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Mitchish mosaic run on its own branch #350
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Original file line number | Diff line number | Diff line change |
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@@ -45,7 +45,6 @@ | |
from .exceptions import OlmoConfigurationError | ||
from .initialization import ModuleType, init_weights | ||
from .torch_util import ensure_finite_ | ||
from .util import pass_through_fn | ||
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__all__ = [ | ||
"LayerNormBase", | ||
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@@ -430,7 +429,7 @@ def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache): | |
self.__cache = cache | ||
assert config.d_model % config.n_heads == 0 | ||
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self._activation_checkpoint_fn = pass_through_fn | ||
self._activation_checkpoint_fn = None | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I find this confusing. Doesn't that mean it will compile only if we don't use checkpointing? As far as I know, compile never likes function pointers? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Or did compile + checkpointing never work anyways? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I'm not sure if we ever got compile to work with checkpointing, but I needed to make this change in order for compile to work without checkpointing. |
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# Dropout. | ||
self.dropout = Dropout(config.residual_dropout) | ||
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@@ -492,7 +491,7 @@ def set_activation_checkpointing(self, strategy: Optional[ActivationCheckpointin | |
if strategy == ActivationCheckpointingStrategy.fine_grained: | ||
self._activation_checkpoint_fn = activation_checkpoint_function(self.config) | ||
else: | ||
self._activation_checkpoint_fn = pass_through_fn | ||
self._activation_checkpoint_fn = None | ||
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@classmethod | ||
def _cast_attn_bias(cls, bias: torch.Tensor, input_dtype: torch.dtype) -> torch.Tensor: | ||
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@@ -673,12 +672,20 @@ def forward( | |
# - for regular attn q, k, v: (batch_size, seq_len, d_model) | ||
# - for multi-query attn q: (batch_size, seq_len, d_model) | ||
# k, v: (batch_size, seq_len, d_model // n_heads) | ||
q, k, v = self.att_proj(self._activation_checkpoint_fn(self.attn_norm, x)).split(self.fused_dims, dim=-1) | ||
if self._activation_checkpoint_fn is not None: | ||
q, k, v = self.att_proj(self._activation_checkpoint_fn(self.attn_norm, x)).split( | ||
self.fused_dims, dim=-1 | ||
) | ||
else: | ||
q, k, v = self.att_proj(self.attn_norm(x)).split(self.fused_dims, dim=-1) | ||
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# Get attention scores. | ||
att, cache = self._activation_checkpoint_fn( | ||
self.attention, q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache | ||
) | ||
if self._activation_checkpoint_fn is not None: | ||
att, cache = self._activation_checkpoint_fn( # type: ignore | ||
self.attention, q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache | ||
) | ||
else: | ||
att, cache = self.attention(q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache) | ||
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# Add attention scores. | ||
# shape: (B, T, C) | ||
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@@ -687,9 +694,15 @@ def forward( | |
# Add feed-forward projection. | ||
# shape: (batch_size, seq_len, d_model) | ||
og_x = x | ||
