R1-Style distributed GRPO

recipes/configs/dev/grpo/3B_full_rl.yaml

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+# Config for multi-node GRPO in dev/grpo_full_finetune_distributed.py
+# using a Llama3.2 3B Base model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Meta-Llama-3.2-3B --output-dir /tmp/Meta-Llama-3.2-3B-Instruct --ignore-patterns "original/consolidated.00.pth"
+#
+# It can be beneficial to first train the base model with SFT using the 3B_sft recipe.
+#
+# To launch on 4 devices, run the following command from root:
+#   tune run --nproc_per_node 4 dev/grpo_full_finetune_distributed --config dev/grpo/3B_full_rl
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nproc_per_node 4 dev/grpo_full_finetune_distributed --config dev/grpo/3B_full_rl checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config works best when the model is being fine-tuned on 2+ GPUs.
+#
+# Furthermore, you can launch it on multiple nodes by going to recipes/dev/ and using
+#  sbatch multinode_grpo.sbatch
+
+base_dir: /tmp
+name: grpo_sft_start
+
+output_dir: ${base_dir}/checkpoints/${name}
+base_model_path: ${base_dir}/Llama-3.2-3B  # Use this to train from the base model
+#base_model_path: ${base_dir}/llama3B_gsm8k_sft_part0/epoch_0  # Use this to train from the slightly trained SFT model
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: ${base_model_path}/original/tokenizer.model
+  max_seq_len: null
+
+# Dataset
+dataset:
+  _component_: torchtune.datasets.gsm8k_dataset
+  partition: 1-9/10
+#  packed: False  # True increases speed
+seed: null
+shuffle: False
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_2.llama3_2_3b
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: ${base_model_path}
+  checkpoint_files: [
+    model-00001-of-00002.safetensors,  # Add ft- if starting from SFT model
+    model-00002-of-00002.safetensors,
+  ]
+  recipe_checkpoint: null
+  output_dir: ${output_dir}
+  model_type: LLAMA3
+resume_from_checkpoint: False
+save_every_n_epochs: 1
+
+# Fine-tuning arguments
+batch_size: 1
+grpo_samples: 16
+forward_batch_size: 1
+max_generated_tokens: 512
+top_k: null
+temperature: 1.0
+
+fsdp_reshard_after_forward: True
+
+ppo_epochs: 1
+ppo_batch_size: ${grpo_samples}  # single-step for the "simple" loss
+
+num_steps: 10000
+
+clip_grad_norm: 1.0
+
+epochs: 10
+optimizer:
+  _component_: torch.optim.AdamW
+  lr: 1e-5
+  fused: True
+lr_scheduler:
+  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
+  num_warmup_steps: 50
+loss:
+  _component_: torchtune.rlhf.loss.GRPOSimpleLoss
+  kl_coeff: 0.04
+  epsilon: 0.2
+max_steps_per_epoch: null
+gradient_accumulation_steps: 1  # Use to increase virtual batch size
+
+# Training env
+device: cuda
+
+# Memory management
+enable_activation_checkpointing: True  # True reduces memory
+enable_activation_offloading: True  # True reduces memory
+compile: False  # pytorch compile, set to true for better perf/memory
+optimizer_in_bwd: False  # True saves memory. Requires gradient_accumulation_steps=1
+
+# Reduced precision
+dtype: bf16
+custom_sharded_layers: ['decoder.tok_embeddings']
+
+
+# Logging
+metric_logger:
+  _component_: torchtune.training.metric_logging.WandBLogger
+  project: grpo_gsm8k_torchtune
+  log_dir: ${output_dir}/logs
+  name: ${name}
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: True
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: True
+  with_stack: True
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1

