feat(example): Add Spark-X2.5 model example with hybrid attention support

[dongjiang1989]

Summary

Add examples/models/spark_x2_5/ with support for the Spark-X2.5-1.7B and Spark-X2.5-4B models from XHToken. These are compact, general-purpose language models with a hybrid attention architecture (3 sliding-window attention layers + 1 full-attention layer, repeating) that natively supports context windows up to 1M tokens.

Architecture highlights

• Hybrid attention: pattern of 3 sliding-window + 1 full-attention layer (28 layers for 1.7B, 36 for 4B)
• Per-layer-type RoPE: full-attention layers use rope_theta=5M, partial_rotary_factor=0.25; sliding-attention layers use rope_theta=10K, partial_rotary_factor=1.0
• Headwise attention output gate: per-head sigmoid gate broadcast over head dim (a lighter variant of use_attn_o_gate)
• Sliding window KV cache (RingKVCache, window=512 tokens)
• GELU activation in MLP (vs default SiLU)
• Tied word embeddings

Files added (in examples/models/spark_x2_5/)

• convert_weights.py — HF safetensors → Meta format conversion with fused QKV split, sharded checkpoint support, and tied embeddings handling
• config/ — JSON configs for 1.7B and 4B variants; XNNPack (fp32, q8da4w), CoreML, and MLX export configs
• test_spark_x2_5.py — config validation and model registration tests
• BUCK, README.md — build target and usage documentation

Core changes to existing files

• examples/models/llama/model_args.py — add headwise_attn_output_gate and rope_parameters fields
• examples/models/llama/feed_forward.py — FeedForward now accepts act_fn parameter (was hardcoded to SiLU)
• examples/models/llama/attention.py — headwise attention output gate (dim→n_heads, broadcast over head_dim), per-layer is_sliding detection, RingKVCache for sliding-window layers, mutual-exclusion validation for gate flags
• examples/models/llama/llama_transformer.py — per-layer-type RoPE via _build_ropes() helper, freqs_by_type dispatch in _forward_layers(), act_fn plumbed to FeedForward
• examples/models/llama/export_llama_lib.py — register spark_x2_5_1_7b and spark_x2_5_4b in EXECUTORCH_DEFINED_MODELS, HUGGING_FACE_REPO_IDS, and weight-conversion dispatch
• extension/llm/export/config/llm_config.py — add spark_x2_5_1_7b and spark_x2_5_4b to ModelType enum

Example export

python -m extension.llm.export.export_llm \
  --config examples/models/spark_x2_5/config/spark_x2_5_xnnpack_q8da4w.yaml \
  +base.model_class="spark_x2_5_1_7b" \
  +base.params="examples/models/spark_x2_5/config/spark_x2_5_1_7b_config.json" \
  +export.output_name="spark_x2_5_1_7b_8da4w.pte"

Test plan

Unit tests

pytest examples/models/spark_x2_5/test_spark_x2_5.py -v
# 3 passed

Model construction + forward pass (both variants)

from executorch.examples.models.llama.model_args import ModelArgs
from executorch.examples.models.llama.llama_transformer import construct_transformer

# 1.7B: 28 layers, dim=2048
# 4B:   36 layers, dim=2560
# Both: prefill + multi-step decode with KV cache succeed
# Layer types: sliding→RingKVCache, full→KVCache
# Per-layer RoPE: 2 distinct RoPE instances (full_attention, sliding_attention)

Sharded checkpoint conversion

# Spark-X2.5-1.7B uses 2 shards, 4B uses 5 shards
# Verified: 283 keys converted correctly, QKV split matches, tied embeddings preserved

Lint

flake8 examples/models/spark_x2_5/ examples/models/llama/model_args.py \
    examples/models/llama/feed_forward.py examples/models/llama/attention.py \
    examples/models/llama/llama_transformer.py examples/models/llama/export_llama_lib.py \
    extension/llm/export/config/llm_config.py --max-line-length=120
# No warnings

This PR was authored with AI assistance (Claude Code).

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/22865

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⚠️ 22 Awaiting Approval

As of commit 0ea99af with merge base c65f1dc ():

AWAITING APPROVAL - The following workflows need approval before CI can run:

• Build Aarch64 Linux CUDA Wheels (gh)
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