Operator block parameter sequential search and multi-protocol model adaptation method and device

Abstract

The embodiment provides an operator block parameter sequential search and multi-protocol model adaptation method, traverses a skill directory and parses preposed metadata in the order of an internal layer, a user general layer, a user local layer and a project local layer to construct a skill index, and injects the skill content into a next round of request in the role of a developer when a skill calling mark is checked out; candidate value sets and feasible regions are determined according to block parameter definition and shape constraints of an operator source code, the block parameter is transformed into an environment variable reading so that a single trial calculation does not need to be recompiled, parameter samples are divided into a superior group and an inferior group according to the trial calculation records, and the larger one of the densities of the two groups is taken as the next group of trial parameters, and a time delay is backfilled on a fixed device; an optimal parameter vector is output after a termination condition is reached, a family configuration is obtained according to model identification prefix matching, a request address and a conversion tool specification are constructed according to a line protocol, tool calls are grouped according to conflict relations, and token budgets are allocated according to information density.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence chip operator development technology, specifically to a method and apparatus for sequential search of operator block parameters and adaptation of multi-protocol models. Background Technology

[0002] In operator development for dedicated accelerator chips, the values ​​of block parameters directly determine the execution latency of the operator on the hardware. Current practices rely heavily on manual experience, with developers experimenting with different combinations of block size, slice size, and loop unrolling factor based on their understanding of the hardware architecture, and then selecting the optimal combination by comparing latency. This approach is highly dependent on accumulated personal experience; the optimization results for the same operator vary significantly among different developers, and existing experience must be re-accumulated after hardware architecture changes.

[0003] Another approach involves exhaustively traversing the parameter space, compiling, installing, and testing each combination on real hardware. The number of parameter combinations grows exponentially with the parameter dimension, and each test takes tens of seconds, resulting in an overall time consumption of several hours or more. Yet another approach builds a hardware performance model and derives parameter values ​​accordingly. However, the microarchitectural characteristics of actual hardware, such as caching behavior, pipeline usage, and memory access bandwidth, are difficult to accurately characterize, and theoretical values ​​often deviate from the experimentally optimal values. Using a general session model to assist in writing operator code lacks specific knowledge of the target hardware, and the generated code frequently violates interface specifications and alignment requirements; correcting these errors is itself very time-consuming. Furthermore, random sampling parameter searches are independent of historical results, making it impossible to guide the search direction with observed parameter-latency relationships, resulting in low sampling efficiency in scenarios where single evaluations are expensive.

[0004] Regarding model integration, existing systems often use hard-coded integration for a single interface protocol. Switching to services with different request and response formats requires significant code modifications to request construction, response parsing, and error handling. While specifying service addresses and credentials via configuration files allows for configuration of connection parameters, it fails to address the differences in parallel tool call support, inference digest support, and context window capacity among different models. When a model supporting parallel calls is replaced with one that does not, the system does not automatically degrade to serial execution, leading to call failures. In terms of tool execution, tools are called sequentially without leveraging their independence. Regarding context management, various outputs are truncated according to the same fixed budget, resulting in high-value content being removed while low-value content occupies space. Therefore, a method combining layered injection of domain skills, sequential parameter search, multi-protocol adaptation, and orchestration control is needed. Summary of the Invention

[0005] To address the problems of existing block parameter tuning relying on manual experience and exhaustive traversal, discrepancies between static modeling and actual measurements, lack of target hardware knowledge in general session models, inability to utilize historical results in random sampling, strong coupling between model access and a single protocol without being able to perceive capability differences, and serial execution of tools and context truncation according to a fixed budget, this application provides a method and apparatus for sequential search of operator block parameters and adaptation of multi-protocol models. It can embed domain specifications and tuning processes into the session through sequential loading and tag-triggered injection of a four-layer skill catalog, reduce the turnaround time of a single trial calculation from minutes to seconds through shape constraint-driven feasible domain pruning and environmental variable modification, guide the direction of subsequent sampling with historical results through density ratio sampling of superior and inferior groups, eliminate differences between devices through hardware-in-the-loop fixed device evaluation, enable new services to be added with only additional configuration without code modification through model family configuration and line protocol routing, and control execution time and context occupancy through concurrent read and write operations of conflict groups and token budget allocation based on information density hierarchy.

[0006] To solve at least one of the above problems, this application provides the following technical solution: Firstly, this application provides a method for sequential search of operator block parameters and adaptation to multi-protocol models, including: traversing the skill directory in the order of built-in layer, user general layer, user local layer, and project local layer, and parsing the pre-metadata of its description files; constructing a skill index based on the rule that later loaders overwrite earlier loaders with the same name; when a skill call flag is detected in the session output, injecting the skill content as a developer into the next round of requests; listing only the name and path for user skills and omitting the description when the cumulative bytes of the description exceed the budget; determining the candidate set and feasible domain of each parameter based on the block parameter definition and shape constraints of the operator source code; and transforming the block parameters into those that are read through environment variables and retain their original values. The system eliminates the need for recompilation during a single trial calculation; it divides parameter samples into superior and inferior groups based on the already calculated records and estimates the probability density for each group, taking the larger of the two densities as the next set of trial parameters; it performs benchmark tests on fixed equipment and backfills latency; it outputs the optimal parameters when one of the following conditions is met: the number of trials reaches the upper limit or the number of adjacent improvement rounds is below the threshold; it obtains the family configuration by matching the model identifier prefix and constructs the request path and conversion tool specifications according to its line protocol, downgrading when capabilities are missing and switching to a backup address when there is a connection layer error; it groups tool calls according to conflict relationships to enable read-only concurrent writing serially, allocates token budgets according to information density, and compresses history and reclaims skills when approaching the window limit.

[0007] Furthermore, it also includes: performing a breadth-first traversal of the skill catalog at each layer to retrieve its description files; parsing the initial metadata of the description files to obtain the skill name and skill description, where the skill name has a maximum character limit and the skill description has a maximum byte limit; registering diagnostic information for directories containing files but lacking the description files and skipping them; aggregating the parsed skill names, skill descriptions, and file paths into a skill index, and loading it sequentially in the order of built-in layer, user general layer, user local layer, and project local layer, with later loaded skills of the same name overriding earlier loaded ones, so that customized content in the project layer takes precedence over global content; the skill index uses the skill name as the key for name-based retrieval during the injection process.

[0008] Furthermore, it also includes: detecting preset skill call markers in the session output text and extracting the skill names contained therein; locating the skill by name in the skill index and injecting the complete content of the skill description file as a developer role message into the next round of requests; text mentioning the skill name outside the marker does not trigger injection; for built-in layer skills, only the name and description are listed in the system prompts and their complete content is not inlined; for user layer skills, only the name, description, and path are listed in the system prompts and the session reads their files as needed to obtain detailed guidance; the number of bytes in the skill description is accumulated for each skill; for skills whose accumulated value exceeds a preset budget, only the name and path are listed and their description is omitted; when a single description exceeds a preset number of characters, it is truncated and an omission mark is added.

[0009] Furthermore, it also includes: retrieving the block parameter definitions in the operator source code to obtain each adjustable parameter, and determining its candidate value set according to the coverage principle, the discrimination principle, and the constraint pruning principle. The coverage principle requires that the candidate values ​​cover the typical value range of the parameter and contain a power of 2 sequence that fits the hardware alignment characteristics. The discrimination principle requires that the performance difference between adjacent candidate values ​​is sufficient to distinguish them. The constraint pruning principle excludes candidate values ​​that exceed the on-chip storage capacity. Retrieving the shape constraints of the operator to obtain the minimum dimension, alignment requirements, and dimensional relationships, converting them into feasible domain constraints, and pruning the candidate value set so that combinations that do not meet the alignment and dimensional relationships do not enter the trial calculation. Modifying the block parameters to be read by environment variables and using the original values ​​as default values, backing up the original files before modification, recompiling and installing after modification, and verifying the value effectiveness, so that a single trial calculation only requires setting environment variables and does not require recompiling the source code.

