feat(dataset): add CodingContentGenerator for pseudo-realistic coding-trace replay

docs/cli-options.md

@@ -470,6 +470,11 @@ Number of text inputs to include in each request for batch processing endpoints.
 <br/>_Constraints: ≥ 0_
 <br/>_Default: `1`_
 
+#### `--prompt-corpus` `<str>`
+
+Source corpus for synthetic prompt text generation. `sonnet` uses Shakespeare sonnets (default for most loaders). `coding` uses pseudo-realistic, template-filled coding content (code, bash output, JSON, error tracebacks, git diffs, configs, markdown) — filler whose token distribution approximates real coding-agent traffic, driving realistic expert-routing patterns on Mixture-of-Experts models (sonnet over-activates English-prose experts and underweights the broader expert set hit by real agentic-coding workloads). When unset, the active dataset loader's default applies — most loaders default to 'sonnet'; agentic-coding loaders override to 'coding'.
+<br/>_Choices: [`sonnet`, `coding`]_
+
 ### Prefix Prompt
 
 #### `--prompt-prefix-pool-size`, `--prefix-prompt-pool-size`, `--num-prefix-prompts` `<int>`
@@ -1784,6 +1789,11 @@ Number of text inputs to include in each request for batch processing endpoints.
 <br/>_Constraints: ≥ 0_
 <br/>_Default: `1`_
 
+#### `--prompt-corpus` `<str>`
+
+Source corpus for synthetic prompt text generation. `sonnet` uses Shakespeare sonnets (default for most loaders). `coding` uses pseudo-realistic, template-filled coding content (code, bash output, JSON, error tracebacks, git diffs, configs, markdown) — filler whose token distribution approximates real coding-agent traffic, driving realistic expert-routing patterns on Mixture-of-Experts models (sonnet over-activates English-prose experts and underweights the broader expert set hit by real agentic-coding workloads). When unset, the active dataset loader's default applies — most loaders default to 'sonnet'; agentic-coding loaders override to 'coding'.
+<br/>_Choices: [`sonnet`, `coding`]_
+
 ### Prefix Prompt
 
 #### `--prompt-prefix-pool-size`, `--prefix-prompt-pool-size`, `--num-prefix-prompts` `<int>`

src/aiperf/common/enums/__init__.py

@@ -33,6 +33,7 @@
     OptimizationDirection,
     PrerequisiteKind,
     PrometheusMetricType,
+    PromptCorpus,
     PromptSource,
     RequestContentType,
     ServerMetricsDiscoveryMode,
@@ -135,6 +136,7 @@
     "PowerMetricUnitInfo",
     "PrerequisiteKind",
     "PrometheusMetricType",
+    "PromptCorpus",
     "PromptSource",
     "RequestContentType",
     "SSEEventType",

src/aiperf/common/enums/enums.py

@@ -430,6 +430,19 @@ def _missing_(cls, value: Any) -> Self:
             return cls.UNKNOWN
 
 
+class PromptCorpus(CaseInsensitiveStrEnum):
+    """Corpus used for synthetic prompt text generation.
+
+    Defined in :mod:`aiperf.common.enums.prompt_corpus` and re-exported here so
+    leaf modules (e.g. ``aiperf.plugin.schema.schemas``) can import it without
+    triggering this package's ``__init__`` chain. Keep that file as the
+    authoritative definition.
+    """
+
+    SONNET = "sonnet"
+    CODING = "coding"
+
+
 class PromptSource(CaseInsensitiveStrEnum):
     SYNTHETIC = "synthetic"
     FILE = "file"

