diff --git a/contrib/models/InternVL3-8B-Instruct/README.md b/contrib/models/InternVL3-8B-Instruct/README.md new file mode 100644 index 00000000..d12f5698 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/README.md @@ -0,0 +1,212 @@ +# Contrib Model: InternVL3-8B-Instruct + +InternVL3-8B-Instruct is a vision-language model (VLM) running on AWS Trainium2 via NxD Inference. It supports both text-only and multimodal (text + image) inference using the NeuronBaseForImageToText framework. + +**Maintainer:** Jim Burtoft ([@jimburtoft](https://github.com/jimburtoft)) + +## Model Information + +- **HuggingFace ID:** [`OpenGVLab/InternVL3-8B-Instruct`](https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct) +- **Model Type:** Vision-language model (decoder-only transformer with vision encoder) +- **Parameters:** ~8B total (InternViT-300M vision encoder + Qwen2.5-7B text backbone) +- **Architecture:** GQA (28 heads / 4 KV heads), RoPE, RMSNorm (text); LayerNorm, GELU, absolute position embeddings (vision); pixel shuffle downsampling + 2-layer MLP projector +- **License:** MIT (Apache-2.0 for Qwen2.5 component) +- **Precision:** BF16 + +### Architecture Overview + +| Component | Details | +|-----------|---------| +| **Vision encoder** | InternViT-300M-448px-V2.5: 24 layers, hidden=1024, 16 heads, patch_size=14, image_size=448 | +| **Projector** | Pixel shuffle (downsample_ratio=0.5, 1024→256 tokens) + LayerNorm + Linear(4096, 3584) + GELU + Linear(3584, 3584) | +| **Text backbone** | Qwen2.5-7B: 28 layers, hidden=3584, intermediate=18944, GQA (28/4), vocab=151674, tie_word_embeddings=False | + +## Validation Results + +**Validated:** 2026-04-28 +**Instance:** trn2.3xlarge (LNC=2, TP=4) +**SDK:** Neuron SDK 2.29 (NxDI 0.9.17334, neuronx-cc 2.24.5133.0, PyTorch 2.9) + +### Benchmark Results + +#### Performance (TP=4, batch_size=1) + +| Sequence Length | TTFT (ms) | TKG Throughput (tok/s) | +|----------------|-----------|------------------------| +| 2048 | 138 | 75.1 | +| 4096 | 230 | 58.9 | +| 8192 | 482 | 40.0 | +| 16384 | 1019 | 23.6 | +| 32768 | 2438 | 11.4 | + +Vision encoder latency: 34.5 ms per 448x448 tile (batch=1). + +#### GPU Comparison (1x NVIDIA L40S, BF16, SDPA) + +| Metric | GPU (L40S) | Neuron (trn2.3xlarge TP=4) | Speedup | +|--------|------------|---------------------------|---------| +| TTFT (2048 input tokens) | 153.5 ms | 138 ms | 1.11x | +| Output tok/s (BS=1) | 40.5 | 75.1 | **1.85x** | + +### Accuracy Validation + +| Test | Status | Metrics | +|------|--------|---------| +| CTE logit comparison (vs CPU FP32) | PASS | cosine=0.9984, top-1 match, top-5 5/5, top-10 8/10 | +| TKG text generation | PASS | Correct, coherent output ("The capital of France is Paris.") | +| Multimodal generation | PASS | Vision encoder + text pipeline end-to-end working | + +## Usage + +### Prerequisites + +```bash +# Activate NxDI environment +source /opt/aws_neuronx_venv_pytorch_2_9_nxd_inference/bin/activate + +# Download model +huggingface-cli download OpenGVLab/InternVL3-8B-Instruct --local-dir /mnt/models/InternVL3-8B-Instruct/ +``` + +### Compile and Run + +```python +import sys +import torch +from pathlib import Path +from transformers import AutoTokenizer + +# Add contrib src to path +sys.path.insert(0, str(Path("contrib/models/InternVL3-8B-Instruct/src"))) + +from modeling_internvl3 import NeuronInternVL3ForCausalLM, InternVL3InferenceConfig +from neuronx_distributed_inference.models.config import NeuronConfig + +MODEL_PATH = "/mnt/models/InternVL3-8B-Instruct/" +COMPILED_PATH = "/mnt/models/neuron_models/InternVL3-8B-Instruct/" + +# Configure +text_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=2048, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + save_sharded_checkpoint=True, +) +vision_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=256, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + buckets=[1], + fused_qkv=True, + save_sharded_checkpoint=True, +) + +config = InternVL3InferenceConfig.from_pretrained( + MODEL_PATH, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, +) + +# Compile (first time only) +model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) +model.compile(COMPILED_PATH) + +# Load and generate +model.load(COMPILED_PATH) + +tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) +prompt = "The capital of France is" +input_ids = tokenizer(prompt, return_tensors="pt").input_ids + +outputs = model(input_ids=input_ids) +next_token = outputs.logits[0, -1].argmax().item() +print(f"{prompt} {tokenizer.decode([next_token])}") +# Output: The capital of France is Paris +``` + +### Multimodal Inference + +```python +# Build input with vision tokens +IMG_CONTEXT_ID = 151667 # +IMG_START_ID = 151665 # +IMG_END_ID = 151666 # + +text_ids = tokenizer("Describe this image:", return_tensors="pt").input_ids[0] +img_tokens = torch.full((256,), IMG_CONTEXT_ID, dtype=torch.long) + +input_ids = torch.cat([ + text_ids, + torch.tensor([IMG_START_ID]), + img_tokens, + torch.tensor([IMG_END_ID]), +]).unsqueeze(0) + +# Pixel values for a single 448x448 tile +pixel_values = preprocess_image(image) # [1, 3, 448, 448] + +outputs = model(input_ids=input_ids, pixel_values=pixel_values) +``` + +## Compatibility Matrix + +| Instance | SDK 2.29 | SDK 2.28 | +|----------|----------|----------| +| trn2.3xlarge (LNC=2, TP=4) | **VALIDATED** | Not tested | + +## Example Checkpoints + +* [OpenGVLab/InternVL3-8B-Instruct](https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct) + +## Testing Instructions + +```bash +# Ensure model is compiled first (see Usage above), then: +cd contrib/models/InternVL3-8B-Instruct/ +pytest test/integration/test_model.py -v --tb=short +``` + +## Implementation Notes + +### Three-File VLM Architecture + +The model uses the NxDI `NeuronBaseForImageToText` framework with three files: + +- `src/modeling_internvl3.py` — Top-level VLM orchestrating vision + text +- `src/modeling_internvl3_text.py` — Text model (Qwen2.5-7B) with vision embedding injection via `scatter_by_index_put()` +- `src/modeling_internvl3_vision.py` — Vision encoder (InternViT-300M) compiled via `torch_neuronx.trace()` with pixel shuffle and MLP projector + +### Weight Mapping + +| HuggingFace Key | NxDI Key | +|-----------------|----------| +| `language_model.model.layers.{i}.*` | `layers.{i}.*` | +| `language_model.model.embed_tokens.weight` | `embed_tokens.weight` | +| `language_model.model.norm.weight` | `norm.weight` | +| `language_model.lm_head.weight` | `lm_head.weight` | +| `vision_model.*` | Vision encoder (separate NEFF) | +| `mlp1.*` | Projector (part of vision NEFF) | + +### Special Tokens + +| Token | ID | Purpose | +|-------|-----|---------| +| `` | 151667 | Visual token placeholder in text sequence | +| `` | 151665 | Image region start marker | +| `` | 151666 | Image region end marker | +| `<|im_end|>` | 151645 | EOS token | + +## Known Issues + +- **V2PE not implemented**: Variable Visual Position Encoding (described in InternVL2.5/3 papers) is not implemented in the HuggingFace model code and is not included here. Standard position IDs are used. Accuracy validation passes without V2PE. +- **Batch size > 1**: Single-request batch inference (batch_size > 1) has a known issue with sampling_params shape. Use vLLM for multi-request concurrent serving. +- **trust_remote_code**: The HuggingFace tokenizer requires `trust_remote_code=True`. The NxDI model code reads config.json directly and does not require it. +- **NKI kernels**: Not applicable for this model. Qwen2.5-7B's `intermediate_size=18944` is incompatible with NxDI NKI `mlp_kernel` and `attn_block_tkg` kernels at tested TP degrees. + +## vLLM Integration + +This model can be served through vLLM-neuron with patches to the vllm-neuron worker. See the [NxD Inference vLLM User Guide](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/libraries/nxd-inference/developer_guides/vllm-user-guide.html) for general vLLM setup. InternVL3 requires modifications to vllm-neuron's model loader and runner to register the custom architecture. Contact the maintainer for patch details. diff --git a/contrib/models/InternVL3-8B-Instruct/accuracy_test.py b/contrib/models/InternVL3-8B-Instruct/accuracy_test.py new file mode 100644 index 00000000..a2f28e66 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/accuracy_test.py @@ -0,0 +1,499 @@ +#!/usr/bin/env python3 +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Apples-to-apples TKG accuracy comparison: CPU reference vs Neuron. + +Generates identical tokenized inputs, runs both CPU and Neuron generation, +and compares tokens position-by-position. + +Phase 1: Generate CPU references (slow, ~5-15 min per prompt for 7B in bf16) +Phase 2: Run Neuron generation with same inputs +Phase 3: Compare token-by-token + +Usage: + # Generate CPU references (run once, saves to disk) + python accuracy_test.py --cpu-ref + + # Run Neuron comparison against saved references + python accuracy_test.py --neuron --skip-compile + + # Run both (full test) + python accuracy_test.py --full --skip-compile +""" + +import argparse +import json +import os +import sys +import time +from pathlib import Path + +import torch + +sys.path.insert(0, str(Path(__file__).parent / "src")) + +MODEL_PATH = "/mnt/models/InternVL3-8B-Instruct/" +COMPILED_PATH = "/mnt/models/neuron_models/InternVL3-8B-VLM/" +REF_DIR = "/mnt/models/accuracy_refs/" +TEST_IMAGE = "/mnt/models/test_image.png" + +# Test prompts +TEST_PROMPTS = [ + { + "name": "france_capital", + "prompt": "What is the capital of France?", + "type": "text", + "max_new_tokens": 32, + }, + { + "name": "meaning_of_life", + "prompt": "I believe the meaning of life is", + "type": "text", + "max_new_tokens": 32, + }, + { + "name": "simple_math", + "prompt": "What is 2 + 2? Answer with just the number:", + "type": "text", + "max_new_tokens": 10, + }, + { + "name": "describe_image", + "prompt": "Describe this image briefly.", + "type": "multimodal", + "max_new_tokens": 32, + }, +] + +# Special tokens +IMG_CONTEXT_ID = 151667 +IMG_START_ID = 151665 +IMG_END_ID = 151666 + + +def build_inputs(prompt_info, tokenizer, pixel_values=None): + """Build identical inputs for both CPU and Neuron using chat template.""" + prompt = prompt_info["prompt"] + + # Use chat template for proper instruction formatting + messages = [{"role": "user", "content": prompt}] + templated = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = tokenizer(templated, return_tensors="pt") + input_ids = inputs.input_ids + + if prompt_info["type"] == "multimodal" and pixel_values is not None: + # Insert image tokens before the text: *256 + # InternVL3 convention: image tokens come in the user message + text_ids = input_ids[0] + img_tokens = torch.full((256,), IMG_CONTEXT_ID, dtype=torch.long) + img_seq = torch.cat( + [ + torch.tensor([IMG_START_ID]), + img_tokens, + torch.tensor([IMG_END_ID]), + ] + ) + # Insert image tokens right after the "user\n" tokens + # Find position of user content in the templated string + input_ids = torch.cat([text_ids, img_seq]).unsqueeze(0) + + attention_mask = torch.ones_like(input_ids) + return input_ids, attention_mask + + +def generate_cpu_references(tokenizer): + """Generate reference outputs using HF model on CPU (slow but golden). + + For text-only prompts: uses the inner Qwen2ForCausalLM language model directly. + For multimodal: uses InternVLChatModel.chat() which handles vision processing. + """ + print("=" * 60) + print("PHASE 1: Generating CPU reference outputs") + print("=" * 60) + print("WARNING: This is slow (~5-15 min per prompt for 7B bf16 on CPU)") + + from transformers import AutoModelForCausalLM + + print("\nLoading HF InternVLChatModel on CPU (bf16)...") + start = time.time() + vlm_model = AutoModelForCausalLM.from_pretrained( + MODEL_PATH, + torch_dtype=torch.bfloat16, + trust_remote_code=True, + device_map="cpu", + ) + vlm_model.eval() + # InternVLChatModel.generate() asserts img_context_token_id is set + vlm_model.img_context_token_id = IMG_CONTEXT_ID + # Get the inner language model for text-only generation + lm = vlm_model.language_model + lm.eval() + print(f"Model loaded in {time.time() - start:.1f}s") + + # Load test image + pixel_values = None + if os.path.exists(TEST_IMAGE): + from PIL import Image + from torchvision import transforms + + img = Image.open(TEST_IMAGE).convert("RGB") + transform = transforms.Compose( + [ + transforms.Resize((448, 448)), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] + ), + ] + ) + pixel_values = transform(img).unsqueeze(0).to(torch.bfloat16) + print(f"Test image loaded: {TEST_IMAGE}") + + os.makedirs(REF_DIR, exist_ok=True) + results = {} + + # EOS for InternVL3 is <|im_end|> = 151645 + eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>") + print(f"Using eos_token_id={eos_token_id} (<|im_end|>)") + + for pinfo in TEST_PROMPTS: + name = pinfo["name"] + if pinfo["type"] == "multimodal" and pixel_values is None: + print(f"\nSkipping {name} (no test image)") + continue + + # Skip if reference already exists + ref_path = os.path.join(REF_DIR, f"{name}.json") + if os.path.exists(ref_path): + print(f"\n--- {name}: already exists, skipping ---") + with open(ref_path) as f: + results[name] = json.load(f) + continue + + print(f"\n--- {name} ---") + pv = pixel_values if pinfo["type"] == "multimodal" else None + input_ids, attention_mask = build_inputs(pinfo, tokenizer, pv) + prompt_len = input_ids.shape[-1] + print(f"Input length: {prompt_len} tokens") + + # Generate on CPU + print(f"Generating {pinfo['max_new_tokens']} tokens on CPU...") + start = time.time() + with torch.no_grad(): + if pinfo["type"] == "multimodal" and pv is not None: + # Use full VLM model for multimodal (handles vision encoder) + output_ids = vlm_model.generate( + input_ids=input_ids, + attention_mask=attention_mask, + pixel_values=pv, + max_new_tokens=pinfo["max_new_tokens"], + do_sample=False, + eos_token_id=eos_token_id, + ) + else: + # Use inner LM directly for text-only (cleaner, avoids custom generate) + output_ids = lm.