[0010] Furthermore, it also includes: sorting the recorded delay values ​​in ascending order and dividing the parameter samples into superior and inferior groups based on a preset quantile; constructing the probability density of each group in each parameter dimension using kernel density estimation, with the dimension of discrete values ​​constructed according to the frequency of values; calculating the ratio of the density of the superior group to the density of the inferior group for each parameter combination in the candidate value set, and taking the larger ratio as the next set of trial parameters, so as to adaptively balance between fine sampling in known superior areas and exploration in insufficiently sampled areas; after each round of trial calculation, the set of parameters and its delay are added to the recorded data and the two densities are re-estimated; when the number of records is less than a preset lower limit, uniform sampling is used to fill the gaps.

[0011] Furthermore, it also includes: performing benchmark tests on real devices to obtain latency; investigating the occupancy status of each device before testing and selecting the one with lower occupancy; fixing the same device number throughout the trial calculation to ensure consistent evaluation conditions for each round; judging rounds with non-numerical and infinite values ​​in the test output as invalid and treating them as invalid values ​​without counting them in the already calculated records; terminating the trial calculation when the number of trial calculations reaches a preset upper limit or the optimal latency improvement of several adjacent rounds is less than a threshold condition; outputting the optimal parameter vector, its measured latency, the comparison result with the default parameters, and the converted computing power; and performing a round of comparative testing on the same device with the optimal parameters and the original parameters to verify the difference; after the verification is passed, the optimal parameter vector is fixed as the new default value.

[0012] Furthermore, it also includes: obtaining model family configuration by prefix matching of model identifiers, wherein the model family configuration registers the family's inference summary support, parallel tool call support, context window occupancy ratio, and preferred terminal tool category; constructing a request address by concatenating the corresponding path after the base address according to its line protocol category, and converting the same set of tool specifications into the description format required by the protocol, downgrading to serial delivery when parallel tool calls are not supported, and not delivering the corresponding fields when inference summaries are not supported; switching to a backup address with an atomic flag and maintaining it in the current process when a connection layer error such as domain name resolution failure, connection timeout, or connection rejection is detected, wherein the switching is idempotent and triggers security multiple times; grouping the current round of tool calls according to conflict relationships, with read-only tools taking shared locks for concurrent execution and write tools taking exclusive locks for serial execution, allocating token budgets according to information density to prevent file content from being truncated, truncating subtask results according to the larger budget, and truncating other outputs according to the beginning and end retention strategy, and compressing history and reclaiming injected skill content when the sum of input and output tokens reaches the context window occupancy ratio.

[0013] Secondly, this application provides an operator block parameter sequential search and multi-protocol model adaptation device, comprising: a skill loading module, used to traverse the skill directory in breadth-first order according to the built-in layer, user general layer, user local layer, and project local layer, parse the pre-database of the specification file to obtain the skill name and description, and build a skill index by overwriting the earlier loaded skill with the same name by the later loaded skill; a skill injection module, used to detect the skill call flag in the session output and inject the skill content as a developer into the next request, listing only the name, description, and path for user-layer skills, and omitting the description when the accumulated bytes of the description exceed the budget; and a space construction module, used to retrieve the block parameter definition and shape constraints of the operator source code and determine the candidate value set according to the three principles of coverage, resolution, and constraint pruning. The feasible region modifies the block parameters to read from the environment variables and retain the original default values; the sequential search module is used to divide the parameter samples into superior and inferior groups according to the pre-set quantiles based on the trial records, and estimate the probability density of each group. The larger of the two densities is taken as the next trial parameter. Benchmark tests are performed on fixed devices and the latency is backfilled until the termination condition is met, and the optimal parameter vector is output; the adaptation orchestration module is used to obtain the family configuration by matching the model identifier prefix and construct the request address and conversion tool specification according to its line protocol, degrade when the capability is missing, switch the backup address with atomic flag when the connection layer is wrong, group the tool calls according to the conflict relationship to make read-only concurrent writing serial, allocate the token budget according to the information density, and compress the history and reclaim skill content when it is close to the window limit.

[0014] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the operator block parameter sequential search and multi-protocol model adaptation method.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the operator block parameter sequential search and multi-protocol model adaptation method.

[0016] Fifthly, this application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the operator block parameter sequential search and multi-protocol model adaptation method.

[0017] As can be seen from the above technical solutions, this application provides a method and apparatus for sequential search of operator block parameters and adaptation of multi-protocol models. It prioritizes project-level customization through skill-level layered loading and name-based overriding; avoids accidental triggering by text mentions through tag-triggered injection; narrows the search range through the three-principle design of candidate value sets and shape constraint pruning; eliminates recompilation for single trials through environment variable-driven parameter control; achieves adaptive balance between exploration and utilization through sampling the ratio of superior to inferior group probability densities; ensures convergence of the search within controllable rounds through benchmark testing on fixed equipment and dual termination conditions; achieves unified access to heterogeneous services through capability degradation driven by model family configuration and line protocol routing; and ensures reliable execution and effective utilization of context through primary / backup switching of atomic flags, concurrent orchestration of read / write locks, and token budget allocation with information density hierarchy. This improves the cycle and consistency of operator tuning, the scalability of model access, and the efficiency of execution orchestration. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0019] Figure 1 This is a flowchart illustrating the operator block parameter sequential search and multi-protocol model adaptation method in the embodiments of this application. Figure 2 This is a schematic diagram of the operator block parameter sequential search and multi-protocol model adaptation device in the embodiments of this application. Figure 3 This is a schematic diagram of the layered skill loading and injection process in the embodiments of this application; Figure 4 This is a schematic diagram of the block parameter feasible domain construction process in the embodiments of this application; Figure 5 This is a schematic diagram of the iterative process for sampling the probability density ratio of superior and inferior groupings in an embodiment of this application; Figure 6 This is a schematic diagram of the concurrent orchestration process for tool call conflict groups in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this application, but not all embodiments.

[0021] Considering the problems of existing block parameter tuning relying on manual experience and exhaustive traversal, deviations between static modeling and actual measurement, strong coupling between model access and a single protocol with no ability to perceive differences, and serial execution of tools and context truncation according to a fixed budget, this application provides a method for sequential search of operator block parameters and multi-protocol model adaptation. Through skill layer loading and injection, feasible domain construction and environmental variable modification, sequential sampling and hardware-in-the-loop evaluation, and multi-protocol adaptation and orchestration management, it provides end-to-end automated assistance for operator tuning and model access.

[0022] This application provides an embodiment of an operator block parameter sequential search and multi-protocol model adaptation method, see [link to embodiment]. Figure 1 The method specifically includes the following: Step S101: Traverse the skill directory in the order of built-in layer, user general layer, user local layer and project local layer and parse the pre-metadata of its description file. Construct the skill index according to the rule that the later loader overwrites the earlier loader with the same name. When the skill call flag is detected in the session output, inject the skill content into the next round of request as a developer. For user skills, only the name and path are listed and the description is omitted when the cumulative bytes of the description exceed the budget.

[0023] In this embodiment, the method runs on the operator development assistance session side for dedicated acceleration chips. Its domain specifications and optimization processes are maintained in the form of skills rather than hard-coded into the session logic. Figure 3 As shown.

[0024] Skills are sourced in four layers. The built-in layer is distributed along with the program's binary; the user-general layer is loaded from the general skills directory under the user's home directory; the user-local layer is loaded from the local skills directory under the user's home directory; and the project-local layer is loaded from the skills directory under the current project directory. The four layers are loaded sequentially in the above order, with later loaded skills overriding earlier loaded skills of the same name, ensuring that customized content from the project layer takes precedence over global content.

[0025] A breadth-first traversal is performed on each level of the skill directory to retrieve its documentation files. The initial metadata of the documentation files is parsed to obtain the skill name and skill description. The skill name has a maximum character limit, and the skill description has a maximum byte limit. Directory entries containing files but lacking the documentation files are registered with diagnostic information and skipped without interrupting the overall loading process.

[0026] In one feasible implementation, the skill index uses the skill name as the key and registers three items for each skill: skill description, file path, and source level. The source level is used to distinguish between built-in skills and user skills when determining coverage and displaying the results.

