[Doc] Update ai_accelerator/hpu-gaudi.inc.md

docs/source/getting_started/installation/ai_accelerator/hpu-gaudi.inc.md

@@ -13,11 +13,11 @@ There are no pre-built wheels or images for this device, so you must build vLLM
 - Intel Gaudi accelerator
 - Intel Gaudi software version 1.18.0
 
-Please follow the instructions provided in the [Gaudi Installation
-Guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html)
+Please follow the instructions provided in the
+[Gaudi Installation Guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html)
 to set up the execution environment. To achieve the best performance,
-please follow the methods outlined in the [Optimizing Training Platform
-Guide](https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Optimization_in_Training_Platform.html).
+please follow the methods outlined in the
+[Optimizing Training Platform Guide](https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Optimization_in_Training_Platform.html).
 
 ## Configure a new environment
 
@@ -32,15 +32,13 @@ pip list | grep habana # verify that habana-torch-plugin, habana-torch-dataloade
 pip list | grep neural # verify that neural_compressor is installed
 ```
 
-Refer to [Intel Gaudi Software Stack
-Verification](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html#platform-upgrade)
+Refer to [Intel Gaudi Software Stack Verification](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html#platform-upgrade)
 for more details.
 
 ### Run Docker Image
 
 It is highly recommended to use the latest Docker image from Intel Gaudi
-vault. Refer to the [Intel Gaudi
-documentation](https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#pull-prebuilt-containers)
+vault. Refer to the [Intel Gaudi documentation](https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#pull-prebuilt-containers)
 for more details.
 
 Use the following commands to run a Docker image:
@@ -278,8 +276,9 @@ Lower value corresponds to less usable graph memory reserved for prefill stage,
 :::
 
 User can also configure the strategy for capturing HPU Graphs for prompt and decode stages separately. Strategy affects the order of capturing graphs. There are two strategies implemented:
-\- `max_bs` - graph capture queue will sorted in descending order by their batch sizes. Buckets with equal batch sizes are sorted by sequence length in ascending order (e.g. `(64, 128)`, `(64, 256)`, `(32, 128)`, `(32, 256)`, `(1, 128)`, `(1,256)`), default strategy for decode
-\- `min_tokens` - graph capture queue will be sorted in ascending order by the number of tokens each graph processes (`batch_size*sequence_length`), default strategy for prompt
+
+- `max_bs` - graph capture queue will sorted in descending order by their batch sizes. Buckets with equal batch sizes are sorted by sequence length in ascending order (e.g. `(64, 128)`, `(64, 256)`, `(32, 128)`, `(32, 256)`, `(1, 128)`, `(1,256)`), default strategy for decode
+- `min_tokens` - graph capture queue will be sorted in ascending order by the number of tokens each graph processes (`batch_size*sequence_length`), default strategy for prompt
 
 When there's large amount of requests pending, vLLM scheduler will attempt to fill the maximum batch size for decode as soon as possible. When a request is finished, decode batch size decreases. When that happens, vLLM will attempt to schedule a prefill iteration for requests in the waiting queue, to fill the decode batch size to its previous state. This means that in a full load scenario, decode batch size is often at its maximum, which makes large batch size HPU Graphs crucial to capture, as reflected by `max_bs` strategy. On the other hand, prefills will be executed most frequently with very low batch sizes (1-4), which is reflected in `min_tokens` strategy.
 
@@ -326,8 +325,7 @@ INFO 08-02 17:38:43 hpu_executor.py:91] init_cache_engine took 37.92 GiB of devi
 - We recommend running inference on Gaudi 2 with `block_size` of 128
   for BF16 data type. Using default values (16, 32) might lead to
   sub-optimal performance due to Matrix Multiplication Engine
-  under-utilization (see [Gaudi
-  Architecture](https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html)).
+  under-utilization (see [Gaudi Architecture](https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html)).
 - For max throughput on Llama 7B, we recommend running with batch size
   of 128 or 256 and max context length of 2048 with HPU Graphs enabled.
   If you encounter out-of-memory issues, see troubleshooting section.
@@ -336,11 +334,11 @@ INFO 08-02 17:38:43 hpu_executor.py:91] init_cache_engine took 37.92 GiB of devi
 
 **Diagnostic and profiling knobs:**
 
-- `VLLM_PROFILER_ENABLED`: if `true`, high level profiler will be enabled. Resulting JSON traces can be viewed in [perfetto.habana.ai](https://perfetto.habana.ai/#!/viewer). Disabled by default.
-- `VLLM_HPU_LOG_STEP_GRAPH_COMPILATION`: if `true`, will log graph compilations per each vLLM engine step, only when there was any - highly recommended to use alongside `PT_HPU_METRICS_GC_DETAILS=1`. Disabled by default.
-- `VLLM_HPU_LOG_STEP_GRAPH_COMPILATION_ALL`: if `true`, will log graph compilations per each vLLM engine step, always, even if there were none. Disabled by default.
-- `VLLM_HPU_LOG_STEP_CPU_FALLBACKS`: if `true`, will log cpu fallbacks per each vLLM engine step, only when there was any. Disabled by default.
-- `VLLM_HPU_LOG_STEP_CPU_FALLBACKS_ALL`: if `true`, will log cpu fallbacks per each vLLM engine step, always, even if there were none. Disabled by default.
+- `VLLM_PROFILER_ENABLED`: If `true`, enable the high level profiler. Resulting JSON traces can be viewed in [perfetto.habana.ai](https://perfetto.habana.ai/#!/viewer). `false` by default.
+- `VLLM_HPU_LOG_STEP_GRAPH_COMPILATION`: If `true`, log graph compilations for each vLLM engine step when any occurs. Highly recommended to use with `PT_HPU_METRICS_GC_DETAILS=1`. `false` by default.
+- `VLLM_HPU_LOG_STEP_GRAPH_COMPILATION_ALL`: If `true`, always log graph compilations for each vLLM engine step even if none occurred. `false` by default.
+- `VLLM_HPU_LOG_STEP_CPU_FALLBACKS`: If `true`, log CPU fallbacks for each vLLM engine step when any occurs. `false` by default.
+- `VLLM_HPU_LOG_STEP_CPU_FALLBACKS_ALL`: if `true`, always log CPU fallbacks for each vLLM engine step even if none occurred. `false` by default.
 
 **Performance tuning knobs:**
 
@@ -381,7 +379,7 @@ INFO 08-02 17:38:43 hpu_executor.py:91] init_cache_engine took 37.92 GiB of devi
 
 Additionally, there are HPU PyTorch Bridge environment variables impacting vLLM execution:
 
-- `PT_HPU_LAZY_MODE`: if `0`, PyTorch Eager backend for Gaudi will be used, if `1` PyTorch Lazy backend for Gaudi will be used, `1` is default
+- `PT_HPU_LAZY_MODE`: if `0`, PyTorch Eager backend for Gaudi will be used; if `1`, PyTorch Lazy backend for Gaudi will be used. `1` is default.
 - `PT_HPU_ENABLE_LAZY_COLLECTIVES`: required to be `true` for tensor parallel inference with HPU Graphs
 
 ## Troubleshooting: tweaking HPU graphs