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=pinfo["max_new_tokens"], + do_sample=False, + eos_token_id=eos_token_id, + ) + elapsed = time.time() - start + print(f"Generated in {elapsed:.1f}s") + + generated_ids = output_ids[0, prompt_len:].tolist() + generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True) + print(f"Output: {generated_text!r}") + + ref = { + "name": name, + "prompt": pinfo["prompt"], + "type": pinfo["type"], + "input_ids": input_ids[0].tolist(), + "prompt_len": prompt_len, + "generated_ids": generated_ids, + "generated_text": generated_text, + "max_new_tokens": pinfo["max_new_tokens"], + "cpu_time_s": round(elapsed, 1), + } + + ref_path = os.path.join(REF_DIR, f"{name}.json") + with open(ref_path, "w") as f: + json.dump(ref, f, indent=2) + print(f"Saved: {ref_path}") + results[name] = ref + + del vlm_model, lm + torch.cuda.empty_cache() if torch.cuda.is_available() else None + return results + + +def run_neuron_comparison(tokenizer, skip_compile=False): + """Run Neuron generation and compare against CPU references.""" + print("\n" + "=" * 60) + print("PHASE 2: Neuron generation + comparison") + print("=" * 60) + + from modeling_internvl3 import InternVL3InferenceConfig, NeuronInternVL3ForCausalLM + from neuronx_distributed_inference.models.config import NeuronConfig + from neuronx_distributed_inference.utils.hf_adapter import ( + HuggingFaceGenerationAdapter, + ) + + # Create config and load model + text_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=2048, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + save_sharded_checkpoint=True, + ) + vision_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=256, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + buckets=[1], + fused_qkv=True, + save_sharded_checkpoint=True, + ) + + config = InternVL3InferenceConfig.from_pretrained( + MODEL_PATH, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + ) + + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + if not skip_compile: + model.compile(COMPILED_PATH) + model.load(COMPILED_PATH) + + adapter = HuggingFaceGenerationAdapter(model) + + # Load test image + pixel_values = None + if os.path.exists(TEST_IMAGE): + from PIL import Image + from torchvision import transforms + + img = Image.open(TEST_IMAGE).convert("RGB") + transform = transforms.Compose( + [ + transforms.Resize((448, 448)), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] + ), + ] + ) + pixel_values = transform(img).unsqueeze(0) + + all_results = {} + + for pinfo in TEST_PROMPTS: + name = pinfo["name"] + ref_path = os.path.join(REF_DIR, f"{name}.json") + if not os.path.exists(ref_path): + print(f"\n--- {name}: SKIP (no CPU reference) ---") + continue + + with open(ref_path) as f: + ref = json.load(f) + + print(f"\n--- {name} ---") + print(f"Prompt: {ref['prompt']!r}") + + # Build IDENTICAL inputs + pv = pixel_values if pinfo["type"] == "multimodal" else None + input_ids, attention_mask = build_inputs(pinfo, tokenizer, pv) + prompt_len = input_ids.shape[-1] + + # Verify inputs match reference + ref_input_ids = ref["input_ids"] + actual_input_ids = input_ids[0].tolist() + if actual_input_ids != ref_input_ids: + print(f"WARNING: Input IDs differ!") + print(f" Neuron: {actual_input_ids[:10]}... (len={len(actual_input_ids)})") + print(f" CPU: {ref_input_ids[:10]}... (len={len(ref_input_ids)})") + + # Generate on Neuron + gen_kwargs = dict( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=pinfo["max_new_tokens"], + do_sample=False, + ) + if pv is not None: + gen_kwargs["pixel_values"] = pv + + start = time.time() + output_ids = adapter.generate(**gen_kwargs) + neuron_time = time.time() - start + + neuron_generated_ids = output_ids[0, prompt_len:].tolist() + neuron_text = tokenizer.decode(neuron_generated_ids, skip_special_tokens=True) + + # Compare token-by-token (trim at first EOS for both sides) + cpu_ids = ref["generated_ids"] + eos_id = tokenizer.convert_tokens_to_ids("<|im_end|>") + + # Trim at first EOS + def trim_at_eos(ids, eos): + for i, t in enumerate(ids): + if t == eos: + return ids[:i] + return ids + + neuron_trimmed = trim_at_eos(neuron_generated_ids, eos_id) + cpu_trimmed = trim_at_eos(cpu_ids, eos_id) + + n_compare = min( + max(len(neuron_trimmed), len(cpu_trimmed)), + pinfo["max_new_tokens"], + ) + # Use max length for comparison (missing tokens count as mismatches) + if n_compare == 0: + n_compare = max(len(neuron_generated_ids), len(cpu_ids)) + + matches = 0 + first_mismatch = None + details = [] + for i in range(n_compare): + n_id = neuron_trimmed[i] if i < len(neuron_trimmed) else -1 + c_id = cpu_trimmed[i] if i < len(cpu_trimmed) else -1 + match = n_id == c_id + if match: + matches += 1 + elif first_mismatch is None: + first_mismatch = i + + n_tok = tokenizer.decode([n_id]) if n_id >= 0 else "" + c_tok = tokenizer.decode([c_id]) if c_id >= 0 else "" + details.append( + { + "pos": i, + "match": match, + "neuron_id": n_id, + "neuron_token": n_tok, + "cpu_id": c_id, + "cpu_token": c_tok, + } + ) + + match_rate = matches / n_compare if n_compare > 0 else 0 + + # Compute prefix match (up to shorter EOS-trimmed sequence) + min_len = min(len(neuron_trimmed), len(cpu_trimmed)) + prefix_matches = 0 + for i in range(min_len): + if neuron_trimmed[i] == cpu_trimmed[i]: + prefix_matches += 1 + else: + break # Stop at first divergence for prefix match + prefix_rate = prefix_matches / min_len if min_len > 0 else 1.0 + + # Print results + print(f"CPU output: {ref['generated_text']!r}") + print(f"Neuron output: {neuron_text!r}") + print( + f"Tokens (EOS-trimmed): neuron={len(neuron_trimmed)}, cpu={len(cpu_trimmed)}" + ) + print(f"Prefix match: {prefix_matches}/{min_len} ({prefix_rate:.1%})") + print(f"Full match: {matches}/{n_compare} ({match_rate:.1%})") + if first_mismatch is not None: + d = details[first_mismatch] + print( + f"First mismatch at pos {first_mismatch}: " + f"neuron={d['neuron_token']!r} vs cpu={d['cpu_token']!r}" + ) + + # Detailed per-position output + for d in details: + flag = "MATCH" if d["match"] else "MISS " + print( + f" pos {d['pos']:2d}: {flag} | neuron={d['neuron_token']!r:12s} cpu={d['cpu_token']!r:12s}" + ) + + # Status based on prefix match (more meaningful for EOS-trimmed comparison) + status = ( + "PASS" if prefix_rate >= 0.9 else ("WARN" if prefix_rate >= 0.5 else "FAIL") + ) + print(f"Status: {status}") + + all_results[name] = { + "status": status, + "prefix_match": f"{prefix_matches}/{min_len} ({prefix_rate:.1%})", + "full_match": f"{matches}/{n_compare} ({match_rate:.1%})", + "neuron_tokens": len(neuron_trimmed), + "cpu_tokens": len(cpu_trimmed), + "first_mismatch_pos": first_mismatch, + "neuron_text": neuron_text[:200], + "cpu_text": ref["generated_text"][:200], + "neuron_time_s": round(neuron_time, 3), + "cpu_time_s": ref.get("cpu_time_s"), + "details": details, + } + + # Summary + print("\n" + "=" * 60) + print("ACCURACY COMPARISON SUMMARY") + print("=" * 60) + overall_pass = True + for name, r in all_results.items(): + print( + f" {name:25s} {r['status']:5s} prefix={r['prefix_match']} full={r['full_match']}" + ) + if r["status"] == "FAIL": + overall_pass = False + + result_path = os.path.join(REF_DIR, "accuracy_results.json") + with open(result_path, "w") as f: + json.dump(all_results, f, indent=2, default=str) + print(f"\nResults saved to {result_path}") + + return overall_pass, all_results + + +def main(): + parser = argparse.ArgumentParser(description="Apples-to-apples accuracy test") + parser.add_argument( + "--cpu-ref", action="store_true", help="Generate CPU references only" + ) + parser.add_argument( + "--neuron", action="store_true", help="Run Neuron comparison only" + ) + parser.add_argument( + "--full", action="store_true", help="Run both CPU ref + Neuron comparison" + ) + parser.add_argument( + "--skip-compile", action="store_true", help="Skip Neuron compilation" + ) + args = parser.parse_args() + + if not any([args.cpu_ref, args.neuron, args.full]): + parser.print_help() + print("\nSpecify --cpu-ref, --neuron, or --full") + sys.exit(1) + + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) + + if args.cpu_ref or args.full: + generate_cpu_references(tokenizer) + + if args.neuron or args.full: + passed, results = run_neuron_comparison(tokenizer, args.skip_compile) + sys.exit(0 if passed else 1) + + +if __name__ == "__main__": + main() diff --git a/contrib/models/InternVL3-8B-Instruct/compile_internvl3_vlm.py b/contrib/models/InternVL3-8B-Instruct/compile_internvl3_vlm.py new file mode 100644 index 00000000..4e26b846 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/compile_internvl3_vlm.py @@ -0,0 +1,193 @@ +#!/usr/bin/env python3 +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Compile InternVL3-8B-Instruct full VLM on Neuron. + +This compiles three NEFFs: +1. Vision encoder: InternViT-300M + pixel shuffle + MLP projector +2. Text CTE: Qwen2.5-7B context encoding (with vision embedding injection) +3. Text TKG: Qwen2.5-7B token generation + +Usage: + python compile_internvl3_vlm.py [--text-only] [--vision-only] + +Target: trn2.3xlarge LNC=2 TP=4 +""" + +import argparse +import sys +import time +from pathlib import Path + +import torch + +# Add contrib src to path +sys.path.insert(0, str(Path(__file__).parent / "src")) + +from modeling_internvl3 import InternVL3InferenceConfig, NeuronInternVL3ForCausalLM +from neuronx_distributed_inference.models.config import NeuronConfig + +MODEL_PATH = "/mnt/models/InternVL3-8B-Instruct/" +COMPILED_PATH = "/mnt/models/neuron_models/InternVL3-8B-Instruct/" + + +def create_config(): + """Create InternVL3 VLM inference config with text + vision NeuronConfigs.""" + + # Text NeuronConfig: TP=4 on trn2.3xlarge LNC=2 + text_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=2048, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + save_sharded_checkpoint=True, + ) + + # Vision NeuronConfig: Must match text TP degree (NxDI requirement) + # Vision encoder is small; weights are replicated across TP ranks. + # Bucket = [1] (one image at a time) + vision_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=256, # 256 vision tokens after pixel shuffle + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + buckets=[1], # number of images + fused_qkv=True, # vision encoder has fused QKV weights + save_sharded_checkpoint=True, + ) + + config = InternVL3InferenceConfig.from_pretrained( + MODEL_PATH, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + ) + + return config + + +def compile_and_test(): + """Compile full VLM and run smoke tests.""" + print("=" * 60) + print("InternVL3-8B-Instruct: Full VLM Compilation") + print("=" * 60) + + config = create_config() + + print(f"\nModel path: {MODEL_PATH}") + print(f"Compiled path: {COMPILED_PATH}") + print(f"\n--- Text Config ---") + print(f" TP degree: {config.text_config.neuron_config.tp_degree}") + print(f" Seq len: {config.text_config.neuron_config.seq_len}") + print(f" Batch size: {config.text_config.neuron_config.max_batch_size}") + print(f" hidden_size: {config.text_config.hidden_size}") + print(f" num_hidden_layers: {config.text_config.num_hidden_layers}") + print(f" vocab_size: {config.text_config.vocab_size}") + print(f"\n--- Vision Config ---") + print(f" TP degree: {config.vision_config.neuron_config.tp_degree}") + print(f" Buckets: {config.vision_config.neuron_config.buckets}") + + # Create model + print("\n--- Creating model ---") + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + + # Compile + print("\n--- Compiling (text + vision) ---") + start = time.time() + model.compile(COMPILED_PATH) + elapsed = time.time() - start + print(f"\nCompilation completed in {elapsed:.1f}s ({elapsed / 60:.1f} min)") + + # Load + print("\n--- Loading compiled model ---") + start = time.time() + model.load(COMPILED_PATH) + elapsed = time.time() - start + print(f"Load completed in {elapsed:.1f}s") + + # Smoke test 1: Text-only + print("\n--- Smoke test: text-only ---") + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) + prompt = "The capital of France is" + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs.input_ids + seq_len = input_ids.shape[-1] + position_ids = torch.arange(seq_len, dtype=torch.int32).unsqueeze(0) + seq_ids = torch.zeros(1, dtype=torch.int32) + + print(f"Prompt: {prompt}") + with torch.no_grad(): + outputs = model( + input_ids=input_ids, + position_ids=position_ids, + seq_ids=seq_ids, + ) + logits = outputs.logits + top5 = torch.topk(logits[0, -1].float(), 5) + print("Top-5 next tokens:") + for t, v in zip(top5.indices, top5.values): + print( + f" {tokenizer.decode([t.item()])!r} (id={t.item()}, logit={v.item():.4f})" + ) + + # Smoke test 2: Multimodal (synthetic image) + print("\n--- Smoke test: multimodal ---") + # Build input with 256 tokens + IMG_CONTEXT_ID = 151667 + IMG_START_ID = 151665 + IMG_END_ID = 151666 + + text_before = "Describe this image:" + text_before_ids = tokenizer(text_before, return_tensors="pt").input_ids[0] + + # Construct: *256 + img_tokens = torch.full((256,), IMG_CONTEXT_ID, dtype=torch.long) + full_ids = torch.cat( + [ + text_before_ids, + torch.tensor([IMG_START_ID]), + img_tokens, + torch.tensor([IMG_END_ID]), + ] + ).unsqueeze(0) + + full_seq_len = full_ids.shape[-1] + full_position_ids = torch.arange(full_seq_len, dtype=torch.int32).unsqueeze(0) + full_seq_ids = torch.zeros(1, dtype=torch.int32) + + # Synthetic pixel values (random noise as placeholder) + pixel_values = torch.randn(1, 3, 448, 448) + + print(f"Input IDs shape: {full_ids.shape}") + print(f"Pixel values shape: {pixel_values.shape}") + print( + f"Number of tokens: {(full_ids == IMG_CONTEXT_ID).sum().item()}" + ) + + with torch.no_grad(): + outputs = model( + input_ids=full_ids, + position_ids=full_position_ids, + seq_ids=full_seq_ids, + pixel_values=pixel_values, + ) + logits = outputs.logits + top5 = torch.topk(logits[0, -1].float(), 5) + print("Top-5 next tokens (multimodal):") + for t, v in zip(top5.indices, top5.values): + print( + f" {tokenizer.decode([t.item()])!r} (id={t.item()}, logit={v.item():.4f})" + ) + + print("\n" + "=" * 60) + print("SUCCESS: InternVL3 full VLM compiled and running on Neuron") + print("=" * 60) + + +if __name__ == "__main__": + compile_and_test() diff --git a/contrib/models/InternVL3-8B-Instruct/gpu_benchmark.py b/contrib/models/InternVL3-8B-Instruct/gpu_benchmark.py new file mode 100644 index 00000000..115eec05 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/gpu_benchmark.py @@ -0,0 +1,457 @@ +#!