doc updates; config fix

docs/config.qmd

@@ -686,9 +686,10 @@ ddp_broadcast_buffers:
 # E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
 # subsequences, or set to 4 to split into four equal-sized subsequences.
 # See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
-sequence_parallel_degree:
-# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
-# Must evenly divide the number of KV heads in your model.
+sequence_parallel_degree: 4  # Set to the number of GPUs to split sequences across
+flash_attention: true  # SP requires flash attention
+micro_batch_size: 1  # SP requires this is set to 1
+# (optional) strides across the key dimension; larger values use more memory but should make training a bit faster
 heads_k_stride: 1
 
 # Path to torch distx for optim 'adamw_anyprecision'

docs/sequence_parallelism.qmd

@@ -23,9 +23,10 @@ Use sequence parallelism when:
 To enable sequence parallelism, add the following to your configuration file:
 
 ```yaml
-# Set to a divisor (> 1) of the number of GPUs available
-sequence_parallel_degree: 4  # Split sequences across 4 GPUs
-# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
+sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
+flash_attention: true  # SP requires flash attention
+micro_batch_size: 1  # SP requires this is set to 1
+# (optional) strides across the key dimension; larger values use more memory but should make training a bit faster
 heads_k_stride: 1
 ```
 
@@ -66,15 +67,16 @@ sequence_len: 8192
 ...
 
 sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
-flash_attention: true  # Required with sequence parallelism
-# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
+flash_attention: true  # SP requires flash attention
+micro_batch_size: 1  # SP requires this is set to 1
+# (optional) strides across the key dimension; larger values use more memory but should make training a bit faster
 heads_k_stride: 1
 
 ...
 ```
 
-This will train the Llama 3 8B model with 8K context length, with each sequence split
-into 2 subsequences of length 4096 across 2 GPUs.
+This will train the Llama 3 8B model with 8192 context length, with each sequence split
+into 4 subsequences of length 2048 across 4 GPUs.
 
 ## Sample Packing with Sequence Parallelism
 
@@ -86,12 +88,14 @@ Sequence parallelism is compatible with Axolotl's sample packing functionality.
 
 ## Effect on Batch Size
 
+First, note that sequence parallelism supports only the case where `micro_batch_size: 1`.
+
 When using sequence parallelism, your effective global batch size is **divided** by the `sequence_parallel_degree`. This happens because:
 
 - Each group of `sequence_parallel_degree` GPUs works on the same batch (just different parts of each sequence)
 - The number of batches processed per step decreases
 
 For example:
-- With 8 GPUs and no sequence parallelism: 8 different batches processed per step
+- With 8 GPUs and no sequence parallelism: 8 different batches are processed per step
 - With 8 GPUs and `sequence_parallel_degree=4`: Only 2 different batches processed per step (each split across 4 GPUs)
-- If your per-GPU `micro_batch_size` is 2, the global batch size decreases from 16 to 4
+- If your per-GPU `micro_batch_size` is 1, the global batch size decreases from 8 to 2

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -82,3 +82,6 @@ deepspeed:
 weight_decay: 0.0
 fsdp:
 fsdp_config:
+
+special_tokens:
+  pad_token: "<|end_of_text|>"