Mistral-NeMo-12B recipe

nemo/collections/llm/__init__.py

@@ -70,6 +70,7 @@
     MaskedTokenLossReduction,
     MistralConfig7B,
     MistralModel,
+    MistralNeMoConfig12B,
     MixtralConfig8x3B,
     MixtralConfig8x7B,
     MixtralConfig8x22B,
@@ -115,6 +116,7 @@
     "t5_forward_step",
     "MaskedTokenLossReduction",
     "MistralConfig7B",
+    "MistralNeMoConfig12B",
     "MistralModel",
     "MixtralConfig8x3B",
     "MixtralConfig8x7B",

nemo/collections/llm/gpt/model/__init__.py

@@ -53,7 +53,7 @@
     LlamaConfig,
     LlamaModel,
 )
-from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralModel
+from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralModel, MistralNeMoConfig12B
 from nemo.collections.llm.gpt.model.mixtral import (
     MixtralConfig8x3B,
     MixtralConfig8x7B,

nemo/collections/llm/gpt/model/mistral.py

@@ -59,7 +59,7 @@ class MistralConfig7B(GPTConfig):
 
 
 @dataclass
-class MistralNeMo2407Config12B(MistralConfig7B):
+class MistralNeMoConfig12B(MistralConfig7B):
     """
     https://mistral.ai/news/mistral-nemo/
     """
@@ -75,7 +75,7 @@ class MistralNeMo2407Config12B(MistralConfig7B):
 
 
 @dataclass
-class MistralNeMo2407Config123B(MistralConfig7B):
+class MistralNeMoConfig123B(MistralConfig7B):
     """
     https://mistral.ai/news/mistral-large-2407/
     """

nemo/collections/llm/recipes/__init__.py

@@ -21,7 +21,8 @@
     llama3_70b_16k,
     llama3_70b_64k,
     llama31_405b,
-    mistral,
+    mistral_7b,
+    mistral_nemo_12b,
     mixtral_8x7b,
     mixtral_8x7b_16k,
     mixtral_8x7b_64k,
@@ -48,7 +49,8 @@
     "llama3_70b_16k",
     "llama3_70b_64k",
     "llama31_405b",
-    "mistral",
+    "mistral_7b",
+    "mistral_nemo_12b",
     "mixtral_8x7b",
     "mixtral_8x7b_16k",
     "mixtral_8x7b_64k",

nemo/collections/llm/recipes/mistral.py → nemo/collections/llm/recipes/mistral_7b.py

@@ -33,7 +33,7 @@
 from nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed
 from nemo.utils.exp_manager import TimingCallback
 
-NAME = "mistral"
+NAME = "mistral_7b"
 
 
 @run.cli.factory(name=NAME)