/usr/bin/env python3 +"""GPU benchmark for InternVL3-8B-Instruct. + +Measures TTFT, output tok/s, E2E latency across batch sizes and optimizations. +Follows GPU Benchmark Standard from OpencodeDocs/steering/gpu-benchmark-standard.md. +""" + +import argparse +import json +import time +import gc +import os + +import torch +import numpy as np +from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedModel + +# Monkey-patch for transformers 5.7.0 compatibility with InternVL3 custom model. +# InternVLChatModel.__init__ doesn't call super().__init__ in a way that sets +# the new all_tied_weights_keys attribute required by transformers >= 5.7.0. +_orig_ptm_init = PreTrainedModel.__init__ + + +def _patched_ptm_init(self, *args, **kwargs): + _orig_ptm_init(self, *args, **kwargs) + if not hasattr(self, "all_tied_weights_keys"): + self.all_tied_weights_keys = {} + + +PreTrainedModel.__init__ = _patched_ptm_init + + +def get_gpu_memory_mb(): + """Return current GPU memory allocated in MB.""" + if torch.cuda.is_available(): + return torch.cuda.memory_allocated() / 1024 / 1024 + return 0 + + +def get_gpu_memory_peak_mb(): + """Return peak GPU memory allocated in MB.""" + if torch.cuda.is_available(): + return torch.cuda.max_memory_allocated() / 1024 / 1024 + return 0 + + +def percentiles(data, pcts=[50, 95, 99]): + """Compute percentiles from list of values.""" + arr = np.array(data) + return {f"p{p}": float(np.percentile(arr, p)) for p in pcts} + + +def build_prompt(tokenizer, target_tokens): + """Build a prompt of approximately target_tokens length.""" + base_text = ( + "The quick brown fox jumps over the lazy dog. " + "In a world of artificial intelligence and machine learning, " + "we find ourselves at the intersection of technology and creativity. " + "The possibilities are endless when we consider the potential applications. " + ) + # Repeat to exceed target, then truncate + repeated = base_text * (target_tokens // 10 + 10) + ids = tokenizer.encode(repeated, add_special_tokens=False)[:target_tokens] + return tokenizer.decode(ids) + + +def benchmark_generate(model, tokenizer, prompt, max_new_tokens, device, batch_size=1): + """Run a single generation and measure E2E time. + + Uses model.language_model.generate() to bypass the VLM wrapper's + img_context_token_id assertion, since we're benchmarking text-only + generation (matching the Neuron TKG benchmark methodology). + + Returns dict with e2e_s, num_output_tokens, num_input_tokens, batch_size. + """ + inputs = tokenizer([prompt] * batch_size, return_tensors="pt", padding=True).to( + device + ) + input_len = inputs["input_ids"].shape[1] + + # Use language_model directly to avoid VLM wrapper assertion + gen_model = getattr(model, "language_model", model) + + torch.cuda.synchronize() + t_start = time.perf_counter() + + with torch.no_grad(): + outputs = gen_model.generate( + **inputs, + max_new_tokens=max_new_tokens, + do_sample=False, + use_cache=True, + ) + + torch.cuda.synchronize() + t_end = time.perf_counter() + + total_time = t_end - t_start + num_output_tokens = outputs.shape[1] - input_len + + return { + "e2e_s": total_time, + "num_output_tokens": int(num_output_tokens), + "num_input_tokens": int(input_len), + "batch_size": batch_size, + } + + +def measure_ttft(model, tokenizer, prompt, device, batch_size=1): + """Measure TTFT by generating exactly 1 token. + + Uses model.language_model.generate() for text-only benchmarking. + """ + inputs = tokenizer([prompt] * batch_size, return_tensors="pt", padding=True).to( + device + ) + + # Use language_model directly to avoid VLM wrapper assertion + gen_model = getattr(model, "language_model", model) + + torch.cuda.synchronize() + t_start = time.perf_counter() + + with torch.no_grad(): + gen_model.generate( + **inputs, + max_new_tokens=1, + do_sample=False, + use_cache=True, + ) + + torch.cuda.synchronize() + t_end = time.perf_counter() + + return (t_end - t_start) * 1000 # ms + + +def run_workload( + model, + tokenizer, + prompt, + max_new_tokens, + device, + batch_size, + num_warmup=5, + num_runs=10, +): + """Run a complete workload with warmup and measurement.""" + print(f" Warmup ({num_warmup} runs)...") + for _ in range(num_warmup): + benchmark_generate(model, tokenizer, prompt, max_new_tokens, device, batch_size) + + # Measure TTFT separately + print(f" Measuring TTFT ({num_runs} runs)...") + ttft_values = [] + for _ in range(num_runs): + ttft = measure_ttft(model, tokenizer, prompt, device, batch_size) + ttft_values.append(ttft) + + # Measure full generation + print(f" Measuring generation ({num_runs} runs)...") + results = [] + for _ in range(num_runs): + r = benchmark_generate( + model, tokenizer, prompt, max_new_tokens, device, batch_size + ) + results.append(r) + + # Compute derived metrics + ttft_stats = percentiles(ttft_values) + + e2e_values = [r["e2e_s"] * 1000 for r in results] # ms + e2e_stats = percentiles(e2e_values) + + # output tok/s = (num_output_tokens - 1) / (e2e - ttft) + # Using median TTFT for TPOT calculation + median_ttft_s = ttft_stats["p50"] / 1000 + output_tps_values = [] + for r in results: + decode_time = r["e2e_s"] - median_ttft_s + if decode_time > 0 and r["num_output_tokens"] > 1: + tps = (r["num_output_tokens"] - 1) / decode_time + output_tps_values.append(tps) + output_tps_stats = ( + percentiles(output_tps_values) + if output_tps_values + else {"p50": 0, "p95": 0, "p99": 0} + ) + + # Total throughput (batch * per-request tok/s) + total_tps = output_tps_stats["p50"] * batch_size + + peak_mem = get_gpu_memory_peak_mb() + + return { + "ttft_ms": ttft_stats, + "output_tps": output_tps_stats, + "total_tps": total_tps, + "e2e_ms": e2e_stats, + "num_output_tokens": int(np.median([r["num_output_tokens"] for r in results])), + "num_input_tokens": results[0]["num_input_tokens"], + "batch_size": batch_size, + "peak_gpu_mem_mb": peak_mem, + "ttft_raw": ttft_values, + "e2e_raw": e2e_values, + "output_tps_raw": output_tps_values, + } + + +WORKLOADS = { + "short-short": (128, 128), + "short-long": (128, 512), + "long-short": (2048, 128), + "long-long": (2048, 512), +} + + +def main(): + parser = argparse.ArgumentParser( + description="GPU benchmark for InternVL3-8B-Instruct" + ) + parser.add_argument( + "--model", + default="/home/ubuntu/models/InternVL3-8B-Instruct", + help="Model path", + ) + parser.add_argument( + "--batch-sizes", + nargs="+", + type=int, + default=[1, 2, 4, 8], + help="Batch sizes to test", + ) + parser.add_argument( + "--workloads", + nargs="+", + default=["short-short", "short-long", "long-short", "long-long"], + help="Workload IDs to test", + ) + parser.add_argument("--num-warmup", type=int, default=5, help="Warmup runs") + parser.add_argument("--num-runs", type=int, default=10, help="Measurement runs") + parser.add_argument("--compile", action="store_true", help="Use torch.compile") + parser.add_argument( + "--flash-attn", action="store_true", help="Use flash_attention_2" + ) + parser.add_argument( + "--llm-only", + action="store_true", + help="Load Qwen2ForCausalLM directly with SDPA (bypasses VLM wrapper eager attention)", + ) + parser.add_argument("--dtype", choices=["bfloat16", "float16"], default="bfloat16") + parser.add_argument( + "--output", default="gpu_benchmark_results.json", help="Output JSON file" + ) + args = parser.parse_args() + + device = "cuda" if torch.cuda.is_available() else "cpu" + dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16 + + print(f"=== GPU Benchmark: InternVL3-8B-Instruct ===") + print(f"Device: {device}") + if torch.cuda.is_available(): + print(f"GPU: {torch.cuda.get_device_name(0)}") + print( + f"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB" + ) + print(f"dtype: {args.dtype}") + print(f"torch.compile: {args.compile}") + print(f"flash_attention_2: {args.flash_attn}") + print(f"LLM-only (SDPA): {args.llm_only}") + print(f"Batch sizes: {args.batch_sizes}") + print(f"Workloads: {args.workloads}") + print() + + # Load model + print("Loading tokenizer...") + tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + print("Loading model...") + t_load_start = time.perf_counter() + attn_impl = "flash_attention_2" if args.flash_attn else "sdpa" + + if args.llm_only: + # Load Qwen2ForCausalLM directly with specified attn_implementation. + # This avoids the InternVLChatModel wrapper which forces eager attention + # and doesn't support attn_implementation parameter. + from transformers import Qwen2ForCausalLM, AutoConfig + from safetensors.torch import load_file + import glob + + config = AutoConfig.from_pretrained(args.model, trust_remote_code=True) + llm_config = config.llm_config + llm_config._attn_implementation = attn_impl + + # Create model on meta device (zero memory), load weights to GPU, + # then materialize buffers (rotary_emb.inv_freq) on GPU. + with torch.device("meta"): + model = Qwen2ForCausalLM(llm_config) + + shard_files = sorted(glob.glob(os.path.join(args.model, "model*.safetensors"))) + prefix = "language_model." + for sf in shard_files: + shard = load_file(sf, device="cuda:0") + for k, v in shard.items(): + if k.startswith(prefix): + param_name = k[len(prefix) :] + parts = param_name.split(".") + obj = model + for p in parts[:-1]: + obj = getattr(obj, p) + setattr( + obj, + parts[-1], + torch.nn.Parameter(v.to(dtype), requires_grad=False), + ) + del shard + gc.collect() + torch.cuda.empty_cache() + + # Materialize rotary embedding buffers (computed from config, not weights) + rope = model.model.rotary_emb + dim = llm_config.hidden_size // llm_config.num_attention_heads + base = getattr(llm_config, "rope_theta", 10000.0) + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.float32, device="cuda") / dim) + ) + rope.inv_freq = inv_freq + if hasattr(rope, "original_inv_freq"): + rope.original_inv_freq = inv_freq.clone() + else: + # Load full VLM (uses eager attention by default) + model = AutoModelForCausalLM.from_pretrained( + args.model, + dtype=dtype, + device_map="auto", + trust_remote_code=True, + ) + model.eval() + t_load = time.perf_counter() - t_load_start + print(f"Model loaded in {t_load:.1f}s") + print(f"GPU memory after load: {get_gpu_memory_mb():.0f} MB") + + if args.compile: + print("Compiling model with torch.compile...") + t_compile_start = time.perf_counter() + model = torch.compile(model, mode="reduce-overhead") + # Trigger compilation with a small forward pass + dummy = tokenizer("Hello", return_tensors="pt").to(device) + with torch.no_grad(): + model.generate(**dummy, max_new_tokens=2, do_sample=False) + t_compile = time.perf_counter() - t_compile_start + print(f"Compilation done in {t_compile:.1f}s") + + # Build prompts for each workload + prompts = {} + for wl_id in args.workloads: + input_tokens, _ = WORKLOADS[wl_id] + prompts[wl_id] = build_prompt(tokenizer, input_tokens) + + # Run benchmarks + all_results = { + "metadata": { + "model": args.model, + "gpu": torch.cuda.get_device_name(0) + if torch.cuda.is_available() + else "N/A", + "vram_gb": torch.cuda.get_device_properties(0).total_memory / 1024**3 + if torch.cuda.is_available() + else 0, + "dtype": args.dtype, + "torch_compile": args.compile, + "flash_attention_2": args.flash_attn, + "llm_only": args.llm_only, + "attn_implementation": attn_impl, + "load_time_s": t_load, + "torch_version": torch.