn_end_dropout = nn.Dropout(model_config.pair_dropout_rate) + self.second_body_second_axis = SecondBodyAxialAttention( + model_config.second_body_second_axis, global_config) + self.second_body_second_axis_dropout = nn.Dropout(model_config.pair_dropout_rate) self.pair_ffn = FeedForwardNetwork( model_config.pair_ffn, pair_channel) @@ -476,24 +476,24 @@ def forward(self, node_acts, pair_acts, triple_acts, mask_dict): triple_mask = mask_dict['triple'] # node track - residual = self.node_attn(node_acts, pair_acts, node_mask, pair_mask) - node_acts += self.node_attn_dropout(residual) + residual = self.first_body_axial_attention(node_acts, pair_acts, node_mask, pair_mask) + node_acts += self.first_body_axial_attention_dropout(residual) residual = self.node_ffn(node_acts) node_acts += self.node_ffn_dropout(residual) # outer - outer = self.outer_product(node_acts, node_mask) - pair_acts += self.outer_product_dropout(outer) + outer = self.low2high(node_acts, node_mask) + pair_acts += self.low2high_dropout(outer) # pair track pair_acts = self.pair_before_ln(pair_acts) - residual = self.triangle_attn_start(pair_acts, triple_acts, triple_mask) - pair_acts += self.triangle_attn_start_dropout(residual) + residual = self.second_body_first_axis(pair_acts, triple_acts, triple_mask) + pair_acts += self.second_body_first_axis_dropout(residual) - residual = self.triangle_attn_end(pair_acts, triple_acts, triple_mask) - pair_acts += self.triangle_attn_end_dropout(residual) + residual = self.second_body_second_axis(pair_acts, triple_acts, triple_mask) + pair_acts += self.second_body_second_axis_dropout(residual) residual = self.pair_ffn(pair_acts) pair_acts += self.pair_ffn_dropout(residual) @@ -534,6 +534,15 @@ def init_weights(self, layer): elif isinstance(layer, nn.LayerNorm): layer._epsilon = 1e-12 + def reduce_dropout(self): + """ + setting the model's dropout rate to 0 + """ + def reduce_p(layer): + if isinstance(layer, nn.Dropout): + layer.p = 0 + self.apply(reduce_p) + def _create_mask(self, batch): node_mask = batch["node_mask"] # (B, N) diff --git a/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py b/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py index 84bee268..fb9e878f 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py +++ b/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py @@ -75,15 +75,13 @@ def get_optimizer(args, train_config, model): return optimizer, scheduler -def get_train_steps_per_epoch(dataset_len): - if args.DEBUG: - return 20 - # add as argument - min_data_len = paddle.to_tensor(dataset_len) - from paddle.distributed import ReduceOp - dist.all_reduce(min_data_len, ReduceOp.MIN) - dataset_len = min_data_len.numpy()[0] - logging.info(f'min dataset len: {dataset_len}') +def get_train_steps_per_epoch(dataset_len, args): + if args.distributed: + min_data_len = paddle.to_tensor(dataset_len) + from paddle.distributed import ReduceOp + dist.all_reduce(min_data_len, ReduceOp.MIN) + dataset_len = min_data_len.numpy()[0] + logging.info(f'min dataset len: {dataset_len}') return int(dataset_len / args.batch_size) - 5 @@ -135,7 +133,10 @@ def load_data(args, trainer_id, trainer_num, model_config, dataset_config, trans if args.DEBUG: train_npz_files = train_npz_files[:16] valid_npz_files = valid_npz_files[:8] - train_dataset = InMemoryDataset(npz_data_files=train_npz_files[trainer_id::trainer_num]) + if args.inference: + train_dataset = [] + else: + train_dataset = InMemoryDataset(npz_data_files=train_npz_files[trainer_id::trainer_num]) valid_dataset = InMemoryDataset(npz_data_files=valid_npz_files[trainer_id::trainer_num]) if model_config.data.get('post_transform', False): logging.info('post transform') @@ -284,6 +285,10 @@ def _read_json(path): ema_start_step = 0 if args.DEBUG else 30 optimizer, scheduler = get_optimizer(args, train_config, model) + if