[0027] Injection is triggered by a flag. A preset skill call flag is detected in the session output text, and the skill name it contains is extracted. After locating the skill by name in the skill index, the complete content of the skill description file is injected as a developer role message into the next request. Text mentioning the skill name outside of the flag does not trigger injection, ensuring that the session does not mistakenly inject its content when discussing a skill, thus avoiding wasting space.

[0028] Built-in skills are listed in the system prompts only with their names and descriptions, without their full content being inlined; their existence and content are not presented to the user. User-level skills are listed in the system prompts only with their names, descriptions, and paths; the session reads their files as needed to obtain detailed instructions. The two types of skills are arranged in separate sections in the system prompts, with the built-in section preceding the user section.

[0029] The length budget is controlled cumulatively by bytes. The number of bytes in the skill description is accumulated for each skill. Skills whose accumulated value exceeds the preset budget are listed only with their name and path, omitting their description and marked with an omission flag. If a single description exceeds the preset character limit, it is truncated and marked with an omission flag. This mechanism prevents the number of skills from growing uncontrollably and consuming excessive context space.

[0030] With this skill index and injection channel ready, the injection optimization process guides the parameter space construction and sequential search stages. Search results and adaptation orchestration results flow back to the skill content recycling stage, such as... Figure 1 As shown.

[0031] Step S102: Based on the block parameter definition and shape constraints of the operator source code, determine the candidate set and feasible region of each parameter, modify the block parameters to be read by environment variables and retain the original values ​​as default so that no recompilation is required for a single trial calculation; divide the parameter samples into superior and inferior groups according to the trial calculation records and estimate the probability density of each group, take the larger of the two densities as the next group of trial calculation parameters, perform benchmark tests on fixed equipment and backfill the delay.

[0032] The parameter space is constructed by first retrieving the block parameter definitions from the operator source code to obtain each adjustable parameter, such as... Figure 4 As shown. The adjustable parameters cover block size, slice size, loop unrolling factor, and parallelism configuration, and their definitions are located in the template matching function of the operator.

[0033] The optimization problem is described as a black box. Given a parameter vector... , among which , For the first A set of candidate values ​​for each parameter. Dimension of parameters; objective function Take the execution latency of this set of parameters on a real device, and solve for the time required to achieve this within the feasible region. Take a minimal parameter vector. The objective function has no analytical expression and its gradient cannot be taken; a single evaluation requires running a test on the device, the cost of which is not negligible.

[0034] The candidate value set is determined according to three principles. The coverage principle requires that the candidate values ​​cover the typical range of values ​​for the parameter and contain a power-of-2 sequence that fits the hardware alignment characteristics; the discrimination principle requires that the performance difference between adjacent candidate values ​​is sufficient to distinguish them, so that the sampling can distinguish the effects of different combinations; the constraint pruning principle excludes candidate values ​​that exceed the on-chip storage capacity.

[0035] Total number of combinations Estimate, of which For the first The number of elements in the set. In one specific implementation, each of the three parameters takes five candidate values. The value is 125. Exhaustive traversal requires 125 rounds of testing, while sequential sampling usually converges to a near-optimal combination within dozens of rounds.

[0036] Shape constraint retrieval obtains the minimum dimension, alignment requirements, and dimensional relationships, transforms them into feasible region constraints, and prunes the candidate value set. In one specific implementation, when the operator requires the input sequence length to be no less than 1024 and an integer multiple of 16, only candidate values ​​for the input dimension that satisfy these two conditions are retained, preventing invalid combinations from entering the trial calculation.

[0037] The environment variable modification changes the block parameters from source code constants to being read via environment variables with the original values ​​as default. Before the modification, the original files are backed up; after the modification, the system is recompiled, installed, and the values ​​are verified to be effective. The original boundary checks are retained to prevent out-of-bounds values ​​from exceeding hardware limits. After the modification, a single test calculation only requires setting the environment variables and running the test; recompiling the source code is no longer necessary, reducing the turnaround time for a single evaluation from minutes to seconds.

[0038] Sequential sampling is performed based on the pre-calculated records. The records are arranged in ascending order of time delay and divided into superior and inferior groups using a preset quantile as the boundary. The quantile is the expected proportion of superior samples, which in one feasible implementation is 0.25. For each group, probability densities are constructed using kernel density estimation for each parameter dimension. The density of the superior group is calculated as follows: Calculation, where For the number of samples in the optimal group, For the first A superior sample group Kernel function; inferior group density Calculate the inferior sample using the same formula.

[0039] Calculate the density ratio for each parameter combination in the candidate value set. ,Pick The larger value is used as the next set of trial parameters to achieve an adaptive balance between fine sampling in known optimal areas and exploration in insufficiently sampled areas. After each round of trial calculations, this set of parameters and its time delay are added to the already calculated records and the two sets of densities are re-estimated; if the number of records is less than the preset lower limit, uniform sampling is used to fill the gaps, so that the initial stage covers the entire feasible region.

[0040] Step S103: Output the optimal parameters when one of the following conditions is met: the number of trial calculations reaches the upper limit or the number of adjacent rounds of improvement is below the threshold; obtain the family configuration by matching the model identifier prefix and construct the request path and conversion tool specification according to its line protocol; downgrade when the capability is missing and switch to the backup address when the connection layer is faulty; group the tool calls according to the conflict relationship to make read-only concurrent writing serial; allocate the token budget according to the information density and compress the history and reclaim skills when approaching the window limit.

[0041] Benchmark tests are performed on real devices, such as Figure 5 As shown. Before testing, the occupancy status of each device was investigated and the device with the lowest occupancy was selected. The same device number was used for the entire round of trials to ensure that the evaluation conditions were consistent and to eliminate observation disturbances introduced by performance differences between devices.

[0042] The single-round trial calculation process involves setting block parameter environment variables, running the benchmark test program, parsing its delay output, and backfilling it into the already calculated record. Rounds in which the test output contains non-numerical or infinite values ​​are judged as invalid and treated as invalid values, and are not included in the already calculated record. If the same set of parameters is invalid for two consecutive rounds, the set is marked as infeasible and excluded from the candidates.

[0043] Before the trial calculation, the preconditions are verified, covering four items: the operator has been successfully compiled, the installation package has been deployed, the calling interface has been successfully executed, and the test program outputs normally. If the preconditions are not met, assistance will be provided to complete the compilation, installation, and testing before proceeding with the trial calculation, ensuring that the objective function can be evaluated across the entire feasible domain without interruption due to unavailability.

[0044] Termination is determined under two conditions: Termination occurs when the number of trial runs reaches a preset upper limit; termination also occurs when the optimal delay improvement in several adjacent rounds falls below a threshold condition, with the improvement range determined by... Calculation, where For the first The optimal delay of the wheel, To review the number of rounds, To improve the extent.

[0045] After termination, the optimal parameter vector, its measured latency, the comparison results with the default parameters, and the calculated computing power are output. A round of comparative tests is performed on the same device with the optimal parameters and the original parameters to verify the differences. After the verification is passed, the optimal parameter vector is solidified as the new default value and written back to the source code.

[0046] Model adaptation obtains the model family configuration by prefix matching of the model identifier. The model family configuration registers four items: inference summary support, parallel tool call support, context window occupancy ratio, and preferred terminal tool category. Prefix matching allows multiple versions of the same family to share a single configuration without requiring code rewriting during version updates. The request address is constructed by appending the corresponding path to the base address based on its protocol category, and the same set of tool specifications is converted into the description format required by that protocol.

[0047] When capabilities are missing, the system will downgrade according to configuration. If parallel tool calls are not supported, the system will downgrade to serial delivery; if inference digests are not supported, the corresponding fields will not be delivered; if specific patch guidance is required, the corresponding instructions will be automatically injected. When domain name resolution failure, connection timeout, or connection rejection errors are detected at the connection layer, the system will switch to a backup address with an atomic flag and maintain this switch within the current process. This switching is idempotent and triggers security multiple times. The system will continue to use the default address after the process restarts.