src/aiperf/common/hash_id_random_generator.py

@@ -0,0 +1,77 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Hash-ID-based random generator for parallel processing with reproducibility.
+
+Enables parallel processing of traces with hash_ids while maintaining
+reproducibility. Each (trace_id, hash_id) pair produces a deterministic random
+sequence regardless of worker count or processing order.
+
+Architecture:
+    Global Seed -> Base RNG -> (trace_id, hash_id) -> Deterministic tokens
+
+The trace_id (typically a content hash of the trace file) ensures that different
+trace files with overlapping hash_id values produce different content, while the
+same trace file always produces identical results.
+"""
+
+import hashlib
+
+from aiperf.common.random_generator import RandomGenerator
+
+__all__ = ["HashIdRandomGenerator"]
+
+
+class _DisabledNumpyRNG:
+    """Raises on any attribute access to prevent NumPy RNG usage."""
+
+    def __getattr__(self, name):
+        raise RuntimeError(
+            "HashIdRandomGenerator does not support NumPy RNG operations. "
+            "Use Python RNG methods (randrange, choice, etc.) instead."
+        )
+
+
+class HashIdRandomGenerator(RandomGenerator):
+    """RandomGenerator that re-seeds deterministically per (trace_id, hash_id).
+
+    Designed for parallel processing where multiple workers need to generate
+    identical content for the same hash_id within a trace file.
+
+    Thread Safety:
+        NOT thread-safe. Each worker process must have its own instance.
+    """
+
+    @classmethod
+    def from_base_rng(cls, base_rng: RandomGenerator) -> "HashIdRandomGenerator":
+        """Create from a base RandomGenerator (typically from rng.derive())."""
+        base_seed = base_rng.seed or base_rng.randrange(0, 2**64)
+        return cls(base_seed, _internal=True)
+
+    def __init__(self, base_seed: int, *, _internal: bool = False):
+        super().__init__(base_seed, _internal=_internal)
+        self._numpy_rng = _DisabledNumpyRNG()
+        self._trace_id: str = ""
+
+    def set_trace_id(self, trace_id: str) -> None:
+        """Set trace identifier to scope hash_ids to a specific trace file.
+
+        Args:
+            trace_id: Content hash or unique identifier for the trace file.
+                      Different trace files must use different trace_ids.
+        """
+        self._trace_id = trace_id
+
+    def reseed_for_hash_id(self, hash_id: int) -> None:
+        """Re-seed RNG deterministically for a specific hash_id.
+
+        After calling, all random operations use the derived seed until
+        the next reseed_for_hash_id call.
+
+        Args:
+            hash_id: KV block hash ID from trace data.
+        """
+        seed_bytes = hashlib.sha256(
+            f"{self.seed}:{self._trace_id}:{hash_id}".encode()
+        ).digest()
+        self._python_rng.seed(int.from_bytes(seed_bytes[:8], "big"))

src/aiperf/config/dataset/content.py

@@ -27,6 +27,7 @@
     AudioFormat,
     ImageFormat,
     ImageSource,
+    PromptCorpus,
 )
 from aiperf.config.base import BaseConfig
 from aiperf.config.types import (
@@ -125,6 +126,25 @@ class PromptConfig(BaseConfig):
         ),
     ]
 
+    corpus: Annotated[
+        PromptCorpus | None,
+        Field(
+            default=None,
+            description="Source corpus for synthetic prompt text generation. "
+            "'sonnet' uses Shakespeare sonnets (default for most loaders). "
+            "'coding' uses pseudo-realistic, template-filled coding content "
+            "(code, bash output, JSON, error tracebacks, git diffs, configs, "
+            "markdown) — filler whose token distribution approximates real "
+            "coding-agent traffic, driving realistic expert-routing patterns "
+            "on Mixture-of-Experts models (sonnet over-activates English-prose "
+            "experts and underweights the broader expert set hit by real "
+            "agentic-coding workloads). "
+            "When unset, the active dataset loader's default applies — most "
+            "loaders default to 'sonnet'; agentic-coding loaders override to "
+            "'coding'.",
+        ),
+    ]
+
     @field_validator("sequence_distribution")
     @classmethod
     def validate_sequence_probabilities(

src/aiperf/config/flags/_converter_dataset.py

@@ -79,6 +79,8 @@ def _build_prompts(cli: CLIConfig) -> dict[str, Any]:
         prompts["block_size"] = cli.prompt_input_tokens_block_size
     if "prompt_batch_size" in s:
         prompts["batch_size"] = cli.prompt_batch_size
+    if "prompt_corpus" in s and cli.prompt_corpus is not None:
+        prompts["corpus"] = cli.prompt_corpus
     return prompts
 
