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Update QAT: add grad clipping, torch.compile, collate fn #1854

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merged 1 commit into from
Nov 8, 2024
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Update QAT: add grad clipping, torch.compile, collate fn #1854

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2 changes: 1 addition & 1 deletion recipes/configs/llama2/7B_qat_full.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ device: cuda

# Memory management
enable_activation_checkpointing: True # True reduces memory
memory_efficient_fsdp_wrap: False
enable_activation_offloading: False # True reduces memory

# Reduced precision
dtype: bf16
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9 changes: 5 additions & 4 deletions recipes/configs/llama3/8B_qat_full.yaml
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Expand Up @@ -44,8 +44,6 @@ resume_from_checkpoint: False
# Fine-tuning arguments
batch_size: 2
epochs: 3
compile: False # pytorch compile, set to true for better perf/memory
optimizer_in_bwd: False # True saves memory. Requires gradient_accumulation_steps=1

# QAT arguments
quantizer:
Expand All @@ -60,13 +58,16 @@ loss:
_component_: torchtune.modules.loss.CEWithChunkedOutputLoss
max_steps_per_epoch: null
gradient_accumulation_steps: 1 # Use to increase virtual batch size
compile: False # pytorch compile, set to true for better perf/memory
optimizer_in_bwd: False # True saves memory. Requires gradient_accumulation_steps=1

# Training env
device: cuda

# Memory management
enable_activation_checkpointing: True # True reduces memory
memory_efficient_fsdp_wrap: True
enable_activation_offloading: False # True reduces memory
custom_sharded_layers: ['tok_embeddings', 'output'] # Layers to shard separately (useful for large vocab size models). Lower Memory, but lower speed.

# Reduced precision
dtype: bf16
Expand All @@ -75,7 +76,7 @@ dtype: bf16
metric_logger:
_component_: torchtune.training.metric_logging.DiskLogger
log_dir: ${output_dir}
output_dir: /tmp/alpaca-llama3-finetune
output_dir: /tmp/full-llama3-finetune
log_every_n_steps: 1
log_peak_memory_stats: True

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