From 059ddeb2eb989151cdd0ca27c0b77274832530e6 Mon Sep 17 00:00:00 2001 From: RonanSynnottArm Date: Fri, 15 Aug 2025 17:30:24 +0900 Subject: [PATCH 01/99] Zena debug part 1 --- .../automotive/zenacssdebug/_index.md | 51 ++++++++++++ .../automotive/zenacssdebug/_next-steps.md | 8 ++ .../automotive/zenacssdebug/config.md | 53 +++++++++++++ .../automotive/zenacssdebug/configdb.png | Bin 0 -> 27998 bytes .../automotive/zenacssdebug/connect.md | 67 ++++++++++++++++ .../automotive/zenacssdebug/launch.md | 52 +++++++++++++ .../automotive/zenacssdebug/zena.md | 73 ++++++++++++++++++ 7 files changed, 304 insertions(+) create mode 100644 content/learning-paths/automotive/zenacssdebug/_index.md create mode 100644 content/learning-paths/automotive/zenacssdebug/_next-steps.md create mode 100644 content/learning-paths/automotive/zenacssdebug/config.md create mode 100644 content/learning-paths/automotive/zenacssdebug/configdb.png create mode 100644 content/learning-paths/automotive/zenacssdebug/connect.md create mode 100644 content/learning-paths/automotive/zenacssdebug/launch.md create mode 100644 content/learning-paths/automotive/zenacssdebug/zena.md diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md new file mode 100644 index 0000000000..2f1b2e78da --- /dev/null +++ b/content/learning-paths/automotive/zenacssdebug/_index.md @@ -0,0 +1,51 @@ +--- +title: Debug Arm Zena CSS Reference Software Stack with Arm Development Studio + +description: Learn how to set up a debug environment for Arm Zena CSS Reference Software Stack + +minutes_to_complete: 60 + +who_is_this_for: This topic is for software developers who wish to use Arm Development Studio to explore and debug the Arm Zena CSS Reference Software Stack + +learning_objectives: + - Set up debug configuration for the Arm Zena CSS FVP + - Debug System Control Processor (SCP) from boot time + - Debug Safety Island (SI) processor + - Debug Linux OS on Applications Processor + - Debug Linux application + +prerequisites: + - Ubuntu 22.04 host machine + - Arm Development Studio 2024.1 (or later) and an appropriate license + - A basic understanding of the Arm Zena CSS software stack + +author: Ronan Synnott + +### Tags +skilllevels: Introductory +subjects: +armips: + - Arm Zena CSS +operatingsystems: + - Linux +tools_software_languages: + - Arm Development Studio + + +further_reading: + - resource: + title: Arm Zena Compute System (CSS) + link: https://developer.arm.com/Compute%20Subsystems/Arm%20Zena%20Compute%20Subsystem + type: website + - resource: + title: Arm Development Studio + link: https://developer.arm.com/Tools%20and%20Software/Arm%20Development%20Studio + type: website + + +### FIXED, DO NOT MODIFY +# ================================================================================ +weight: 1 # _index.md always has weight of 1 to order correctly +layout: "learningpathall" # All files under learning paths have this same wrapper +learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. +--- diff --git a/content/learning-paths/automotive/zenacssdebug/_next-steps.md b/content/learning-paths/automotive/zenacssdebug/_next-steps.md new file mode 100644 index 0000000000..c3db0de5a2 --- /dev/null +++ b/content/learning-paths/automotive/zenacssdebug/_next-steps.md @@ -0,0 +1,8 @@ +--- +# ================================================================================ +# FIXED, DO NOT MODIFY THIS FILE +# ================================================================================ +weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation. +title: "Next Steps" # Always the same, html page title. +layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing. +--- diff --git a/content/learning-paths/automotive/zenacssdebug/config.md b/content/learning-paths/automotive/zenacssdebug/config.md new file mode 100644 index 0000000000..9a63bfe374 --- /dev/null +++ b/content/learning-paths/automotive/zenacssdebug/config.md @@ -0,0 +1,53 @@ +--- +# User change +title: "Model Configuration" + +weight: 4 # 1 is first, 2 is second, etc. + +# Do not modify these elements +layout: "learningpathall" +--- + +# Debug Configuration + +Arm Development Studio requires a `Debug Configuration` of the target that it will connect to. + +As of 2025.0, there is no such configuration provided 'out-of-the-box' for the Zena CSS FVP. However creating such a configuration is straight forward. + +See the Arm Development Studio [Getting Started Guide](https://developer.arm.com/documentation/101469/latest/Migrating-from-DS-5-to-Arm-Development-Studio/Connect-to-new-or-custom-models) for full instructions, but summarized below. + +## Launch FVP + +As per previous section, launch FVP with the Iris server enabled: + +```command +kas shell -c "../layers/meta-arm/scripts/runfvp -t tmux --verbose -- --iris-server --iris-port 7100" +``` + +## Configuration Database + +Debug Configurations are stored in a configuration database. You must first create a local database in which to store the configuration. + +Navigate to `File` > `New` > `Other`, and then select `Configuration Database` > `Configuration Database` from the drop-down list. + +![New Configuration Database](configdb.png) + +Click `Next`. Give the Database a meaningful name, and click `Finish`. + +## Debug Configuration + +Navigate to the same wizard as above, and select `Model Configuration`. + +Click `Next`, and you will be prompted to select the above `Configuration Database`. Click `Next` again, and you will be prompted to select a Model Interface. + +Select `Iris` from the pulldown, and click `Next`. + +You will then be prompted to locate the model to connect to. + +Select `Browse for model running on local host`. 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zcn-G|7Z;l~Pk005X9>4KVdozLwTFRB#D|JwKk@s>EUXN|8J*id|4{KntmfIH1^x4c z;P9_!I?5-!&0bNW)rF((Kl%CwznYVg;afijR2J~BA9Mc9X*}O{Z@!G*O*>w;@Fu@& z+*aZJK8-8pB_=6JRIwn|jrj0KULxe_qW8~=26~8to&)xpSYpFRkIMYX6iehs2(zRde6p9%QU(Y$q|RNd~$e*iD9_iX?G literal 0 HcmV?d00001 diff --git a/content/learning-paths/automotive/zenacssdebug/connect.md b/content/learning-paths/automotive/zenacssdebug/connect.md new file mode 100644 index 0000000000..b975bbb319 --- /dev/null +++ b/content/learning-paths/automotive/zenacssdebug/connect.md @@ -0,0 +1,67 @@ +--- +# User change +title: "Debug Connections" + +weight: 5 # 1 is first, 2 is second, etc. + +# Do not modify these elements +layout: "learningpathall" +--- + +You are now ready to create debug connections for each of the sub-systems within Zena CSS. This section will create the connections, which will be subsequently enhanced in the following section. You may prefer to fully set up one such connection before moving to others. + +Development Studio has full support for Heterogeneous systems such as Zena CSS, and so you can connect to all processors simultaneously. + +### Debug connection project + +It may be sensible to create a project to store these connections (`.launch` files) in. + +Select `File` > `New...