[0048] Tools are grouped according to conflict relationships, such as Figure 6 As shown. Read-only tools acquire shared locks and execute concurrently, while write tools acquire exclusive locks and execute serially. The total duration of concurrent orchestration is as follows: Estimate, of which to The time consumed by each read-only tool, This is the sum of the time taken by each writing tool. Number of read-only tools The number of write tools; when the time taken by each read-only tool is similar, the time saving ratio compared to serial arrangement is approximately .

[0049] The token budget is allocated hierarchically based on information density. The allocation problem is described as minimizing the sum of information losses under the total budget constraint, i.e., within the total budget... satisfy Under the constraints Take the smallest, where To be assigned to the The budget for the output item, Its original length, For its information density, For the total budget, This represents the number of output items for this round of the tool. The solution is approximated using a hierarchical heuristic approach, and the file content is taken as... Instead of truncating, the results of subtasks are truncated according to the larger budget, while the remaining outputs are truncated according to the strategy of keeping the beginning and end but omitting the middle section.

[0050] Compression is triggered when the sum of input and output tokens reaches the specified context window occupancy ratio. Compression replaces intermediate history with a session-generated summary while retaining the initial context and recent user messages. During compression, if a window exceeds the bounds error, the oldest item is removed one by one before retrying. Compression also reclaims injected skill content, ensuring that expired skill content no longer occupies space and is re-injected as needed in the next round, flowing back to the skill loading stage to form a closed loop of injection, search, adaptation, and reclamation.

[0051] In one feasible implementation, with a parameter space containing five candidate values ​​for each of the three parameters, sequential sampling typically converges to a near-optimal parameter combination within 15 to 25 rounds of trial calculations, while exhaustive traversal requires testing 125 combinations one by one, resulting in a difference of 5 to 10 times in the number of trial calculations. The tuning cycle for manual trial-and-error methods is typically several days to several weeks, and the results vary depending on the developer's experience. The aforementioned values ​​are taken from trial calculation records of the same operator on a fixed device, and their values ​​vary with the parameter dimension, the density of candidate values, and the performance sensitivity of the operator itself.

[0052] As can be seen from the above description, the operator block parameter sequential search and multi-protocol model adaptation method provided in this application embodiment can embed domain specifications into the session through layered skill injection, shorten the single trial calculation turnaround through feasible domain pruning and environmental variable modification, guide the search direction through superior-inferior density ratio sampling, and unify heterogeneous service access through model family configuration and line protocol routing.

[0053] In one embodiment of the operator block parameter sequential search and multi-protocol model adaptation method of this application, see [link to documentation]. Figure 3 It can also specifically include the following: Step S201: Perform a breadth-first traversal of the skill catalog at each level to retrieve its description file, parse the preceding metadata at the beginning of the description file to obtain the skill name and skill description, the skill name is limited to a maximum number of characters, the skill description is limited to a maximum number of bytes, and the catalog containing the file but lacking the description file is registered with diagnostic information and skipped; Step S202: The parsed skill names, skill descriptions, and file paths are aggregated into a skill index, which is then loaded sequentially in the order of built-in layer, user general layer, user local layer, and project local layer. Skills with the same name are loaded later and overwritten earlier, so that customized content in the project layer takes precedence over global content. The skill index uses the skill name as the key for the injection process to retrieve by name.

[0054] Skill loading is performed once when the session starts and re-executed whenever the skill catalog changes, such as... Figure 3 As shown. The paths of the four directories are: built-in resources of the program, the general skills directory under the user's home directory, the local skills directory under the user's home directory, and the skills directory under the current project directory.

[0055] A breadth-first search is performed on each level of the skill directory, traversing subdirectories to retrieve their documentation. The breadth-first order ensures that shallower skills are retrieved before deeper skills; in cases of identical names, deeper skills are loaded later, overwriting shallower ones. Directories containing the required documentation but lacking it are recorded with diagnostic information, including the directory path and a description of the missing document, to help users troubleshoot why the skill is not working.

[0056] The skill name and skill description are obtained by parsing the initial metadata of the documentation. The initial metadata is written in key-value format within the delimiter at the beginning of the file. The keys are limited to name and description, and other keys are ignored without causing an error, allowing the skill file to attach other keys for its own use.

[0057] The skill name has a maximum character limit, which is 100 characters in one feasible implementation; the skill description has a maximum byte limit, which is 5000 bytes in one feasible implementation. Entries exceeding the limits are truncated and a truncation flag is registered to prevent excessively large metadata from consuming excessive loading time and presentation space.

[0058] The skill index uses the skill name as the key and records three items for each skill: skill description, file path, and source level. The source level is used to distinguish between built-in skills and user skills when determining coverage and presentation. Built-in skills are not shown to the user, while user skills are presented in groups according to their source level.

[0059] The four layers are loaded sequentially: the built-in layer, the user-general layer, the user-local layer, and the project-local layer. For skills with the same name, later loaders overwrite earlier loaders, and the source layer of the overwritten skill is recorded for troubleshooting. This order ensures that customized content in the project layer takes precedence over the user and built-in layers, allowing the same developer to use customized skills with the same name in different projects.

[0060] In one specific implementation, changes to the skill catalog are detected by the file system observation interface. When a change occurs, only the affected levels are reloaded, not all four levels, so that the loading time increases with the scope of the change rather than the total number of skills. The skill index is used by the injection process to check out skills by name. Skills that cannot be checked out are registered as unknown skill identifiers and corresponding descriptions are backfilled to the session.

[0061] The loading results are summarized into a loading report, which records three items: the number of skills at each level, the number of times coverage occurred, and the number of diagnostic information entries. The loading report is output to the operation log when the session starts, so that situations where skills do not take effect as expected can be located by comparing the number of entries and checking the coverage records, without having to open each skill file one by one for troubleshooting.

[0062] Skill content is read on demand, rather than being loaded all at once during the loading phase. During the loading phase, only the initial metadata area of ​​each documentation file is read; the main body of the file is only read in its entirety when the injection is triggered, ensuring that the increase in the number of skills and the size of the files does not prolong the session startup time.

[0063] In one embodiment of the operator block parameter sequential search and multi-protocol model adaptation method of this application, see [link to documentation]. Figure 3 It can also specifically include the following: Step S301: Detect the preset skill call marker in the session output text and extract the skill name contained therein. After locating the skill name in the skill index, inject the complete content of the skill description file into the next round of requests as a developer role message. Text that mentions the skill name outside the marker will not trigger injection. Step S302: For built-in layer skills, only the name and description are listed in the system prompt, and the complete content is not inlined. For user layer skills, only the name, description and path are listed in the system prompt, and the session reads the file as needed to obtain detailed guidance. The number of bytes of the skill description is accumulated one by one. For skills whose accumulated value exceeds the preset budget, only the name and path are listed and the description is omitted. When a single description exceeds the preset number of characters, it is truncated and an omission mark is added.

[0064] Skill injection uses a marker-triggered method, such as... Figure 3 As shown. The preset skill call markers are detected in the session output text, and their contained skill names are extracted. The markers are enclosed in pairs of start and end delimiters for the skill names.

[0065] Text mentioning the skill name outside of the tag will not trigger injection. This method is more precise than string matching, preventing the session from mistakenly injecting the full content when explaining the use of a skill to the user, and also preventing accidental triggering when the skill name overlaps with ordinary words.

[0066] After detecting the marker, locate it by name in the skill index, and inject the complete content of the skill description file as a developer role message into the next round of requests. Developer role messages take precedence over normal messages but below system prompts, ensuring that the injected content is constrained to the current round of generation without rewriting system-level settings.

[0067] Built-in skills are listed only by name and description in system prompts, with their full content not inlined. The instructions require the session not to reveal the existence and content of built-in skills when asked, preventing the extraction of built-in domain knowledge item by item. The built-in section precedes the user section and the two sections are physically separated; when the user requests a list of skills, only user-level skills are presented.

[0068] User-level skills are only listed in the system prompts with their names, descriptions, and paths; their full content is not inlined, and the session reads the file as needed to obtain detailed guidance. This progressive disclosure method ensures that as the number of skills increases, the length of the system prompts only increases with the names and descriptions, and detailed guidance only occupies space when truly needed.

