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Use an async checkpointer to initialize JDI in tests #1170

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Use an async checkpointer to initialize JDI in tests #1170

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5 changes: 1 addition & 4 deletions MaxText/layers/linears.py
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Original file line number Diff line number Diff line change
Expand Up @@ -432,10 +432,7 @@ def gmm(inputs, kernel, group_sizes):
)
else:
if self.quant is not None:
raise NotImplementedError(
"Quantization is not yet supported with ragged_dot, please set"
" megablox=True"
)
raise NotImplementedError("Quantization is not yet supported with ragged_dot, please set" " megablox=True")
output = jax.lax.ragged_dot(
lhs=inputs,
rhs=kernel,
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9 changes: 5 additions & 4 deletions end_to_end/tpu/gemma/7b/2_test_gemma.sh
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Expand Up @@ -36,16 +36,17 @@ export RUN_NAME=unscanned_chkpt
# We defined path to unscanned checkpoint created in 1_test_gemma.sh
export UNSCANNED_CKPT_PATH=${BASE_OUTPUT_PATH}/${RUN_NAME}/checkpoints/0/items

export ASYNC_CHECKPOINTING=True # True so that the jax distributed system is initialized
# We run decoding on the `UNSCANNED_CKPT_PATH` for efficient decoding on the unscanned version of the checkpoint. Note that this checkpoint only has parameters and no optimizer state.
# So, we use it by specifying`load_parameters_path=${CONVERTED_CHECKPOINT}`
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.gemma load_parameters_path=${UNSCANNED_CKPT_PATH} per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic steps=10 async_checkpointing=false scan_layers=false model_name=gemma-7b attention=dot_product prompt="I love to"
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.gemma load_parameters_path=${UNSCANNED_CKPT_PATH} per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic steps=10 async_checkpointing=${ASYNC_CHECKPOINTING} scan_layers=false model_name=gemma-7b attention=dot_product prompt="I love to"

# We can also run decoding (albeit in a bit unoptimized way) by using the scanned converted checkpoint located at `CONVERTED_CHECKPOINT`. Note again that this checkpoint only has parameters and no optimizer state. So, we use it by specifying`load_parameters_path=${CONVERTED_CHECKPOINT}`
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.gemma load_parameters_path=${CONVERTED_CHECKPOINT} per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic steps=10 async_checkpointing=false model_name=gemma-7b attention=dot_product prompt="I love to"
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.gemma load_parameters_path=${CONVERTED_CHECKPOINT} per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic steps=10 async_checkpointing=${ASYNC_CHECKPOINTING} model_name=gemma-7b attention=dot_product prompt="I love to"

# Alternatively, we skip to running finetuning by using the scanned converted checkpoint located at `CONVERTED_CHECKPOINT`. Again, we use it by specifying`load_parameters_path=${CONVERTED_CHECKPOINT}`. Note that scanned checkpoint helps with efficient finetuning
export FINETUNE_RUN_NAME=runner_finetune
python MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} tokenizer_path=assets/tokenizer.gemma load_parameters_path=${CONVERTED_CHECKPOINT} per_device_batch_size=1 run_name=${FINETUNE_RUN_NAME} max_target_length=8192 steps=10 async_checkpointing=false model_name=gemma-7b checkpoint_period=5
python MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} tokenizer_path=assets/tokenizer.gemma load_parameters_path=${CONVERTED_CHECKPOINT} per_device_batch_size=1 run_name=${FINETUNE_RUN_NAME} max_target_length=8192 steps=10 async_checkpointing=${ASYNC_CHECKPOINTING} model_name=gemma-7b checkpoint_period=5

# We also run pre-training, this is similar to the finetuning command except we don't pass any checkpoint directory to load parameters from
python MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} tokenizer_path=assets/tokenizer.gemma per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_target_length=8192 steps=5 enable_checkpointing=false model_name=gemma-7b
Expand All @@ -57,7 +58,7 @@ export PARAM_RUN_NAME=param_chkpt
python MaxText/generate_param_only_checkpoint.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} load_full_state_path=${BASE_OUTPUT_PATH}/${FINETUNE_RUN_NAME}/checkpoints/5/items run_name=${PARAM_RUN_NAME} model_name='gemma-7b' force_unroll=true

# Now, run decoding on the checkpoint generated from our finetune run.
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.gemma load_parameters_path=${BASE_OUTPUT_PATH}/${PARAM_RUN_NAME}/checkpoints/0/items per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic steps=10 async_checkpointing=false scan_layers=false model_name=gemma-7b attention=dot_product prompt="I love to"
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.gemma load_parameters_path=${BASE_OUTPUT_PATH}/${PARAM_RUN_NAME}/checkpoints/0/items per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic steps=10 async_checkpointing=${ASYNC_CHECKPOINTING} scan_layers=false model_name=gemma-7b attention=dot_product prompt="I love to"

# We recommend training/finetuning Gemma on v5e-256 using the following sharding strategy to achieve optimal performance.
# This below command does Ahead Of Time Cross Compilation (https://github.com/google/maxtext?tab=readme-ov-file#ahead-of-time-compilation-aot) for our recommended v5e-256 configuration for Gemma 7B.
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12 changes: 7 additions & 5 deletions end_to_end/tpu/llama2/70b/2_test_llama2_70b.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -40,16 +40,18 @@ export RUN_NAME=unscanned_chkpt
# We defined path to unscanned checkpoint created in 1_test_llama2_70b.sh
export UNSCANNED_CKPT_PATH=${BASE_OUTPUT_PATH}/${RUN_NAME}/checkpoints/0/items

export ASYNC_CHECKPOINTING=true # True so that jax distributed system is initialized