` > `Project` > `General` > `Project`, and give it a meaningful name (`Connections`). + +## RSE (Cortex-M55) + +Runtime Security Engine (RSE) is based on [Cortex-M55](https://developer.arm.com/Processors/Cortex-M55) core and is a security subsystem fulfilling the role of Root of Trust. + +Select `File` > `New` > `Model Connection`. + +{{% notice Note %}} +You can also use `File` > `New` > `Other` > `Arm Debugger` > `Model Connection`, or + +`Create a debug connection...` shortcut in the `Debug Control` pane. +{{% /notice %}} + +Specify a connection name (`RSE`), and associate with the above project. Click `Next`. + +Locate the FVP based on the name you gave it previously (`Zena_CSS_FVP`). The text filter can help locate easily. + +You will then be presented with the `Edit configuration` pane. In the `Connection` tab, scroll down to locate `Bare Metal Debug` > `Arm_Cortex-M55`. + +As we shall be later launching the FVP with the software stack loaded, select `Connect to an already running model`. + +Assuming the same host will be running both the FVP and the debugger, specify the `Connection address` as the default `127.0.0.1:7100`. + +{{% notice Note %}} +`127.0.0.1` is the same as `localhost`. It is also possible to connect to a remote host by specifying appropriate address. + +`7100` is the default port number. You may need to change this if necessary. +{{% /notice %}} + +Click `Apply` to save the connection information, and `Close`. Observe that `RSE.launch` is created inside the `Connections` project. + +## Safety Island (Cortex-R82AE) + +The Safety Island is a subsystem based on [Cortex-R82AE](https://developer.arm.com/Processors/Cortex-R82AE) core. The software running on the Safety Island is responsible for power, clock and CMN control. + +The procedure to create this connection is very similar to the above, other than to select `Bare Metal Debug` > `Arm_Cortex-R82AE` from the pull down. + +You can copy-and-paste `RSE.launch` as `SI.launch` and open to change the CPU. + +## Primary Compute (Cortex-A720AE) + +The Primary Compute consists of four processor clusters to run a rich OS such as Linux. Each processor cluster includes four [Cortex-A720AE](https://developer.arm.com/Processors/Cortex-A720AE) cores and a [DSU-120AE](https://developer.arm.com/Processors/DSU-120AE) DynamIQ Shared Unit. + +The application processors will be debugged in an SMP configuration with Linux Kernel awareness. + +As above, create `Primary.launch` connection and scroll to `Linux Kernel Debug` > `ARM_Cortex-A720AEx16 SMP Cluster 1`. + +This will connect to all Cortex-A720AE processors present in the FVP, though only cores 0-3 are used. diff --git a/content/learning-paths/automotive/zenacssdebug/launch.md b/content/learning-paths/automotive/zenacssdebug/launch.md new file mode 100644 index 0000000000..dc81227454 --- /dev/null +++ b/content/learning-paths/automotive/zenacssdebug/launch.md @@ -0,0 +1,52 @@ +--- +# User change +title: "Launch FVP" + +weight: 3 # 1 is first, 2 is second, etc. + +# Do not modify these elements +layout: "learningpathall" +--- + +From the [documentation](https://arm-auto-solutions.docs.arm.com/en/v2.0/rd-aspen/user_guide/reproduce.html#run-the-fvp) the default command to launch the FVP with the software stack loaded is: + +```command +kas shell -c "../layers/meta-arm/scripts/runfvp -t tmux --verbose" +``` + +However this does not enable the Iris debug server inside the model, and so will not be debuggable. Additional command options are necessary. + +We will use the following. See output of `FVP_RD_Aspen --help` for full list and explanation. Options are case-sensitive. + +| Option | Alias | Notes | +|---------------------- |--------- |---------------------------------------------- | +| \-\-iris-server | -I | Start Iris Debug Server | +| \-\-iris-port | | Specify a port number (default = `7100`) | +| \-\-run | -R | Run simulation when debug server started | +| \-\-iris-allow-remote | -A | Allow remote connections (if different hosts) | + +## Launch FVP with additional options + +To launch the FVP with additional options, modify the above command by adding `--` and then the options. + +For example, to launch the model with the debug server and hold at the initial reset condition: + +```command +kas shell -c "../layers/meta-arm/scripts/runfvp -t tmux --verbose -- --iris-server --iris-port 7100" +``` + +To launch the model and start running (so that it can start to boot up): + +```command +kas shell -c "../layers/meta-arm/scripts/runfvp -t tmux --verbose -- --iris-server --iris-port 7100 --run" +``` + +To launch the model so that remote hosts can access it (not recommended if not needed), using options aliases: + +```command +kas shell -c "../layers/meta-arm/scripts/runfvp -t tmux --verbose -- -I -A --iris-port 7100" +``` + +{{% notice Note %}} +It is recommended to specify the port number used even if it is the default as that must match the debug connection setting (see later). +{{% /notice %}} diff --git a/content/learning-paths/automotive/zenacssdebug/zena.md b/content/learning-paths/automotive/zenacssdebug/zena.md new file mode 100644 index 0000000000..5e439d5805 --- /dev/null +++ b/content/learning-paths/automotive/zenacssdebug/zena.md @@ -0,0 +1,73 @@ +--- +# User change +title: "Getting started" + +weight: 2 # 1 is first, 2 is second, etc. + +# Do not modify these elements +layout: "learningpathall" +--- + +# Arm Zena Compute Subsystem + +The Arm Zena Compute Subsystem (CSS) consists of a high-performance Arm Cortex-A720AE Application Processor (Primary Compute) system augmented with an Arm Cortex-R82AE based Safety Island (SI) and real-time domain to host additional system safety monitoring and real-time services. + +The system additionally includes a Runtime Security Engine (RSE) used for the secure boot of the system elements and the runtime secure services. + +The Arm Zena CSS software stack provides an open-source, integrated solution running on a Fixed Virtual Platform (FVP). + +The reference software stack and the FVP are freely available. + +For more information, see [Arm Zena Compute Subsystem (CSS)](https://developer.arm.com/Compute%20Subsystems/Arm%20Zena%20Compute%20Subsystem) and associated links. + +## Build software stack + +The steps to download and build the software stack are described in the [User Guide](https://arm-auto-solutions.docs.arm.com/en/v2.0/rd-aspen/user_guide/reproduce.html). + +The default `Arm Automotive Solutions Demo` build