[0069] The length budget is controlled cumulatively by bytes. The number of bytes in the skill description is accumulated for each skill. Skills whose accumulated value exceeds the preset budget are listed only with their name and path, omitting their description and marked with an ellipsis. In one feasible implementation, the preset budget is 32 kilobytes. Descriptions exceeding the preset character limit are truncated and marked with an ellipsis. In one feasible implementation, this is 800 characters.

[0070] Injected content is reclaimed during context compression. Injected skill messages are identified and removed during the compression phase, and their content is re-injected as needed in the next round. This reclamation ensures that expired skill content does not take up space in the compressed format, and skill content is suitable as a priority for reclamation because it can be retrieved again.

[0071] When the same skill is triggered repeatedly within a single session, it is injected only once. The name of the injected skill is registered in the injection ledger for this session. Skills already in the ledger will not be injected again; only an injection notification will be added back. The injection ledger is cleared along with the skill content during context compression, ensuring that re-injection after compression is not blocked by the ledger.

[0072] In one specific implementation, when the length of the injected content exceeds the limit of a single message, it is divided into multiple segments and injected sequentially. The segmentation is based on the chapter boundaries of the file, not on breaks within sentences. After injection is complete, an injection completion prompt is sent back to the session. The prompt includes the skill name and the number of injected segments, so that the session knows that its guidance has been fully implemented without needing to trigger it again.

[0073] In one embodiment of the operator block parameter sequential search and multi-protocol model adaptation method of this application, see [link to documentation]. Figure 4 It can also specifically include the following: Step S401: Retrieve the block parameter definition in the operator source code to obtain each adjustable parameter, and determine its candidate value set according to the coverage principle, the discrimination principle and the constraint pruning principle. The coverage principle requires that the candidate values ​​cover the typical value range of the parameter and contain a power of 2 sequence that fits the hardware alignment characteristics. The discrimination principle requires that the performance difference between adjacent candidate values ​​is sufficient to distinguish them. The constraint pruning principle excludes candidate values ​​that exceed the on-chip storage capacity. Step S402: Obtain the minimum dimension, alignment requirements, and dimensional relationships of the shape constraints of the retrieval operator, convert them into feasible domain constraints, and prune the candidate value set so that combinations that do not meet the alignment and dimensional relationships are not included in the trial calculation; modify the block parameters to be read by environment variables and use the original values ​​as default values. Back up the original files before modification, and recompile and install after modification and verify the value to take effect, so that a single trial calculation only requires setting environment variables and does not require recompiling the source code.

[0074] The parameter space is constructed by first retrieving the block parameter definitions from the operator source code to obtain each adjustable parameter, such as... Figure 4 As shown. The search locates the template matching function by matching the source code text, and extracts the parameter name, current value and its position in the source code item by item.

[0075] The candidate value set is determined according to three principles. The coverage principle requires that the candidate values ​​cover the typical range of values ​​for the parameter and contain a power of 2 sequence that fits the hardware alignment characteristics. In a specific implementation, the candidate values ​​for the block size are 16, 32, 64, 128 and 256, so that each candidate value satisfies the memory access alignment requirements and does not produce unaligned access.

[0076] The discrimination principle requires that the performance difference between adjacent candidate values ​​be sufficient to distinguish them. If the candidate values ​​are too dense, the time delay difference between adjacent values ​​falls within the measurement jitter, making it impossible to distinguish their quality and wasting sampling rounds; if the candidate values ​​are too sparse, the optimal value may fall between two candidates and cannot be selected. The density of candidate values ​​is determined by the sensitivity of the time delay to the parameter obtained from the pre-trial calculation, with denser values ​​in sensitive areas and sparser values ​​in less sensitive areas.

[0077] The constraint pruning principle excludes candidate values ​​that exceed the on-chip storage capacity. Candidate values ​​whose block size and data type width product exceed the on-chip buffer capacity are directly eliminated, so that infeasible combinations do not occupy trial rounds.

[0078] Shape constraint retrieval obtains the minimum dimension, alignment requirements, and dimensional relationships, and transforms them into feasible region constraints. In one specific implementation, when the operator requires the input sequence length to be no less than 1024 and an integer multiple of 16, only candidate values ​​for the input dimensions that satisfy these two conditions are retained; when there are product or division relationships between dimensions, they are jointly pruned according to these relationships to avoid invalid combinations caused by independent dimensional selection.

[0079] The environment variable modification changes the block parameters from source code constants to reading them via environment variables and using the original values ​​as default values. The modification inserts environment variable read calls and string-to-integer conversions for each parameter, while retaining the original boundary checks to prevent out-of-bounds values ​​from exceeding hardware limits.

[0080] Before the modification, the original files were backed up and distinguished by their extensions. After the modification, the system was recompiled and installed, and the environment variable values ​​were verified to be effective. Verification was done by setting a non-default value and observing the latency changes. The modification allows a single trial calculation to be run by simply setting the environment variables, without recompiling the source code. The turnaround time for a single evaluation has been reduced from minutes to seconds, thus increasing the number of rounds that can be completed within the same time budget.

[0081] The candidate value set and feasible domain, along with the estimated total number of combinations, are provided for sequential search. The total number of combinations is estimated by the product of the number of elements in each set, which is used to determine whether it is worthwhile to use sequential search instead of direct traversal.

[0082] In one embodiment of the operator block parameter sequential search and multi-protocol model adaptation method of this application, see [link to documentation]. Figure 5 It can also specifically include the following: Step S501: Sort the time delay values ​​that have been calculated and recorded in ascending order and divide the parameter samples into superior and inferior groups with the preset quantiles as the boundary. Construct the probability density of each group in each parameter dimension using kernel density estimation. The dimension of discrete values ​​is constructed according to the frequency of values. Step S502: Calculate the ratio of the density of the superior group to the density of the inferior group for each parameter combination in the candidate value set, and take the larger ratio as the next set of trial parameters to achieve an adaptive balance between fine sampling in the known superior area and exploration in the insufficiently sampled area; after each round of trial calculation, add the set of parameters and its time delay to the already calculated record and re-estimate the two densities. If the number of records is less than the preset lower limit, uniform sampling is used to fill the gap.

[0083] Sequential search is performed based on the already calculated records, such as... Figure 5 As shown. The calculated records use the round number as the primary key, and each record registers the parameter vector, measured delay, and effective state of that round.

[0084] The records are arranged in ascending order by time delay and divided into superior and inferior groups based on a preset quantile. The quantile is the expected proportion of superior samples, which is set to 0.25 in one feasible implementation, meaning that the top quartile of samples is assigned to the superior group and the rest to the inferior group. If the quantile value is too small, the number of superior samples will be too small, resulting in unstable density estimation; if the value is too large, the distinction between superior and inferior samples will decrease.

[0085] For each of the two groups, construct their probability density along each parameter dimension using kernel density estimation. The density of the superior group is then calculated as follows: Calculation, where For the number of samples in the optimal group, For the first A superior sample group Kernel function; inferior group density The same formula is used to calculate the density of the inferior group samples. The dimension of the discrete values ​​is changed to construct its density based on the frequency of the values.

[0086] The bandwidth of the kernel function is determined by the number of samples and the range of values. A larger bandwidth is used when there are few samples to smooth the density, while a smaller bandwidth is used when there are many samples to make the density fit the sample distribution. The joint density is obtained by multiplying the independent density estimates of each dimension by the dimensional independence assumption. This assumption makes the sample requirement for density estimation grow linearly with the dimension rather than exponentially.

[0087] Calculate the density ratio for each parameter combination in the candidate value set. ,Pick The larger value is used as the next set of trial parameters. The ratio is taken as a large value where the density of the superior group is high and the density of the inferior group is low, so that the sampling tends to be close to the region where the known superior samples are not occupied by inferior samples, thus achieving an adaptive balance between fine sampling in the known superior region and exploration in the insufficiently sampled region.

[0088] After each round of trial calculations, the set of parameters and its time delay are added to the previously calculated record, and the two densities are re-estimated so that subsequent sampling reflects the latest observations in real time. Parameter combinations that have already been calculated are not sampled again; they are marked as already calculated in the candidate list and included in the calculation. Excluded at that time.