__version__, + "cuda_version": torch.version.cuda if torch.cuda.is_available() else "N/A", + }, + "results": {}, + } + + for wl_id in args.workloads: + input_tokens, output_tokens = WORKLOADS[wl_id] + print(f"\n--- Workload: {wl_id} (in={input_tokens}, out={output_tokens}) ---") + + for bs in args.batch_sizes: + print(f" Batch size: {bs}") + torch.cuda.reset_peak_memory_stats() + + try: + result = run_workload( + model, + tokenizer, + prompts[wl_id], + output_tokens, + device, + bs, + args.num_warmup, + args.num_runs, + ) + + key = f"{wl_id}_bs{bs}" + all_results["results"][key] = result + + print(f" TTFT P50: {result['ttft_ms']['p50']:.1f} ms") + print(f" Output tok/s P50: {result['output_tps']['p50']:.1f}") + print(f" Total tok/s: {result['total_tps']:.1f}") + print(f" E2E P50: {result['e2e_ms']['p50']:.1f} ms") + print(f" Peak GPU: {result['peak_gpu_mem_mb']:.0f} MB") + + except torch.cuda.OutOfMemoryError: + print( + f" OOM at batch_size={bs}, skipping larger batches for this workload" + ) + key = f"{wl_id}_bs{bs}" + all_results["results"][key] = {"error": "OOM"} + gc.collect() + torch.cuda.empty_cache() + break + except Exception as e: + print(f" Error: {e}") + key = f"{wl_id}_bs{bs}" + all_results["results"][key] = {"error": str(e)} + + # Save results + # Remove raw arrays for clean JSON (keep percentiles) + for key, val in all_results["results"].items(): + if isinstance(val, dict): + val.pop("ttft_raw", None) + val.pop("e2e_raw", None) + val.pop("output_tps_raw", None) + + with open(args.output, "w") as f: + json.dump(all_results, f, indent=2) + print(f"\nResults saved to {args.output}") + + # Print summary table + print("\n=== SUMMARY ===") + print( + f"{'Workload':<15} {'BS':>3} {'TTFT P50':>10} {'tok/s P50':>10} {'Total tok/s':>12} {'E2E P50':>10} {'Peak MB':>8}" + ) + print("-" * 75) + for key, val in all_results["results"].items(): + if "error" in val: + print(f"{key:<15} {'':>3} {'ERROR':>10}") + continue + wl = key.rsplit("_bs", 1)[0] + bs = val["batch_size"] + print( + f"{wl:<15} {bs:>3} {val['ttft_ms']['p50']:>9.1f}ms {val['output_tps']['p50']:>9.1f} {val['total_tps']:>11.1f} {val['e2e_ms']['p50']:>9.1f}ms {val['peak_gpu_mem_mb']:>7.0f}" + ) + + +if __name__ == "__main__": + main() diff --git a/contrib/models/InternVL3-8B-Instruct/gpu_eager_results.json b/contrib/models/InternVL3-8B-Instruct/gpu_eager_results.json new file mode 100644 index 00000000..04685720 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/gpu_eager_results.json @@ -0,0 +1,368 @@ +{ + "metadata": { + "model": "/home/ubuntu/models/InternVL3-8B-Instruct", + "gpu": "NVIDIA L40S", + "vram_gb": 44.39215087890625, + "dtype": "bfloat16", + "torch_compile": false, + "flash_attention_2": false, + "attn_implementation": "eager", + "load_time_s": 2.7835791559999734, + "torch_version": "2.11.0+cu130", + "cuda_version": "13.0" + }, + "results": { + "short-short_bs1": { + "ttft_ms": { + "p50": 32.354057999953056, + "p95": 33.15108340003121, + "p99": 33.32482948002621 + }, + "output_tps": { + "p50": 35.77090147724594, + "p95": 35.82600723018595, + "p99": 35.83171407477799 + }, + "total_tps": 35.77090147724594, + "e2e_ms": { + "p50": 3582.725865000043, + "p95": 3590.407341399998, + "p99": 3591.927379479987 + }, + "num_output_tokens": 128, + "num_input_tokens": 128, + "batch_size": 1, + "peak_gpu_mem_mb": 15187.84716796875 + }, + "short-short_bs2": { 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166.07691539979896, + "p99": 168.71254307975505 + }, + "output_tps": { + "p50": 42.23309606359, + "p95": 42.30755131546222, + "p99": 42.30923809368367 + }, + "total_tps": 42.23309606359, + "e2e_ms": { + "p50": 12259.857311499673, + "p95": 12473.311311049429, + "p99": 12478.515446209485 + }, + "num_output_tokens": 512, + "num_input_tokens": 2048, + "batch_size": 1, + "peak_gpu_mem_mb": 14992.86865234375 + }, + "long-long_bs2": { + "ttft_ms": { + "p50": 350.13424400040094, + "p95": 363.21154429965645, + "p99": 364.2686288592722 + }, + "output_tps": { + "p50": 39.66795834426845, + "p95": 39.76727403069557, + "p99": 39.77517209438742 + }, + "total_tps": 79.3359166885369, + "e2e_ms": { + "p50": 13232.075230000191, + "p95": 13285.502536349895, + "p99": 13298.716810470114 + }, + "num_output_tokens": 512, + "num_input_tokens": 2048, + "batch_size": 2, + "peak_gpu_mem_mb": 15378.43115234375 + }, + "long-long_bs4": { + "ttft_ms": { + "p50": 667.7430545000789, + "p95": 678.1529508004041, + "p99": 681.4914525602308 + }, + "output_tps": { + "p50": 38.25393678850787, + "p95": 38.28818350222392, + "p99": 38.290061948362975 + }, + "total_tps": 153.0157471540315, + "e2e_ms": { + "p50": 14025.845337499959, + "p95": 14048.56425299954, + "p99": 14051.948389799372 + }, + "num_output_tokens": 512, + "num_input_tokens": 2048, + "batch_size": 4, + "peak_gpu_mem_mb": 16161.55615234375 + }, + "long-long_bs8": { + "ttft_ms": { + "p50": 1317.572557499716, + "p95": 1323.4350110004925, + "p99": 1323.876147800347 + }, + "output_tps": { + "p50": 35.87654712657549, + "p95": 36.01936773697751, + "p99": 36.02395023602966 + }, + "total_tps": 287.0123770126039, + "e2e_ms": { + "p50": 15560.860764499921, + "p95": 15619.46175729986, + "p99": 15624.53301465971 + }, + "num_output_tokens": 512, + "num_input_tokens": 2048, + "batch_size": 8, + "peak_gpu_mem_mb": 17724.80615234375 + } + } +} \ No newline at end of file diff --git a/contrib/models/InternVL3-8B-Instruct/nki_benchmark.py b/contrib/models/InternVL3-8B-Instruct/nki_benchmark.py new file mode 100644 index 00000000..4baf570c --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/nki_benchmark.py @@ -0,0 +1,350 @@ +#!/usr/bin/env python3 +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +NKI kernel benchmark for InternVL3-8B-Instruct. + +Compiles with opt-in NKI kernels enabled, then benchmarks TTFT and TKG +throughput. Compares against baseline (non-NKI) numbers. + +NKI kernels enabled: + - qkv_kernel_enabled: Fused QKV projection with NKI RMSNorm (CTE + TKG) + - mlp_kernel_enabled: Fused MLP (gate/up/down) kernel with NKI RMSNorm (CTE + TKG) + +Note: attn_block_tkg_nki_kernel_enabled triggers NCC_ISTP902 compiler bug in SDK 2.29 +(StaticProfiler error in O-proj all-reduce). Omitted until SDK fix. + +Usage: + # Full compile + benchmark + python nki_benchmark.py + + # Skip compile, just benchmark + python nki_benchmark.py --skip-compile + + # Accuracy validation after compile + python nki_benchmark.py --accuracy-only --skip-compile + +Target: trn2.3xlarge LNC=2 TP=4 +""" + +import argparse +import json +import os +import sys +import time +from pathlib import Path + +import torch + +sys.path.insert(0, str(Path(__file__).parent / "src")) + +from modeling_internvl3 import InternVL3InferenceConfig, NeuronInternVL3ForCausalLM +from neuronx_distributed_inference.models.config import NeuronConfig +from neuronx_distributed_inference.utils.hf_adapter import HuggingFaceGenerationAdapter + +MODEL_PATH = "/mnt/models/InternVL3-8B-Instruct/" +COMPILED_NKI_PATH = "/mnt/models/neuron_models/InternVL3-8B-VLM-NKI/" +COMPILED_BASELINE_PATH = "/mnt/models/neuron_models/InternVL3-8B-VLM/" +RESULTS_PATH = "/mnt/models/nki_benchmark_results.json" + +# Baseline numbers (from Task 015 scale_test at seq_len=2048 batch_size=1) +BASELINE = { + "ttft_ms": 138, + "tkg_tok_s": 75.1, +} + + +def create_nki_config(seq_len=2048, batch_size=1): + """Create config with NKI kernels enabled for the text backbone.""" + text_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=batch_size, + seq_len=seq_len, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + save_sharded_checkpoint=True, + fused_qkv=True, # Required for NKI QKV kernel + # NKI kernel flags + qkv_kernel_enabled=True, # Fused QKV projection with NKI RMSNorm + mlp_kernel_enabled=True, # Fused MLP with NKI RMSNorm + # NOTE: attn_block_tkg_nki_kernel_enabled=True triggers NCC_ISTP902 + # compiler bug in SDK 2.29 (StaticProfiler error in O-proj all-reduce). + # Omitted until SDK fix is available. + ) + + # Vision NeuronConfig: same as baseline (no NKI for vision encoder) + vision_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=256, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + buckets=[1], + fused_qkv=True, + save_sharded_checkpoint=True, + ) + + config = InternVL3InferenceConfig.from_pretrained( + MODEL_PATH, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + ) + + return config + + +def compile_nki(config, compiled_path): + """Compile model with NKI kernels.""" + print("=" * 60) + print("COMPILING WITH NKI KERNELS") + print("=" * 60) + nc = config.text_config.neuron_config + print(f" qkv_kernel_enabled: {nc.qkv_kernel_enabled}") + print(f" qkv_nki_kernel_enabled: {nc.qkv_nki_kernel_enabled}") + print( + f" attn_block_tkg_nki_kernel_enabled: {nc.attn_block_tkg_nki_kernel_enabled}" + ) + print(f" mlp_kernel_enabled: {nc.mlp_kernel_enabled}") + print(f" fused_qkv: {nc.fused_qkv}") + print(f" seq_len: {nc.seq_len}, batch_size: {nc.max_batch_size}") + print(f" Output: {compiled_path}") + + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + + start = time.time() + model.compile(compiled_path) + compile_time = time.time() - start + print( + f"\nCompilation completed in {compile_time:.1f}s ({compile_time / 60:.1f} min)" + ) + + return model, compile_time + + +def load_model(config, compiled_path): + """Load already-compiled model.""" + print(f"\nLoading compiled model from {compiled_path}") + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + start = time.time() + model.load(compiled_path) + load_time = time.time() - start + print(f"Loaded in {load_time:.1f}s") + return model + + +def measure_ttft(model, tokenizer, n_runs=5): + """Measure TTFT for text-only CTE.""" + messages = [{"role": "user", "content": "What is the capital of France?"}] + templated = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = tokenizer(templated, return_tensors="pt") + input_ids = inputs.input_ids + + seq_len = input_ids.shape[-1] + position_ids = torch.arange(seq_len, dtype=torch.int32).unsqueeze(0) + seq_ids = torch.zeros(1, dtype=torch.int32) + + # Warmup + model.reset() + with torch.no_grad(): + model(input_ids=input_ids, position_ids=position_ids, seq_ids=seq_ids) + + # Measure + ttft_times = [] + for _ in range(n_runs): + model.reset() + start = time.perf_counter() + with torch.no_grad(): + model(input_ids=input_ids, position_ids=position_ids, seq_ids=seq_ids) + ttft_times.append(time.perf_counter() - start) + + avg = sum(ttft_times) / len(ttft_times) + return avg * 1000 # ms + + +def measure_tkg(model, tokenizer, num_tokens=64, n_runs=3): + """Measure TKG throughput using HuggingFaceGenerationAdapter.""" + adapter = HuggingFaceGenerationAdapter(model) + + messages = [ + {"role": "user", "content": "Tell me a detailed story about a brave knight."} + ] + templated = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = tokenizer(templated, return_tensors="pt") + input_ids = inputs.input_ids + attention_mask = torch.ones_like(input_ids) + + eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>") + + # Warmup + with torch.no_grad(): + adapter.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=num_tokens, + do_sample=False, + eos_token_id=eos_token_id, + ) + + # Measure + tkg_results = [] + for _ in range(n_runs): + start = time.perf_counter() + with torch.no_grad(): + output_ids = adapter.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=num_tokens, + do_sample=False, + eos_token_id=eos_token_id, + ) + total_time = time.perf_counter() - start + generated = output_ids.shape[-1] - input_ids.shape[-1] + tkg_results.append((generated, total_time)) + + # Average tokens/sec (excluding TTFT — approximate by subtracting first-token overhead) + avg_tokens = sum(r[0] for r in tkg_results) / len(tkg_results) + avg_time = sum(r[1] for r in tkg_results) / len(tkg_results) + tok_s = avg_tokens / avg_time if avg_time > 0 else 0.0 + + return tok_s, avg_tokens, avg_time + + +def run_accuracy_check(adapter, tokenizer): + """Quick accuracy sanity check.""" + prompts = [ + ("france", "What is the capital of France?"), + ("math", "What is 2 + 2? Answer with just the number:"), + ] + + eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>") + results = {} + + for name, prompt_text in prompts: + messages = [{"role": "user", "content": prompt_text}] + templated = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = tokenizer(templated, return_tensors="pt") + input_ids = inputs.input_ids + attention_mask = torch.ones_like(input_ids) + + with torch.no_grad(): + output_ids = adapter.