is used. + +{{% notice Note %}} +This learning path is focused on **debugging** the software stack only. +{{% /notice %}} + +## Verify correct build and execution + +Once the software stack has been built, you can verify that it runs successfully with the command: + +``` command +kas shell -c "../layers/meta-arm/scripts/runfvp -t tmux --verbose" +``` + +The system will run through the boot process until a Linux prompt is available (in `terminal_ns_uart0`). + +Use `Ctrl+C` on the command terminal to terminate. + +## Install FVP (optional) + +The FVP is downloaded and installed as part of the build process above. + +The `Arm-Zena-CSS-FVP` can also be independently downloaded from the Arm Developer [website](https://developer.arm.com/Tools%20and%20Software/Fixed%20Virtual%20Platforms/Automotive%20FVPs). + +See also the Arm Ecosystem FVPs and Architecture Envelope Models [Install Guide](/install-guides/fm_fvp/eco_fvp/). + +{{% notice Note %}} +For legacy reasons the FVP is named is `FVP_RD_Aspen`. +{{% /notice %}} + +# Arm Development Studio + +Arm Development Studio is a software development solution with support of multicore debug for Arm CPUs. It provides the earliest support for the latest processors. + +The CPUs implemented within Arm Zena CSS are supported by Arm Development Studio 2024.0 and later, though 2024.1 or later is recommended for appropriate Linux OS support. At time of writing the latest version available is 2025.0, and that is the version used for screenshots etc in this learning path. + +For more information see [Arm Development Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Development%20Studio). + +Arm Development Studio is a commercial, license managed, product. For installation and set up instructions, see this [Install Guide](/install-guides/armds/). + +Launch the IDE. We recommend creating a new workspace folder. + +If prompted by the launcher (this is disabled by default) create a new folder there, else select `File` > `Switch Workspace` > `Other...`. + +{{% notice %}} +To enable this prompt by default, navigate to `Window` > `Preferences` > `General` > `Startup and Shutdown` > `Workspaces`, and enable `Prompt for workspace on startup`. +{{% /notice %}} From e089ca2d2f51101f2d8875b91a43f13bf4d05611 Mon Sep 17 00:00:00 2001 From: RonanSynnottArm Date: Mon, 18 Aug 2025 11:51:34 +0900 Subject: [PATCH 02/99] test from linux --- content/learning-paths/automotive/zenacssdebug/_index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md index 2f1b2e78da..902ab114b8 100644 --- a/content/learning-paths/automotive/zenacssdebug/_index.md +++ b/content/learning-paths/automotive/zenacssdebug/_index.md @@ -5,7 +5,7 @@ description: Learn how to set up a debug environment for Arm Zena CSS Reference minutes_to_complete: 60 -who_is_this_for: This topic is for software developers who wish to use Arm Development Studio to explore and debug the Arm Zena CSS Reference Software Stack +who_is_this_for: This topic is for software developers who wish to use Arm Development Studio to explore and debug the Arm Zena CSS Reference Software Stack. learning_objectives: - Set up debug configuration for the Arm Zena CSS FVP From 137c3f644a7bacd123b78976a2bc1732db9a1786 Mon Sep 17 00:00:00 2001 From: RonanSynnottArm Date: Tue, 19 Aug 2025 13:44:26 +0900 Subject: [PATCH 03/99] Ready for review --- .../automotive/zenacssdebug/_index.md | 2 +- .../automotive/zenacssdebug/config.md | 2 - .../automotive/zenacssdebug/connect.md | 22 +++-- .../zenacssdebug/debugger_commands.png | Bin 0 -> 147990 bytes .../automotive/zenacssdebug/launch.md | 18 ++-- .../automotive/zenacssdebug/primarycompute.md | 70 +++++++++++++++ .../automotive/zenacssdebug/rse.md | 83 ++++++++++++++++++ .../automotive/zenacssdebug/safetyisland.md | 67 ++++++++++++++ 8 files changed, 247 insertions(+), 17 deletions(-) create mode 100644 content/learning-paths/automotive/zenacssdebug/debugger_commands.png create mode 100644 content/learning-paths/automotive/zenacssdebug/primarycompute.md create mode 100644 content/learning-paths/automotive/zenacssdebug/rse.md create mode 100644 content/learning-paths/automotive/zenacssdebug/safetyisland.md diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md index 902ab114b8..dc5b4f100a 100644 --- a/content/learning-paths/automotive/zenacssdebug/_index.md +++ b/content/learning-paths/automotive/zenacssdebug/_index.md @@ -17,7 +17,7 @@ learning_objectives: prerequisites: - Ubuntu 22.04 host machine - Arm Development Studio 2024.1 (or later) and an appropriate license - - A basic understanding of the Arm Zena CSS software stack + - A basic understanding of the Arm Zena CSS software stack and Arm processors author: Ronan Synnott diff --git a/content/learning-paths/automotive/zenacssdebug/config.md b/content/learning-paths/automotive/zenacssdebug/config.md index 9a63bfe374..ae5eafe666 100644 --- a/content/learning-paths/automotive/zenacssdebug/config.md +++ b/content/learning-paths/automotive/zenacssdebug/config.md @@ -30,8 +30,6 @@ Debug Configurations are stored in a configuration database. You must first crea Navigate to `File` > `New` > `Other`, and then select `Configuration Database` > `Configuration Database` from the drop-down list. -![New Configuration Database](configdb.png) - Click `Next`. Give the Database a meaningful name, and click `Finish`. ## Debug Configuration diff --git a/content/learning-paths/automotive/zenacssdebug/connect.md b/content/learning-paths/automotive/zenacssdebug/connect.md index b975bbb319..2379ca5508 100644 --- a/content/learning-paths/automotive/zenacssdebug/connect.md +++ b/content/learning-paths/automotive/zenacssdebug/connect.md @@ -8,6 +8,8 @@ weight: 5 # 1 is first, 2 is second, etc. layout: "learningpathall" --- +## Debug Connections + You are now ready to create debug connections for each of the sub-systems within Zena CSS. This section will create the connections, which will be subsequently enhanced in the following section. You may prefer to fully set up one such connection before moving to others. Development Studio has full support for Heterogeneous systems such as Zena CSS, and so you can connect to all processors simultaneously. @@ -18,7 +20,7 @@ It may be sensible to create a project to store these connections (`.launch` fil Select `File` > `New...