x = self._activation_checkpoint_fn(self.ff_norm, x) | ||
if self._activation_checkpoint_fn is not None: | ||
x = self._activation_checkpoint_fn(self.ff_norm, x) # type: ignore | ||
else: | ||
x = self.ff_norm(x) | ||
x = self.ff_proj(x) | ||
x = self._activation_checkpoint_fn(self.act, x) | ||
if self._activation_checkpoint_fn is not None: | ||
x = self._activation_checkpoint_fn(self.act, x) # type: ignore | ||
else: | ||
x = self.act(x) | ||
x = self.ff_out(x) | ||
x = self.dropout(x) | ||
x = og_x + x | ||
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@@ -753,23 +766,35 @@ def forward( | |
# - for multi-query attn q: (batch_size, seq_len, d_model) | ||
# k, v: (batch_size, seq_len, d_model // n_heads) | ||
# shape of ff: (batch_size, seq_len, hidden_size) | ||
q, k, v, ff = self.fused_attn_ff_proj(self._activation_checkpoint_fn(self.norm, x)).split( | ||
self.fused_dims, dim=-1 | ||
) | ||
if self._activation_checkpoint_fn is not None: | ||
q, k, v, ff = self.fused_attn_ff_proj(self._activation_checkpoint_fn(self.norm, x)).split( | ||
self.fused_dims, dim=-1 | ||
) | ||
else: | ||
q, k, v, ff = self.fused_attn_ff_proj(self.norm(x)).split(self.fused_dims, dim=-1) | ||
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# Get attention scores. | ||
# shape: (B, T, C) | ||
att, cache = self._activation_checkpoint_fn( | ||
self.attention, q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache | ||
) | ||
if self._activation_checkpoint_fn is not None: | ||
att, cache = self._activation_checkpoint_fn( # type: ignore | ||
self.attention, q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache | ||
) | ||
else: | ||
att, cache = self.attention(q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache) | ||
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# Apply output projections (and activation function) and sum the results. | ||
# We keep these projections separate because we found that we got better throughput this | ||
# way compared to fusing them. | ||
return ( | ||
x + self.dropout(self.ff_out(self._activation_checkpoint_fn(self.act, ff))) + self.dropout(att), | ||
cache, | ||
) | ||
if self._activation_checkpoint_fn is not None: | ||
return ( | ||
x + self.dropout(self.ff_out(self._activation_checkpoint_fn(self.act, ff))) + self.dropout(att), | ||
cache, | ||
) | ||
else: | ||
return ( | ||
x + self.dropout(self.ff_out(self.act(ff))) + self.dropout(att), | ||
cache, | ||
) | ||
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class OlmoLlamaBlock(OlmoBlock): | ||
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@@ -874,9 +899,15 @@ def forward( | |
# Add feed-forward projection. | ||
# shape: (batch_size, seq_len, d_model) | ||
og_x = x | ||
x = self._activation_checkpoint_fn(self.ff_norm, x) | ||
if self._activation_checkpoint_fn is not None: | ||
x = self._activation_checkpoint_fn(self.ff_norm, x) # type: ignore | ||
else: | ||
x = self.ff_norm(x) | ||
x = self.ff_proj(x) | ||
x = self._activation_checkpoint_fn(self.act, x) | ||
if self._activation_checkpoint_fn is not None: | ||
x = self._activation_checkpoint_fn(self.act, x) # type: ignore | ||
else: | ||
x = self.act(x) | ||
x = self.ff_out(x) | ||
x = self.dropout(x) | ||
x = og_x + x | ||
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@@ -945,7 +976,7 @@ def forward( | |
) | ||
): | ||
# shape: (batch_size, seq_len, d_model) | ||
x, cache = self._activation_checkpoint_fn( | ||
x, cache = self._activation_checkpoint_fn( # type: ignore | ||
block, x, attention_bias=attention_bias, layer_past=layer_past, use_cache=use_cache | ||
) | ||
else: | ||
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@@ -0,0 +1,30 @@ | ||