 

src/aiperf/config/flags/_section_fields.py

@@ -74,6 +74,7 @@
         "prompt_prefix_shared_system_length",
         "prompt_prefix_user_context_length",
         "prompt_sequence_distribution",
+        "prompt_corpus",
         # ----- image modality -----
         "image_width_mean",
         "image_width_stddev",

src/aiperf/config/flags/cli_config.py

@@ -45,6 +45,7 @@
     GPUTelemetryMode,
     ImageFormat,
     ModelSelectionStrategy,
+    PromptCorpus,
     RequestContentType,
     ServerMetricsFormat,
     SweepMode,
@@ -1006,6 +1007,29 @@ def url(self) -> str:
         ),
     ] = None
 
+    prompt_corpus: Annotated[
+        PromptCorpus | None,
+        Field(
+            default=None,
+            description="Source corpus for synthetic prompt text generation. "
+            "`sonnet` uses Shakespeare sonnets (default for most loaders). "
+            "`coding` uses pseudo-realistic, template-filled coding content "
+            "(code, bash output, JSON, error tracebacks, git diffs, configs, "
+            "markdown) — filler whose token distribution approximates real "
+            "coding-agent traffic, driving realistic expert-routing patterns "
+            "on Mixture-of-Experts models (sonnet over-activates English-prose "
+            "experts and underweights the broader expert set hit by real "
+            "agentic-coding workloads). "
+            "When unset, the active dataset loader's default applies — most "
+            "loaders default to 'sonnet'; agentic-coding loaders override to "
+            "'coding'.",
+        ),
+        CLIParameter(
+            name=("--prompt-corpus",),
+            group=Groups.PROMPT,
+        ),
+    ] = None
+
     ##############################################################################
     # Output Sequence Length (OSL)
     ##############################################################################

src/aiperf/config/schema/aiperf-config.schema.json

@@ -9324,11 +9324,32 @@
           "default": null,
           "description": "Distribution of (ISL, OSL) pairs with probabilities for mixed workload simulation. Each entry specifies {isl, osl, probability}. Probabilities are percentages (0-100) and must sum to 100. When specified, requests are sampled from this distribution instead of using isl/osl fields.",
           "title": "Sequencedistribution"
+        },
+        "corpus": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/PromptCorpus"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null,
+          "description": "Source corpus for synthetic prompt text generation. 'sonnet' uses Shakespeare sonnets (default for most loaders). 'coding' uses pseudo-realistic, template-filled coding content (code, bash output, JSON, error tracebacks, git diffs, configs, markdown) — filler whose token distribution approximates real coding-agent traffic, driving realistic expert-routing patterns on Mixture-of-Experts models (sonnet over-activates English-prose experts and underweights the broader expert set hit by real agentic-coding workloads). When unset, the active dataset loader's default applies — most loaders default to 'sonnet'; agentic-coding loaders override to 'coding'."
         }
       },
       "title": "PromptConfig",
       "type": "object"
     },
+    "PromptCorpus": {
+      "description": "Corpus used for synthetic prompt text generation.\n\nDefined in :mod:`aiperf.common.enums.prompt_corpus` and re-exported here so\nleaf modules (e.g. ``aiperf.plugin.schema.schemas``) can import it without\ntriggering this package's ``__init__`` chain. Keep that file as the\nauthoritative definition.",
+      "enum": [
+        "sonnet",
+        "coding"
+      ],
+      "title": "PromptCorpus",
+      "type": "string"
+    },
     "PublicDataset": {
       "additionalProperties": false,
       "description": "Public dataset configuration.\n\nUses well-known public benchmarking datasets that are\nautomatically downloaded and processed by AIPerf.",

src/aiperf/dataset/composer/base.py

@@ -6,7 +6,11 @@
 from typing import TYPE_CHECKING
 
 from aiperf.common import random_generator as rng
-from aiperf.common.enums import ConversationContextMode, ModelSelectionStrategy
+from aiperf.common.enums import (
+    ConversationContextMode,
+    ModelSelectionStrategy,
+    PromptCorpus,
+)
 from aiperf.common.mixins import AIPerfLoggerMixin
 from aiperf.common.models import Conversation, Turn
 from aiperf.common.models.sequence_distribution import (
@@ -30,6 +34,7 @@
     )
     from aiperf.config.distributions import SamplingDistribution
     from aiperf.config.resolution.plan import BenchmarkRun
+    from aiperf.dataset.generator.coding_content import CodingContentGenerator
 
 
 class BaseDatasetComposer(AIPerfLoggerMixin, ABC):
@@ -66,14 +71,8 @@ def __init__(
         )
 