` > `Project` > `General` > `Project`, and give it a meaningful name (`Connections`). -## RSE (Cortex-M55) +### RSE (Cortex-M55) Runtime Security Engine (RSE) is based on [Cortex-M55](https://developer.arm.com/Processors/Cortex-M55) core and is a security subsystem fulfilling the role of Root of Trust. @@ -41,27 +43,33 @@ As we shall be later launching the FVP with the software stack loaded, select `C Assuming the same host will be running both the FVP and the debugger, specify the `Connection address` as the default `127.0.0.1:7100`. {{% notice Note %}} -`127.0.0.1` is the same as `localhost`. It is also possible to connect to a remote host by specifying appropriate address. +`127.0.0.1` is the same as `localhost`, that is the same host machine as is running the FVP. + +It is also possible to connect to a remote host by specifying appropriate IP address, and launching FVP with the `-A` option. `7100` is the default port number. You may need to change this if necessary. {{% /notice %}} Click `Apply` to save the connection information, and `Close`. Observe that `RSE.launch` is created inside the `Connections` project. -## Safety Island (Cortex-R82AE) +### Safety Island (Cortex-R82AE) The Safety Island is a subsystem based on [Cortex-R82AE](https://developer.arm.com/Processors/Cortex-R82AE) core. The software running on the Safety Island is responsible for power, clock and CMN control. The procedure to create this connection is very similar to the above, other than to select `Bare Metal Debug` > `Arm_Cortex-R82AE` from the pull down. -You can copy-and-paste `RSE.launch` as `SI.launch` and open to change the CPU. +{{% notice %}} +For convenience you can copy-and-paste `RSE.launch` as `SI.launch` and simply modify the CPU. +{{% /notice %}} -## Primary Compute (Cortex-A720AE) +### Primary Compute (Cortex-A720AE) The Primary Compute consists of four processor clusters to run a rich OS such as Linux. Each processor cluster includes four [Cortex-A720AE](https://developer.arm.com/Processors/Cortex-A720AE) cores and a [DSU-120AE](https://developer.arm.com/Processors/DSU-120AE) DynamIQ Shared Unit. The application processors will be debugged in an SMP configuration with Linux Kernel awareness. -As above, create `Primary.launch` connection and scroll to `Linux Kernel Debug` > `ARM_Cortex-A720AEx16 SMP Cluster 1`. +As above, create `Primary_init.launch` connection and scroll to `Bare Metal Debug` > `ARM_Cortex-A720AE_0`. This will connect to just CPU0, leaving the other CPUs free to run. + +To debug the Linux kernel we can make use of the [OS awareness](https://developer.arm.com/documentation/101470/latest/Debugging-Embedded-Systems/About-OS-awareness) feature of the Arm Debugger. -This will connect to all Cortex-A720AE processors present in the FVP, though only cores 0-3 are used. +Create `Primary_Linux.launch` connection and scroll to `Linux Kernel Debug` > `ARM_Cortex-A720AEx16 SMP Cluster 1`. 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content/learning-paths/automotive/zenacssdebug/_index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md index dc5b4f100a..28732c1161 100644 --- a/content/learning-paths/automotive/zenacssdebug/_index.md +++ b/content/learning-paths/automotive/zenacssdebug/_index.md @@ -9,10 +9,10 @@ who_is_this_for: This topic is for software developers who wish to use Arm Devel learning_objectives: - Set up debug configuration for the Arm Zena CSS FVP - - Debug System Control Processor (SCP) from boot time - - Debug Safety Island (SI) processor - - Debug Linux OS on Applications Processor - - Debug Linux application + - Debug Runtime Security Engine (RSE) from boot time + - Debug Safety Island (SI) + - Debug Linux OS on Primary Compute cores +## - Debug Linux application prerequisites: - Ubuntu 22.04 host machine From 11819fa0649f8511a64c26a2fe3e561d61177f4c Mon Sep 17 00:00:00 2001 From: Zenon Xiu Date: Sat, 23 Aug 2025 19:43:01 +0800 Subject: [PATCH 05/99] use Streamline to analyze LLM running on CPU with llama.cpp --- ..._generation_at_Prefill_and_Decode_stage.md | 179 ++++++++++++++++++ .../llama_cpp_streamline/Conclusion.md | 13 ++ .../llama_cpp_streamline/Deep_dive.md | 132 +++++++++++++ .../llama_cpp_streamline/Introduction.md | 19 ++ .../Introduction_to_llama_cpp.md | 38 ++++ .../llama_cpp_streamline/Multi_threads.md | 38 ++++ .../llama_cpp_streamline/_index.md | 55 ++++++ .../llama_cpp_streamline/_next-steps.md | 8 + .../images/Decode_only.png | Bin 0 -> 124434 bytes .../images/Prefill_only.png | Bin 0 -> 117659 bytes .../images/annotation_marker_1.png | Bin 0 -> 8068 bytes .../images/annotation_marker_2.png | Bin 0 -> 20620 bytes .../images/annotation_marker_prefill.png | Bin 0 -> 162753 bytes .../images/annotation_pmu_stall.png | Bin 0 -> 69293 bytes .../images/annotation_prefill_call_stack.png | Bin 0 -> 95931 bytes 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a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Analyzing_token_generation_at_Prefill_and_Decode_stage.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Analyzing_token_generation_at_Prefill_and_Decode_stage.md new file mode 100644 index 0000000000..2629adbb5a --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Analyzing_token_generation_at_Prefill_and_Decode_stage.md @@ -0,0 +1,179 @@ +--- +title: Analyzing token generation at Prefill and Decode stage +weight: 3 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +# Analyzing token generation at Prefill and Decode stage +We use Annotation Marker feature of Streamline and integrate the Annotation Marker generation code to the llama.cpp project. We can have a visible token generation view at Prefill and Decode stage. +You can find more information about Annotation Marker feature [here](https://developer.arm.com/documentation/101816/9-7/Annotate-your-code?lang=en). + +## Steps of llama.cpp integration and Streamline setup + +### Step 1: Build Streamline Annotation Library +Install ArmDS or Arm Streamline on your host PC first. +You can get Streamline Annotation support code in the installation directory like Arm\Development Studio 2024.1\sw\streamline\gator\annotate. +You can also get the Annotation support code here, https://github.com/ARM-software/gator/tree/main , please download the proper code that matches the version of your Streamline tool. + +Then you can build the Streamline Annotation Library by running +```bash +make CROSS_COMPILE=/path/to/aarch64_linux_gcc_tool +``` + +for example, +```bash +make CROSS_COMPILE=./Work/arm-gnu-toolchain-13.3.rel1-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu- +``` +The static linked library, libstreamline_annotate.a, will be produced. + +### Step 2: Integrate Annotation Marker code to llama.cpp +Download llama.cpp code from https://github.com/ggml-org/llama.cpp/archive/refs/tags/b6202.tar.gz +Go to llama.cpp root directory and create a directory ‘streamline_annotation’ under the llama.cpp root directory +```bash +cd ./llama.cpp +mkdir streamline_annotation +``` + +Copy the library ‘libstreamline_annotate.a’ and the header file ‘streamline_annotate.h’ from Step 1 to the directory ‘streamline_annotation’. + +To link