[Optimization 6/n] Introduce Optimization Worker

kernel_perf_agent/README.md

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+# Kernel Performance Utilities
+
+Low-level, reusable utilities for kernel optimization.

kernel_perf_agent/kernel_opt/diagnose_prompt/__init__.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Diagnose Prompt Module for Hardware Bottleneck Analysis.
+
+This module provides prompt building utilities for the Judge LLM that
+analyzes NCU profiling metrics to identify performance bottlenecks.
+"""
+
+from .gpu_specs import get_gpu_specs
+from .judger_prompts import (
+    build_judge_optimization_prompt,
+    extract_judge_response,
+    validate_judge_response,
+)
+
+__all__ = [
+    "get_gpu_specs",
+    "build_judge_optimization_prompt",
+    "extract_judge_response",
+    "validate_judge_response",
+]

kernel_perf_agent/kernel_opt/diagnose_prompt/gpu_specs.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+GPU Specifications Database for Bottleneck Analysis
+
+This module provides GPU hardware specifications needed for performance analysis
+and bottleneck identification. It includes peak compute performance, memory bandwidth,
+cache sizes, and SM counts for common NVIDIA GPUs.
+
+"""
+
+import subprocess
+from typing import Any
+
+from kernel_perf_agent.kernel_opt.diagnose_prompt.gpu_specs_database import (
+    GPU_SPECS_DATABASE,
+)
+
+__all__ = ["GPU_SPECS_DATABASE", "query_gpu_name", "get_gpu_specs"]
+
+
+def query_gpu_name() -> str | None:
+    """
+    Query GPU name using nvidia-smi.
+
+    Returns:
+        GPU name string, or None if query fails
+    """
+    try:
+        result = subprocess.run(
+            ["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
+            capture_output=True,
+            text=True,
+            timeout=5,
+        )
+        if result.returncode == 0:
+            # Take only the first GPU (nvidia-smi returns one line per GPU)
+            gpu_name = result.stdout.strip().split("\n")[0].strip()
+            return gpu_name
+    except (subprocess.TimeoutExpired, FileNotFoundError, Exception):
+        pass
+    return None
+
+
+def get_gpu_specs(gpu_name: str | None = None) -> dict[str, Any]:
+    """
+    Get GPU specifications for bottleneck analysis.
+
+    This function returns hardware specifications needed for performance analysis,
+    including peak compute performance, memory bandwidth, cache sizes, and SM counts.
+
+    Args:
+        gpu_name: GPU name (if None, auto-detect with nvidia-smi)
+
+    Returns:
+        Dictionary with GPU specifications containing:
+        - name: GPU name
+        - architecture: GPU architecture (e.g., "Ampere", "Hopper")
+        - peak_fp32_tflops: Peak FP32 compute performance in TFLOPS
+        - peak_fp16_tflops: Peak FP16 compute performance in TFLOPS
+        - peak_bf16_tflops: Peak BF16 compute performance in TFLOPS (0 if not supported)
+        - peak_memory_bw_gbps: Peak memory bandwidth in GB/s
+        - sm_count: Number of streaming multiprocessors
+        - max_threads_per_sm: Maximum threads per SM
+        - l1_cache_kb: L1 cache size in KB per SM
+        - l2_cache_mb: Total L2 cache size in MB
+        - memory_gb: Total GPU memory in GB
+        - memory_type: Memory type (e.g., "HBM2e", "GDDR6X")
+
+    Examples:
+        >>> specs = get_gpu_specs()  # Auto-detect
+        >>> print(f"Peak BW: {specs['peak_memory_bw_gbps']} GB/s")
+
+        >>> specs = get_gpu_specs("NVIDIA A100")
+        >>> print(f"SM Count: {specs['sm_count']}")
+    """
+    # Auto-detect if not provided
+    if gpu_name is None:
+        gpu_name = query_gpu_name()
+
+    # Return default if detection failed
+    if gpu_name is None:
+        print("⚠️  GPU auto-detection failed, using A100 specs as fallback")
+        return GPU_SPECS_DATABASE["NVIDIA A100"].copy()
+
+    # Try exact match
+    if gpu_name in GPU_SPECS_DATABASE:
+        return GPU_SPECS_DATABASE[gpu_name].copy()
+
+    # Try fuzzy match (contains or partial match)
+    gpu_name_lower = gpu_name.lower()
+    for key, specs in GPU_SPECS_DATABASE.items():
+        key_lower = key.lower()
+        # Check if either name contains the other
+        if gpu_name_lower in key_lower or key_lower in gpu_name_lower:
+            print(f"ℹ️  Matched '{gpu_name}' to '{key}' (fuzzy match)")
+            return specs.copy()
+
+    # Fallback to A100 specs with warning
+    print(f"⚠️  Unknown GPU: '{gpu_name}', using A100 specs as fallback")
+    print(f"    Available GPUs: {', '.join(GPU_SPECS_DATABASE.keys())}")
+    return GPU_SPECS_DATABASE["NVIDIA A100"].copy()
+
+
+if __name__ == "__main__":
+    print("GPU Specifications Module")
+    print("=" * 60)
+
+    # Auto-detect GPU
+    detected_name = query_gpu_name()
+    if detected_name:
+        print(f"\nDetected GPU: {detected_name}")
+    else:
+        print("\nNo GPU detected (nvidia-smi not available)")
+        exit()
+
+    # Get specs
+    specs = get_gpu_specs()
+    print(
+        f"\nUsing specs for: {specs['name']} ({specs.get('architecture', 'Unknown')})"
+    )
+    print(f"  - Peak Memory Bandwidth: {specs['peak_memory_bw_gbps']} GB/s")
+    print(f"  - Peak FP32 Performance: {specs['peak_fp32_tflops']} TFLOPS")
+    print(f"  - SM Count: {specs['sm_count']}")
+
+    # Show all available GPUs
+    print(f"\n{'=' * 60}")
+    print("Available GPU specifications in database:")
+    for gpu_name in sorted(GPU_SPECS_DATABASE.keys()):
+        print(f"  - {gpu_name}")

