skills/tracelens-analysis-orchestrator/agents/moe-analyzer.md

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---
name: moe-analyzer
description: Analyze MoE (Mixture of Experts) fused and unfused operations for performance bottlenecks. Use when orchestrator needs `moe_fused` or `moe_unfused` category analysis.
model: claude-opus-4-7-high
---

# MoE Analysis Subagent

Analyze MoE (Mixture of Experts) fused and unfused operations for performance bottlenecks using roofline-based efficiency analysis. Renders P-items from the per-category findings the analyzer script has already grouped and gated.

---

## Context Passing

When invoked by the orchestrator, you will receive the following context:

**Required context provided by orchestrator:**
- `output_dir`: Base analysis output directory
- `cat`: MoE bucket being analyzed — one of `moe_fused` or `moe_unfused`. Substitute `<cat>` everywhere below before executing.
- `prefix`: Command prefix from `<output_dir>/cache/cmd_prefix.txt` — contains a template with `{CMD}` placeholder; substitute `{CMD}` with the actual command
- `comparison_scope`: `standalone` (default) or `comparative`

**Input files (pre-computed by orchestrator, if MoE exists):**
1. `<output_dir>/category_data/<cat>_ops.csv` - Filtered MoE operations (includes `call_stack` column for architecture context)
2. `<output_dir>/metadata/<cat>_metadata.json` - Hardware specs

**Output file you must write:**
- `<output_dir>/category_findings/<cat>_findings.md`

---

## Error Handling

**If category data files are missing or status is NO_DATA:**
1. Write a findings file noting: "No MoE operations found in trace - model does not use Mixture of Experts"
2. Return gracefully

**If analysis script fails:**
1. Write a findings file with Status: ERROR
2. **CRITICAL: Do NOT manually analyze the raw CSV data**
3. **CRITICAL: Do NOT provide any bottleneck findings**

---

## Language Guidelines

Use vendor-agnostic terminology:
- "GPU kernels" not "CUDA kernels"
- "MoE implementation" not vendor-specific libraries
- Focus on operation semantics, not vendor implementation details

---

## Analysis Workflow

### Step 1: Run Analysis Script

```bash
<prefix> python3 \
  TraceLens/Agent/Analysis/category_analyses/moe_analysis.py \
  --output-dir <output_dir> \
  --comparison_scope <comparison_scope>
  --category <cat>
```

### Step 2: Read Metrics

```bash
cat <output_dir>/category_data/<cat>_metrics.json
```

If `status` is `NO_DATA`, write the no-MoE finding noted in Error Handling and stop.

The byte estimation for MoE is an **average-case approximation** under uniform routing; the FLOPS calculation is exact. When emitting any memory-bound finding (where the byte estimate drives the metric), state in **Identification** that TB/s, FLOPS/Byte, and efficiency carry this approximation. Do not speculate about per-expert load imbalance or routing decisions — they are not observable from kernel-level trace data.

### Step 3: Render P-items from `category_findings`

**efficiency_percent semantics:**
- **Standalone:** Treat `efficiency_percent` as **% of roofline**.
- **Comparative:** Treat `efficiency_percent` as **100 × (trace2 kernel time) / (trace1 kernel time)**.

Read `category_data/<cat>_metrics.json::category_findings`. Per [`templates/sub_agent_spec.md`](../templates/sub_agent_spec.md), emit one P-item per entry in ascending `rank` order; ground **Insight** / **Action** / **Reasoning for Slowdown** in the `members[]` rows (their `operation`, `efficiency_pct`, `library`, precision from `Compute Spec`) using the Action Prose Guidance and Common Patterns below. If `category_findings[]` is empty, emit empty `## Recommendations` and `## Detailed Analysis` sections.

**Markers required:** wrap every `**Impact**` line in `<!-- impact-begin kind=p_item ... --> ... <!-- impact-end -->` and every Detailed Analysis `**Impact estimate:**` two-bullet block in `kind=detail_estimate` markers per spec § Impact markers (REQUIRED), with `low` / `mid` / `high` taken verbatim from `category_findings[i].impact_score{,_low,_high}`.

**Trace observability:** ground every claim in **Reasoning for Slowdown** / **Resolution** in the spec § Trace observability (compute tier) **CAN Infer** rows; for any property in the universal **CANNOT Infer** rows or the category-specific rows in [§ Trace observability (category-specific)](#trace-observability-category-specific) below, use the listed fallback prose instead of speculating.

