Add trainer.validate example for GPT (#6794)

* add trainer.validate example

Signed-off-by: ericharper <[email protected]>

* clean up white space

Signed-off-by: ericharper <[email protected]>

* add mbs and gbs to the config

Signed-off-by: ericharper <[email protected]>

---------

Signed-off-by: ericharper <[email protected]>

examples/nlp/language_modeling/conf/megatron_gpt_validate_config.yaml

@@ -0,0 +1,22 @@
+trainer:
+  devices: 1
+  num_nodes: 1
+  accelerator: gpu
+  logger: False # logger provided by exp_manager
+  precision: 16 # 16, 32, or bf16
+  log_every_n_steps: 1
+  limit_val_batches: 10
+  limit_test_batches: 50
+  max_steps: 100 # needed to setup dataloaders
+  max_epochs: null
+  replace_sampler_ddp: False
+
+tensor_model_parallel_size: ??? # should be set the same as the pretrained model that is being restored from
+pipeline_model_parallel_size: ??? # should be set the same as the pretrained model that is being restored from 
+micro_batch_size: null # limited by GPU memory, defaults to pretrained model config
+global_batch_size: null # will use more micro batches to reach global batch size, defaults to pretrained model config
+virtual_pipeline_model_parallel_size: null
+gpt_model_file: null  # GPT nemo file path
+checkpoint_dir: null # checkpoint file dir. This is used to load the PTL checkpoint generated during the GPT training
+checkpoint_name: null # PTL checkpoint file name, only used for PTL checkpoint loading
+hparams_file: null # model configuration file, only used for PTL checkpoint loading