nemo/collections/llm/recipes/mistral_nemo_12b.py

@@ -0,0 +1,285 @@
+# Copyright (c) 2024, 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.
+
+
+from typing import Callable, Optional
+
+import nemo_run as run
+import pytorch_lightning as pl
+import torch
+from megatron.core.distributed import DistributedDataParallelConfig
+from pytorch_lightning.callbacks.callback import Callback
+
+from nemo import lightning as nl
+from nemo.collections.llm.api import finetune, pretrain
+from nemo.collections.llm.gpt.data.mock import MockDataModule
+from nemo.collections.llm.gpt.data.squad import SquadDataModule
+from nemo.collections.llm.gpt.model.mistral import MistralModel, MistralNeMoConfig12B
+from nemo.collections.llm.peft.lora import LoRA
+from nemo.collections.llm.recipes.finetune_default import default_finetune_recipe
+from nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger
+from nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing
+from nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed
+from nemo.lightning.pytorch.callbacks.megatron_comm_overlap import MegatronCommOverlapCallback
+from nemo.utils.exp_manager import TimingCallback
+
+NAME = "mistral_nemo_base_12b"
+
+
+@run.cli.factory(name=NAME)
+def model() -> run.Config[pl.LightningModule]:
+    """
+    Factory function to create a Mistral-Nemo-Base-12B model configuration.
+
+    Returns:
+        run.Config[pl.LightningModule]: Configuration for the Mistral-Nemo-Base-12B model.
+
+    Examples:
+        CLI usage:
+            $ nemo llm pretrain model=mistral_nemo_base_12b ...
+
+        Python API usage:
+            >>> model_config = model()
+            >>> print(model_config)
+    """
+    return run.Config(MistralModel, config=run.Config(MistralNeMoConfig12B))
+
+
+def trainer(
+    tensor_parallelism: int = 2,
+    pipeline_parallelism: int = 1,
+    pipeline_parallelism_type: Optional[torch.dtype] = None,
+    virtual_pipeline_parallelism: Optional[int] = None,
+    context_parallelism: int = 2,
+    sequence_parallelism: bool = True,
+    num_nodes: int = 1,
+    num_gpus_per_node: int = 8,
+    max_steps: int = 1168251,
+    callbacks: Optional[list[run.Config[Callback]]] = None,
+) -> run.Config[nl.Trainer]:
+    """
+    Configure the NeMo Lightning Trainer for Mistral-Nemo-Base-12B model.
+
+    This function sets up the distributed training strategy and other training parameters.
+
+    Args:
+        tensor_parallelism (int): Degree of tensor model parallelism.
+        pipeline_parallelism (int): Degree of pipeline model parallelism.
+        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.
+        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.
+        context_parallelism (int): Degree of context parallelism.
+        sequence_parallelism (bool): Whether to use sequence parallelism.
+        num_nodes (int): Number of compute nodes to use.
+        num_gpus_per_node (int): Number of GPUs per node.
+        max_steps (int): Maximum number of training steps.
+        callbacks (Optional[list[run.Config[Callback]]]): List of callback configurations.
+
+    Returns:
+        run.Config[nl.Trainer]: Configuration for the NeMo Lightning Trainer.
+
+    Examples:
+        CLI usage:
+            $ nemo llm pretrain trainer=mistral_nemo_base_12b ...
+
+        Python API usage:
+            >>> trainer_config = trainer(num_nodes=2, num_gpus_per_node=8)
+            >>> print(trainer_config)
+
+    Note:
+        For more information on distributed training strategies, refer to the
+        NeMo documentation on multi-GPU and multi-node training.
+    """
+    strategy = run.Config(
+        nl.MegatronStrategy,
+        tensor_model_parallel_size=tensor_parallelism,
+        pipeline_model_parallel_size=pipeline_parallelism,
+        pipeline_dtype=pipeline_parallelism_type,
+        virtual_pipeline_model_parallel_size=virtual_pipeline_parallelism,
+        context_parallel_size=context_parallelism,
+        sequence_parallel=sequence_parallelism,
+        gradient_as_bucket_view=True,
+        ckpt_async_save=True,
+        ckpt_parallel_load=True,
+        ddp=run.Config(
+            DistributedDataParallelConfig,
+            check_for_nan_in_grad=True,
+            grad_reduce_in_fp32=True,
+            overlap_grad_reduce=True,
+            overlap_param_gather=True,
+        ),
+    )
+
+    trainer = run.Config(
+        nl.Trainer,
+        accelerator="gpu",
+        accumulate_grad_batches=1,
+        callbacks=callbacks,
+        devices=num_gpus_per_node,
+        limit_test_batches=50,
+        limit_val_batches=32,
+        log_every_n_steps=10,
+        max_steps=max_steps,
+        num_nodes=num_nodes,
+        plugins=bf16_mixed(),
+        strategy=strategy,
+        use_distributed_sampler=False,
+        val_check_interval=2000,
+    )
+
+    return trainer
+
+
+@run.cli.factory(target=pretrain, name=NAME)
+def pretrain_recipe(
+    dir: Optional[str] = None, name: str = "default", num_nodes: int = 1, num_gpus_per_node: int = 8, fn=pretrain
+) -> run.Partial:
+    """
+    Create a pre-training recipe for Mistral-Nemo-Base-12B model.
+
+    This function sets up a complete configuration for pre-training, including
+    model, trainer, data, logging, optimization, and resumption settings.
+
+    Args:
+        dir (Optional[str]): Directory for saving logs and checkpoints.
+        name (str): Name of the pre-training run.
+        num_nodes (int): Number of compute nodes to use.
+        num_gpus_per_node (int): Number of GPUs per node.
+        fn (Callable): The pre-training function to use.
+
+    Returns:
+        run.Partial: Partial configuration for pre-training.
+
+    Examples:
+        CLI usage:
+            $ nemo llm pretrain --factory mistral_nemo_base_12b
+            $ nemo llm pretrain --factory "mistral_nemo_base_12b(num_nodes=2, name='my_pretrain')"
+
+        Python API usage:
+            >>> recipe = pretrain_recipe(name="mistral_nemo_base_12b", num_nodes=2)
+            >>> print(recipe)
+
+    Note:
+        For more details on pre-training LLMs with NeMo, see the pre-training
+        guide in the `examples/llm/pretrain/` directory.
+    """
+    return run.Partial(
+        fn,
+        model=model(),
+        trainer=trainer(
+            num_nodes=num_nodes,
+            num_gpus_per_node=num_gpus_per_node,
+            callbacks=[run.Config(TimingCallback)],
+        ),
+        data=run.Config(MockDataModule, seq_length=8192, global_batch_size=512, micro_batch_size=1),
+        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),
+        optim=distributed_fused_adam_with_cosine_annealing(max_lr=3e-4),
+        resume=default_resume(),
+    )
+
+
+@run.cli.factory(target=pretrain, name=NAME + "_optimized")
+def pretrain_recipe_performance(
+    dir: Optional[str] = None,
+    name: str = "default",
+    num_nodes: int = 1,
+    num_gpus_per_node: int = 8,
+    fn: Callable = pretrain,
+) -> run.Partial:
+    """
+    Create a performance-optimized pre-training recipe for Mistral-Nemo-Base-12B model.
+
+    This recipe enables performance optimizations that may not be suitable for all use cases.
+    It builds upon the standard pre-training recipe and adds additional performance enhancements.
+
+    Args:
+        dir (Optional[str]): Directory for saving logs and checkpoints.
+        name (str): Name of the pre-training run.
+        num_nodes (int): Number of compute nodes to use.
+        num_gpus_per_node (int): Number of GPUs per node.
+        fn (Callable): The pre-training function to use.
+
+    Returns:
+        run.Partial: Partial configuration for performance-optimized pre-training.
+
+    Examples:
+            $ nemo llm pretrain --factory mistral_nemo_base_12b_optimized
+
+        Python API usage:
+            >>> recipe = pretrain_recipe_performance(name="mistral_nemo_base_12b_perf", num_nodes=4)
+            >>> print(recipe)
+
+    Note:
+        Use this recipe with caution and only when you need maximum performance.
+        It may not be suitable for all hardware configurations or use cases.
+    """
+    recipe = pretrain_recipe(name=name, dir=dir, num_nodes=num_nodes, num_gpus_per_node=num_gpus_per_node, fn=fn)
+
+    recipe.trainer.callbacks.append(
+        run.Config(
+            MegatronCommOverlapCallback,
+            tp_comm_overlap=True,
+        )
+    )
+    return recipe
+
+
+@run.cli.factory(target=finetune, name=NAME)
+def finetune_recipe(
+    dir: Optional[str] = None,
+    name: str = "default",
+    num_nodes: int = 1,
+    num_gpus_per_node: int = 8,
+    peft_scheme: Optional[str] = 'lora',
+) -> run.Partial:
+    """
+    Create a fine-tuning recipe for Mistral-Nemo-Base-12B model.
+
+    This function sets up a complete configuration for fine-tuning, including
+    model, trainer, data, logging, optimization, and resumption settings.
+    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.
+
+    Args:
+        dir (Optional[str]): Directory for saving logs and checkpoints.
+        name (str): Name of the fine-tuning run.
+        num_nodes (int): Number of compute nodes to use.
+        num_gpus_per_node (int): Number of GPUs per node.
+        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning. Allowed values: 'lora', 'none'/None.
+
+    Returns:
+        run.Partial: Partial configuration for fine-tuning.
+
+    Examples:
+        CLI usage:
+            $ nemo llm finetune --factory mistral_nemo_base_12b
+
+        Python API usage:
+            >>> recipe = finetune_recipe(name="mistral_nemo_base_12b_finetune", num_nodes=2)
+            >>> print(recipe)
+
+    Note:
+        This recipe uses the SQuAD dataset for fine-tuning. For more information
+        on fine-tuning LLMs with NeMo, see the fine-tuning guide in the
+        `examples/llm/finetune/` directory.
+    """
+    recipe = default_finetune_recipe(
+        model(), "mistralai/Mistral-Nemo-Base-2407", dir, name, num_nodes, num_gpus_per_node
+    )
+    if peft_scheme is None or peft_scheme.lower() == 'none':
+        recipe.optim.config.lr = 5e-6
+    elif peft_scheme.lower() == 'lora':
+        recipe.peft = run.Config(LoRA, target_modules=['linear_qkv', 'linear_proj'], dim=32)
+        recipe.optim.config.lr = 1e-4
+    else:
+        raise ValueError(f"Unrecognized peft scheme: {peft_scheme}")
+    return recipe