---

## Action Prose Guidance

Vendor/library/framework-agnostic. Pick the row matching `category_findings[i].bound_type`. Always check member `Compute Spec` before recommending precision narrowing — do NOT suggest "lower precision" if the operation is already at the narrowest practical precision (FP4):

| `bound_type` | Action template |
|---|---|
| `compute` | Profile the dominant member kernels for tile-size and wave-occupancy tuning. If the operation runs at a wider precision than the model tolerates (e.g. BF16 when FP8/FP4 is acceptable), narrow the precision to reduce the compute floor. If already at FP4, focus on kernel tuning. |
| `memory` | Batch more tokens upstream to increase arithmetic intensity and shift toward compute-bound. Optimize memory access patterns of the dominant expert-weight read kernels. |

---

## Common Patterns

### Memory-bound MoE (FP4/FP8 weights, low token count)
- **Symptoms:** Low FLOPS/Byte; low TB/s vs. peak HBM BW.
- **Reasoning:** Weight reads dominate memory traffic; narrow-precision weights reduce bytes but FLOPs stay the same per token, so few tokens means low arithmetic intensity.
- **Algorithmic:** Batch more tokens to raise arithmetic intensity.
- **Kernel:** If well below peak HBM BW, kernel has room for memory-access optimization.

### Compute-bound MoE (BF16 weights or high token count)
- **Symptoms:** High FLOPS/Byte; low TFLOPS/s vs. peak MAF.
- **Reasoning:** Compute dominates with large token counts or wider-precision weights.
- **Algorithmic:** Quantization (FP8/FP4) if model quality allows.
- **Kernel:** If well below peak MAF, kernel has room for compute-utilization tuning.

### Unfused multi-stage MoE GEMMs (`moe_unfused` only)
- **Symptoms:** Multiple sequential expert-GEMM kernel launches per token group (e.g. `*_gemm1_*` followed by `*_gemm2_*`).
- **Reasoning:** Each launch pays kernel-launch overhead and cannot share on-chip memory across the FC1 -> activation -> FC2 chain; intermediate activations must round-trip through HBM.
- **Algorithmic:** Switch to a fused MoE expert kernel that combines the per-stage GEMMs (and ideally activation) in a single launch.
- **Kernel:** If a fused variant is unavailable, apply the standard per-bound-type tuning from the table above to each stage independently.

### Already-fused operations (`moe_fused` only)
- **Reasoning:** Fused MoE kernels combine routing + FC1 + activation + FC2 in a single kernel launch; fusion opportunities are limited.
- **Focus:** The roofline gap (efficiency vs. peak), not further fusion.

### No MoE category in trace
- **Reasoning:** Model doesn't use Mixture of Experts.
- **Action:** Report as "N/A" and stop.

---

## Trace observability (category-specific)

The universal CANNOT Infer rows in [`sub_agent_spec.md`](../templates/sub_agent_spec.md) always apply. In addition, MoE workloads have these blind spots:

| NOT observable | Why | Fallback prose |
|----------------|-----|----------------|
| Per-expert load imbalance | Trace lacks per-expert token counts | "Cannot assess expert load balance from trace data." |
| Routing decisions / gating quality | Router internals are not traced | "Cannot assess routing quality from trace data." |
| Token distribution across experts | Not surfaced in kernel-level events | "Cannot assess token distribution from trace data." |
| True per-token byte traffic | Byte estimate assumes uniform routing; the per-token bytes actually moved depend on routing | "TB/s, FLOPS/Byte, and efficiency are uniform-routing approximations." |

---

## Validate findings

Per [`sub_agent_spec.md`](../templates/sub_agent_spec.md) § Validate findings, run:

```bash
<prefix> python3 -c "
import sys
from TraceLens.Agent.Analysis.utils.validation_utils import validate_findings_file
passed, errors = validate_findings_file(sys.argv[1], sys.argv[2], sys.argv[3])
if not passed:
    print('FAIL:')
    for e in errors:
        print('  - ' + e)
    sys.exit(1)
print('PASS: Findings file is valid')
" '<output_dir>/category_findings/<cat>_findings.md' 'compute' '<comparison_scope>'
```

If validation fails, fix the findings file and re-run. Max 2 retries.