libstreamline_annotate.a for building llama-cli, change llama.cpp\CMakeLists.txt by adding following lines, + +```script +set(STREAMLINE_LIB_PATH ${CMAKE_SOURCE_DIR}/streamline_annotation/libstreamline_annotate.a) +target_include_directories(llama-cli PRIVATE ${CMAKE_SOURCE_DIR}/streamline_annotation) +target_link_libraries(${TARGET} PRIVATE ${STREAMLINE_LIB_PATH} ) +``` + +To add Annotation Markers to llama-cli, change the llama-cli code ./llama.cpp/tools/main/main.cpp by adding +```c +#include "streamline_annotate.h" +``` +And + +```c + for (int i = 0; i < (int) embd.size(); i += params.n_batch) { + int n_eval = (int) embd.size() - i; + if (n_eval > params.n_batch) { + n_eval = params.n_batch; + } + + LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); + + // Add annotation marker code for Streamline + { + char printf_buf[200]; + sprintf(printf_buf, "past %d, n_eval %d", n_past,n_eval ); + ANNOTATE_MARKER_STR(printf_buf); + } + // End of annotation marker + + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { + LOG_ERR("%s : failed to eval\n", __func__); + return 1; + } +``` + + +### Step 3: Build llama-cli executable +For convenience, we build the llama-cli by using static link. +You can get the aarch64 gcc compiler toolchain here, https://developer.arm.com/downloads/-/arm-gnu-toolchain-downloads . + +Firstly, create a new directory ‘build’ understand llama.cpp directory and go into it. +```bash +mkdir ./build & cd ./build +``` +Then configure the project by running +```bash +cmake .. -DCMAKE_SYSTEM_NAME=Linux -DCMAKE_SYSTEM_PROCESSOR=arm -DCMAKE_C_COMPILER=aarch64-none-linux-gnu-gcc -DCMAKE_CXX_COMPILER=aarch64-none-linux-gnu-g++ -DLLAMA_NATIVE=OFF -DLLAMA_F16C=OFF -DLLAMA_GEMM_ARM=ON -DBUILD_SHARED_LIBS=OFF -DCMAKE_EXE_LINKER_FLAGS="-static -g" -DGGML_OPENMP=OFF -DCMAKE_C_FLAGS="-march=armv8.2-a+i8mm+dotprod -g" -DCMAKE_CXX_FLAGS="-march=armv8.2-a+dotprod+i8mm -g" -DGGML_CPU_KLEIDIAI=ON -DGGML_OPENMP=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_EXAMPLES=ON -DLLAMA_CURL=OFF +``` + +Set CMAKE_C_COMPILER and DCMAKE_CXX_COMPILER to your cross compiler path. Make sure that “-march” in DCMAKE_C_FLAGS and CMAKE_CXX_FLAGS matches your Arm CPU hardware. + +In this guide, we use an Arm CPU which supports NEON Dotprod and I8MM instructions, so ‘-march’ is specified as ‘armv8.2-a+dotprod+i8mm’. We also specify ‘-static’ and ‘-g’ options so that the produced llama-cli executable is statically linked and with debug info. By doing this, it makes source code/function level profiling easier and the llama-cli executable runnable on various version of Arm64 Linux/Android. + +Now, we can build the project by running +```bash +cmake --build ./ --config Release +``` + +After the building process, you should find the llama-cli executable in ./build/bin/ directory. + +### Step 4: Run llama-cli and use Streamline for analysis +Copy following files to your Arm64 platform, +* llama-cli executable +* the ‘gatord’ executable in ArmDS or Streamline installation folder, such as ‘Arm\Development Studio 2024.1\sw\streamline\bin\linux\arm64’ for Linux and ‘Arm\Development Studio 2024.1\sw\streamline\bin\android\arm64’ for Android +* the LLM model, Qwen1_5-0_5b-chat-q4_0.gguf + +Then run the gatord on your Arm64 target +```bash +./gatord +``` +You should see some messages below, + +``` bash +Streamline Data Recorder v9.4.0 (Build 9b1e8f8) +Copyright (c) 2010-2024 Arm Limited. All rights reserved. +Gator ready +``` + +Then launch the Streamline application on your host PC, connect to the gatord running on your Arm64 target with either TCP or ADB connection. You can select PMU events to be monitored at this point. +![text#center](images/streamline_capture.png "Figure 5. Streamline Capture") + +Please set the path of llama-cli executable is specified for Streamline so that the debug info in the executable can be used for analysis. + +![text#center](images/streamline_capture_image.png "Figure 6. Streamline config") + +You can click ‘Start Capture’ button on Streamline to start collecting data from the Arm64 target. + +Note: This guide is not intended to introduce how to use Streamline, if you encounter any issue during setting up gatord or Streamline, please seek for help from Arm support. + +Now, run the llama-cli executable as below, + +``` bash +./llama-cli -m qwen1_5-0_5b-chat-q4_0.gguf -p "<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n<|im_start|>user\nTell me a story about a fox and a crow? Please do not tell the traditional story in Aesop's fables. Please tell me a positive story about friendship and love. The story should have no more than 400 words<|im_end|>\n<|im_start|>assistant\n" -st -t 1 +``` + +After a while, you can stop the Streamline data collection by clicking ‘Stop’ button on Streamline. Then Streamline on your host PC will start the data analysis. + +## Analyze the data with Streamline +From the timeline view of Streamline, we can see some Annotation Markers, since we add an Annotation Marker before llama_decode function, each Annotation Marker marks the start time of a token generation. +![text#center](images/annotation_marker_1.png "Figure 7. Annotation Marker") + +We also add a string that includes the position input the tokens and number of input tokens to process in each Annotation Marker. The string can be shown when clicking those Annotation Markers. For example, + +![text#center](images/annotation_marker_2.png "Figure 8. Annotation Marker 2") + +The number after ‘past’ indicates the position of input tokens, the number after ‘n_eval’ indicates the number of tokens to be processed this time. +As shown in the timeline view below, with help of Annotation Markers, we can clearly identify the Prefill stage and Decode stage. +![text#center](images/annotation_marker_prefill.png "Figure 9. Annotation Marker Prefill and Decode stage") + +We can see that the first token generated at Prefill stage takes more time, since 78 input tokens have to be processed at Prefill stage, it involves a lot of GEMM operations. At Decode stage, tokens are generated one by one at mostly equal speed, each token takes less time, thanks to the effect of KV cache. At Decode stage, it involves many GEMV operations. + +We can further investigate it with PMU event counters that are captured by Streamline. At Prefill stage, the amount of computation, which are indicated by PMU event counters counting number of Advanced SIMD (NEON), Floating point, Integer data processing instruction, is large. However, the memory access is relatively low. Especially, the number of L3 cache refill/miss is much lower than that of Decode stage. + +At Decode stage, the amount of computation is relatively less (since the time of each token is less), but the