tests/collections/llm/gpt/model/test_mistral.py

@@ -1,6 +1,6 @@
 import torch.nn.functional as F
 
-from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralNeMo2407Config12B, MistralNeMo2407Config123B
+from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralNeMoConfig12B, MistralNeMoConfig123B
 
 
 def test_mistral_config7b():
@@ -25,7 +25,7 @@ def test_mistral_config7b():
 
 
 def test_mistral_nemo_config_12b():
-    config = MistralNeMo2407Config12B()
+    config = MistralNeMoConfig12B()
     assert config.normalization == "RMSNorm"
     assert config.activation_func == F.silu
     assert config.position_embedding_type == "rope"
@@ -49,7 +49,7 @@ def test_mistral_nemo_config_12b():
 
 
 def test_mistral_nemo_config_123b():
-    config = MistralNeMo2407Config123B()
+    config = MistralNeMoConfig123B()
     assert config.normalization == "RMSNorm"
     assert config.activation_func == F.silu
     assert config.position_embedding_type == "rope"

tests/collections/llm/recipes/test_mistral.py

@@ -6,7 +6,7 @@
 from nemo.collections.llm.gpt.data.squad import SquadDataModule
 from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralModel
 from nemo.collections.llm.peft.lora import LoRA
-from nemo.collections.llm.recipes import mistral
+from nemo.collections.llm.recipes import mistral_7b as mistral
 from nemo.lightning import AutoResume, Trainer