examples/nlp/language_modeling/megatron_gpt_validate.py

@@ -0,0 +1,155 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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 os
+import tempfile
+
+from omegaconf import OmegaConf, open_dict
+from pytorch_lightning.trainer.trainer import Trainer
+
+from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
+from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel
+from nemo.collections.nlp.parts.nlp_overrides import (
+    MegatronHalfPrecisionPlugin,
+    NLPDDPStrategy,
+    NLPSaveRestoreConnector,
+    PipelineMixedPrecisionPlugin,
+)
+from nemo.core.config import hydra_runner
+from nemo.utils import logging
+from nemo.utils.app_state import AppState
+from nemo.utils.model_utils import inject_model_parallel_rank
+
+""" Example script showing how to run validation on a MegatronGPT model.
+
+    Sample usage:
+
+    From nemo model:
+
+    python megatron_gpt_validate.py \
+        trainer.devices=4 \
+        trainer.num_nodes=1 \
+        trainer.limit_val_batches=10 \
+        trainer.max_steps=100 \
+        tensor_model_parallel_size=1 \
+        pipeline_model_parallel_size=4 \
+        trainer.precision=bf16 \
+        gpt_model_file=/path/to/megatron_gpt_tp_1_pp4.nemo
+    
+    from PTL checkpoint:
+    python megatron_gpt_validate.py \
+        trainer.devices=4 \
+        trainer.num_nodes=1 \
+        trainer.limit_val_batches=10 \
+        trainer.max_steps=100 \
+        tensor_model_parallel_size=1 \
+        pipeline_model_parallel_size=4 \
+        virtual_pipeline_model_parallel_size=4 \
+        trainer.precision=bf16 \
+        checkpoint_dir='/path/to/experiment/checkpoints' \
+        checkpoint_name='megatron_gpt--val_loss=7.78-step=100-consumed_samples=6336.0-last.ckpt' \
+        hparams_file='/path/to/experiment/hparams.yaml
+
+"""
+
+
+def modify_pretrained_cfg(pretrained_cfg, trainer, cfg):
+    with open_dict(pretrained_cfg):
+        OmegaConf.set_struct(pretrained_cfg, True)
+        pretrained_cfg.sequence_parallel = False
+        pretrained_cfg.activations_checkpoint_granularity = None
+        pretrained_cfg.activations_checkpoint_method = None
+        pretrained_cfg.precision = trainer.precision
+        if cfg.micro_batch_size is not None:
+            pretrained_cfg.micro_batch_size = cfg.micro_batch_size
+        if cfg.global_batch_size is not None:
+            pretrained_cfg.global_batch_size = cfg.global_batch_size
+        if trainer.precision == "16":
+            pretrained_cfg.megatron_amp_O2 = False
+    return pretrained_cfg
+
+
+@hydra_runner(config_path="conf", config_name="megatron_gpt_validate_config")
+def main(cfg) -> None:
+
+    trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer)
+
+    assert (
+        cfg.trainer.devices * cfg.trainer.num_nodes
+        == cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size
+    ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size"
+
+    if cfg.gpt_model_file:
+        logging.info(f"Restoring model from {cfg.gpt_model_file}")
+        save_restore_connector = NLPSaveRestoreConnector()
+        if os.path.isdir(cfg.gpt_model_file):
+            save_restore_connector.model_extracted_dir = cfg.gpt_model_file
+
+        pretrained_cfg = MegatronGPTModel.restore_from(
+            restore_path=cfg.gpt_model_file,
+            trainer=trainer,
+            return_config=True,
+            save_restore_connector=save_restore_connector,
+        )
+        pretrained_cfg = modify_pretrained_cfg(pretrained_cfg, trainer, cfg)
+        model = MegatronGPTModel.restore_from(
+            restore_path=cfg.gpt_model_file,
+            trainer=trainer,
+            override_config_path=pretrained_cfg,
+            save_restore_connector=save_restore_connector,
+            map_location=f'cuda:{trainer.local_rank}',  # map_location is needed for converted models
+        )
+    elif cfg.checkpoint_dir:
+        logging.info(
+            f"Restoring model from checkpoint_dir: {cfg.checkpoint_dir} with checkpoint name: {cfg.checkpoint_name}"
+        )
+        app_state = AppState()
+        if cfg.tensor_model_parallel_size > 1 or cfg.pipeline_model_parallel_size > 1:
+            app_state.model_parallel_size = cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size
+            app_state.tensor_model_parallel_size = cfg.tensor_model_parallel_size
+            app_state.pipeline_model_parallel_size = cfg.pipeline_model_parallel_size
+            app_state.virtual_pipeline_model_parallel_size = cfg.virtual_pipeline_model_parallel_size
+            (
+                app_state.tensor_model_parallel_rank,
+                app_state.pipeline_model_parallel_rank,
+                app_state.model_parallel_size,
+                app_state.data_parallel_size,
+                app_state.pipeline_model_parallel_split_rank,
+                app_state.virtual_pipeline_model_parallel_rank,
+            ) = fake_initialize_model_parallel(
+                world_size=app_state.model_parallel_size,
+                rank=trainer.global_rank,
+                tensor_model_parallel_size_=cfg.tensor_model_parallel_size,
+                pipeline_model_parallel_size_=cfg.pipeline_model_parallel_size,
+                virtual_pipeline_model_parallel_size_=cfg.virtual_pipeline_model_parallel_size,
+            )
+        checkpoint_path = inject_model_parallel_rank(os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name))
+        pretrained_cfg = OmegaConf.load(cfg.hparams_file)
+        pretrained_cfg = modify_pretrained_cfg(pretrained_cfg.cfg, trainer, cfg)
+        with tempfile.NamedTemporaryFile(suffix='.yaml') as f:
+            OmegaConf.save(config=pretrained_cfg, f=f.name)
+            model = MegatronGPTModel.load_from_checkpoint(
+                checkpoint_path=checkpoint_path, trainer=trainer, hparams_file=f.name,
+            )
+    else:
+        raise ValueError("need at least a nemo file or checkpoint dir")
+
+    logging.info("\n\n**************  Model configuration ***********")
+    logging.info(f'\n{OmegaConf.to_yaml(model.cfg)}')
+
+    trainer.validate(model=model)
+
+
+if __name__ == '__main__':
+    main()  # noqa pylint: disable=no-value-for-parameter

nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py

@@ -1019,17 +1019,18 @@ def setup(self, stage=None):
             self.setup_validation_data(self.cfg.data)
             self.setup_test_data(self.cfg.data)
 
-        # when using pipeline model parallel the final stage need to initialize word embeddings
-        if parallel_state.get_pipeline_model_parallel_world_size() > 1:
-            if isinstance(self.model, list):
-                for i, module in enumerate(self.model):
-                    parallel_state.set_virtual_pipeline_model_parallel_rank(i)
+        if stage == 'fit':
+            # when using pipeline model parallel the final stage need to initialize word embeddings
+            if parallel_state.get_pipeline_model_parallel_world_size() > 1:
+                if isinstance(self.model, list):
+                    for i, module in enumerate(self.model):
+                        parallel_state.set_virtual_pipeline_model_parallel_rank(i)
+                        if self.cfg.get('share_embeddings_and_output_weights', True):
+                            module.sync_initial_word_embeddings()
+                    parallel_state.set_virtual_pipeline_model_parallel_rank(0)
+                else:
                     if self.cfg.get('share_embeddings_and_output_weights', True):
-                        module.sync_initial_word_embeddings()
-                parallel_state.set_virtual_pipeline_model_parallel_rank(0)
-            else:
-                if self.cfg.get('share_embeddings_and_output_weights', True):
-                    self.model.sync_initial_word_embeddings()
+                        self.model.sync_initial_word_embeddings()
 
         if self.cfg.get('transformer_engine', False):
             self.setup_transformer_engine_tp_groups()