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Update gemma-3-1b-it contrib: fix head_dim=256 issues, add chunked attention and SWA support #129

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Update gemma-3-1b-it contrib: fix head_dim=256 issues, add chunked attention and SWA support #129

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214 changes: 138 additions & 76 deletions contrib/models/gemma-3-1b-it/README.md
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# Contrib Model: gemma 3 1b it
# Contrib Model: Gemma 3 1B IT

NeuronX Distributed Inference implementation of gemma 3 1b it.
NeuronX Distributed Inference support for **google/gemma-3-1b-it** (1B parameter variant).

## Model Information

- **HuggingFace ID:** `gemma-3-1b-it`
- **Model Type:** Decoder-only transformer
- **License:** Check HuggingFace model card

## Architecture Details


## Validation Results

**Validated:** 2026-02-06
**Configuration:** TP=1, batch_size=1, seq_len=128, bfloat16

### Test Results
This contrib subclasses the official `models/gemma3/` implementation and adds
the minimal overrides needed for the 1B variant's unusual architecture.

| Test | Status | Result |
|------|--------|--------|
| Smoke Test | ✅ PASS | Model loads successfully |
| Token Matching | ✅ PASS | **100% match** (best of multiple prompts) |

**Test Prompt:** `"def fibonacci(n):"`

**Status:** ✅ VALIDATED

### Device Profiling Metrics

**Configuration:** TP=1, batch_size=1, seq_len=128, bfloat16
**Instance:** trn1.32xlarge | **Profiled:** 2026-03-18

| Metric | Context Encoding | Token Generation |
|--------|-----------------|------------------|
| MFU (%) | 0.20 | 0.00 |
| MBU (%) | 0.50 | 0.59 |
| HFU (%) | 0.21 | 0.00 |
| Execution Time (us) | 0.01 | 0.01 |
| HBM Read | 2.00 GB | 2.00 GB |
| HBM Write | 4.82 MB | 1.10 MB |

**Throughput:** 87.64 tok/s | **Compile Time:** 253.40s
## Model Information

> Metrics from `neuron-profile capture` on compiled NEFFs. MFU = Model FLOPs Utilization,
> MBU = Memory Bandwidth Utilization, HFU = Hardware FLOPs Utilization.
- **HuggingFace ID:** `google/gemma-3-1b-it`
- **Model Type:** Decoder-only transformer (causal LM)
- **Parameters:** 1B
- **License:** Gemma license (see HuggingFace model card)

## Why a Separate Contrib?

The official `models/gemma3/` targets the 4B/12B/27B variants (head_dim=128).
The 1B variant has several unusual architecture parameters that require
additional handling:

| Parameter | 1B | 4B/12B/27B |
|-----------|-----|-----------|
| head_dim | **256** | 128 |
| vocab_size | **262144** | 262208 |
| num_kv_heads | **1** | 4-16 |
| num_attention_heads | **4** | 8-32 |

### Issues Addressed

1. **Chunked attention for head_dim=256** -- The Neuron compiler generates DGE
scatter/gather instructions that produce out-of-bounds memory accesses when
head_dim exceeds 128. All Q@K^T and scores@V matmuls are split into
128-wide chunks along head_dim. Mathematically identical, avoids hardware
addressing limits.

2. **vocab_size from HF config** -- The upstream `Gemma3InferenceConfig`
hardcodes `vocab_size=262208`. This contrib reads the actual value from
the HuggingFace config (262144 for 1B).

3. **Auto-disable NKI attention kernel** -- The NKI flash attention kernel
asserts `head_dim <= 128`. This contrib auto-disables it when head_dim
exceeds that limit.

4. **k_cache_transposed + SWA + GQA fix** -- The base class forces
`k_cache_transposed=False` for sliding window layers, but the KV cache
manager stores K in BHDS layout for ALL layers when `k_cache_transposed=True`
in the config. This creates a layout mismatch: `repeat_kv` assumes BHSD but
receives BHDS, producing incorrect GQA expansion. The fix restores the
config value and transposes K around `repeat_kv`.

5. **query_pre_attn_scalar weight fusion** -- NxDI uses `QK^T / sqrt(head_dim)`
for attention scaling, but Gemma 3 specifies `QK^T / sqrt(query_pre_attn_scalar)`.
Rather than modifying the attention kernel (which risks breaking optimizations),
we fuse the correction factor into Q/K weight matrices at load time. Zero
runtime overhead. Pattern from Pierre Lienhart's gemma3-vision contrib.

