Add Quick search support for ensemble composing model parameter ranges

Fix copyrights

Fix tests

Author: the-david-oy

docs/config_search.md

@@ -1,17 +1,6 @@
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 # Table of Contents
@@ -240,7 +229,8 @@ manual sweep:
 
 _This mode has the following limitations:_
 
-- If model config parameters are specified, they can contain only one possible combination of parameters
+- Top-level models can contain only one possible combination of model config parameters
+- Composing models (ensemble/BLS sub-models) can specify parameter ranges if they follow specific patterns (see [Ensemble Composing Model Parameter Ranges](#ensemble-composing-model-parameter-ranges))
 
 This mode uses a hill climbing algorithm to search the configuration space, looking for
 the maximal objective value within the specified constraints. In the majority of cases
@@ -262,6 +252,77 @@ profile_models:
 
 ---
 
+### **Ensemble Composing Model Parameter Ranges**
+
+When profiling ensemble or BLS models in Quick search mode, composing models (sub-models) can specify parameter ranges for `instance_group` count. This enables optimization of composing models with different resource requirements, such as:
+
+- CPU-bound models (tokenizers, preprocessors) that may benefit from higher instance counts
+- GPU-bound models (inference models, embeddings) with limited GPU memory
+
+**Supported Instance Count Patterns:**
+
+Model Analyzer supports two types of instance count sequences that map to Quick search's coordinate system:
+
+1. **Powers of 2**: `[1, 2, 4, 8, 16, 32]` or subsets like `[2, 4, 8]`
+   - Maps to exponential search dimensions
+   - Recommended for most use cases
+
+2. **Contiguous sequences**: `[1, 2, 3, 4, 5]` or ranges like `[5, 6, 7, 8]`
+   - Maps to linear search dimensions
+   - Useful for fine-grained control
+
+**Important Notes:**
+
+- Only composing models can specify instance count ranges in Quick mode
+- Top-level models (non-composing) must still have a single parameter combination
+- Only powers of 2 or contiguous sequences are supported; arbitrary value lists (e.g., `[1, 3, 7, 15]`) are not supported
+- Composing models are identified using `cpu_only_composing_models` or `bls_composing_models` configuration
+
+---
+
+_An example with ensemble containing CPU tokenizer and GPU inference model:_
+
+```yaml
+model_repository: /path/to/model/repository/
+
+run_config_search_mode: quick
+export_path: /tmp/results
+override_output_model_repository: true
+
+cpu_only_composing_models:
+  - tokenizer
+
+profile_models:
+  tokenizer:
+    model_config_parameters:
+      instance_group:
+        - kind: KIND_CPU
+          count: [1, 2, 4, 8, 16, 32]  # Powers of 2 sequence
+      dynamic_batching:
+        max_queue_delay_microseconds: [0]
+
+  inference_model:
+    model_config_parameters:
+      instance_group:
+        - kind: KIND_GPU
+          count: [1, 2, 4, 8]  # Subset of powers of 2
+      dynamic_batching:
+        max_queue_delay_microseconds: [0]
+
+  ensemble_model:
+    model_config_parameters:
+      dynamic_batching:
+        max_queue_delay_microseconds: [0]
+```
+
+In this example:
+- The tokenizer (CPU model) searches instance counts from 1 to 32
+- The inference model (GPU model) searches instance counts from 1 to 8
+- Quick search explores both dimensions in parallel to find the optimal combination
+- The ensemble model itself has fixed parameters (single combination)
+
+---
+
 ### **Limiting Batch Size, Instance Group, and Client Concurrency**
 
 Using the `--run-config-search-<min/max>...` config options you have the ability to clamp the algorithm's upper or lower bounds for the model's batch size and instance group count, as well as the client's request concurrency.
@@ -398,6 +459,10 @@ _This mode has the following limitations:_
 
 Ensemble models can be optimized using the Quick Search mode's hill climbing algorithm to search the composing models' configuration spaces in parallel, looking for the maximal objective value within the specified constraints. Model Analyzer has observed positive outcomes towards finding the maximum objective value; with runtimes under one hour (compared to the days it would take a brute force run to complete) for ensembles that contain up to four composing models.
 
+**Composing Model Parameter Ranges:**
+
+Composing models within ensembles can specify instance count ranges to optimize models with different resource requirements (e.g., CPU tokenizers vs GPU inference models). See [Ensemble Composing Model Parameter Ranges](#ensemble-composing-model-parameter-ranges) for details on supported patterns and configuration examples.
+
 After Model Analyzer has found the best config(s), it will then sweep the top-N configurations found (specified by `--num-configs-per-model`) over the concurrency range before generation of the summary reports.
 