args.inference: + mean_mae = evaluate(args, 0, model, valid_dataset, collate_fn) + print(f"mean mae : {mean_mae}") + exit(0) ### start train data_writer = None @@ -292,7 +297,7 @@ def _read_json(path): data_writer = SummaryWriter(f'{args.log_dir}/tensorboard_log_dir', max_queue=0) except Exception as ex: print(f'Create data_writer failed: {ex}') - train_steps = get_train_steps_per_epoch(len(train_dataset)) + train_steps = get_train_steps_per_epoch(len(train_dataset), args) print("train_steps per epoch : ", train_steps) mean_mae_list = [] for _ in range(args.start_step): @@ -301,6 +306,9 @@ def _read_json(path): ## ema register if epoch_id >= ema_start_step and not ema.is_registered: ema.register() + + if epoch_id == 69: + model.encoder.reduce_dropout() ## train s_time = time.time() @@ -362,6 +370,7 @@ def _read_json(path): parser.add_argument("--model_config", type=str) parser.add_argument("--encoder_config", type=str) parser.add_argument("--train_config", type=str) + parser.add_argument("--inference", action='store_true', default=False) parser.add_argument("--init_model", type=str) parser.add_argument("--start_step", type=int) parser.add_argument("--model_dir", type=str, default="./debug_models") From aaf938f3b7022fab512f02c6f82d9342c0fa793f Mon Sep 17 00:00:00 2001 From: hedonglong Date: Wed, 26 Oct 2022 16:01:03 +0800 Subject: [PATCH 3/3] add c128 checkponit with corresponding config, refine reduce_dropout function in distribtued mode --- apps/pretrained_compound/ChemRL/GEM-2/README.md | 4 ++++ .../ChemRL/GEM-2/configs/model_configs/opt3d_l12_c128.json | 5 +++++ apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py | 3 ++- 3 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 apps/pretrained_compound/ChemRL/GEM-2/configs/model_configs/opt3d_l12_c128.json diff --git a/apps/pretrained_compound/ChemRL/GEM-2/README.md b/apps/pretrained_compound/ChemRL/GEM-2/README.md index 8893a471..effb9e3d 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/README.md +++ b/apps/pretrained_compound/ChemRL/GEM-2/README.md @@ -100,7 +100,11 @@ To reproduce the result from the ogb leaderboard, you can download the checkponi Then put it under the local `./model` folder and run the inference command: sh scripts/inference.sh +We also provide a checkpoint with smaller embedding size(128), you can download it from [here](https://baidu-nlp.bj.bcebos.com/PaddleHelix/models/molecular_modeling/gem2_l12_c128.pdparams) . +Then change the `encoder_config` to `opt3d_l12_c128.json`, `init_model` to `gem2_l12_c128.pdparams` in `inference.sh`. + +Now you can run the inference command with the new checkpoint. ## Citing this work diff --git a/apps/pretrained_compound/ChemRL/GEM-2/configs/model_configs/opt3d_l12_c128.json b/apps/pretrained_compound/ChemRL/GEM-2/configs/model_configs/opt3d_l12_c128.json new file mode 100644 index 00000000..aa9e659b --- /dev/null +++ b/apps/pretrained_compound/ChemRL/GEM-2/configs/model_configs/opt3d_l12_c128.json @@ -0,0 +1,5 @@ +{ + "node_channel": 128, + "pair_channel": 128, + "triple_channel": 128 +} diff --git a/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py b/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py index fb9e878f..ad82d697 100644 --- a/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py +++ b/apps/pretrained_compound/ChemRL/GEM-2/train_gem2.py @@ -274,6 +274,7 @@ def _read_json(path): ### build model model = MolRegressionModel(model_config, encoder_config) + single_model = model print("parameter size:", calc_parameter_size(model.parameters())) if args.distributed: model = paddle.DataParallel(model) @@ -308,7 +309,7 @@ def _read_json(path): ema.register() if epoch_id == 69: - model.encoder.reduce_dropout() + single_model.encoder.reduce_dropout() ## train s_time = time.time()

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