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=32, + do_sample=False, + eos_token_id=eos_token_id, + ) + + generated_ids = output_ids[0, input_ids.shape[-1] :].tolist() + text = tokenizer.decode(generated_ids, skip_special_tokens=True) + results[name] = text + print(f" {name}: {text!r}") + + # Basic sanity + france_ok = "paris" in results.get("france", "").lower() + math_ok = "4" in results.get("math", "") + return france_ok and math_ok + + +def main(): + parser = argparse.ArgumentParser(description="NKI kernel benchmark") + parser.add_argument("--skip-compile", action="store_true", help="Skip compilation") + parser.add_argument( + "--accuracy-only", action="store_true", help="Only run accuracy check" + ) + parser.add_argument("--seq-len", type=int, default=2048, help="Sequence length") + parser.add_argument("--batch-size", type=int, default=1, help="Batch size") + args = parser.parse_args() + + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) + + config = create_nki_config(seq_len=args.seq_len, batch_size=args.batch_size) + compile_time = None + + if not args.skip_compile: + model, compile_time = compile_nki(config, COMPILED_NKI_PATH) + # After compile(), model already has weights loaded in memory. + # Call load() to load the compiled NEFFs onto Neuron devices + # and save sharded weight checkpoints for future --skip-compile runs. + model.load(COMPILED_NKI_PATH) + else: + model = load_model(config, COMPILED_NKI_PATH) + + # Accuracy check + print("\n--- Accuracy Sanity Check ---") + adapter = HuggingFaceGenerationAdapter(model) + accuracy_ok = run_accuracy_check(adapter, tokenizer) + print(f"Accuracy: {'PASS' if accuracy_ok else 'FAIL'}") + + if args.accuracy_only: + return + + # TTFT benchmark + print("\n--- TTFT Benchmark ---") + ttft_ms = measure_ttft(model, tokenizer, n_runs=5) + ttft_delta = ((ttft_ms - BASELINE["ttft_ms"]) / BASELINE["ttft_ms"]) * 100 + print( + f" TTFT: {ttft_ms:.1f} ms (baseline: {BASELINE['ttft_ms']} ms, delta: {ttft_delta:+.1f}%)" + ) + + # TKG benchmark + print("\n--- TKG Throughput Benchmark ---") + tok_s, avg_tokens, avg_time = measure_tkg(model, tokenizer, num_tokens=64, n_runs=3) + tkg_delta = ((tok_s - BASELINE["tkg_tok_s"]) / BASELINE["tkg_tok_s"]) * 100 + print( + f" TKG: {tok_s:.1f} tok/s (baseline: {BASELINE['tkg_tok_s']} tok/s, delta: {tkg_delta:+.1f}%)" + ) + print(f" Avg tokens generated: {avg_tokens:.0f}") + print(f" Avg total time: {avg_time:.3f}s") + + # Summary + print("\n" + "=" * 60) + print("NKI BENCHMARK SUMMARY") + print("=" * 60) + print(f" Config: seq_len={args.seq_len}, batch_size={args.batch_size}") + print(f" NKI kernels: qkv + mlp (fused RMSNorm)") + print(f" Accuracy: {'PASS' if accuracy_ok else 'FAIL'}") + print( + f" TTFT: {ttft_ms:.1f} ms (baseline: {BASELINE['ttft_ms']} ms, {ttft_delta:+.1f}%)" + ) + print( + f" TKG: {tok_s:.1f} tok/s (baseline: {BASELINE['tkg_tok_s']} tok/s, {tkg_delta:+.1f}%)" + ) + if compile_time: + print(f" Compile time: {compile_time:.1f}s ({compile_time / 60:.1f} min)") + + # Save results + results = { + "config": { + "seq_len": args.seq_len, + "batch_size": args.batch_size, + "nki_kernels": [ + "qkv_kernel_enabled", + "mlp_kernel_enabled", + ], + }, + "accuracy": "PASS" if accuracy_ok else "FAIL", + "ttft_ms": round(ttft_ms, 1), + "tkg_tok_s": round(tok_s, 1), + "ttft_delta_pct": round(ttft_delta, 1), + "tkg_delta_pct": round(tkg_delta, 1), + "baseline": BASELINE, + "compile_time_s": round(compile_time, 1) if compile_time else None, + } + + with open(RESULTS_PATH, "w") as f: + json.dump(results, f, indent=2) + print(f"\nResults saved to {RESULTS_PATH}") + + +if __name__ == "__main__": + main() diff --git a/contrib/models/InternVL3-8B-Instruct/scale_test.py b/contrib/models/InternVL3-8B-Instruct/scale_test.py new file mode 100644 index 00000000..c8fbed36 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/scale_test.py @@ -0,0 +1,474 @@ +#!/usr/bin/env python3 +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Task 015-016: Sequence length and batch size scaling tests for InternVL3-8B-Instruct. + +Tests compilation and inference at various seq_len and batch_size configurations. +Records compilation time, TTFT, TKG throughput, and HBM usage. + +Usage: + # Run all scaling tests (compiles each configuration) + python scale_test.py + + # Test specific seq_len + python scale_test.py --seq-len 4096 + + # Test specific batch size at given seq_len + python scale_test.py --seq-len 4096 --batch-size 4 + + # Skip compilation (use existing compiled models) + python scale_test.py --skip-compile --seq-len 2048 + +Target: trn2.3xlarge LNC=2 TP=4 +""" + +import argparse +import gc +import json +import os +import sys +import time +import traceback +from pathlib import Path + +import torch + +# Add contrib src to path +sys.path.insert(0, str(Path(__file__).parent / "src")) + +from modeling_internvl3 import InternVL3InferenceConfig, NeuronInternVL3ForCausalLM +from neuronx_distributed_inference.models.config import NeuronConfig + +MODEL_PATH = "/mnt/models/InternVL3-8B-Instruct/" +COMPILED_BASE = "/mnt/models/neuron_models/" +RESULTS_PATH = "/mnt/models/scale_test_results.json" + +# Special token IDs +IMG_CONTEXT_ID = 151667 +IMG_START_ID = 151665 +IMG_END_ID = 151666 + + +def get_compiled_path(seq_len, batch_size): + """Get compiled model path for a specific configuration.""" + return os.path.join(COMPILED_BASE, f"InternVL3-8B-s{seq_len}-b{batch_size}") + + +def create_config(seq_len, batch_size): + """Create InternVL3 config for given seq_len and batch_size.""" + text_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=batch_size, + seq_len=seq_len, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + save_sharded_checkpoint=True, + ) + + vision_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=256, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + buckets=[1], + fused_qkv=True, + save_sharded_checkpoint=True, + ) + + config = InternVL3InferenceConfig.from_pretrained( + MODEL_PATH, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + ) + + return config + + +def get_hbm_usage(): + """Get current HBM usage from neuron-monitor.""" + try: + import subprocess + + result = subprocess.run( + [ + "neuron-monitor", + "-c", + '{"period":"1s","neuron_runtimes":[{"tag_filter":".*","metrics":[{"type":"memory_info"}]}]}', + ], + capture_output=True, + text=True, + timeout=5, + ) + # Simple parsing - just get overall usage + if result.returncode == 0: + import re + + matches = re.findall(r'"hbm_total_bytes":\s*(\d+)', result.stdout) + if matches: + return int(matches[0]) / (1024**3) # GB + except Exception: + pass + return None + + +def measure_ttft(model, tokenizer, prompt, seq_len, batch_size, pixel_values=None): + """Measure time to first token (TTFT) for a given prompt.""" + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs.input_ids + + if pixel_values is not None: + # Build multimodal input + text_ids = input_ids[0] + img_tokens = torch.full((256,), IMG_CONTEXT_ID, dtype=torch.long) + input_ids = torch.cat( + [ + text_ids, + torch.tensor([IMG_START_ID]), + img_tokens, + torch.tensor([IMG_END_ID]), + ] + ).unsqueeze(0) + + # Repeat for batch + if batch_size > 1: + input_ids = input_ids.repeat(batch_size, 1) + + actual_seq_len = input_ids.shape[-1] + position_ids = torch.arange(actual_seq_len, dtype=torch.int32).unsqueeze(0) + if batch_size > 1: + position_ids = position_ids.repeat(batch_size, 1) + seq_ids = torch.arange(batch_size, dtype=torch.int32) + + # Warmup + model.reset() + with torch.no_grad(): + model( + input_ids=input_ids, + position_ids=position_ids, + seq_ids=seq_ids, + pixel_values=pixel_values, + ) + + # Measure TTFT (average of 3 runs) + ttft_times = [] + for _ in range(3): + model.reset() + start = time.perf_counter() + with torch.no_grad(): + outputs = model( + input_ids=input_ids, + position_ids=position_ids, + seq_ids=seq_ids, + pixel_values=pixel_values, + ) + ttft = time.perf_counter() - start + ttft_times.append(ttft) + + avg_ttft = sum(ttft_times) / len(ttft_times) + return avg_ttft, actual_seq_len + + +def measure_tkg_throughput(model, tokenizer, prompt, num_tokens=32, batch_size=1): + """Measure TKG throughput (tokens/sec) for autoregressive generation.""" + from neuronx_distributed_inference.utils.hf_adapter import ( + HuggingFaceGenerationAdapter, + ) + + adapter = HuggingFaceGenerationAdapter(model) + + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs.input_ids + if batch_size > 1: + input_ids = input_ids.repeat(batch_size, 1) + + attention_mask = torch.ones_like(input_ids) + + # Warmup + with torch.no_grad(): + adapter.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=4, + do_sample=False, + ) + + # Measure (average of 3 runs) + tkg_times = [] + for _ in range(3): + start = time.perf_counter() + with torch.no_grad(): + out = adapter.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=num_tokens, + do_sample=False, + ) + elapsed = time.perf_counter() - start + tkg_times.append(elapsed) + + avg_time = sum(tkg_times) / len(tkg_times) + # Total tokens generated = num_tokens * batch_size + total_tokens = num_tokens * batch_size + throughput = total_tokens / avg_time + + return throughput, avg_time, num_tokens + + +def run_test(seq_len, batch_size, skip_compile=False): + """Run a single scaling test configuration.""" + compiled_path = get_compiled_path(seq_len, batch_size) + result = { + "seq_len": seq_len, + "batch_size": batch_size, + "compiled_path": compiled_path, + "status": "pending", + } + + print(f"\n{'=' * 60}") + print(f"Testing: seq_len={seq_len}, batch_size={batch_size}") + print(f"{'=' * 60}") + + try: + # Create config + config = create_config(seq_len, batch_size) + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + + # Compile + if not skip_compile: + print(f"\nCompiling to {compiled_path}...") + start = time.time() + model.compile(compiled_path) + compile_time = time.time() - start + result["compile_time_s"] = round(compile_time, 1) + print(f"Compilation: {compile_time:.1f}s ({compile_time / 60:.1f} min)") + else: + print(f"\nSkipping compile, loading from {compiled_path}") + result["compile_time_s"] = "skipped" + + # Load + print("Loading compiled model...") + start = time.time() + model.load(compiled_path) + load_time = time.time() - start + result["load_time_s"] = round(load_time, 1) + print(f"Load: {load_time:.1f}s") + + # HBM usage + hbm = get_hbm_usage() + if hbm: + result["hbm_usage_gb"] = round(hbm, 2) + print(f"HBM usage: {hbm:.2f} GB") + + # Tokenizer + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) + + # Test 1: Text-only TTFT + print("\n--- Text-only TTFT ---") + prompt = "What is the capital of France?" + ttft, actual_seq = measure_ttft(model, tokenizer, prompt, seq_len, batch_size) + result["text_ttft_ms"] = round(ttft * 1000, 1) + result["text_ttft_input_len"] = actual_seq + print( + f"TTFT: {ttft * 1000:.1f} ms (input_len={actual_seq}, batch={batch_size})" + ) + + # Test 2: TKG throughput + print("\n--- TKG throughput ---") + throughput, gen_time, num_tok = measure_tkg_throughput( + model, tokenizer, prompt, num_tokens=32, batch_size=batch_size + ) + result["tkg_throughput_tok_s"] = round(throughput, 1) + result["tkg_gen_time_s"] = round(gen_time, 3) + result["tkg_num_tokens"] = num_tok + print( + f"Throughput: {throughput:.1f} tok/s ({gen_time:.3f}s for {num_tok} tokens, batch={batch_size})" + ) + + # Test 3: Multimodal TTFT (only for batch_size=1) + if batch_size == 1: + print("\n--- Multimodal TTFT ---") + pixel_values = torch.randn(1, 3, 448, 448) + mm_ttft, mm_seq = measure_ttft( + model, + tokenizer, + "Describe this image:", + seq_len, + batch_size, + pixel_values=pixel_values, + ) + result["multimodal_ttft_ms"] = round(mm_ttft * 1000, 1) + result["multimodal_input_len"] = mm_seq + print(f"Multimodal TTFT: {mm_ttft * 1000:.1f} ms (input_len={mm_seq})") + + # Test 4: Long prompt TTFT (fill most of seq_len) + if seq_len >= 4096: + print(f"\n--- Long prompt TTFT (target ~{seq_len // 2} tokens) ---") + # Generate a long prompt by repeating text + base_text = "The quick brown fox jumps over the lazy dog. " * 100 + long_inputs = tokenizer( + base_text, return_tensors="pt", max_length=seq_len // 2, truncation=True + ) + long_prompt_len = long_inputs.input_ids.shape[-1] + long_ids = long_inputs.input_ids + if batch_size > 1: + long_ids = long_ids.repeat(batch_size, 1) + long_pos = torch.arange(long_prompt_len, dtype=torch.int32).unsqueeze(0) + if batch_size > 1: + long_pos = long_pos.repeat(batch_size, 1) + long_seq_ids = torch.arange(batch_size, dtype=torch.int32) + + model.reset() + long_ttft_times = [] + for _ in range(3): + model.reset() + start = time.perf_counter() + with torch.no_grad(): + model( + input_ids=long_ids, position_ids=long_pos, seq_ids=long_seq_ids + ) + long_ttft_times.append(time.perf_counter() - start) + + long_ttft = sum(long_ttft_times) / len(long_ttft_times) + result["long_ttft_ms"] = round(long_ttft * 1000, 1) + result["long_ttft_input_len"] = long_prompt_len + print( + f"Long TTFT: {long_ttft * 1000:.1f} ms (input_len={long_prompt_len}, batch={batch_size})" + ) + + result["status"] = "PASS" + print(f"\nResult: PASS") + + except Exception as e: + result["status"] = "FAIL" + result["error"] = str(e) + result["traceback"] = traceback.format_exc() + print(f"\nResult: FAIL - {e}") + + finally: + # Cleanup to free HBM + if "model" in locals(): + del model + gc.collect() + + return result + + +def main(): + parser = argparse.ArgumentParser(description="InternVL3 scaling tests") + parser.add_argument( + "--seq-len", + type=int, + default=None, + help="Test specific seq_len (default: test 2048, 4096, 8192)", + ) + parser.add_argument( + "--batch-size", + type=int, + default=None, + help="Test specific batch_size (default: test 1, 2, 4)", + ) + parser.add_argument( + "--skip-compile", + action="store_true", + help="Skip compilation, use existing compiled models", + ) + args = parser.parse_args() + + print("=" * 60) + print("InternVL3-8B-Instruct: Scaling Tests (Tasks 015-016)") + print("=" * 60) + print(f"Instance: trn2.3xlarge LNC=2 TP=4") + print(f"Model: {MODEL_PATH}") + + all_results = [] + + if args.seq_len and args.batch_size: + # Single specific test + result = run_test(args.seq_len, args.batch_size, args.skip_compile) + all_results.append(result) + elif args.seq_len: + # Test specific seq_len with batch_size sweep + for bs in [1, 2, 4]: + result = run_test(args.seq_len, bs, args.skip_compile) + all_results.append(result) + if result["status"] == "FAIL": + print(f"\nStopping batch sweep at batch_size={bs} (failed)") + break + elif args.batch_size: + # Test specific batch_size with seq_len sweep + for sl in [2048, 4096, 8192]: + result = run_test(sl, args.batch_size, args.skip_compile) + all_results.append(result) + if result["status"] == "FAIL": + print(f"\nStopping seq_len sweep at seq_len={sl} (failed)") + break + else: + # Full scaling matrix + # Phase 1: seq_len sweep at batch_size=1 + print("\n\n*** PHASE 1: Sequence