Add gemma 7b it benchmark

examples/huggingface/README.md

@@ -31,3 +31,12 @@ Throughput improves by around 10%, while GPU memory usage drops by 50%.
 
 ![Throughput](img/qwen_tps.png)
 ![GPU Memory Allocated](img/qwen_mem_alloc.png)
+
+
+### GEMMA 7B
+Benchmark conditions: Gemma-7B, Alpaca Dataset, Max seq len = 512, Data Type = bf16, Optimizer = AdamW, Gradient Checkpointing = True, Distributed Strategy = FSDP1 on 4 A100s.
+
+Throughput improves by around 24%, while GPU memory usage drops by 33%. 
+
+![Throughput](img/gemma_7b_mem.png)
+![GPU Memory Allocated](img/gemma_7b_tp.png)

examples/huggingface/run_gemma.sh

@@ -0,0 +1,20 @@
+torchrun --nnodes=1 --nproc-per-node=4 training.py \
+    --model_name "google/gemma-7b-it" \
+    --bf16 \
+    --max_steps 20 \
+    --per_device_train_batch_size 24 \
+    --per_device_eval_batch_size 1 \
+    --eval_strategy "no" \
+    --save_strategy "no" \
+    --learning_rate 6e-6 \
+    --weight_decay 0.05 \
+    --warmup_ratio 0.1 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --include_num_input_tokens_seen \
+    --report_to none \
+    --fsdp "full_shard auto_wrap" \
+    --fsdp_config config/fsdp_config.json \
+    --seed 42 \
+    --use_liger True \
+    --output_dir alpaca_finetuning

examples/huggingface/training.py

@@ -53,8 +53,8 @@ def train():
             torch_dtype=torch.bfloat16,
             # These args will get passed to the appropriate apply_liger_kernel_to_* function
             # to override the default settings
-            cross_entropy=True,
-            fused_linear_cross_entropy=False,
+            # cross_entropy=True,
+            # fused_linear_cross_entropy=False,
         )
     else:
         model = transformers.AutoModelForCausalLM.from_pretrained(