#!/usr/bin/env bash | ||
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set -ex | ||
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CONFIG_PATH=configs/v1_5-mix-medium-mitch-ish-s3.yaml | ||
NUM_NODES=4 | ||
ARGS='--activation_checkpointing=fine_grained wandb.name=v1_5-mix-mitch-ish-mcli-final --epoch=1 --optimizer.learning_rate=0.000023 --scheduler.t_warmup=556000 --scheduler.t_max=557000 --scheduler.alpha_f=0.001 --stop_at=557000' | ||
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gantry run \ | ||
--allow-dirty \ | ||
--workspace ai2/llm-testing \ | ||
--task-name mitchish-mcli-final \ | ||
--description mitchish-mcli-final \ | ||
--priority high \ | ||
--beaker-image olmo-torch2-gantry \ | ||
--cluster ai2/general-cirrascale-a100-80g-ib \ | ||
--gpus 8 \ | ||
--replicas "${NUM_NODES}" \ | ||
--nfs \ | ||
--mount /net/nfs.cirrascale/allennlp/petew/cache:/root/.cache \ | ||
--env LOG_FILTER_TYPE=local_rank0_only \ | ||
--env OMP_NUM_THREADS=8 \ | ||
--env OLMO_TASK=model \ | ||
--env-secret WANDB_API_KEY=WANDB_API_KEY \ | ||
--env-secret AWS_ACCESS_KEY_ID=AWS_ACCESS_KEY_ID \ | ||
--env-secret AWS_SECRET_ACCESS_KEY=AWS_SECRET_ACCESS_KEY \ | ||
--shared-memory 10GiB \ | ||
--venv base \ | ||
--yes \ | ||
-- /bin/bash -c "torchrun --nnodes ${NUM_NODES}:${NUM_NODES} --nproc-per-node 8 --rdzv_id=101 --rdzv_backend=c10d --rdzv_endpoint=\$BEAKER_LEADER_REPLICA_HOSTNAME:29400 scripts/train.py ${CONFIG_PATH} ${ARGS}" |
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@@ -0,0 +1,52 @@ | ||
#!/bin/bash | ||
#SBATCH --job-name=v1.5-mix-medium-mitch-ish | ||
#SBATCH --account=kempner_lab | ||
#SBATCH --output=/n/holyscratch01/kempner_lab/Lab/logs-petew/%j.log | ||
#SBATCH --nodes=8 # Total number of nodes | ||
#SBATCH --ntasks-per-node=4 | ||
#SBATCH --gpus-per-node=4 # Allocate one gpu per MPI rank | ||
#SBATCH --cpus-per-task=16 | ||
#SBATCH --time=167:00:00 | ||
#SBATCH --mem=0 # All memory on the node | ||
#SBATCH --partition=kempner_project | ||
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export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK | ||
export MPICH_GPU_SUPPORT_ENABLED=1 | ||
export MIOPEN_USER_DB_PATH=/tmp/${USER}-miopen-cache-${SLURM_JOB_ID} | ||
export MIOPEN_CUSTOM_CACHE_DIR=${MIOPEN_USER_DB_PATH} | ||
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export PYTHONPATH=.:${PYTHONPATH} | ||
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# Try playing with max_split_size_mb if you run into OOM errors. | ||
# export PYTORCH_HIP_ALLOC_CONF=max_split_size_mb:512 | ||
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export DATA_PATH=/n/home06/dgroeneveld/data/preprocessed/olmo-mix | ||
export EVAL_DATA_PATH=/n/home06/dgroeneveld/data/eval-data | ||
export CHECKPOINTS_PATH=/n/home06/dgroeneveld/checkpoints | ||
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export PYTORCH_KERNEL_CACHE_PATH=/tmp/pytorch_kernel_cache/ | ||
mkdir -p $PYTORCH_KERNEL_CACHE_PATH | ||
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LOAD_PATH=s3://ai2-llm/checkpoints/7b/v1_5-mix-mitch-ish/step556000-unsharded | ||
# SAVE_PATH=s3://ai2-llm/checkpoints/7b/v1_5-mix-mitch-ish-final-tulu | ||
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srun \ | ||
"--cpus-per-task=$SLURM_CPUS_PER_TASK" \ | ||
--distribution=block:block \ | ||
--kill-on-bad-exit \ | ||
scripts/run_with_environment.sh \ | ||
$HOME/miniconda3/envs/LLM/bin/python -u scripts/train.py configs/v1_5-mix-medium-mitch-ish-s3.yaml \ | ||
"--run_name=kempner_${SLURM_JOB_ID}" \ | ||
--wandb.name=v1_5-mix-mitch-ish-final-tulu \ | ||
