[contrib] Add Bloom-1b7 NeuronX port

contrib/models/bloom-1b7/README.md

@@ -0,0 +1,111 @@
+# Contrib Model: Bloom-1b7
+
+NeuronX Distributed Inference implementation of bigscience/bloom-1b7.
+
+## Model Information
+
+- **HuggingFace ID:** `bigscience/bloom-1b7`
+- **Model Type:** Decoder-only transformer
+- **Parameters:** 1.7B
+- **License:** BigScience RAIL License v1.0
+
+## Architecture Details
+
+| Property | Value |
+|----------|-------|
+| Hidden Size | 2048 |
+| Num Attention Heads | 16 (MHA) |
+| Num Hidden Layers | 24 |
+| Vocab Size | 250,880 |
+| Position Encoding | ALiBi (Attention with Linear Biases) |
+| Activation | GELU (tanh approximation) |
+| Normalization | LayerNorm (with embedding LayerNorm) |
+| QKV Format | Fused interleaved [num_heads, 3, head_dim, hidden_size] |
+| Weight Tying | LM head tied to token embeddings |
+
+### Key Implementation Notes
+
+- **ALiBi positional encoding:** No position embeddings — bias is computed and added in custom `perform_prefill` and `compute_for_token_gen` overrides
+- **Fused QKV weights:** Split into separate Q, K, V during weight conversion
+- **Embedding LayerNorm:** Applied after token embeddings via `get_model_output` override
+- **Standard LayerNorm** (not RMSNorm) with `layer_norm_epsilon`
+
+## Validation Results
+
+**Validated:** 2026-03-04
+**Configuration:** TP=1, batch_size=1, seq_len=128, bfloat16
+
+### Test Results
+
+| Test | Status | Result |
+|------|--------|--------|
+| Smoke Test | PASS | Model loads successfully |
+| Greedy Token Matching | PASS | **76.9% match** (vs HF bfloat16 reference) |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer
+from neuronx_distributed_inference.models.config import NeuronConfig
+
+from src.modeling_bloom import NeuronBloomForCausalLM, BloomInferenceConfig
+
+model_path = "/path/to/bloom-1b7/"
+compiled_model_path = "/path/to/compiled/"
+
+neuron_config = NeuronConfig(
+    tp_degree=1,
+    batch_size=1,
+    seq_len=128,
+    max_context_length=128,
+    torch_dtype=torch.bfloat16,
+)
+
+config = BloomInferenceConfig.from_pretrained(
+    model_path, neuron_config=neuron_config,
+)
+
+model = NeuronBloomForCausalLM(model_path, config)
+model.compile(compiled_model_path)
+model.load(compiled_model_path)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+# ... (see integration test for full example)
+```
+
+## Performance
+
+Profiled on trn1.32xlarge (single NeuronCore utilization):
+
+| Metric | Context Encoding | Token Generation |
+|--------|-----------------|------------------|
+| Throughput | - | 63.3 tok/s |
+| MBU (Memory) | 18.6% | 16.1% |
+| MFU (Compute) | 7.4% | 0.1% |
+
+*Batch size 1, sequence length 128, BF16 precision, TP=1*
+
+## Compatibility Matrix
+
+| Instance/Version | 2.20+ | 2.19 and earlier |
+|------------------|-------|------------------|
+| Trn1             | Working | Not tested |
+| Inf2             | Not tested | Not tested |
+
+## Testing
+
+Run integration tests:
+
+```bash
+pytest contrib/models/bloom-1b7/test/integration/test_model.py --capture=tee-sys
+```
+
+## Example Checkpoints
+
+* bigscience/bloom-1b7
+
+## Maintainer
+
+Neuroboros Team - Annapurna Labs
+
+**Last Updated:** 2026-03-04

contrib/models/bloom-1b7/src/__init__.py

@@ -0,0 +1,3 @@
+from .modeling_bloom import NeuronBloomForCausalLM, BloomInferenceConfig
+
+__all__ = ["NeuronBloomForCausalLM", "BloomInferenceConfig"]