Qwen2 Recipe (#10974)

* Add qwen recipe * Apply isort and black reformatting Signed-off-by: suiyoubi <[email protected]> * change to TP1 for small models Signed-off-by: Ao Tang <[email protected]> --------- Signed-off-by: suiyoubi <[email protected]> Signed-off-by: Ao Tang <[email protected]> Co-authored-by: suiyoubi <[email protected]>
NVIDIA · Oct 27, 2024 · a814e4a · a814e4a
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diff --git a/nemo/collections/llm/gpt/model/qwen2.py b/nemo/collections/llm/gpt/model/qwen2.py
@@ -296,7 +296,7 @@ def _import_qkv_bias(ctx: io.TransformCTX, q, k, v):
     k = k.view(*new_kv_tensor_shape)
     v = v.view(*new_kv_tensor_shape)
 
-    qkv_bias = torch.empty((0, head_size))
+    qkv_bias = torch.empty((0, head_size)).type_as(q)
     for i in range(num_query_groups):
         qkv_bias = torch.cat((qkv_bias, q[i * heads_per_group : (i + 1) * heads_per_group, :]))
         qkv_bias = torch.cat((qkv_bias, k[i : i + 1, :]))

diff --git a/nemo/collections/llm/recipes/__init__.py b/nemo/collections/llm/recipes/__init__.py
@@ -50,6 +50,11 @@
     nemotron4_22b_16k,
     nemotron4_22b_64k,
     nemotron4_340b,
+    qwen2,
+    qwen2_1p5b,
+    qwen2_7b,
+    qwen2_72b,
+    qwen2_500m,
 )
 from nemo.collections.llm.recipes.log.default import default_log, default_resume
 from nemo.collections.llm.recipes.optim import adam
@@ -90,6 +95,11 @@
     "nemotron4_22b_16k",
     "nemotron4_22b_64k",
     "nemotron4_340b",
+    "qwen2",
+    "qwen2_500m",
+    "qwen2_1p5b",
+    "qwen2_7b",
+    "qwen2_72b",
     "gpt3_175b",
     "adam",
     "default_log",