kernel_perf_agent/kernel_opt/diagnose_prompt/gpu_specs_database.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+GPU Specifications Database
+
+This module contains the GPU hardware specifications database used for
+performance analysis and bottleneck identification. Separated into its
+own file to allow easier module overriding.
+
+Sources: NVIDIA official specifications, manufacturer datasheets
+"""
+
+GPU_SPECS_DATABASE: dict[str, dict[str, object]] = {
+    "NVIDIA A100": {
+        "name": "NVIDIA A100",
+        "architecture": "Ampere",
+        "peak_fp32_tflops": 19.5,
+        "peak_fp16_tflops": 312.0,
+        "peak_bf16_tflops": 312.0,
+        "peak_memory_bw_gbps": 1555,
+        "sm_count": 108,
+        "max_threads_per_sm": 2048,
+        "l1_cache_kb": 192,
+        "l2_cache_mb": 40,
+        "memory_gb": 40,
+        "memory_type": "HBM2e",
+    },
+    "NVIDIA H100": {
+        "name": "NVIDIA H100",
+        "architecture": "Hopper",
+        "peak_fp32_tflops": 51.0,
+        "peak_fp16_tflops": 989.0,
+        "peak_bf16_tflops": 989.0,
+        "peak_memory_bw_gbps": 3352,
+        "sm_count": 132,
+        "max_threads_per_sm": 2048,
+        "l1_cache_kb": 256,
+        "l2_cache_mb": 50,
+        "memory_gb": 80,
+        "memory_type": "HBM3",
+    },
+    "NVIDIA RTX 4090": {
+        "name": "NVIDIA RTX 4090",
+        "architecture": "Ada Lovelace",
+        "peak_fp32_tflops": 82.6,
+        "peak_fp16_tflops": 165.0,
+        "peak_bf16_tflops": 165.0,
+        "peak_memory_bw_gbps": 1008,
+        "sm_count": 128,
+        "max_threads_per_sm": 1536,
+        "l1_cache_kb": 128,
+        "l2_cache_mb": 72,
+        "memory_gb": 24,
+        "memory_type": "GDDR6X",
+    },
+    "NVIDIA RTX 5080": {
+        "name": "NVIDIA RTX 5080",
+        "architecture": "Blackwell",
+        "peak_fp32_tflops": 57.0,
+        "peak_fp16_tflops": 114.0,
+        "peak_bf16_tflops": 114.0,
+        "peak_memory_bw_gbps": 960,
+        "sm_count": 84,
+        "max_threads_per_sm": 1536,
+        "l1_cache_kb": 128,
+        "l2_cache_mb": 64,
+        "memory_gb": 16,
+        "memory_type": "GDDR7",
+    },
+}