Length Sweep (batch_size=1) ***") + max_passing_seq_len = 2048 + for sl in [2048, 4096, 8192]: + result = run_test(sl, 1, args.skip_compile) + all_results.append(result) + if result["status"] == "PASS": + max_passing_seq_len = sl + else: + print( + f"\nSeq_len ceiling: {sl} fails. Max passing: {max_passing_seq_len}" + ) + break + + # Phase 2: batch_size sweep at recommended seq_len + recommended_seq_len = min(max_passing_seq_len, 4096) + print(f"\n\n*** PHASE 2: Batch Size Sweep (seq_len={recommended_seq_len}) ***") + for bs in [2, 4]: + result = run_test(recommended_seq_len, bs, args.skip_compile) + all_results.append(result) + if result["status"] == "FAIL": + print( + f"\nBatch size ceiling at seq_len={recommended_seq_len}: bs={bs} fails" + ) + break + + # Summary + print("\n\n" + "=" * 60) + print("SCALING TEST SUMMARY") + print("=" * 60) + + print( + f"\n{'seq_len':<10} {'batch':<8} {'status':<8} {'compile':<12} {'TTFT(ms)':<12} {'tok/s':<10} {'mm_TTFT':<12}" + ) + print("-" * 72) + for r in all_results: + compile_t = r.get("compile_time_s", "?") + if isinstance(compile_t, (int, float)): + compile_str = f"{compile_t:.0f}s" + else: + compile_str = str(compile_t) + + ttft = r.get("text_ttft_ms", "?") + ttft_str = f"{ttft}" if isinstance(ttft, str) else f"{ttft:.1f}" + + tps = r.get("tkg_throughput_tok_s", "?") + tps_str = f"{tps}" if isinstance(tps, str) else f"{tps:.1f}" + + mm = r.get("multimodal_ttft_ms", "-") + mm_str = f"{mm}" if isinstance(mm, str) else f"{mm:.1f}" + + print( + f"{r['seq_len']:<10} {r['batch_size']:<8} {r['status']:<8} {compile_str:<12} {ttft_str:<12} {tps_str:<10} {mm_str:<12}" + ) + + # Save results + with open(RESULTS_PATH, "w") as f: + json.dump(all_results, f, indent=2, default=str) + print(f"\nResults saved to {RESULTS_PATH}") + + +if __name__ == "__main__": + main() diff --git a/contrib/models/InternVL3-8B-Instruct/src/__init__.py b/contrib/models/InternVL3-8B-Instruct/src/__init__.py new file mode 100644 index 00000000..88cea97a --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/src/__init__.py @@ -0,0 +1,28 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +from modeling_internvl3 import ( + InternVL3InferenceConfig, + NeuronInternVL3ForCausalLM, +) +from modeling_internvl3_text import ( + InternVL3TextModelWrapper, + NeuronInternVL3TextForCausalLM, + NeuronInternVL3TextModel, +) +from modeling_internvl3_vision import ( + InternVL3VisionModelWrapper, + NeuronInternVL3VisionModel, + convert_vision_hf_to_neuron_state_dict, +) + +__all__ = [ + "InternVL3InferenceConfig", + "NeuronInternVL3ForCausalLM", + "InternVL3TextModelWrapper", + "NeuronInternVL3TextForCausalLM", + "NeuronInternVL3TextModel", + "InternVL3VisionModelWrapper", + "NeuronInternVL3VisionModel", + "convert_vision_hf_to_neuron_state_dict", +] diff --git a/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3.py b/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3.py new file mode 100644 index 00000000..763bcd33 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3.py @@ -0,0 +1,585 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +NxDI contrib model for InternVL3-8B-Instruct (OpenGVLab). + +Architecture: InternViT-300M vision encoder + pixel shuffle MLP projector + Qwen2.5-7B text backbone. + +This is the top-level VLM class that inherits from NeuronBaseForImageToText, +orchestrating both the vision encoder (separate NEFF) and text decoder (CTE + TKG NEFFs). + +Vision pipeline: + pixel_values [B,3,448,448] -> InternViT -> strip CLS -> pixel_shuffle -> MLP projector + -> [1, seq_len, 3584] padded vision embeddings + +Text pipeline: + input_ids -> embed_tokens -> scatter vision embeddings at positions + -> 28 Qwen2.5 decoder layers -> lm_head -> logits + +Special tokens: + = 151667 (image placeholder token in input_ids) + = 151665 (image start) + = 151666 (image end) +""" + +import copy +import json +import logging +import os +from typing import Callable, Dict, List, Optional, Tuple, Type, Union + +import torch +from transformers.modeling_outputs import CausalLMOutputWithPast + +from neuronx_distributed_inference.models.config import InferenceConfig, NeuronConfig +from neuronx_distributed_inference.models.image_to_text_model_base import ( + ImageToTextInferenceConfig, + NeuronBaseForImageToText, +) +from neuronx_distributed_inference.models.llama4.utils.encoder_utils import ( + generate_positions_from_mask, + pad_positions, +) +from neuronx_distributed_inference.models.model_wrapper import VISION_ENCODER_MODEL_TAG + +from modeling_internvl3_text import ( + InternVL3TextModelWrapper, + NeuronInternVL3TextForCausalLM, + NeuronInternVL3TextModel, +) +from modeling_internvl3_vision import ( + InternVL3VisionModelWrapper, + NeuronInternVL3VisionModel, + convert_vision_hf_to_neuron_state_dict, +) + +logger = logging.getLogger("Neuron") + +# InternVL3 special token ID for image context placeholder +IMG_CONTEXT_TOKEN_ID = 151667 + +# Keys from top-level config that must be copied to text_config +INTERNVL3_TEXT_CONFIG_KEYS = [ + "hidden_size", + "num_attention_heads", + "num_hidden_layers", + "num_key_value_heads", + "pad_token_id", + "vocab_size", + "intermediate_size", + "max_position_embeddings", + "rms_norm_eps", + "rope_theta", + "hidden_act", + "bos_token_id", + "eos_token_id", + "tie_word_embeddings", +] + + +# --------------------------------------------------------------------------- +# Config +# --------------------------------------------------------------------------- + + +class InternVL3InferenceConfig(ImageToTextInferenceConfig): + """ + Inference configuration for InternVL3 on Neuron. + + Requires two NeuronConfig objects: + - text_neuron_config: for the Qwen2.5-7B text decoder (CTE + TKG) + - vision_neuron_config: for the InternViT-300M vision encoder + + The HF config.json has text params under "llm_config" and vision params + under "vision_config". This class handles the mapping. + """ + + def __init__( + self, + text_neuron_config, + vision_neuron_config, + fused_spec_config=None, + load_config=None, + metadata: Optional[Dict] = None, + **kwargs, + ): + # Wrap load_config to map InternVL's llm_config -> text_config + # The NxDI base class calls load_config(self) which sets attributes from + # config.to_dict(). InternVL HF config has "llm_config" not "text_config", + # so we intercept to create text_config from llm_config before validation. + if load_config is not None: + original_load_config = load_config + + def _patched_load_config(config_obj): + original_load_config(config_obj) + # Map llm_config -> text_config if needed + if ( + not hasattr(config_obj, "text_config") + or config_obj.text_config is None + ): + llm_cfg = getattr(config_obj, "llm_config", None) + if llm_cfg is not None: + # llm_config can be a dict, PretrainedConfig, or SimpleNamespace + if isinstance(llm_cfg, dict): + text_config_dict = dict(llm_cfg) + elif hasattr(llm_cfg, "to_dict"): + text_config_dict = llm_cfg.to_dict() + elif hasattr(llm_cfg, "__dict__"): + text_config_dict = dict(vars(llm_cfg)) + else: + text_config_dict = { + k: getattr(llm_cfg, k) + for k in dir(llm_cfg) + if not k.startswith("_") + } + # Set HF defaults needed by NxDI + text_config_dict.setdefault("output_attentions", False) + text_config_dict.setdefault("output_hidden_states", False) + config_obj.text_config = text_config_dict + # Also propagate text config keys to top level + for key in INTERNVL3_TEXT_CONFIG_KEYS: + if key in text_config_dict and not hasattr(config_obj, key): + setattr(config_obj, key, text_config_dict[key]) + + load_config = _patched_load_config + + super().__init__( + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + fused_spec_config=fused_spec_config, + load_config=load_config, + metadata=metadata, + **kwargs, + ) + self.add_special_config() + self.validate_model_supported_configs() + + def add_special_config(self): + """Set InternVL3-specific config defaults.""" + self.num_cores_per_group = 1 + + # Qwen2.5 text backbone: QKV bias, no O bias + self.qkv_bias = True + self.o_bias = False + + # Image token ID for vision mask generation + self.image_token_id = IMG_CONTEXT_TOKEN_ID + + # Copy text keys from top-level config to text_config + for key in INTERNVL3_TEXT_CONFIG_KEYS: + if hasattr(self, key): + setattr(self.text_config, key, getattr(self, key)) + + self.pad_token_id = getattr(self.text_config, "pad_token_id", 0) + + def validate_model_supported_configs(self): + """Validate and disable unsupported NeuronConfig options.""" + # Validate text config keys match + for key in INTERNVL3_TEXT_CONFIG_KEYS: + if hasattr(self, key) and hasattr(self.text_config, key): + top_val = getattr(self, key) + text_val = getattr(self.text_config, key) + if top_val != text_val: + logger.warning( + f"Config mismatch: {key} top={top_val} vs text={text_val}, using top" + ) + setattr(self.text_config, key, top_val) + + # Disable unsupported text model features + TEXT_UNSUPPORTED = [ + "is_block_kv_layout", + "is_prefix_caching", + "is_chunked_prefill", + "is_medusa", + "enable_fused_speculation", + ] + for cfg_name in TEXT_UNSUPPORTED: + if getattr(self.text_config.neuron_config, cfg_name, False) is not False: + setattr(self.text_config.neuron_config, cfg_name, False) + logger.warning( + f"InternVL3 text model: '{cfg_name}' unsupported, disabled." + ) + + # Disable unsupported vision model features + VISION_UNSUPPORTED = [ + "sequence_parallel_enabled", + "flash_decoding_enabled", + "qkv_kernel_enabled", + "attn_block_tkg_nki_kernel_cache_update", + "attn_block_tkg_nki_kernel_enabled", + ] + for cfg_name in VISION_UNSUPPORTED: + if getattr(self.vision_config.neuron_config, cfg_name, False) is not False: + setattr(self.vision_config.neuron_config, cfg_name, False) + logger.warning( + f"InternVL3 vision model: '{cfg_name}' unsupported, disabled." + ) + + def get_required_attributes(self) -> List[str]: + return [ + "text_config", + "vision_config", + "text_config.hidden_size", + "text_config.num_attention_heads", + "text_config.num_hidden_layers", + "text_config.num_key_value_heads", + "text_config.pad_token_id", + "text_config.vocab_size", + "text_config.max_position_embeddings", + "text_config.rope_theta", + "text_config.rms_norm_eps", + "text_config.hidden_act", + "vision_config.hidden_size", + "vision_config.num_attention_heads", + "vision_config.num_hidden_layers", + "vision_config.image_size", + "vision_config.patch_size", + ] + + @classmethod + def get_neuron_config_cls(cls) -> Type[NeuronConfig]: + return NeuronConfig + + @classmethod + def from_pretrained( + cls, + model_path: str, + text_neuron_config=None, + vision_neuron_config=None, + **kwargs, + ) -> "InternVL3InferenceConfig": + """ + Load configuration from a pretrained InternVL3 model directory. + + InternVL3 config.json structure: + - Top-level: model_type, downsample_ratio, force_image_size, etc. + - llm_config: Qwen2.5-7B text params (model_type=qwen2) + - vision_config: InternViT params (model_type=intern_vit_6b) + + The ImageToTextInferenceConfig parent expects "text_config" and + "vision_config" keys in kwargs. We map llm_config -> text_config. + """ + config_path = os.path.join(model_path, "config.json") + if not os.path.exists(config_path): + raise FileNotFoundError(f"config.json not found at {config_path}") + + with open(config_path, "r") as f: + config_dict = json.load(f) + + # Extract text config (InternVL uses "llm_config", not "text_config") + llm_config = config_dict.get("llm_config", {}) + vision_config = config_dict.get("vision_config", {}) + + # Build the config dict that ImageToTextInferenceConfig expects + # Must have "text_config" and "vision_config" at top level + inference_kwargs = {} + + # Copy top-level InternVL params + for key in [ + "downsample_ratio", + "force_image_size", + "select_layer", + "model_type", + "architectures", + "tie_word_embeddings", + ]: + if key in config_dict: + inference_kwargs[key] = config_dict[key] + + # Copy text params from llm_config -> text_config + text_config_dict = {} + for key in [ + "hidden_size", + "num_attention_heads", + "num_hidden_layers", + "num_key_value_heads", + "vocab_size", + "max_position_embeddings", + "rope_theta", + "rms_norm_eps", + "hidden_act", + "intermediate_size", + "pad_token_id", + "bos_token_id", + "eos_token_id", + "tie_word_embeddings", + ]: + if key in llm_config: + text_config_dict[key] = llm_config[key] + + # HF PretrainedConfig defaults required by NxDI model_base._setup_func_config() + # These are normally set by HF's from_pretrained() but we build config manually + text_config_dict.setdefault("output_attentions", False) + text_config_dict.setdefault("output_hidden_states", False) + # Note: do NOT set use_return_dict here — it's a read-only computed attribute + # in PretrainedConfig and will raise AttributeError when HuggingFaceGenerationAdapter + # converts our config via to_pretrained_config() → PretrainedConfig(**text_config_dict) + + # Also set at top level (required by ImageToTextInferenceConfig) + for key, value in text_config_dict.items(): + inference_kwargs[key] = value + + inference_kwargs["text_config"] = text_config_dict + + # Copy vision config as-is + inference_kwargs["vision_config"] = vision_config + + # Set image_token_id + inference_kwargs["image_token_id"] = IMG_CONTEXT_TOKEN_ID + + # Set _name_or_path + inference_kwargs["_name_or_path"] = model_path + + # Merge user kwargs + inference_kwargs.update(kwargs) + + return cls( + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + **inference_kwargs, + ) + + +# --------------------------------------------------------------------------- +# Top-level VLM class +# --------------------------------------------------------------------------- + + +class NeuronInternVL3ForCausalLM(NeuronBaseForImageToText): + """ + InternVL3 vision-language model for Neuron inference. + + Orchestrates: + - Vision encoder NEFF (InternViT-300M + pixel shuffle + projector) + - Text decoder CTE NEFF (Qwen2.5-7B context encoding with vision embedding injection) + - Text decoder TKG NEFF (Qwen2.5-7B token generation) + + Usage: + text_nc = NeuronConfig(tp_degree=4, max_batch_size=1, seq_len=4096, ...) + vision_nc = NeuronConfig(tp_degree=1, max_batch_size=1, buckets=[1], ...) + config = InternVL3InferenceConfig.from_pretrained( + model_path, text_neuron_config=text_nc, vision_neuron_config=vision_nc + ) + model = NeuronInternVL3ForCausalLM(config) + model.compile(compiled_path) + model.load(compiled_path) + output = model(input_ids, attention_mask, position_ids, seq_ids, + sampling_params, pixel_values=pixel_values) + """ + + text_model_cls = NeuronInternVL3TextModel + vision_model_cls = NeuronInternVL3VisionModel + text_model_wrapper = InternVL3TextModelWrapper + vision_model_wrapper = InternVL3VisionModelWrapper + + def __init__(self, *args, **kwargs): + super().