Revert "fix some typos" (#6244)

3rdparty/amd/profiling/PROFILING.md

@@ -356,7 +356,7 @@ client.sh
 # Start profiling via API
 curl http://localhost:30000/start_profile -H "Content-Type: application/json"
 
-# Benchmark serving using SGLang with a random dataset and tokenizer
+# Benchmark serving using sglang with random dataset and tokenizer
 # Define the log file with a timestamp
 TIMESTAMP=$(date +%Y%m%d_%H%M%S)
 LOGFILE="sglang_client_log_$TIMESTAMP.json"

3rdparty/amd/tuning/TUNING.md

@@ -93,21 +93,21 @@ TORCHINDUCTOR_MAX_AUTOTUNE=1 TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1 TORCHINDU
 #Inference with large improvement on AMD GPU
 TORCHINDUCTOR_FREEZING=1 your_script.sh
 ```
-## 4. Fused MoE kernel
-To maximize MoE kernel efficiency, need to use below scripts to find out the best launch configuration
+## 4. Fused MOE kernel
+To maximize moe kernel efficiency, need to use below scripts to find out the best launch configuration
 
 ### Key parameters:
-- **--model**: what MoE model type to do tuning, it will automatically decide the size of d_model, model_intermediate_size, num_layers
+- **--model**: what moe model type to do tuning, it will automatically decide the size of d_model, model_intermediate_size, num_layers
 - **--tp-size**: simulate the whole model run configuration to set the dimension size using tp correctly
-- **--batch**: M dimension size of MoE kernel, for prefill MoE kernel the value is batch*input_len, for decode MoE kernel the value is batch
+- **--batch**: M dimension size of moe kernel, for prefill moe kernel the value is batch*input_len, for decode moe kernel the value is batch
 - **--dtype**: computation type
 
 ```bash
 #Tuning
-#for example, we have one case like this "python3 -m sglang.bench_latency --model dummy_grok1/ --load-format dummy --tokenizer-path Xenova/grok-1-tokenizer --tp 8 --batch-size 32 --input 1024 --output 8 --attention-backend triton --sampling-backend pytorch --quantization fp8" to run, it defined batch-size 32 input length 1024 and output length 8, from "--batch" in MoE view point, the prefill batch is 32*1024 = 32768, the decode batch is 32*1(only one output token generated in each run).
-#so we can tune decode MoE use below command
+#for example, we have one case like this "python3 -m sglang.bench_latency --model dummy_grok1/ --load-format dummy --tokenizer-path Xenova/grok-1-tokenizer --tp 8 --batch-size 32 --input 1024 --output 8 --attention-backend triton --sampling-backend pytorch --quantization fp8" to run, it defined batch-size 32 input length 1024 and output length 8, from "--batch" in moe view point, the prefill batch is 32*1024 = 32768, the decode batch is 32*1(only one output token generated in each run).
+#so we can tune decode moe use below command
 python benchmark_moe_rocm.py --model grok1 --tp-size 8 --dtype float8 --batch "32"
-# and use this command to tune prefill MoE
+# and use this command to tune prefill moe
 python benchmark_moe_rocm.py --model grok1 --tp-size 8 --dtype float8 --batch "32768"
 ```