[0089] When the number of records is insufficient to meet the preset lower limit, uniform sampling is used to fill the gap, ensuring that the initial stage covers the entire feasible region and does not prematurely converge to a local area. In one feasible implementation, uniform sampling is used for the first few rounds, followed by density ratio sampling. The switching point of the sampling method is determined according to the number of records.

[0090] The next set of trial calculation parameters is read by the hardware in-loop evaluation stage, and the evaluation delay is backfilled into the trial calculation record to form an iterative closed loop of the search.

[0091] In one embodiment of the operator block parameter sequential search and multi-protocol model adaptation method of this application, see [link to documentation]. Figure 5 It can also specifically include the following: Step S601: Perform benchmark testing on real equipment to obtain latency. Before testing, investigate the occupancy status of each device and select the one with lower occupancy. Fix the same device number for the whole round of trial calculation to ensure that the evaluation conditions are consistent in each round. Rounds with test output containing non-numerical and infinite values ​​are judged as invalid and treated as invalid values ​​and not included in the already calculated records. Step S602: When the number of trial calculations reaches the preset upper limit and the optimal delay improvement of several adjacent rounds is less than the threshold condition, the trial calculation is terminated. The optimal parameter vector, the actual measured delay, the comparison result with the default parameter and the converted computing power are output. On the same device, a round of comparison test is performed with the optimal parameter and the original parameter to verify the difference. After the verification is passed, the optimal parameter vector is fixed as the new default value.

[0092] Hardware-in-the-loop assessments obtain latency by performing benchmark tests on real devices, such as... Figure 5 As shown. Before testing, the usage status of each device was checked, and the device with the lowest usage was selected. The usage rate and remaining video memory were read through the device management interface.

[0093] The same equipment number is fixed throughout the trial run, and this number is specified by environmental variables and remains unchanged throughout the trial run. This ensures consistent evaluation conditions across all runs and eliminates observational disturbances introduced by performance differences between equipment. Fixing the equipment is equivalent to controlling the remaining variables of the evaluation environment, thus attributing delay differences to the parameters themselves rather than the operating environment.

[0094] The single-round trial calculation process involves setting block parameter environment variables, running the benchmark test program, parsing its delay output, and backfilling it into the previously calculated record. The delay is taken as the median of multiple runs rather than a single value, so that individual jitters do not affect the judgment; the number of runs is determined based on the repeatability of the delay, and the number of runs is increased if the repeatability is poor.

[0095] Rounds whose test output contains non-numerical or infinite values ​​are considered invalid and treated as invalid values, and are not included in the already calculated records. If the same set of parameters is invalid for two consecutive rounds, the set is marked as infeasible and excluded from the candidates. The parameter vector of invalid rounds and their outputs are recorded together for checking whether shape constraints are omitted.

[0096] Before the trial calculation, the preconditions are verified, covering four items: the operator has been successfully compiled, the installation package has been deployed, the calling interface has been successfully executed, and the test program outputs normally. If the preconditions are not met, assistance will be provided to complete the compilation, installation, and testing before proceeding with the trial calculation, ensuring that the objective function can be evaluated across the entire feasible domain without interruption due to unavailability.

[0097] Termination is determined by either of two conditions: The trial run reaches a preset upper limit; the optimal delay improvement in several adjacent rounds falls below a threshold condition, with the improvement rate determined by... Calculation, where For the first The optimal delay of the wheel, To review the number of rounds.

[0098] After termination, the system outputs four items: the optimal parameter vector, the measured latency, the comparison result with the default parameters, and the calculated computing power. A round of comparative tests is performed on the same device with both the optimal and original parameters to verify the differences. The comparative tests are conducted adjacently in the same time period to ensure that device state drift does not affect the comparison conclusion.

[0099] After successful verification, the optimal parameter vector is solidified as a new default value and written back to the source code. The environment variable reading channel is retained so that the parameters can still be adjusted later. If the verification fails, the original default value is retained and the reason for the verification failure is recorded. The optimal parameter vector and the comparison report are presented to the user in the adaptation and orchestration process.

[0100] In one embodiment of the operator block parameter sequential search and multi-protocol model adaptation method of this application, see [link to documentation]. Figure 6 It can also specifically include the following: Step S701: Obtain the model family configuration by matching the prefix of the model identifier. The model family configuration registers the inference summary support, parallel tool call support, context window occupancy ratio and preferred terminal tool category of the family. Construct the request address by concatenating the corresponding path after the base address according to its line protocol category, and convert the same set of tool specifications into the description format required by the protocol. When parallel tool calls are not supported, it is downgraded to serial delivery. When inference summaries are not supported, the corresponding fields are not delivered. Step S702: When a connection layer error such as domain name resolution failure, connection timeout, or connection rejection is detected, switch to an alternate address with an atomic flag and maintain it within the current process. The switch is idempotent and triggers security multiple times. Group the tool calls in this round according to conflict relationships. Read-only tools acquire shared locks and execute concurrently, while write tools acquire exclusive locks and execute serially. Allocate token budgets according to information density to prevent file content from being truncated. Truncate subtask results according to the larger budget and truncate other outputs according to the beginning and end retention strategy. When the sum of input and output tokens reaches the context window occupancy ratio, compress the history and reclaim the injected skill content.

[0101] In the model adaptation phase, the model family configuration is obtained by matching the prefix of the model identifier, such as... Figure 1 As shown. The matching process compares the initial characters of the identifier with the prefix pattern of each family. If a match is found, the family configuration is returned; otherwise, the default configuration is returned.

[0102] The model family configuration registers four items: inference summary support, parallel tool invocation support, context window occupancy ratio, and preferred terminal tool category. Prefix matching allows multiple versions of the same family to share a single configuration, and the matching logic does not need to be modified when versions are updated; differences in context window capacity and maximum output length between versions are registered separately for each version.

[0103] The request address is constructed by appending the corresponding path after the base address, based on the wire protocol category. The three wire protocols correspond to reactive interfaces, dialog completion interfaces, and message interfaces, respectively. Their paths are different and cannot be automatically discovered at runtime; they must be explicitly declared by the provider in the configuration. The query parameters are concatenated into a query string of addresses according to the configuration.

[0104] The same set of tool specifications is converted into the description format required by the protocol. The conversion first generates an intermediate form in a unified specification construction phase, and then converts it into its respective format according to the protocol. This way, adding a new protocol only requires adding a conversion branch without affecting the tool specifications themselves. During the conversion, the mode of the tool parameters is recursively normalized, and the type of nodes with missing types is inferred based on their contained keywords and the numerical type is normalized.

[0105] When a capability is missing, the system will degrade according to the configuration. If parallel tool calls are not supported, the system will degrade to serial delivery; if inference summaries are not supported, the corresponding fields will not be delivered; if a specific patch is required, the corresponding documentation will be automatically injected. Degradation is configuration-driven and does not require changes to business logic, allowing the same set of calling logic to serve multiple models with different capabilities.

[0106] When a connection layer error such as domain name resolution failure, connection timeout, or connection rejection is detected, the process switches to an alternate address using an atomic flag. The alternate address is a direct connection address that bypasses domain name resolution. The switch is performed atomically, setting the flag, and multiple triggers result in the same outcome without the need for locking. After the switch, subsequent requests within the current process use the alternate address, and the process resumes using the alternate address after restarting.

[0107] Tool orchestration is grouped according to conflict relationships. Read-only tools acquire shared locks and execute concurrently, while write tools acquire exclusive locks and execute serially. Conflict relationships are determined by whether two tools access the same resource; conflicts exist between two write operations and between one read and one write operation. The total duration of concurrent orchestration is based on... Estimate, of which For the number of read-only tools, For the number of write tools, when the time taken by each read-only tool is similar, the time saving ratio relative to serial arrangement is approximately .