number of L3 cache refill/miss goes much higher. +By monitoring more PMU events, Backend Stall Cycles and Backend Stall Cycles due to Memory stall, +![text#center](images/annotation_pmu_stall.png "Figure 10. Annotation PMU stall cycle") + +We can see that at Prefill stage, Backend Stall Cycles due to Memory stall are only about 10% of total Backend Stall Cycles. However, at Decode stage, Backend Stall Cycles due to Memory stall are only around 50% of total Backend Stall Cycles. +All those PMU event counters indicate that it is compute-bound at Prefill stage, and memory-bound at Decode stage. + +Now, let us further profile the code execution with Streamline. In the ‘Call Paths’ view of Streamline, we can see the percentage of running time of call stack. +![text#center](images/annotation_prefill_call_stack.png "Figure 11. Call stack") + +In the ‘Functions’ view of Streamline, we can see the percentage of running time of functions. +![text#center](images/annotation_prefill_functions.png "Figure 12. Functions") + +As we can see, the function, graph_compute, takes the largest part of the running time. With understanding of llama.cpp code, it shows that large amounts of GEMM and GEMV operation take most of the time. +* The computation (GEMM and GEMV) of Q, K, V vectors and most of FFN layers have weights with Q4_0 data type, and the input activations are with FP32 data type. The computation is forwarded to KleidiAI trait by ggml_cpu_extra_compute_forward. KleidiAI ukernels implemented with NEON Dotprod and I8MM vector instructions are used to accelerate the computation. + - At Prefill stage, kai_run_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm ukernel is used for GEMM MAT_MUL(Matrix Multiply) operators. It takes the advantage of NEON I8MM instruction. Since Prefill stage only takes small percentage of the whole time, the percentage of this function is small as shown in figures above. However, if we focus on Prefill stage only, with ‘Samplings’ view in Timeline. We can see kai_run_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm takes the largest part of the whole Prefill stage. + ![text#center](images/Prefill_only.png "Figure 13. Prefill only") + + - At Decode stage, kai_run_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod ukernel is used for GEMV MAT_MUL operators. It takes advantage of NEON Dotprod instruction. If we focus on Decode stage only, we can see this function takes the second largest percentage. + ![text#center](images/Decode_only.png "Figure 14. Decode only") + +* There is a result output linear layer in qwen1_5-0_5b-chat-q4_0 model, the wights are with Q6_K data type. The computation for this layer is a huge [1, 1024] x [1024, 151936] GEMV operation, where 1024 is the embedding size and 151936 is the vocabulary size. This operation cannot be handled by KleidiAI yet, it is handled by the ggml_vec_dot_q6_K_q8_K function in ggml-cpu library. +* The tensor nodes for computation of for Multi-Head attention are presented as three-dimension matrices with FP16 data type (KV cache also holds FP16 values), they are computed by ggml_vec_dot_f16 function in ggml-cpu library. +* The computation of RoPE, Softmax, RMSNorm layers does not take significant portion of the running time. \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Conclusion.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Conclusion.md new file mode 100644 index 0000000000..9a51e2f797 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Conclusion.md @@ -0,0 +1,13 @@ +--- +title: Conclusion +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +# Conclusion +By leveraging the Streamline tool together with a good understanding of the llama.cpp code, the execution process of the LLM model can be visualized, which helps analyze code efficiency and investigate potential optimization. + +Note that addtional annotation code in llama.cpp and gatord might somehow affect the performance. + diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Deep_dive.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Deep_dive.md new file mode 100644 index 0000000000..c022292eb2 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Deep_dive.md @@ -0,0 +1,132 @@ +--- +title: Deep dive into individual operators +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +# Deep dive into individual operators +The session provides a guide of how to use the Streamline Annotation Channel feature to analyze running time of each node in the compute graph. +More information about Streamline Annotation Channel can be found here https://developer.arm.com/documentation/101816/9-7/Annotate-your-code/User-space-annotations/Group-and-Channel-annotations?lang=en + +## Integrate Annotation Channel code to llama.cpp +In llama.cpp project, tensor nodes in compute graph are computed by the function ggml_graph_compute_thread in CPU backend, llama.cpp\ggml\src\ggml-cpu\ggml-cpu.c +```c +for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) { + struct ggml_tensor * node = cgraph->nodes[node_n]; + + ggml_compute_forward(¶ms, node); +``` +To monitor the execution time of each node, we create a annotation channel for each type of operators (GGML_OP_MUL_MAT, GGML_OP_SOFTMAX, GGML_OP_ROPE, GGML_OP_MUL etc), since GGML_OP_MAT_MUL including both GEMM and GEMV operation takes significant portion of execution time, two dedicated annotation channels are created for GEMM and GEMV respectively. + +The annotation channel starts at the beginning of ggml_compute_forward’, it stops at the end of ‘ggml_compute_forward’, so that the computation of tensor node/operator can be monitored. +Firstly, add Streamline Annotation header file to ggml-cpu.c, +```c +#include "streamline_annotate.h" +``` +Then add annotation channel code in ggml_graph_compute_thread function, +```c +for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) { + struct ggml_tensor * node = cgraph->nodes[node_n]; + // Start Annotation Channel for Streamline + { + char printf_buf[256]; + sprintf(printf_buf," %s, %s", node->name, ggml_get_name(node)); + + if(node->op==GGML_OP_MUL_MAT ) + { + if (node->src[1]->ne[1] == 1) + ANNOTATE_CHANNEL(0, printf_buf); + else + ANNOTATE_CHANNEL(1, printf_buf); + } + else + ANNOTATE_CHANNEL((node->op)+2, printf_buf); + } + + + + ggml_compute_forward(¶ms, node); + + + // End Annotation Channel for Streamline + { + + if(node->op==GGML_OP_MUL_MAT) + { + if (node->src[1]->ne[1] == 1) + ANNOTATE_CHANNEL_END(0); + else + ANNOTATE_CHANNEL_END(1); + } + + else + ANNOTATE_CHANNEL_END((node->op)+2); + } +``` + + +We also add tensor node names and the names of operation to the string annotation channels. If information of the shape and size of source tensors is required, we can change the