# We run decoding on the `UNSCANNED_CKPT_PATH` for efficient decoding on the unscanned version of the checkpoint. Note that this checkpoint only has parameters and no optimizer state.
# So, we use it by specifying`load_parameters_path=${CONVERTED_CHECKPOINT}`
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.llama2 load_parameters_path=${UNSCANNED_CKPT_PATH} per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic async_checkpointing=false scan_layers=false model_name=${MODEL_VARIATION} attention=dot_product prompt="I love to"
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.llama2 load_parameters_path=${UNSCANNED_CKPT_PATH} per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic async_checkpointing=${ASYNC_CHECKPOINTING} scan_layers=false model_name=${MODEL_VARIATION} attention=dot_product prompt="I love to"

# We can also run decoding (albeit in a bit unoptimized way) by using the scanned converted checkpoint located at `CONVERTED_CHECKPOINT`. Note again that this checkpoint only has parameters and no optimizer state. So, we use it by specifying`load_parameters_path=${CONVERTED_CHECKPOINT}`
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.llama2 load_parameters_path=${CONVERTED_CHECKPOINT} per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic async_checkpointing=false model_name=${MODEL_VARIATION} attention=dot_product prompt="I love to"
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.llama2 load_parameters_path=${CONVERTED_CHECKPOINT} per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic async_checkpointing=${ASYNC_CHECKPOINTING} model_name=${MODEL_VARIATION} attention=dot_product prompt="I love to"

# Alternatively, we skip to running finetuning by using the scanned converted checkpoint located at `CONVERTED_CHECKPOINT`. Again, we use it by specifying`load_parameters_path=${CONVERTED_CHECKPOINT}`. Note that scanned checkpoint helps with efficient finetuning
export FINETUNE_RUN_NAME=runner_finetune
python MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} tokenizer_path=assets/tokenizer.llama2 load_parameters_path=${CONVERTED_CHECKPOINT} per_device_batch_size=1 run_name=${FINETUNE_RUN_NAME} steps=10 async_checkpointing=false model_name=${MODEL_VARIATION} checkpoint_period=5
python MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} tokenizer_path=assets/tokenizer.llama2 load_parameters_path=${CONVERTED_CHECKPOINT} per_device_batch_size=1 run_name=${FINETUNE_RUN_NAME} steps=10 async_checkpointing=${ASYNC_CHECKPOINTING} model_name=${MODEL_VARIATION} checkpoint_period=5

# We also run pre-training, this is similar to the finetuning command except we don't pass any checkpoint directory to load parameters from
python MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} tokenizer_path=assets/tokenizer.llama2 per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) steps=5 enable_checkpointing=false model_name=${MODEL_VARIATION}
Expand All @@ -61,7 +63,7 @@ export PARAM_RUN_NAME=param_chkpt
python MaxText/generate_param_only_checkpoint.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} load_full_state_path=${BASE_OUTPUT_PATH}/${FINETUNE_RUN_NAME}/checkpoints/5/items run_name=${PARAM_RUN_NAME} model_name=${MODEL_VARIATION} force_unroll=true

# Now, run decoding on the checkpoint generated from our finetune run.
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.llama2 load_parameters_path=${BASE_OUTPUT_PATH}/${PARAM_RUN_NAME}/checkpoints/0/items per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic steps=10 async_checkpointing=false scan_layers=false model_name=${MODEL_VARIATION} attention=dot_product prompt="I love to"
python MaxText/decode.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs tokenizer_path=assets/tokenizer.llama2 load_parameters_path=${BASE_OUTPUT_PATH}/${PARAM_RUN_NAME}/checkpoints/0/items per_device_batch_size=1 run_name=runner_$(date +%Y-%m-%d-%H-%M) max_prefill_predict_length=4 max_target_length=16 dataset_type=synthetic steps=10 async_checkpointing=${ASYNC_CHECKPOINTING} scan_layers=false model_name=${MODEL_VARIATION} attention=dot_product prompt="I love to"

# We also test whether the forward pass logits match the golden logits for Llama2-70b
python3 MaxText/tests/forward_pass_logit_checker.py --atol=0.2 --rtol=0.2 MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} load_parameters_path=${UNSCANNED_CKPT_PATH} run_name=forward_pass_test per_device_batch_size=1 model_name=llama2-70b ici_tensor_parallelism=4 max_prefill_predict_length=4 max_target_length=4 dataset_type=synthetic dtype=float32 scan_layers=false async_checkpointing=false
python3 MaxText/tests/forward_pass_logit_checker.py --atol=0.2 --rtol=0.2 MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} load_parameters_path=${UNSCANNED_CKPT_PATH} run_name=forward_pass_test per_device_batch_size=1 model_name=llama2-70b ici_tensor_parallelism=4 max_prefill_predict_length=4 max_target_length=4 dataset_type=synthetic dtype=float32 scan_layers=false async_checkpointing=${ASYNC_CHECKPOINTING}
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