__init__( + self.text_model_cls, + self.vision_model_cls, + self.text_model_wrapper, + self.vision_model_wrapper, + *args, + **kwargs, + ) + + def get_vision_compiler_args(self) -> str: + """Compiler args for vision encoder NEFF.""" + return "--auto-cast=matmult --model-type=transformer -O1" + + def get_compiler_args(self) -> str: + """Compiler args for text model NEFFs (CTE + TKG).""" + return "--auto-cast=matmult --model-type=transformer -O1" + + def get_required_kwargs(self) -> List[str]: + """Additional input args for HuggingFaceGenerationAdapter.""" + return ["pixel_values", "vision_mask"] + + def enable_vision_encoder( + self, enable_wlt_optimization: bool = True, **model_init_kwargs + ): + """Create the vision encoder model wrapper.""" + new_config = copy.deepcopy(self.config) + self.vision_encoder_model = self.vision_model_wrapper( + config=new_config, + model_cls=self.vision_model_cls, + tag=VISION_ENCODER_MODEL_TAG, + compiler_args=self.get_vision_compiler_args(), + model_init_kwargs=model_init_kwargs, + priority_model_idx=(0 if enable_wlt_optimization else None), + pipeline_execution=True, + return_ranked_to_cpu=False, + ) + self.vision_models.append(self.vision_encoder_model) + + def get_padding_length(self, input_ids): + """Get the CTE bucket size for the given input length.""" + buckets = self.context_encoding_model.config.neuron_config.buckets + for val in buckets: + if val >= input_ids.shape[1]: + return val + raise RuntimeError( + f"No bucket found for input_ids length {input_ids.shape[1]}. " + f"Available buckets: {buckets}" + ) + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + seq_ids: Optional[torch.LongTensor] = None, + sampling_params: Optional[torch.FloatTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + vision_mask: Optional[torch.FloatTensor] = None, + adapter_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + medusa_args=None, + input_capture_hook: Optional[Callable] = None, + tensor_capture_hook: Optional[Callable] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + """ + Forward pass orchestrating vision encoder and text decoder. + + For context encoding with images: + 1. Identify token positions in input_ids + 2. Run vision encoder NEFF -> padded vision embeddings + 3. Pass vision_embeddings + vision_mask to text decoder CTE + 4. Inside CTE NEFF: embed_tokens -> scatter vision -> decoder layers -> logits + + For token generation or text-only: + - Pass dummy (zero) vision tensors to text decoder TKG + """ + # Work around NxDI issue: NeuronBaseForImageToText.forward() doesn't + # capture preprocess_inputs() return values, so sampling_params=None + # flows through to the compiled NEFF which expects [batch, 3]. + # Provide default sampling_params here if not supplied by caller. + if sampling_params is None: + sampling_params = self.default_sampling_params + + pad_limit = self.get_padding_length(input_ids) + + if ( + pixel_values is not None + and input_ids.shape[-1] > 1 + and pixel_values.sum() != 0 + ): + # Context encoding with images + # Build vision_mask: find positions in input_ids + vision_mask = (input_ids == self.config.image_token_id).unsqueeze(-1) + vision_mask = vision_mask.to(torch.bool) + vision_mask = generate_positions_from_mask(vision_mask.squeeze()) + vision_mask = pad_positions(vision_mask, pad_limit, (pad_limit - 1)) + + # Run vision encoder NEFF + # The NEFF is compiled for batch=1 (single image). For multi-image + # requests, loop over images and concatenate vision embeddings. + num_images = pixel_values.shape[0] + pv_dtype = self.vision_config.neuron_config.torch_dtype + if num_images == 1: + vision_embeddings = self.vision_encoder_model(pixel_values.to(pv_dtype)) + else: + # Each NEFF call returns [1, text_seq_len, hidden] with the first + # tokens_per_image positions containing real embeddings (rest are + # zero-padded by pad_to_text_seq_len inside the NEFF). + # Extract real tokens from each call, concatenate, re-pad. + tokens_per_image = 256 # (448/14)^2 * downsample_ratio^2 + emb_list = [] + for i in range(num_images): + emb_i = self.vision_encoder_model( + pixel_values[i : i + 1].to(pv_dtype) + ) + # Extract real tokens only: [1, tokens_per_image, hidden] + emb_list.append(emb_i[:, :tokens_per_image, :]) + # Concatenate: [1, num_images * tokens_per_image, hidden] + vision_embeddings = torch.cat(emb_list, dim=1) + # Pad to CTE bucket size (pad_limit) + total_vis_tokens = vision_embeddings.shape[1] + if total_vis_tokens < pad_limit: + pad_zeros = torch.zeros( + 1, + pad_limit - total_vis_tokens, + vision_embeddings.shape[2], + dtype=vision_embeddings.dtype, + device=vision_embeddings.device, + ) + vision_embeddings = torch.cat([vision_embeddings, pad_zeros], dim=1) + elif total_vis_tokens > pad_limit: + vision_embeddings = vision_embeddings[:, :pad_limit, :] + else: + # Token generation or text-only: use dummy zeros + vision_embeddings, vision_mask = ( + self.text_model_wrapper.get_dummy_vision_inputs( + config=self.text_config, + input_ids=input_ids, + n_active_tokens=pad_limit, + fill_value=(pad_limit - 1), + ) + ) + + output_token = super().forward( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + seq_ids=seq_ids, + sampling_params=sampling_params, + input_capture_hook=input_capture_hook, + tensor_capture_hook=tensor_capture_hook, + vision_embeddings=vision_embeddings, + vision_mask=vision_mask, + ) + return output_token + + @classmethod + def get_config_cls(cls): + return InternVL3InferenceConfig + + @staticmethod + def load_hf_model(model_path, **kwargs): + """Load original HF InternVL3 model for CPU reference inference. + + Uses FP32 to avoid -inf logits that occur with BF16, + which cause false failures in logit_validation. + """ + from transformers import AutoModel + + model = AutoModel.from_pretrained( + model_path, trust_remote_code=True, torch_dtype=torch.float32 + ).eval() + # HF generate() requires img_context_token_id to be set + model.img_context_token_id = IMG_CONTEXT_TOKEN_ID + return model + + @staticmethod + def convert_hf_to_neuron_state_dict( + state_dict: dict, inference_config: InternVL3InferenceConfig + ) -> dict: + """ + Convert full InternVL3 HF state dict to Neuron format. + + Delegates to: + - convert_vision_hf_to_neuron_state_dict() for vision + projector weights + - NeuronInternVL3TextForCausalLM.convert_hf_to_neuron_state_dict() for text weights + """ + # Vision weights (encoder + projector) + vision_state_dict = convert_vision_hf_to_neuron_state_dict(state_dict) + + # Text weights + text_state_dict = ( + NeuronInternVL3TextForCausalLM.convert_hf_to_neuron_state_dict( + state_dict, inference_config.text_config + ) + ) + + # Merge (vision and text keys should not overlap) + merged = {} + merged.update(vision_state_dict) + merged.update(text_state_dict) + return merged + + @staticmethod + def update_state_dict_for_tied_weights(state_dict): + """Handle tied embeddings.""" + NeuronInternVL3TextForCausalLM.update_state_dict_for_tied_weights(state_dict) diff --git a/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3_text.py b/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3_text.py new file mode 100644 index 00000000..be8f2094 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3_text.py @@ -0,0 +1,445 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +InternVL3-8B-Instruct: Text backbone (Qwen2.5-7B) for NxDI. + +This module contains the text decoder model, attention, decoder layers, +text model wrapper (ImageToTextModelWrapper), and weight conversion. + +Text backbone: Qwen2.5-7B +- 28 layers, hidden_size=3584, 28 Q heads, 4 KV heads (GQA 7:1) +- Standard RoPE (rope_theta=1e6), RMSNorm, SiLU gated MLP +- QKV bias=True, O bias=False +- vocab_size=151674, tie_word_embeddings=False + +Weight key mapping (HF -> NxDI): + language_model.model.layers.{i}.* -> layers.{i}.* + language_model.model.embed_tokens.weight -> embed_tokens.weight + language_model.model.norm.weight -> norm.weight + language_model.lm_head.weight -> lm_head.weight +""" + +import torch +from torch import nn + +from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm + +from neuronx_distributed.parallel_layers.layers import ( + ColumnParallelLinear, + ParallelEmbedding, +) +from neuronx_distributed.utils import cpu_mode + +from neuronx_distributed_inference.models.config import InferenceConfig, NeuronConfig +from neuronx_distributed_inference.models.image_to_text_model_wrapper import ( + ImageToTextModelWrapper, +) +from neuronx_distributed_inference.models.llama.modeling_llama import NeuronLlamaMLP +from neuronx_distributed_inference.models.llama4.utils.encoder_utils import ( + scatter_by_index_put, +) +from neuronx_distributed_inference.models.model_base import ( + NeuronBaseForCausalLM, + NeuronBaseModel, +) +from neuronx_distributed_inference.modules.attention.attention_base import ( + NeuronAttentionBase, +) +from neuronx_distributed_inference.modules.attention.utils import RotaryEmbedding +from neuronx_distributed_inference.modules.custom_calls import CustomRMSNorm + + +def get_rmsnorm_cls(): + """Get RMSNorm implementation: HF for CPU, CustomRMSNorm for Neuron.""" + return Qwen2RMSNorm if cpu_mode() else CustomRMSNorm + + +# --------------------------------------------------------------------------- +# Attention +# --------------------------------------------------------------------------- + + +class NeuronInternVL3Attention(NeuronAttentionBase): + """ + InternVL3 text attention: GQA with standard RoPE and QKV bias. + + - 28 Q heads, 4 KV heads (7:1 GQA ratio) + - head_dim = 128 + - Q/K/V have bias, O does not + - Standard RoPE (not M-RoPE) + - No Q-K normalization (unlike Qwen3) + """ + + def __init__(self, config): + head_dim = getattr( + config, + "head_dim", + config.hidden_size // config.num_attention_heads, + ) + rotary_emb = RotaryEmbedding( + dim=head_dim, + max_position_embeddings=config.max_position_embeddings, + base=config.rope_theta, + ) + super().__init__( + config=config, + hidden_size=config.hidden_size, + num_attention_heads=config.num_attention_heads, + num_key_value_heads=config.num_key_value_heads, + head_dim=head_dim, + rotary_emb=rotary_emb, + qkv_bias=True, + o_bias=False, + rms_norm_eps=config.rms_norm_eps, + ) + + +# --------------------------------------------------------------------------- +# Decoder layer +# --------------------------------------------------------------------------- + + +class NeuronInternVL3DecoderLayer(nn.Module): + """ + InternVL3 text decoder layer: pre-norm RMSNorm + GQA attention + SwiGLU MLP. + + Supports NKI kernel fused RMSNorm when qkv_kernel_enabled or mlp_kernel_enabled. + """ + + def __init__(self, config): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = NeuronInternVL3Attention(config) + self.mlp = NeuronLlamaMLP(config) + self.input_layernorm = get_rmsnorm_cls()( + config.hidden_size, eps=config.rms_norm_eps + ) + self.post_attention_layernorm = get_rmsnorm_cls()( + config.hidden_size, eps=config.rms_norm_eps + ) + + # NKI kernel flags — fuse RMSNorm into kernels when enabled + neuron_config = config.neuron_config + self.qkv_kernel_enabled = neuron_config.qkv_kernel_enabled + self.mlp_kernel_enabled = neuron_config.mlp_kernel_enabled + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask=None, + position_ids=None, + past_key_value=None, + **kwargs, + ): + residual = hidden_states + + # When QKV kernel is enabled, pass the RMSNorm module to be fused + # into the NKI kernel instead of applying it separately + if self.qkv_kernel_enabled: + qkv_fused_rmsnorm = self.input_layernorm + else: + hidden_states = self.input_layernorm(hidden_states) + qkv_fused_rmsnorm = None + + hidden_states, present_key_value, cos_cache, sin_cache = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + rmsnorm=qkv_fused_rmsnorm, + **kwargs, + ) + + hidden_states = residual + hidden_states + + residual = hidden_states + + # When MLP kernel is enabled, pass the RMSNorm module to be fused + if self.mlp_kernel_enabled: + hidden_states, _ = self.mlp( + hidden_states, + rmsnorm=self.post_attention_layernorm, + ) + else: + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states)[0] + + hidden_states = residual + hidden_states + + return (hidden_states, present_key_value, cos_cache, sin_cache, None) + + +# --------------------------------------------------------------------------- +# Text model wrapper (ImageToTextModelWrapper) +# --------------------------------------------------------------------------- + + +class InternVL3TextModelWrapper(ImageToTextModelWrapper): + """ + Text model wrapper for InternVL3 that includes vision embedding inputs + in the compiled NEFF trace signature. + + Inherits ImageToTextModelWrapper which generates 24-argument input tuples + with vision_embeddings (arg 22) and vision_mask (arg 23). + """ + + def __init__( + self, + config, + model_cls, + tag="", + compiler_args=None, + priority_model_idx=None, + pipeline_execution=True, + return_ranked_to_cpu=True, + model_init_kwargs={}, + ) -> None: + super().