To use the debugger, first ensure that Configure::read(‘debug’) is Using the Debugger Class ¶ class Debugger ¶ Reveal the stack trace, and on the “Code” link to reveal theĮrror-causing lines. That is generated has both a stack trace and a code excerpt from Page and makes an entry in the error.log file. When an error is raised, Debugger both outputs information to the As with all debuggingįunctions, Configure::debug must be set to a value higher than 0. The Debugger’sĮrror handling is used by default in CakePHP. Replacing it with far more useful error reports. The Debugger Class overrides PHP’s default error handling, It has severalįunctions which are invoked statically, and provide dumping, More options for obtaining debugging information. The debugger class was introduced with CakePHP 1.2 and offers even Output from this function is only shown if the core debug variableĬhanged in version 2.1: The output of debug() more resembles var_dump(), and uses The functionĪlso prints out the line and file it is originating from by

HTML-friendly way, set the second parameter to true. First, if you’d like data to be shown in an The debug() functionĪllows you to show the contents of a variable in a number ofĭifferent ways. The debug() function is a globally available function that works $showFrom ( boolean) – Show the line and file the debug() occurred on. Escaping is enabledīy default in 2.0 when serving web requests. $showHTML ( boolean) – Set to true, to enable escaping. $var ( mixed) – The contents to print out. Basic Debugging ¶ debug ( mixed $var, boolean $showHtml = null, $showFrom = true ) Parameters : With any IDE or editor, CakePHP does provide several tools toĪssist in debugging and exposing what is running under the hood of

While CakePHP doesn’t offer any tools that directly connect Code is available at this https URL.Debugging is an inevitable and necessary part of any developmentĬycle. 1.25$ imes$ speed-up in Llama 2 13B on an NVIDIA L40). Despite our method's simplicity, we show with 50% of the FF parameters, GRIFFIN maintains the original model's performance with little to no degradation on a variety of classification and generation tasks, all while improving latency (e.g. This is possible due to a critical observation that many trained LLMs naturally produce highly structured FF activation patterns within a sequence, which we call flocking. To address this, we introduce GRIFFIN, a novel training-free MoE that selects unique FF experts at the sequence level for efficient generation across a plethora of LLMs with different non-ReLU activation functions. However, these techniques can be very costly and inflexible in practice, as they often require training or are restricted to specific types of architectures. Fortunately, some methods such as pruning or constructing a mixture of experts (MoE) aim at exploiting sparsity in transformer feedforward (FF) blocks to gain boosts in speed and reduction in memory requirements. View a PDF of the paper titled Prompt-prompted Mixture of Experts for Efficient LLM Generation, by Harry Dong and 2 other authors View PDF HTML (experimental) Abstract:With the development of transformer-based large language models (LLMs), they have been applied to many fields due to their remarkable utility, but this comes at a considerable computational cost at deployment.

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