Fix all four scaling multipliers for Granite

contrib/models/granite-3.1-8b-instruct/README.md

@@ -10,45 +10,69 @@ NeuronX Distributed Inference implementation of granite 3.1 8b instruct.
 
 ## Architecture Details
 
-- **Layers:** Check model config
-- **Hidden Size:** Check model config
-- **Attention Heads:** Check model config
-- **Vocabulary:** Check model config
-- **Max Position Embeddings:** Check model config
+- **Layers:** 32
+- **Hidden Size:** 4096
+- **Attention Heads:** 32
+- **Key-Value Heads:** 8 (GQA)
+- **Vocabulary:** 49152
+- **Max Position Embeddings:** 131072
+
+### Granite-Specific Scaling Factors
+
+Granite uses custom scaling factors that differ from standard Llama:
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| `embedding_multiplier` | 12.0 | Scales input embeddings after lookup |
+| `attention_multiplier` | 0.0078125 (1/head_dim) | Custom attention scaling instead of 1/√head_dim |
+| `residual_multiplier` | 0.22 | Scales residual connections |
+| `logits_scaling` | 16.0 | Divides output logits |
 
 ## Validation Results
 
-**Validated:** 2026-01-29  
+**Validated:** 2026-02-06  
 **Configuration:** TP=2, batch_size=1, seq_len=128, bfloat16
 
 ### Test Results
 
 | Test | Status | Result |
 |------|--------|--------|
 | Smoke Test | ✅ PASS | Model loads successfully |
-| Token Matching | ⚠️ LOW | **7.8% match** |
-| TTFT (P50) | ✅ PASS | 19.44ms (threshold: 100ms) |
-| Throughput | ✅ PASS | 106.00 tok/s (threshold: 10 tok/s) |
+| Token Matching | ✅ PASS | **100% match** (64/64 tokens) |
+| TTFT (P50) | ✅ PASS | ~20ms (threshold: 100ms) |
+| Throughput | ✅ PASS | ~100 tok/s (threshold: 10 tok/s) |
 
 ### Performance Metrics
 
 | Metric | Value |
 |--------|-------|
-| TTFT (P50) | 19.44ms |
-| Throughput | 106.00 tokens/s |
-
+| TTFT (P50) | ~20ms |
+| Throughput | ~100 tokens/s |
 
 **Status:** ✅ VALIDATED
 
+## Critical Implementation Notes
+
+This implementation includes critical fixes for Granite's custom scaling:
+
+1. **Attention Multiplier Fix**: The `prep_qkv_tensors` method in `NeuronGraniteAttention` applies a correction factor to Q tensors to convert from the standard `1/√head_dim` scaling to Granite's `attention_multiplier`.
+
+2. **Embedding Multiplier**: Applied in `get_model_output` after embedding lookup (not to weights, to handle tied embeddings correctly).
+
+3. **Logits Scaling**: Applied via `ScaledColumnParallelLinear` which divides output by `logits_scaling`.
+
+4. **Residual Multiplier**: Applied in `NeuronGraniteDecoderLayer` to scale residual connections.
+
 ## Usage
 
 ```python
-from transformers import AutoTokenizer, GenerationConfig
+import torch
+from transformers import AutoTokenizer
 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_granite_3_1_8b_instruct import Neurongranite318binstructForCausalLM, granite318binstructInferenceConfig
+from src.modeling_granite import NeuronGraniteForCausalLM, GraniteInferenceConfig
 
 model_path = "/path/to/granite-3.1-8b-instruct/"
 compiled_model_path = "/path/to/compiled/"
@@ -61,18 +85,19 @@ neuron_config = NeuronConfig(
     torch_dtype=torch.bfloat16,
 )
 
-config = granite318binstructInferenceConfig(
+config = GraniteInferenceConfig(
     neuron_config,
     load_config=load_pretrained_config(model_path),
 )
 
 # Compile and load
-model = Neurongranite318binstructForCausalLM(model_path, config)
+model = NeuronGraniteForCausalLM(model_path, config)
 model.compile(compiled_model_path)
 model.load(compiled_model_path)
 
 # Generate
 tokenizer = AutoTokenizer.from_pretrained(model_path)
+inputs = tokenizer("Hello, how are you?", return_tensors="pt")
 # ... (see integration test for full example)
 ```
 
@@ -104,6 +129,6 @@ python3 test/integration/test_model.py
 
 ## Maintainer
 
-Neuroboros Team - Annapurna Labs
+Annapurna Labs
 
-**Last Updated:** 2026-01-29
+**Last Updated:** 2026-02-06