 ---
@@ -412,6 +477,10 @@ _This mode has the following limitations:_
 
 BLS models can be optimized using the Quick Search mode's hill climbing algorithm to search the BLS composing models' configuration spaces, as well as the BLS model's instance count, in parallel, looking for the maximal objective value within the specified constraints. Model Analyzer has observed positive outcomes towards finding the maximum objective value; with runtimes under one hour (compared to the days it would take a brute force run to complete) for BLS models that contain up to four composing models.
 
+**Composing Model Parameter Ranges:**
+
+BLS composing models can specify instance count ranges to optimize models with different resource requirements. Models are identified using the `bls_composing_models` configuration parameter. See [Ensemble Composing Model Parameter Ranges](#ensemble-composing-model-parameter-ranges) for details on supported patterns and configuration examples.
+
 After Model Analyzer has found the best config(s), it will then sweep the top-N configurations found (specified by `--num-configs-per-model`) over the concurrency range before generation of the summary reports.
 
 ---

model_analyzer/config/generate/quick_run_config_generator.py

@@ -25,6 +25,7 @@
 from model_analyzer.config.run.model_run_config import ModelRunConfig
 from model_analyzer.config.run.run_config import RunConfig
 from model_analyzer.constants import LOGGER_NAME
+from model_analyzer.model_analyzer_exceptions import TritonModelAnalyzerException
 from model_analyzer.perf_analyzer.perf_config import PerfAnalyzerConfig
 from model_analyzer.result.run_config_measurement import RunConfigMeasurement
 from model_analyzer.triton.model.model_config import ModelConfig
@@ -425,33 +426,53 @@ def _get_next_model_config_variant(
         )
 
         model_config_params = deepcopy(model.model_config_parameters())
+
+        # Extract user-specified instance_group kind before removing it
+        instance_kind = self._extract_instance_group_kind(model_config_params)
+        if not instance_kind:
+            # Fallback to cpu_only flag
+            instance_kind = "KIND_CPU" if model.cpu_only() else "KIND_GPU"
+
         if model_config_params:
+            # Remove parameters that are controlled by search dimensions
             model_config_params.pop("max_batch_size", None)
+            model_config_params.pop("instance_group", None)
 
-            # This is guaranteed to only generate one combination (check is in config_command)
+            # Generate combinations from remaining parameters
+            # For composing models, this may include dynamic_batching settings, etc.
             param_combos = GeneratorUtils.generate_combinations(model_config_params)
-            assert len(param_combos) == 1
 
-            param_combo = param_combos[0]
+            # Top-level models must have exactly 1 combination (validated earlier)
+            # Composing models can have 1 combination (non-searchable params are fixed)
+            if len(param_combos) > 1:
+                raise TritonModelAnalyzerException(
+                    f"Model {model.model_name()} has multiple parameter combinations "
+                    f"after removing searchable parameters. This should have been caught "
+                    f"during config validation."
+                )
+
+            param_combo = param_combos[0] if param_combos else {}
         else:
             param_combo = {}
 
-        kind = "KIND_CPU" if model.cpu_only() else "KIND_GPU"
+        # Add instance_group with count from dimension and kind from config
         instance_count = self._calculate_instance_count(dimension_values)
-
         param_combo["instance_group"] = [
             {
                 "count": instance_count,
-                "kind": kind,
+                "kind": instance_kind,
             }
         ]
 
+        # Add max_batch_size from dimension if applicable
         if "max_batch_size" in dimension_values:
             param_combo["max_batch_size"] = self._calculate_model_batch_size(
                 dimension_values
             )
 
-        if model.supports_dynamic_batching():
+        # Add default dynamic_batching if model supports it and not already specified
+        # Preserves user-specified dynamic_batching settings (single combinations only)
+        if model.supports_dynamic_batching() and "dynamic_batching" not in param_combo:
             param_combo["dynamic_batching"] = {}
 
         model_config_variant = BaseModelConfigGenerator.make_model_config_variant(
@@ -463,6 +484,37 @@ def _get_next_model_config_variant(
 
         return model_config_variant
 
+    def _extract_instance_group_kind(self, model_config_params: dict) -> str:
+        """
+        Extract the 'kind' field from instance_group in model_config_parameters.
+
+        Returns empty string if not found or if instance_group is not specified.
+        """
+        if not model_config_params or "instance_group" not in model_config_params:
+            return ""
+
+        instance_group = model_config_params["instance_group"]
+
+        # Handle various nested list structures from config parsing
+        if isinstance(instance_group, list) and len(instance_group) > 0:
+            # Handle nested structure: [[ {...} ]]
+            while (
+                isinstance(instance_group, list)
+                and len(instance_group) > 0
+                and isinstance(instance_group[0], list)
+            ):
+                instance_group = instance_group[0]
+
+            # Now should have [{...}] structure
+            if (
+                isinstance(instance_group, list)
+                and len(instance_group) > 0
+                and isinstance(instance_group[0], dict)
+            ):
+                return instance_group[0].get("kind", "")
+
+        return ""
+
     def _create_next_model_run_config(
         self,
         model: ModelProfileSpec,