diff --git a/nemo/collections/llm/recipes/qwen2.py b/nemo/collections/llm/recipes/qwen2.py
@@ -0,0 +1,139 @@
+# 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 Optional
+
+import nemo_run as run
+import pytorch_lightning as pl
+import torch
+from pytorch_lightning.callbacks.callback import Callback
+
+from nemo import lightning as nl
+from nemo.collections.llm.gpt.model.qwen2 import (
+    Qwen2Config1P5B,
+    Qwen2Config7B,
+    Qwen2Config72B,
+    Qwen2Config500M,
+    Qwen2Model,
+)
+from nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed, fp16_mixed
+
+
+def qwen2_model(version: str) -> run.Config[pl.LightningModule]:
+    """
+    A function to create a qwen2 models.
+
+    Args:
+        version (str): The version of the qwen2 model to create. one of ["qwen2_500m", "qwen2_1p5b",
+            "qwen2_7b", "qwen2_72b"].
+
+    Returns:
+        run.Config[pl.LightningModule]: Configuration for the qwen2 model.
+    """
+    config = None
+    if version == "qwen2_500m":
+        config = run.Config(Qwen2Config500M)
+    elif version == "qwen2_1p5b":
+        config = run.Config(Qwen2Config1P5B)
+    elif version == "qwen2_7b":
+        config = run.Config(Qwen2Config7B)
+    elif version == "qwen2_72b":
+        config = run.Config(Qwen2Config72B)
+
+    assert config is not None, f"Invalid version: {version}"
+    return run.Config(Qwen2Model, config=config)
+
+
+def qwen2_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 = 1,
+    sequence_parallelism: bool = False,
+    num_nodes: int = 1,
+    num_gpus_per_node: int = 8,
+    max_steps: int = 1168251,
+    precision: str = "bf16-mixed",
+    accumulate_grad_batches: int = 1,
+    limit_test_batches: int = 32,
+    limit_val_batches: int = 32,
+    log_every_n_steps: int = 10,
+    val_check_interval: int = 2000,
+    callbacks: Optional[list[run.Config[Callback]]] = None,
+) -> run.Config[nl.Trainer]:
+    """
+    Configure the NeMo Lightning Trainer for qwen2 models.
+
+    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.
+        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.
+        accumulate_grad_batches (int): Number of steps per gradient accumulation.
+        limit_test_batches (int): Limit the number of test batches.
+        limit_val_batches (int): Limit the number of validation batches.
+        log_every_n_steps (int): Log every n steps.
+        val_check_interval (int): Run validation every N steps.
+        callbacks (Optional[list[run.Config[Callback]]]): List of callback configurations.
+
+    Returns:
+        run.Config[nl.Trainer]: Configuration for the NeMo Lightning Trainer.
+    """
+    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_include_optimizer=True,
+        ckpt_async_save=True,
+        ckpt_parallel_load=True,
+    )
+
+    precision_plugin = None
+    if precision == "16-mixed":
+        precision_plugin = fp16_mixed()
+    elif precision == "bf16-mixed":
+        precision_plugin = bf16_mixed()
+
+    trainer = run.Config(
+        nl.Trainer,
+        accelerator="gpu",
+        callbacks=callbacks,
+        devices=num_gpus_per_node,
+        accumulate_grad_batches=accumulate_grad_batches,
+        limit_test_batches=limit_test_batches,
+        limit_val_batches=limit_val_batches,
+        log_every_n_steps=log_every_n_steps,
+        max_steps=max_steps,
+        num_nodes=num_nodes,
+        plugins=precision_plugin,
+        strategy=strategy,
+        use_distributed_sampler=False,
+        val_check_interval=val_check_interval,
+    )
+
+    return trainer
diff --git a/nemo/collections/llm/recipes/qwen2_1p5b.py b/nemo/collections/llm/recipes/qwen2_1p5b.py
@@ -0,0 +1,222 @@
+# 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 Optional
+
+import nemo_run as run
+import pytorch_lightning as pl
+import torch
+
+from nemo.collections.llm.api import finetune, pretrain
+from nemo.collections.llm.gpt.data.mock import MockDataModule
+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.qwen2 import qwen2_model, qwen2_trainer
+from nemo.utils.exp_manager import TimingCallback
+
+NAME = "qwen2_1p5b"
+
+
+@run.cli.factory(name=NAME)
+def model() -> run.Config[pl.LightningModule]:
+    """
+    Factory function to create a Qwen2 1.5b model configuration.
+
+    Returns:
+        run.Config[pl.LightningModule]: Configuration for the Qwen2 1.5b model.
+
+    Examples:
+        CLI usage:
+            $ nemo llm pretrain model=qwen2_1p5b ...
+
+        Python API usage:
+            >>> model_config = model()
+            >>> print(model_config)
+    """
+
+    return qwen2_model(version=NAME)
+
+
+@run.cli.factory(target=pretrain, name=NAME)
+def pretrain_recipe(
+    # General
+    dir: Optional[str] = None,
+    name: str = "default",
+    # Trainer
+    tensor_parallelism: int = 1,
+    pipeline_parallelism: int = 1,
+    pipeline_parallelism_type: Optional[torch.dtype] = None,
+    virtual_pipeline_parallelism: Optional[int] = None,
+    context_parallelism: int = 1,
+    sequence_parallelism: bool = False,
+    num_nodes: int = 1,
+    num_gpus_per_node: int = 8,
+    max_steps: int = 300000,
+    precision: str = "bf16-mixed",
+    accumulate_grad_batches: int = 1,
+    gradient_clip_val: float = 1.0,
+    limit_test_batches: int = 32,
+    limit_val_batches: int = 32,
+    log_every_n_steps: int = 10,
+    val_check_interval: int = 500,
+    # Data
+    global_batch_size=32,
+    micro_batch_size=2,
+    seq_length=4096,
+    # Optimizer
+    warmup_steps=500,
+    constant_steps=0,
+    min_lr=3e-5,
+    max_lr=3e-4,
+    # Training function
+    fn=pretrain,
+) -> run.Partial:
+    """
+    Create a pre-training recipe for Qwen2 1.5b 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.
+        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.
+        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.
+        accumulate_grad_batches (int): Number of steps per gradient accumulation.
+        gradient_clip_val (float): Value for gradient clipping.
+        limit_test_batches (int): Limit the number of test batches.
+        limit_val_batches (int): Limit the number of validation batches.
+        log_every_n_steps (int): Log every n steps.
+        val_check_interval (int): Run validation every N steps.
+        global_batch_size (int): Global batch size.
+        micro_batch_size (int): Micro batch size.
+        seq_length (int): Sequence length.
+        warmup_steps (int): Number of warmup steps.
+        constant_steps (int): Number of constant steps.
+        min_lr (float): Minimum learning rate.
+        max_lr (float): Maximum learning rate.
+        fn (Callable): The pre-training function to use.
+
+    Returns:
+        run.Partial: Partial configuration for pre-training.
+
+    Examples:
+        CLI usage:
+            $ nemo llm pretrain --factory qwen2_1p5b
+            $ nemo llm pretrain --factory "qwen2_1p5b(num_nodes=1, name='my_qwen2_pretrain')"
+
+        Python API usage:
+            >>> recipe = pretrain_recipe(name="qwen2_pretrain", num_nodes=1)
+            >>> print(recipe)
+
+    Note:
+        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.
+    """
+    return run.Partial(
+        fn,
+        model=model(),
+        trainer=qwen2_trainer(
+            tensor_parallelism=tensor_parallelism,
+            pipeline_parallelism=pipeline_parallelism,
+            pipeline_parallelism_type=pipeline_parallelism_type,
+            virtual_pipeline_parallelism=virtual_pipeline_parallelism,
+            context_parallelism=context_parallelism,
+            sequence_parallelism=sequence_parallelism,
+            num_nodes=num_nodes,
+            num_gpus_per_node=num_gpus_per_node,
+            max_steps=max_steps,
+            precision=precision,
+            accumulate_grad_batches=accumulate_grad_batches,
+            limit_test_batches=limit_test_batches,
+            limit_val_batches=limit_val_batches,
+            log_every_n_steps=log_every_n_steps,
+            val_check_interval=val_check_interval,
+            callbacks=[run.Config(TimingCallback)],
+        ),
+        data=run.Config(
+            MockDataModule,
+            seq_length=seq_length,
+            global_batch_size=global_batch_size,
+            micro_batch_size=micro_batch_size,
+        ),
+        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),
+        optim=distributed_fused_adam_with_cosine_annealing(
+            precision=precision,
+            warmup_steps=warmup_steps,
+            constant_steps=constant_steps,
+            min_lr=min_lr,
+            max_lr=max_lr,
+            clip_grad=gradient_clip_val,
+        ),
+        resume=default_resume(),
+    )
+
+
+@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 Qwen2 1.5b 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 qwen2_1p5b
+
+        Python API usage:
+            >>> recipe = finetune_recipe(name="qwen2_1p5b_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(), "Qwen/Qwen2-1.5B", 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)
+        recipe.optim.config.lr = 1e-4
+    else:
+        raise ValueError(f"Unrecognized peft scheme: {peft_scheme}")
+    return recipe