### Known Compiler Issue

**CTE buckets < 512 crash at runtime** with head_dim=256 + `input_output_aliases`.
This is a Neuron compiler issue (DGE OOB), not a code issue. Workaround:
always use `context_encoding_buckets: [512]` or larger.

| CTE Bucket | Result |
|-----------|--------|
| 128 | OOB crash |
| 256 | OOB crash |
| 384 | OOB crash |
| 512 | **PASS** |

## Usage

### Standalone (NxDI API)

```python
from transformers import AutoTokenizer, GenerationConfig
import torch
from neuronx_distributed_inference.models.config import NeuronConfig
from neuronx_distributed_inference.utils.hf_adapter import load_pretrained_config

# Import model classes from src
from src.modeling_gemma_3_1b_it import Neurongemma31bitForCausalLM, gemma31bitInferenceConfig
import sys
sys.path.insert(0, "contrib/models/gemma-3-1b-it/src")
from modeling_gemma3 import NeuronGemma3_1B_ForCausalLM, Gemma3_1B_InferenceConfig

model_path = "/path/to/gemma-3-1b-it/"
compiled_model_path = "/path/to/compiled/"
model_path = "google/gemma-3-1b-it"

# Configure
neuron_config = NeuronConfig(
tp_degree=1,
batch_size=None,
batch_size=4,
seq_len=512,
torch_dtype=torch.bfloat16,
attn_kernel_enabled=False,
k_cache_transposed=True,
)

config = gemma31bitInferenceConfig(
config = Gemma3_1B_InferenceConfig(
neuron_config,
load_config=load_pretrained_config(model_path),
)

# Compile and load
model = Neurongemma31bitForCausalLM(model_path, config)
model.compile(compiled_model_path)
model.load(compiled_model_path)
model = NeuronGemma3_1B_ForCausalLM(model_path, config)
model.compile("/tmp/gemma3-1b-compiled")
model.load("/tmp/gemma3-1b-compiled")
```

### vLLM Serving

Requires installing the NxDI fork with the `gemma3` model type registered in
`constants.py` (or using the fork's `fix/gemma3-1b-oob` branch).

# Generate
tokenizer = AutoTokenizer.from_pretrained(model_path)
# ... (see integration test for full example)
```bash
python -m vllm.entrypoints.openai.api_server \
--model google/gemma-3-1b-it \
--tensor-parallel-size 1 \
--max-model-len 512 \
--max-num-seqs 4 \
--dtype bfloat16 \
--no-enable-prefix-caching \
--block-size 128 \
--additional-config '{"override_neuron_config": {
"tp_degree": 1,
"batch_size": 4,
"seq_len": 512,
"n_active_tokens": 4,
"context_encoding_buckets": [512],
"token_generation_buckets": [512],
"on_device_sampling_config": null,
"attn_kernel_enabled": false,
"k_cache_transposed": true
}}'
```

## Compatibility Matrix
## Required Configuration

| Instance/Version | 2.20+ | 2.19 and earlier |
|------------------|-------|------------------|
| Trn1 | ✅ Working | Not tested |
| Inf2 | Not tested | Not tested |
| Parameter | Value | Why |
|-----------|-------|-----|
| `attn_kernel_enabled` | `false` | NKI kernel asserts head_dim <= 128 |
| `k_cache_transposed` | `true` | Required for the SWA+GQA fix |
| `context_encoding_buckets` | `[512]` or larger | Compiler OOB for buckets < 512 |
| `on_device_sampling_config` | `null` | Required (not `false`) |

## Testing
## Compatibility

Run integration tests:
| Instance | Status | Notes |
|----------|--------|-------|
| trn2.3xlarge | Tested | TP=1, batch_size=4/16, CTE bucket 512 |
| inf2.8xlarge | Not tested with this contrib | OOB confirmed on raw official code |
| trn1.* | Not tested | Should work with same config |

```bash
pytest nxdi_contrib_models/models/gemma-3-1b-it/test/integration/test_model.py --capture=tee-sys
```
## Architecture

Or run manually:
This contrib is structured as thin subclasses of the official implementation:

```bash
cd nxdi_contrib_models/models/gemma-3-1b-it
python3 test/integration/test_model.py
```
models/gemma3/modeling_gemma3.py (upstream, unchanged)
|
+-- contrib/gemma-3-1b-it/src/modeling_gemma3.py (this file)
|-- Gemma3_1B_InferenceConfig <-- fixes vocab_size, auto-disables NKI
|-- NeuronGemma3_1B_Attention <-- chunked attn + k_cache_transposed fix
+-- NeuronGemma3_1B_ForCausalLM <-- query_pre_attn_scalar weight fusion
```

## Example Checkpoints
No upstream files are modified. The contrib imports from the official
`models/gemma3/` package and overrides only what is necessary.

* gemma-3-1b-it
## Testing

```bash
# On a Neuron instance (trn2 or inf2):
cd neuronx-distributed-inference
PYTHONPATH="contrib/models/gemma-3-1b-it/src:src:$PYTHONPATH" \
pytest contrib/models/gemma-3-1b-it/test/integration/test_model.py -v --capture=tee-sys
```

## Maintainer

Annapurna Labs
Jim Burtoft (jimburtoft)

**Last Updated:** 2026-02-06
**Last Updated:** 2026-03-27
5 changes: 4 additions & 1 deletion contrib/models/gemma-3-1b-it/src/__init__.py
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from .modeling_gemma3 import NeuronGemma3ForCausalLM, Gemma3InferenceConfig
from .modeling_gemma3 import (
Gemma3_1B_InferenceConfig,
NeuronGemma3_1B_ForCausalLM,
)
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