code as below, +```c + sprintf(printf_buf,"%s %s %d_%d_%d %d_%d_%d", node->name, ggml_get_name(node), \ + node->src[0]? node->src[0]->ne[0] : 0, \ + node->src[0]? node->src[0]->ne[1] : 0 , \ + node->src[0]? node->src[0]->ne[2] : 0 ,\ + node->src[1]? node->src[1]->ne[0] : 0, \ + node->src[1]? node->src[1]->ne[1] : 0, \ + node->src[1]? node->src[1]->ne[2] : 0 \ + ); +``` +Then we need to change llama.cpp\ggml\src\ggml-cpu\CMakeLists.txt to include Streamline Annotation header file and libstreamline_annotate.a library by adding codes as below, +```bash + set(STREAMLINE_LIB_PATH ${CMAKE_SOURCE_DIR}/streamline_annotation/libstreamline_annotate.a) + target_include_directories( ${GGML_CPU_NAME} PRIVATE ${CMAKE_SOURCE_DIR}/streamline_annotation) + target_link_libraries(${GGML_CPU_NAME} PRIVATE ${STREAMLINE_LIB_PATH} ) +``` + +Then build llama-cli executable, run llama-cli and collect profiling data with Streamline as previous session. + + +## Analyze the data with Streamline +String annotations are displayed as text overlays inside the relevant channels in the details panel of the Timeline view, for example inside Channel 0 in the following screenshot. + +![text#center](images/deep_dive_1.png "Figure 15. Annotation Channel") + +The letter A is displayed in the process list to indicate the presence of annotations. +String annotations are also displayed in the Message column in the Log view. + +![text#center](images/deep_dive_2.png "Figure 16. Annotation Channel") + +### View of individual operators at Prefill stage + +The screenshot of annotation channel view at Prefill stage is shown as below, +![text#center](images/prefill_annotation_channel.png "Figure 17. Annotation Channel") + +Note that the name of operator in the screenshot above is manually marked. If the name of operator needs to be shown instead of Channel number by Streamline, ANNOTATE_NAME_CHANNEL can be added to ggml_graph_compute_thread function. +This annotation macro is defined as, +```c +ANNOTATE_NAME_CHANNEL(channel, group, string) +``` +For example, +```c + ANNOTATE_NAME_CHANNEL(0, 0, "MUL_MAT_GEMV"); + ANNOTATE_NAME_CHANNEL(1, 0, "MUL_MAT_GEMM"); +``` +The code above sets the name of annotation channel 0 as ‘MUL_MAT_GEMV’, the name of annotation channel 1 as ‘MUL_MAT_GEMM’. +We can get more detailed information by zooming in the view, +![text#center](images/prefill_annotation_channel_2.png "Figure 18. Annotation Channel") + +When moving the cursor to the Annotation channel, the tensor node name, the name of operation and the dimension of source tensor nodes will be shown. +![text#center](images/prefill_annotation_channel_3.png "Figure 19. Annotation Channel") + +The screenshot above shows a GGML_OP_MUL_MAT operator of FFN_UP node, whose source tensors dimension is [1024, 2816] and [1024, 68]. +The view clearly shows that the major time was spent on MUL_MAT GEMM operation of attention layers and FFN layers at Prefill stage. There is a large MUL_MAT GEMV operation at result output linear layer. Other operators such as MUL, Softmax, Norm, RoPE do not take significant time. + +### View of individual operators at Decode stage +The screenshot of annotation channel view at Decode stage is shown as below, +![text#center](images/decode_annotation_channel.png "Figure 20. Annotation Channel") +We can get more detailed information by zooming in the view, +![text#center](images/decode_annotation_channel_2.png "Figure 21. Annotation Channel") + +The view shows that the major time was spent on MUL_MAT GEMV operations of attention layers and FFN layers at Decode stage. Comparing with Prefill stage, there is no GEMM at those layers, GEMV operations are performed instead. The large MUL_MAT GEMV operation at result output linear layer takes more significant portion of time at Decode stage, since the time spent on each token generation at Decode stage is less due to utilization of KV cache. It corresponds to the percentage of execution time of the function ggml_vec_dot_q6_K_q8_K that we observed in previous session. \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Introduction.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Introduction.md new file mode 100644 index 0000000000..a7d77646e1 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Introduction.md @@ -0,0 +1,19 @@ +--- +title: PLACEHOLDER STEP TITLE 1 +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +# Introduction +Large Language Model (LLM) models run very smoothly on Arm CPUs. The framework for running LLM models is usually complex. To analyze the execution of LLM and utilize profiling information for potential code optimization, a good understanding of various LLMs based on transformers and appropriate analysis tools are required. +This article provides a guide of using llama-cli application built from llama.cpp and Arm’s Streamline tool to analyze the efficiency of LLM running on the CPU. The guide includes, +* How to profile token generation on LLM Prefill and Decode stage +* How to profile individual tensor OP +* How to profile LLM execution on multi-thread and multi-core + +Understanding this article requires prerequisite knowledge of transformer, llama.cpp, and Streamline. + +In this guide, we run Qwen1_5-0_5b-chat-q4_0.gguf model with llama-cli on Arm64 Linux and use Streamline for analysis. This guide should also work on Arm64 Android platform. + diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Introduction_to_llama_cpp.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Introduction_to_llama_cpp.md new file mode 100644 index 0000000000..d1a9a7424d --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Introduction_to_llama_cpp.md @@ -0,0 +1,38 @@ +--- +title: Introduction to llama.cpp +weight: 3 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +# Introduction to llama.cpp +llama.cpp is an LLM framework implemented in C++ that can be used for both training and inference. This article only covers inference on the CPU. +Llama-cli does work as below, +* Load and interpret large language models in .gguf format. +* Build a compute graph according to the model structure. The compute graph can be divided into subgraphs suitable for different backends, and the subgraphs are assigned to the most suitable backend devices. This step converts the model structure into a compute graph with many tensor nodes and operators (such as ADD, MAT_MUL, NORM, SOFTMAX) that can be actually computed. +Since this article only focuses on running LLM on the CPU, all backends are set to CPU. +* Allocate memory for the tensors nodes in the compute graph by the graph planner. +* Compute tensor nodes in the graph compute stage, where the ‘graph_compute’ function forwards the compute subgraphs to the backend