[0108] The token budget is allocated hierarchically based on information density, and the allocation problem is based on the total budget. satisfy Under the constraints Take the minimum description, where To be assigned to the The budget for the output item, Its original length, For its information density, For the total budget, This represents the number of output items for this round of the tool. The solution is approximated using a hierarchical heuristic approach, and the file content is taken as... Instead of truncating, the results of subtasks are truncated according to the larger budget, while the remaining outputs are truncated according to the strategy of keeping the beginning and end but omitting the middle section.

[0109] Compression is triggered when the sum of input and output tokens reaches the specified context window occupancy ratio. In one feasible implementation, the occupancy ratio is 0.9, with the remaining 10% serving as a safety margin for counting deviation. Compression replaces intermediate history with a session-generated summary while retaining the initial context and the most recent rounds of user messages. During compression, if a window out-of-bounds error is encountered, the oldest item is removed one by one before retrying. Simultaneously, compression reclaims injected skill content and returns it to the skill loading stage to form a closed loop.

[0110] To implement all or part of the method, this application provides an embodiment of an apparatus for implementing the operator block parameter sequential search and multi-protocol model adaptation method, see [link to embodiment]. Figure 2 The device specifically includes the following components: The skill loading module is used to traverse the skill directory in breadth-first order of built-in layer, user general layer, user local layer and project local layer, parse the pre-metadata of the description file to obtain the skill name and description, and build the skill index by overwriting the earlier loaded skill with the same name with the later loaded skill. The skill injection module is used to detect the skill call flag in the session output and then inject the skill content into the next request as a developer. For user-level skills, only the name, description and path are listed, and the description is omitted when the cumulative bytes of the description exceed the budget. The spatial construction module is used to retrieve the block parameter definitions and shape constraints of the operator source code and determine the candidate value set and feasible region according to the three principles of coverage, resolution and constraint pruning. The block parameters are transformed into default values ​​that are read by environment variables and retain the original values. The sequential search module is used to divide the parameter samples into superior and inferior groups according to the pre-defined quantiles based on the trial records and estimate the probability density of each group. The larger of the two densities is taken as the next trial parameter. Benchmark tests are performed on a fixed device and the time delay is filled until the termination condition is met, and then the optimal parameter vector is output. The adaptation orchestration module is used to obtain family configurations by matching model identifier prefixes and construct request addresses and conversion tool specifications according to their line protocols, degrade when capabilities are missing, switch backup addresses with atomic flags when there are connection layer errors, group tool calls according to conflict relationships to enable read-only concurrent writing serially, allocate token budgets according to information density, and compress history and reclaim skill content when approaching the window limit.

[0111] The skill loading module performs a breadth-first traversal of each skill directory in the order of built-in layer, user general layer, user local layer, and project local layer. After detecting the documentation, it parses the initial metadata to obtain the skill name and description. For directories containing files but lacking documentation, it registers diagnostic information and skips them. It aggregates the name, description, path, and source level into a skill index and overwrites the earlier loaded ones with the same name with the later loaded ones, ensuring that project-level customized content takes effect before user-level and built-in layers. The skill injection module detects the preset skill call marker in the session output text, locates it by name, and injects the complete skill content as a developer role message into the next request. Text mentioning the name outside the marker does not trigger injection. Built-in layer skills do not show their existence to the user, and user-level skills only list the name, description, and path, omitting the description when the accumulated bytes exceed the budget.

[0112] The spatial construction module retrieves the block parameter definitions from the operator source code to obtain each adjustable parameter. It determines the candidate value set according to the coverage, resolution, and constraint pruning principles. After retrieving the shape constraints to obtain the minimum dimension, alignment requirements, and dimensional relationships, it converts these into feasible region constraints and prunes the candidate value set. The block parameters are then modified to be read from environment variables with the original values ​​as default values. The original files are backed up before modification, and the module is recompiled, installed, and the values ​​are verified to be effective after modification. The sequential search module divides the parameter samples into superior and inferior groups based on the pre-defined quantiles of the already calculated records. For each group, the probability density is estimated using kernel density, and the larger of the two is used as the next set of parameters for calculation. Already calculated combinations are not sampled repeatedly. If the number of records is insufficient, uniform sampling is used to fill the gaps.

[0113] The adaptation orchestration module performs benchmark tests on real devices to obtain latency and fixes the same device number to ensure consistent evaluation conditions. Rounds containing non-numerical and infinite values ​​are treated as invalid. The module terminates when one of the following conditions is met: the number of trial calculations reaches the upper limit or the improvement of several adjacent rounds is below the threshold. The module outputs the optimal parameter vector, measured latency, and comparison results. On the same device, a round of comparative testing is performed with the optimal parameters and the original parameters to verify the results and then solidify them as the new default values. The module further obtains the family configuration of registered inference summary support, parallel tool call support, context window occupancy ratio, and preferred terminal tool category by matching the model identifier prefix. It constructs request addresses according to their line protocol categories and converts tool specifications. When a capability is missing, it downgrades. When there is a connection layer error, it switches to a backup address with an atomic flag. The module groups tool calls according to conflict relationships so that read-only tools acquire shared locks concurrently and write tools acquire exclusive locks serially. The module allocates token budgets according to information density and compresses history and reclaims injected skill content when the sum of input and output tokens reaches the window occupancy ratio.

[0114] As can be seen from the above description, the operator block parameter sequential search and multi-protocol model adaptation device provided in this application embodiment can provide end-to-end automated assistance for operator tuning and model access through the collaboration of five modules: skill loading, skill injection, space construction, sequential search and adaptation orchestration.

[0115] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0116] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0117] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method.

[0118] In this embodiment, a skill index is constructed by sequential loading and name overwriting of a four-layer skill catalog, and content injection is triggered by a call marker. The search range is narrowed by a three-principle design of candidate value sets driven by block parameter definition and shape constraints, and feasible domain pruning. Environmental variable modification enables single trial calculations to be completed without recompilation, reducing turnaround time to the second level. The sampling of the ratio of the probability density of the superior and inferior groups guides subsequent sampling with historical trial calculation results. The optimal parameter vector is output by benchmark testing of fixed equipment and dual termination judgment of trial calculation limit and improvement magnitude threshold condition. Heterogeneous service access is achieved by capability degradation of model family configuration, line protocol routing, and atomic flag master-slave switching. The execution time and context occupation are controlled by the concurrent reading and writing of conflict groups and the token budget allocation of information density hierarchy. When the window limit is approached, the history is compressed and the injected skill content is recycled back to the skill loading to form a closed loop.

[0119] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for sequential search of operator block parameters and adaptation of multi-protocol models, characterized in that, The method includes: The skill catalog is traversed in the order of built-in layer, user general layer, user local layer and project local layer, and the preceding metadata of its description file is parsed. The skill index is constructed according to the rule that the later loader overwrites the earlier loader with the same name. When a skill call flag is detected in the session output, the corresponding skill content is injected into the next round of request as a developer. User skills are only listed with name and path and the description is omitted when the cumulative bytes of the description exceed the budget. Based on the block parameter definition and shape constraints of the operator source code, the candidate set and feasible region of each parameter are determined. The block parameters are modified to be read by environment variables and retain the original values ​​as default so that no recompilation is required for a single trial. The parameter samples are divided into superior and inferior groups according to the trial records and the probability density of each group is estimated. The larger of the two densities is taken as the next group of trial parameters. Benchmark tests are performed on fixed equipment and the delay is backfilled. The optimal parameters are output when one of the following conditions is met: the number of trial calculations reaches the upper limit or the number of adjacent improvement rounds is below the threshold; the family configuration is obtained by matching the model identifier prefix and the request path and conversion tool specification are constructed according to its line protocol; when the capability is missing, it is downgraded and the backup address is switched when the connection layer is faulty; the tool calls are grouped according to the conflict relationship to make read-only concurrent writing serial; the token budget is allocated according to the information density and the history and skills are compressed and recycled when the window is close to the upper limit.