'--data.paths=[s3://ai2-llm/preprocessed/tulu-v2-sft-mixture/gpt-neox-20b-pii-special/data.npy,s3://ai2-llm/preprocessed/olmo-mix/v1_5-sample-9B/gpt-neox-20b-pii-special/data.npy]' \ | ||
--eval_interval=100 \ | ||
--save_interval=500 \ | ||
"--load_path=${LOAD_PATH}" \ | ||
--restore_dataloader=false \ | ||
--optimizer.learning_rate=0.000023 \ | ||
--scheduler.t_warmup=556000 \ | ||
--scheduler.alpha_f=0.001 \ | ||
--scheduler.t_max=558223 \ | ||
--stop_at=558223 \ | ||
--time_limit=$((167 * 60 * 60)) \ | ||
"--save_folder=/n/holyscratch01/kempner_lab/Lab/checkpoints/${SLURM_JOB_ID}/" |
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@@ -0,0 +1,60 @@ | ||
#!/bin/bash | ||
#SBATCH --job-name=v1.5-mix-medium-mitch-ish | ||
#SBATCH --account=project_462000229 | ||
#SBATCH --output=/pfs/lustref1/flash/project_462000229/logs/%j.log | ||
#SBATCH --nodes=256 # Total number of nodes | ||
#SBATCH --ntasks-per-node=8 | ||
#SBATCH --gpus-per-node=8 # Allocate one gpu per MPI rank | ||
#SBATCH --cpus-per-task=6 | ||
#SBATCH --time=48:00:00 | ||
#SBATCH --time-min=24:00:00 | ||
#SBATCH --mem=0 # All memory on the node | ||
#SBATCH --partition=standard-g | ||
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module load LUMI/22.08 partition/G | ||
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# export OLMO_CONTAINER=llm-lumi_latest.sif | ||
export OLMO_CONTAINER=llm-lumi-torch21_latest.sif | ||
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export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK | ||
export MPICH_GPU_SUPPORT_ENABLED=1 | ||
export NCCL_SOCKET_IFNAME=hsn | ||
export NCCL_NET_GDR_LEVEL=3 | ||
export MIOPEN_USER_DB_PATH=/tmp/${USER}-miopen-cache-${SLURM_JOB_ID} | ||
export MIOPEN_CUSTOM_CACHE_DIR=${MIOPEN_USER_DB_PATH} | ||
export CXI_FORK_SAFE=1 | ||
export CXI_FORK_SAFE_HP=1 | ||
export FI_CXI_DISABLE_CQ_HUGETLB=1 | ||
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# We need to set this to avoid "Cassini Event Queue overflow detected." errors. | ||
export FI_CXI_DEFAULT_CQ_SIZE=131072 | ||
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#export NCCL_DEBUG=INFO | ||
export PYTHONPATH=.:${PYTHONPATH} | ||
export ROCM_PATH=/opt/rocm | ||
export SINGULARITYENV_LD_LIBRARY_PATH=/usr/local/lib:/opt/cray/libfabric/1.15.2.0/lib64 | ||
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# Try playing with max_split_size_mb if you run into OOM errors. | ||
#export PYTORCH_HIP_ALLOC_CONF=max_split_size_mb:128 | ||
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export DATA_PATH=$FLASH_DIR/preprocessed/olmo-mix | ||
export CHECKPOINTS_PATH=$FLASH_DIR/checkpoints | ||
export EVAL_DATA_PATH=$SCRATCH_DIR/eval-data | ||
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srun \ | ||
--cpus-per-task=$SLURM_CPUS_PER_TASK \ | ||
--distribution=block:block \ | ||
--kill-on-bad-exit \ | ||
scripts/run_with_environment.sh \ | ||
singularity exec \ | ||
-B"$PROJECT_DIR:$PROJECT_DIR" \ | ||
-B"$FLASH_DIR:$FLASH_DIR" \ | ||
-B"$SCRATCH_DIR:$SCRATCH_DIR" \ | ||
-B /opt/cray:/opt/cray \ | ||
-B /usr/lib64/libcxi.so.1:/usr/lib64/libcxi.so.1 \ | ||
-B /usr/lib64/libjson-c.so.3:/usr/lib64/libjson-c.so.3 \ | ||
$PROJECT_DIR/containers/$OLMO_CONTAINER \ | ||
python scripts/train.py configs/v1_5-mix-medium-mitch-ish.yaml ${@} \ | ||
--run_name=${SLURM_JOB_ID} \ | ||
--global_train_batch_size=4096 \ | ||
--max_duration=238418 |
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I thought this was linear the whole time?
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I guess when we first made this config we were thinking cosine. We've only ran it with linear though.