devices. Then, it schedules the computation of the subgraph. The compute graph is a tree of nodes. The computation is performed by traversing the nodes in the graph. + +The steps above are wrapped in the function ‘llama_decode’. At LLM Prefill and Decode stage, llama-cli calls ‘llama_decode’ repeatedly to generate tokens. However, the parameter ‘llama_batch’ passed to ‘llama_decode' is different at Prefill and Decode stage. ‘llama_batch’ includes information such as input tokens, number of input tokens, the position of input tokens etc. + +The components of llama.cpp include +![text#center](images/llama_componetns.png "Figure 1. llama.cpp components") + +llama.cpp supports various backends such as CPU, GPU, CUDA, OpenCL etc. +For the CPU backend, it provides an optimized ggml-cpu library (mainly utilizing CPU vector instructions). For Arm CPUs, the ggml-cpu library also offers an aarch64 trait that can leverage the new I8MM instructions for acceleration. The ggml-cpu library also integrates the Arm KleidiAI library as an additional trait. + +Most of autoregressive LLMs are Decoder-only model. Let us have a brief introduction to Prefill and Decode stage of autoregressive LLMs. + +![text#center](images/llm_prefill_decode.png "Figure 2. Prefill and Decode stage") + +At the Prefill stage, multiple input tokens of the prompt are processed. It mainly performs GEMM (A matrix is multiplied by another matrix) operations to generate the first output token. + +![text#center](images/transformer_prefill.jpg "Figure 3. Transformer Prefill stage") + +At the Decode stage, by utilizing the KV cache, it mainly performs GEMV (A vector is multiplied by a matrix) operations to generate subsequent output tokens one by one. + +![text#center](images/transformer_decode.jpg "Figure 4. Transformer Decode stage") + +Therefore, the prefill stage is compute-bound, while the decode stage has relatively less computation and is more memory-bound due to KV cache memory access. This can also be seen in the subsequent analysis with Streamline. \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Multi_threads.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Multi_threads.md new file mode 100644 index 0000000000..1858f34b54 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/Multi_threads.md @@ -0,0 +1,38 @@ +--- +title: Use Streamline to analyze multi-core/multi-thread support in llama.cpp +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +# Use Streamline to analyze multi-core/multi-thread support in llama.cpp +The CPU backend in llama.cpp utilizes multi-core/multi-thread to accelerate the computation of operators. +llama.cpp creates a threadpool. The number of threads in threadpool is decided by ‘-t’ command option, if ‘-t’ command option is not specified, then it is the number of CPU cores in the system by default. +The entrypoint of secondary thread is ggml_graph_compute_secondary_thread. +When computing one tensor node/operator in the compute graph, if the worksize is big, llama.cpp splits the computation of the node/operator into multiple parts for the threads. +Let me give an example of MUL_MAT operator to demonstrate how the splitting is done. + +![text#center](images/multi_thread.png "Figure 22. Multi-thread") + +In this example, the result matrix C is split equally between four threads, each thread computes a quarter of matrix C. +The execution of multi-threads on CPU cores can be observed by Streamline. Core Map and Cluster Map modes in the Streamline Timeline view map threads and processes to processor cores or clusters. + +More information about Core Map and Cluster Map modes can be found here +https://developer.arm.com/documentation/101816/9-7/Analyze-your-capture/Viewing-application-activity/Core-Map-and-Cluster-Map-modes + +Run llama-cli with ‘-t 2 -C 0x3’ to specify two threads and thread affinity as CPU core0 and core1 as below, +```bash +./llama-cli -m qwen1_5-0_5b-chat-q4_0.gguf -p "<|im_start|>system\nYou are a helpful AI assistant.<|im_end|>\n<|im_start|>user\nTell me a story about a fox and a crow? Please do not tell the traditional story in Aesop's fables. Please tell me a positive story about friendship and love. The story should have no more than 400 words<|im_end|>\n<|im_start|>assistant\n" -st -t 2 -C 0x3 +``` + +Collect profiling data with Streamline, then select Core Map and Cluster Map modes in the Streamline Timeline view. + +![text#center](images/multi_thread_core_map.png "Figure 23. Multi-thread") + +As shown in the screenshot above, two threads are created and running on CPU core0 and core1 respectively. +Furthermore, individual operator view with annotation channel can be used to present two threads’ operators in parallel. Note that annotation channels are created independently per-thread. + +![text#center](images/multi_thread_annotation_channel.png "Figure 24. Multi-thread") + +As shown in screenshot above, at the specific time, both threads are computing for the same node. In this example, it is result_output linear node. \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md new file mode 100644 index 0000000000..23bd1a5b79 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md @@ -0,0 +1,55 @@ +--- +title: Use Streamline to analyze LLM running on CPU on llama.cpp + +minutes_to_complete: 50 + +who_is_this_for: Engineers who want to learn LLM inference on CPU, engineers who want to proflie and optimize llama.cpp code. + +learning_objectives: + - Be able to use Streamline to profile llama.cpp code + - Learn the execution of LLM on CPU cores + +prerequisites: + - Understanding of llama.cpp + - Understanding of transformer model + - Knowledge of Streamline usage + +author: Zenon(Zhilong) Xiu + +### Tags +skilllevels: 4 +subjects: PLACEHOLDER SUBJECT +armips: + - Cortex-A + - Neoverse +tools_software_languages: + - Arm Streamline + - C++ +operatingsystems: + - Linux + - Android + + + +further_reading: + - resource: + title: llama.cpp project + link: https://github.com/ggml-org/llama.cpp + type: source code + - resource: + title: Qwen1_5-0_5b-chat-q4_0.gguf + link: https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF/blob/main/qwen1_5-0_5b-chat-q4_0.gguf + type: LLM model + - resource: + title: Arm Streamline User Guide + link: https://developer.arm.com/documentation/101816/9-7 + type: website + + + +### FIXED, DO NOT MODIFY +# ================================================================================ +weight: 1 # _index.md always has weight of 1 to order correctly +layout: "learningpathall" # All files under learning paths have this same wrapper +learning_path_main_page: "yes" # This should be surfaced when looking for related content. 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