2. The operator block parameter sequential search and multi-protocol model adaptation method according to claim 1, characterized in that, The process of traversing the skill directory in the order of built-in layer, user general layer, user local layer, and project local layer, parsing the pre-metadata of its description files, and constructing the skill index according to the rule of later loaders overwriting earlier loaders with the same name includes: A breadth-first traversal is performed on the skill catalog at each level to retrieve its description file. The preceding metadata at the beginning of the description file is parsed to obtain the skill name and skill description. The skill name has a maximum character limit, and the skill description has a maximum byte limit. Catalogs that contain files but lack the description file are registered with diagnostic information and skipped. The parsed skill names, skill descriptions, and file paths are aggregated into a skill index, which is loaded sequentially in the order of built-in layer, user general layer, user local layer, and project local layer. Skills with the same name are loaded later and overwrite those loaded earlier, so that customized content in the project layer takes precedence over global content. The skill index uses the skill name as the key for the injection process to retrieve by name.

3. The operator block parameter sequential search and multi-protocol model adaptation method according to claim 1, characterized in that, When a skill call flag is detected in the session output, the skill content is injected into the next request as a developer. User skills are listed only by name and path, and the description is omitted when the cumulative bytes exceed the budget. This includes: The preset skill call marker is detected in the session output text and the skill name contained therein is extracted. After locating the skill by name in the skill index, the complete content of the skill description file is injected into the next round of requests as a developer role message. Text that mentions the skill name outside the marker does not trigger injection. Built-in layer skills are only listed in the system prompt with their name and description, and their full content is not inlined. User layer skills are only listed in the system prompt with their name, description and path, and the session reads their files as needed to obtain detailed instructions. The number of bytes in the description of each skill is accumulated. Skills whose accumulated value exceeds the preset budget are only listed with their name and path and their description is omitted. When a single description exceeds the preset number of characters, it is truncated and an omission mark is added.

4. The operator block parameter sequential search and multi-protocol model adaptation method according to claim 1, characterized in that, The process of determining the candidate set and feasible region of each parameter based on the block parameter definition and shape constraints of the operator source code, and modifying the block parameters to read from environment variables and retain their original default values ​​so that no recompilation is required for a single trial calculation, includes: The block parameter definition in the operator source code is retrieved to obtain each adjustable parameter. The candidate value set is determined according to the coverage principle, the discrimination principle and the constraint pruning principle. The coverage principle requires that the candidate value covers the typical value range of the parameter and contains a power of 2 sequence that fits the hardware alignment characteristics. The discrimination principle requires that the performance difference between adjacent candidate values ​​is sufficient to distinguish them. The constraint pruning principle excludes candidate values ​​that exceed the on-chip storage capacity. The shape constraints of the retrieval operator are used to obtain the minimum dimension, alignment requirements, and dimensional relationships. These are then transformed into feasible domain constraints, and the candidate value set is pruned so that combinations that do not meet the alignment and dimensional relationships are not included in the trial calculation. The block parameters are modified to be read by environment variables and have the original values ​​as default values. The original files are backed up before modification, and after modification, the files are recompiled, installed, and the values ​​are verified to be effective. This ensures that a single trial calculation only requires setting environment variables and does not require recompiling the source code.

5. The operator block parameter sequential search and multi-protocol model adaptation method according to claim 1, characterized in that, The process of dividing the parameter samples into superior and inferior groups based on the already calculated records and estimating the probability density of each group, taking the larger of the two densities as the next set of trial parameters, performing benchmark tests on fixed equipment, and backfilling the delay includes: Arrange the time delay values ​​that have been calculated and recorded in ascending order and divide the parameter samples into superior and inferior groups with preset quantiles as the boundary. Construct the probability density of each group in each parameter dimension using kernel density estimation. Construct the dimension of discrete values ​​according to the frequency of values. For each parameter combination in the candidate value set, calculate the ratio of the superior group density to the inferior group density, and take the larger ratio as the next set of trial parameters to achieve an adaptive balance between fine sampling in known superior areas and exploration in insufficiently sampled areas. After each round of trial calculation, add the set of parameters and its time delay to the already calculated records and re-estimate the two groups of densities. If the number of records is less than the preset lower limit, use uniform sampling to fill the gaps.

6. The operator block parameter sequential search and multi-protocol model adaptation method according to claim 1, characterized in that, The optimal parameters are output when one of the following conditions is met: the number of trial calculations reaches the upper limit or the number of adjacent improvement rounds falls below the threshold; the family configuration is obtained by matching the model identifier prefix and the request path and conversion tool specifications are constructed according to its line protocol; when the capability is missing, the system is downgraded and a backup address is switched when there is a connection layer error, including: The latency was obtained by performing benchmark tests on real equipment. Before the test, the occupancy status of each device was investigated and the device with lower occupancy was selected. The same device number was fixed for the whole round of trial calculation to ensure that the evaluation conditions were consistent in each round. Rounds with test outputs containing non-numerical and infinite values ​​were judged as invalid and treated as invalid values ​​and not included in the trial calculation record. The trial is terminated when one of the following conditions is met: the number of trial calculations reaches the preset upper limit or the improvement of the optimal delay in several adjacent rounds is less than the threshold. The optimal parameter vector, the measured delay, the comparison result with the default parameters, and the calculated computing power are output. A round of comparative testing is performed on the same device with the optimal parameters and the original parameters to verify the difference. After the verification is passed, the optimal parameter vector is fixed as the new default value.

7. The operator block parameter sequential search and multi-protocol model adaptation method according to claim 1, characterized in that, The method of grouping tool calls by conflict relationship to enable read-only concurrent writes serially, allocating token budgets according to information density, and compressing history and recycling skills when approaching the window limit includes: The model family configuration is obtained by matching the prefix of the model identifier. The model family configuration registers the inference summary support, parallel tool call support, context window occupancy ratio and preferred terminal tool category of the corresponding family. The request address is constructed by concatenating the corresponding path after the base address according to its line protocol category, and the same set of tool specifications is converted into the description format required by the protocol. When parallel tool calls are not supported, it is downgraded to serial delivery. When inference summaries are not supported, the corresponding fields are not delivered. When a domain name resolution failure, connection timeout, or connection rejection error is detected, the system switches to an alternate address using an atomic flag and maintains this switch within the current process. This switching is idempotent and triggers security multiple times. The current round of tool calls is grouped according to conflict relationships. Read-only tools acquire shared locks and execute concurrently, while write tools acquire exclusive locks and execute serially. Token budgets are allocated hierarchically according to information density to prevent file content from being truncated. Subtask results are truncated according to the larger budget, and other outputs are truncated according to the beginning and end retention strategy. When the sum of input and output tokens reaches the context window occupancy ratio, the history is compressed and the injected skill content is reclaimed.

8. A device for sequential search of operator block parameters and adaptation of multi-protocol models, characterized in that, The device includes: The skill loading module is used to traverse the skill directory in breadth-first order of built-in layer, user general layer, user local layer and project local layer, parse the pre-metadata of the description file to obtain the skill name and description, and build the skill index by overwriting the earlier loaded skill with the same name with the later loaded skill. The skill injection module is used to detect the skill call flag in the session output and then inject the skill content into the next request as a developer. For user-level skills, only the name, description and path are listed, and the description is omitted when the cumulative bytes of the description exceed the budget. The spatial construction module is used to retrieve the block parameter definitions and shape constraints of the operator source code and determine the candidate value set and feasible region according to the three principles of coverage, resolution and constraint pruning. The block parameters are transformed into default values ​​that are read by environment variables and retain the original values. The sequential search module is used to divide the parameter samples into superior and inferior groups according to the pre-defined quantiles based on the trial records and estimate the probability density of each group. The larger of the two densities is taken as the next trial parameter. Benchmark tests are performed on a fixed device and the time delay is filled until the termination condition is met, and then the optimal parameter vector is output. The adaptation orchestration module is used to obtain family configurations by matching model identifier prefixes and construct request addresses and conversion tool specifications according to their line protocols, degrade when capabilities are missing, switch backup addresses with atomic flags when there are connection layer errors, group tool calls according to conflict relationships to enable read-only concurrent writing serially, allocate token budgets according to information density, and compress history and reclaim skill content when approaching the window limit.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the operator block parameter sequential search and multi-protocol model adaptation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the operator block parameter sequential search and multi-protocol model adaptation method according to any one of claims 1 to 7.

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