         # Create generators (prompt generator requires a tokenizer)
-        self.prompt_generator: PromptGenerator | None = (
-            PromptGenerator(
-                prompts=self._synthetic_prompts,
-                prefix_prompts=self._synthetic_prefix_prompts,
-                tokenizer=tokenizer,
-            )
-            if tokenizer
-            else None
+        self.prompt_generator: PromptGenerator | CodingContentGenerator | None = (
+            self._create_prompt_generator(tokenizer) if tokenizer else None
         )
         self.image_generator = ImageGenerator(self._synthetic_images)
         self.audio_generator = AudioGenerator(self._synthetic_audio)
@@ -95,6 +94,27 @@ def __init__(
         # Cache for turn-level sequence lengths to ensure ISL/OSL pairing consistency
         self._turn_sequence_cache: dict[int, tuple[int, int]] = {}
 
+    def _create_prompt_generator(
+        self, tokenizer: Tokenizer
+    ) -> PromptGenerator | CodingContentGenerator:
+        if (
+            self._synthetic_prompts is not None
+            and self._synthetic_prompts.corpus == PromptCorpus.CODING
+        ):
+            from aiperf.dataset.generator.coding_content import CodingContentGenerator
+
+            return CodingContentGenerator(
+                config=self._synthetic_prompts,
+                prefix_prompts=self._synthetic_prefix_prompts,
+                tokenizer=tokenizer,
+            )
+
+        return PromptGenerator(
+            prompts=self._synthetic_prompts,
+            prefix_prompts=self._synthetic_prefix_prompts,
+            tokenizer=tokenizer,
+        )
+
     @abstractmethod
     def create_dataset(self) -> list[Conversation]:
         """

src/aiperf/dataset/composer/custom.py

@@ -7,11 +7,12 @@
 
 import orjson
 
-from aiperf.common.enums import ConversationContextMode, DatasetFormat
+from aiperf.common.enums import ConversationContextMode, DatasetFormat, PromptCorpus
 from aiperf.common.models import Conversation
 from aiperf.common.tokenizer import Tokenizer
 from aiperf.common.utils import load_json_str
 from aiperf.config.dataset import FileDataset
+from aiperf.config.dataset.content import PromptConfig
 from aiperf.dataset.composer.base import BaseDatasetComposer
 from aiperf.dataset.loader.base_loader import BaseLoader
 from aiperf.dataset.utils import check_file_exists
@@ -254,7 +255,29 @@ def _create_loader_instance(self, dataset_type: CustomDatasetType) -> None:
                     "Trace datasets require a tokenizer for prompt synthesis. "
                     "Ensure the endpoint supports tokenization or provide a --tokenizer."
                 )
-            kwargs["prompt_generator"] = self.prompt_generator
+
+            corpus = (
+                self._synthetic_prompts.corpus
+                if self._synthetic_prompts is not None
+                and self._synthetic_prompts.corpus is not None
+                else loader_metadata.default_prompt_corpus
+            )
+            if corpus == PromptCorpus.CODING:
+                from aiperf.dataset.generator.coding_content import (
+                    CodingContentGenerator,
+                )
+
+                prompt_config = (
+                    self._synthetic_prompts
+                    if self._synthetic_prompts is not None
+                    else PromptConfig()
+                )
+                kwargs["prompt_generator"] = CodingContentGenerator(
+                    config=prompt_config,
+                    tokenizer=self.prompt_generator.tokenizer,
+                )
+            else:
+                kwargs["prompt_generator"] = self.prompt_generator
 
             if loader_metadata.default_block_size is not None:
                 kwargs["default_block_size"] = loader_metadata.default_block_size