recipes/configs/dev/grpo/3B_sft.yaml

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+# Config for multi-device SFT for reasoning in full_finetune_distributed.py
+# using a Llama3.2 3B Base model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Meta-Llama-3.2-3B --output-dir /tmp/Meta-Llama-3.2-3B-Instruct --ignore-patterns "original/consolidated.00.pth"
+#
+# To launch on 4 devices, run the following command from root:
+#   tune run --nproc_per_node 4 full_finetune_distributed --config dev/grpo/3B_sft
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nproc_per_node 4 full_finetune_distributed --config dev/grpo/3B_sft checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config works best when the model is being fine-tuned on 2+ GPUs.
+
+
+base_dir: /tmp
+name: llama3B_gsm8k_sft_part0
+
+output_dir: ${base_dir}/${name}
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: ${base_dir}/Llama-3.2-3B/original/tokenizer.model
+  max_seq_len: null
+
+# Dataset
+dataset:
+  _component_: torchtune.datasets.gsm8k_sft
+  partition: 0-0/10
+seed: null
+shuffle: True
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_2.llama3_2_3b
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: ${base_dir}/Llama-3.2-3B/
+  checkpoint_files: [
+    model-00001-of-00002.safetensors,
+    model-00002-of-00002.safetensors,
+  ]
+  recipe_checkpoint: null
+  output_dir: ${output_dir}
+  model_type: LLAMA3
+resume_from_checkpoint: False
+
+# Fine-tuning arguments
+batch_size: 2
+epochs: 1
+
+optimizer:
+  _component_: torch.optim.AdamW
+  lr: 1e-5
+  fused: True
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+max_steps_per_epoch: null
+clip_grad_norm: null
+compile: False  # torch.compile the model + loss, True increases speed + decreases memory
+optimizer_in_bwd: False  # True saves memory. Requires gradient_accumulation_steps=1
+gradient_accumulation_steps: 1  # Use to increase effective batch size
+
+# Training env
+device: cuda
+
+# Memory management
+enable_activation_checkpointing: True  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+
+# Reduced precision
+dtype: bf16
+
+# Logging
+metric_logger:
+  _component_: torchtune.training.metric_logging.WandBLogger
+  project: gsm8k_sft
+  log_dir: ${output_dir}/logs
+  name: ${name}
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1

recipes/configs/llama3_2/3B_full_rl_single_device_mps.yaml

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+# Config for single device full finetuning in full_finetune_single_device.py
+# using a Llama3.2 3B Instruct model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Llama-3.2-3B --output-dir /Users/ariel/checkpoint/tmp/Llama-3.2-3B --ignore-patterns "original/consolidated.00.pth"
+#
+# The default config uses an optimizer from bitsandbytes. If you do not have it installed,
+# you can install it with
+#   pip install bitsandbytes
+#
+# To launch on a single device, run the following command from root:
+#   tune run full_finetune_single_device --config llama3_2/3B_full_single_device
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run full_finetune_single_device --config llama3_2/3B_full_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config works only for training on single device.
+
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /Users/ariel/checkpoint/tmp/Llama-3.2-3B/original/tokenizer.model
+  max_seq_len: null
+
+# Dataset
+dataset:
+#  _component_: torchtune.datasets.math_dataset
+  _component_: torchtune.datasets.gsm8k_dataset
+seed: null
+shuffle: True
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_2.llama3_2_3b
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /Users/ariel/checkpoint/tmp/Llama-3.2-3B/
+  checkpoint_files: [
+    model-00001-of-00002.safetensors,
+    model-00002-of-00002.safetensors,
+  ]
+  recipe_checkpoint: null
+  output_dir: /Users/ariel/checkpoint/tmp/Llama-3.2-3B/
+  model_type: LLAMA3_2
+resume_from_checkpoint: False
+
+# Fine-tuning arguments
+batch_size: 4
+max_generated_tokens: 100
+top_k: null
+temperature: 0.7
+
+epochs: 1
+optimizer:
+  _component_: bitsandbytes.optim.PagedAdamW8bit
+  lr: 2e-5
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+max_steps_per_epoch: null
+gradient_accumulation_steps: 1  # Use to increase virtual batch size
+optimizer_in_bwd: True  # True saves memory. Requires gradient_accumulation_steps=1
+compile: False  # pytorch compile, set to true for better perf/memory
+
+# Training environment
+device: mps
+
+# Memory management
+enable_activation_checkpointing: True  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+
+# Reduced precision
+dtype: bf16
+
+# Logging
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+output_dir: /Users/ariel/checkpoint/tmp/full-llama3.2-finetune
+log_every_n_steps: 1
+log_peak_memory_stats: False  # for mac
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1