__init__( + config, + model_cls, + tag, + compiler_args, + priority_model_idx, + pipeline_execution, + return_ranked_to_cpu, + model_init_kwargs, + ) + + @staticmethod + def get_dummy_vision_inputs(config, input_ids, n_active_tokens, fill_value): + """ + Create dummy vision tensors for tracing and text-only / token-gen passes. + + For context encoding (seq_len > 1): + - vision_embeddings: [batch, seq_len, hidden_size] zeros + - vision_mask: [batch, n_active_tokens, 1] filled with fill_value (int32 positions) + For token generation (seq_len == 1): + - Both are empty tensors + """ + input_batch_size, input_sequence_len = input_ids.shape[0], input_ids.shape[-1] + if input_sequence_len > 1: + vision_embeddings = torch.zeros( + input_batch_size, + config.neuron_config.seq_len, + config.hidden_size, + dtype=config.neuron_config.torch_dtype, + ) + vision_mask = torch.full( + size=(input_batch_size, n_active_tokens, 1), + fill_value=fill_value, + dtype=torch.int32, + ) + else: + vision_embeddings = torch.zeros((0), dtype=config.neuron_config.torch_dtype) + vision_mask = torch.zeros((0), dtype=torch.bool) + return vision_embeddings, vision_mask + + +# --------------------------------------------------------------------------- +# Text model (traced on Neuron) +# --------------------------------------------------------------------------- + + +class NeuronInternVL3TextModel(NeuronBaseModel): + """ + InternVL3 text model (Qwen2.5-7B backbone) for NxDI. + + Components: + - ParallelEmbedding (vocab=151674, hidden=3584) + - 28x NeuronInternVL3DecoderLayer + - RMSNorm + - ColumnParallelLinear lm_head + + Implements encode_vision_to_input() for merging vision embeddings + into text embeddings during context encoding. + """ + + def setup_attr_for_model(self, config): + self.on_device_sampling = ( + config.neuron_config.on_device_sampling_config is not None + ) + self.tp_degree = config.neuron_config.tp_degree + self.hidden_size = config.hidden_size + self.num_attention_heads = config.num_attention_heads + self.num_key_value_heads = config.num_key_value_heads + self.max_batch_size = config.neuron_config.max_batch_size + self.buckets = config.neuron_config.buckets + + def init_model(self, config): + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = ParallelEmbedding( + config.vocab_size, + config.hidden_size, + self.padding_idx, + dtype=config.neuron_config.torch_dtype, + shard_across_embedding=True, + pad=True, + sequence_parallel_enabled=config.neuron_config.sequence_parallel_enabled, + ) + + self.layers = nn.ModuleList( + [ + NeuronInternVL3DecoderLayer(config) + for _ in range(config.num_hidden_layers) + ] + ) + + self.norm = get_rmsnorm_cls()(config.hidden_size, eps=config.rms_norm_eps) + + self.lm_head = ColumnParallelLinear( + config.hidden_size, + config.vocab_size, + bias=False, + pad=True, + gather_output=not self.on_device_sampling, + dtype=config.neuron_config.torch_dtype, + ) + + def encode_vision_to_input( + self, + inputs_embeds: torch.Tensor, + vision_embeddings: torch.Tensor, + vision_mask: torch.Tensor, + ) -> torch.Tensor: + """ + Scatter vision embeddings into text embeddings at image token positions. + + Called by NeuronBaseModel.get_model_output() during context encoding only. + Runs ON-DEVICE inside the compiled NEFF. + + Args: + inputs_embeds: [batch, seq_len, hidden_size] -- text token embeddings + vision_embeddings: [batch, seq_len, hidden_size] -- padded vision embeddings + vision_mask: [batch, seq_len, 1] -- int32 position indices + + Returns: + inputs_embeds with vision positions replaced by vision embeddings + """ + return scatter_by_index_put(inputs_embeds, vision_embeddings, vision_mask) + + +# --------------------------------------------------------------------------- +# Weight conversion helper (text-only, used by top-level model) +# --------------------------------------------------------------------------- + + +class NeuronInternVL3TextForCausalLM(NeuronBaseForCausalLM): + """ + Helper class for text weight conversion only. + Not used directly for inference -- the top-level NeuronBaseForImageToText + class handles that via NeuronInternVL3TextModel. + """ + + _model_cls = NeuronInternVL3TextModel + + @staticmethod + def load_hf_model(model_path, **kwargs): + return None + + @staticmethod + def convert_hf_to_neuron_state_dict( + state_dict: dict, config: InferenceConfig + ) -> dict: + """ + Convert InternVL3 text weights from HuggingFace to Neuron format. + + HF layout: + language_model.model.embed_tokens.weight + language_model.model.layers.{i}.self_attn.{q,k,v}_proj.{weight,bias} + language_model.model.layers.{i}.self_attn.o_proj.weight + language_model.model.layers.{i}.mlp.{gate,up,down}_proj.weight + language_model.model.layers.{i}.{input,post_attention}_layernorm.weight + language_model.model.norm.weight + language_model.lm_head.weight + + NxDI layout (fused_qkv=False): + embed_tokens.weight + layers.{i}.self_attn.{q,k,v}_proj.{weight,bias} + layers.{i}.self_attn.o_proj.weight + layers.{i}.mlp.{gate,up,down}_proj.weight + layers.{i}.{input,post_attention}_layernorm.weight + norm.weight + lm_head.weight + + NxDI layout (fused_qkv=True): + layers.{i}.self_attn.qkv_proj.Wqkv.{weight,bias} + (q/k/v fused into single Wqkv tensor) + + When fused_qkv=False, separate q/k/v_proj are kept and the GQA + preshard_hook fuses them during weight sharding. + + When fused_qkv=True (required for NKI QKV kernel), we pre-fuse + q/k/v into Wqkv here because the preshard_hook expects it. + """ + neuron_config = config.neuron_config + neuron_state_dict = {} + + # Add rank tensors for tensor parallelism + if neuron_config.vocab_parallel: + neuron_state_dict["embed_tokens.rank_util.rank"] = torch.arange( + 0, neuron_config.local_ranks_size + ) + + for key, value in state_dict.items(): + # Only process text weights (language_model.*) + if not key.startswith("language_model."): + continue + + # Strip the language_model.model. prefix + new_key = key + if key.startswith("language_model.model."): + new_key = key[len("language_model.model.") :] + elif key.startswith("language_model."): + new_key = key[len("language_model.") :] + + neuron_state_dict[new_key] = value.detach().clone() + + # When fused_qkv=True, fuse separate q/k/v weights into Wqkv + if neuron_config.fused_qkv: + for i in range(config.num_hidden_layers): + prefix = f"layers.{i}.self_attn" + # Fuse weights: [q_size, hidden] + [kv_size, hidden] + [kv_size, hidden] + q_w = neuron_state_dict.pop(f"{prefix}.q_proj.weight") + k_w = neuron_state_dict.pop(f"{prefix}.k_proj.weight") + v_w = neuron_state_dict.pop(f"{prefix}.v_proj.weight") + neuron_state_dict[f"{prefix}.qkv_proj.Wqkv.weight"] = torch.cat( + [q_w, k_w, v_w], dim=0 + ) + # Fuse biases (Q/K/V all have bias in InternVL3) + q_b = neuron_state_dict.pop(f"{prefix}.q_proj.bias", None) + k_b = neuron_state_dict.pop(f"{prefix}.k_proj.bias", None) + v_b = neuron_state_dict.pop(f"{prefix}.v_proj.bias", None) + if q_b is not None and k_b is not None and v_b is not None: + neuron_state_dict[f"{prefix}.qkv_proj.Wqkv.bias"] = torch.cat( + [q_b, k_b, v_b], dim=0 + ) + + # Add per-layer rank tensors for attention TP sharding + tp_degree = neuron_config.tp_degree + for i in range(config.num_hidden_layers): + neuron_state_dict[f"layers.{i}.self_attn.rank_util.rank"] = torch.arange( + 0, tp_degree, dtype=torch.int32 + ) + + # Add base model rank tensor + neuron_state_dict["rank_util.rank"] = torch.arange( + 0, tp_degree, dtype=torch.int32 + ) + + return neuron_state_dict + + @staticmethod + def update_state_dict_for_tied_weights(state_dict): + """Handle tied embeddings (InternVL3: tie_word_embeddings=False, but support both).""" + if "lm_head.weight" not in state_dict and "embed_tokens.weight" in state_dict: + state_dict["lm_head.weight"] = state_dict["embed_tokens.weight"].clone() + + @classmethod + def get_config_cls(cls): + # This helper class doesn't need its own config -- used through top-level + from neuronx_distributed_inference.models.config import InferenceConfig + + return InferenceConfig diff --git a/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3_vision.py b/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3_vision.py new file mode 100644 index 00000000..0d5ee256 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/src/modeling_internvl3_vision.py @@ -0,0 +1,447 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +InternVL3-8B-Instruct: Vision encoder (InternViT-300M) for NxDI. + +Architecture: + - InternViT-300M-448px-V2.5 + - 24 layers, hidden_size=1024, 16 heads, head_dim=64 + - Patch size 14x14, image 448x448 -> 1024 patches + CLS = 1025 tokens + - LayerNorm, GELU, LayerScale (ls1, ls2) + - Fused QKV: attn.qkv [3072, 1024] + - Position: absolute learned embeddings [1, 1025, 1024] + +Full vision pipeline: + 1. Patch embedding + CLS + position -> 24 transformer layers -> [B, 1025, 1024] + 2. Strip CLS token: [B, 1024, 1024] + 3. Pixel shuffle (0.5x): [B, 256, 4096] + 4. Projector MLP: LayerNorm(4096) -> Linear(4096,3584) -> GELU -> Linear(3584,3584) + 5. Pad to text seq_len: [1, seq_len, 3584] + +Weight key mapping (HF -> NxDI vision): + vision_model.embeddings.class_embedding -> encoder.class_embedding + vision_model.embeddings.patch_embedding.{weight,bias} -> encoder.patch_embedding.{weight,bias} + vision_model.embeddings.position_embedding -> encoder.position_embedding + vision_model.encoder.layers.{i}.* -> encoder.layers.{i}.* + mlp1.0.{weight,bias} -> proj_norm.{weight,bias} + mlp1.1.{weight,bias} -> proj_linear1.{weight,bias} + mlp1.3.{weight,bias} -> proj_linear2.{weight,bias} +""" + +import math +from typing import List, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from neuronx_distributed_inference.models.config import InferenceConfig +from neuronx_distributed_inference.models.model_wrapper import ( + EncoderModelInstance, + ModelWrapper, +) + +# InternVL3 vision constants +VISION_HIDDEN_SIZE = 1024 +VISION_NUM_HEADS = 16 +VISION_NUM_LAYERS = 24 +VISION_INTERMEDIATE_SIZE = 4096 +VISION_PATCH_SIZE = 14 +VISION_IMAGE_SIZE = 448 +VISION_NUM_PATCHES = (VISION_IMAGE_SIZE // VISION_PATCH_SIZE) ** 2 # 1024 +VISION_NUM_OUTPUT_TOKENS = 256 # after pixel shuffle (0.5x): 1024 / 4 +TEXT_HIDDEN_SIZE = 3584 +DOWNSAMPLE_RATIO = 0.5 + + +# --------------------------------------------------------------------------- +# Vision encoder components (pure PyTorch, for tracing) +# --------------------------------------------------------------------------- + + +class InternVisionAttention(nn.Module): + """InternViT attention with fused QKV projection.""" + + def __init__(self, hidden_size=VISION_HIDDEN_SIZE, num_heads=VISION_NUM_HEADS): + super().__init__() + self.num_heads = num_heads + self.head_dim = hidden_size // num_heads + self.scale = self.head_dim**-0.5 + + self.qkv = nn.Linear(hidden_size, 3 * hidden_size, bias=True) + self.proj = nn.Linear(hidden_size, hidden_size, bias=True) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + batch, seq_len, _ = hidden_states.shape + + qkv = self.qkv(hidden_states) + qkv = qkv.reshape(batch, seq_len, 3, self.num_heads, self.head_dim) + qkv = qkv.permute(2, 0, 3, 1, 4) # [3, B, heads, seq, head_dim] + q, k, v = qkv.unbind(0) + + # Scaled dot-product attention (no causal mask for vision) + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + out = attn @ v # [B, heads, seq, head_dim] + + out = out.transpose(1, 2).reshape(batch, seq_len, -1) + return self.proj(out) + + +class InternVisionMLP(nn.Module): + """InternViT MLP: fc1 -> GELU -> fc2.""" + + def __init__( + self, hidden_size=VISION_HIDDEN_SIZE, intermediate_size=VISION_INTERMEDIATE_SIZE + ): + super().__init__() + self.fc1 = nn.Linear(hidden_size, intermediate_size, bias=True) + self.fc2 = nn.Linear(intermediate_size, hidden_size, bias=True) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return self.fc2(F.gelu(self.fc1(hidden_states))) + + +class InternVisionLayer(nn.Module): + """InternViT transformer layer with pre-norm and LayerScale.""" + + def __init__( + self, + hidden_size=VISION_HIDDEN_SIZE, + num_heads=VISION_NUM_HEADS, + intermediate_size=VISION_INTERMEDIATE_SIZE, + ): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size) + self.attn = InternVisionAttention(hidden_size, num_heads) + self.ls1 = nn.Parameter(torch.ones(hidden_size) * 0.1) + + self.norm2 = nn.LayerNorm(hidden_size) + self.mlp = InternVisionMLP(hidden_size, intermediate_size) + self.ls2 = nn.Parameter(torch.ones(hidden_size) * 0.1) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = hidden_states + self.ls1 * self.attn(self.norm1(hidden_states)) + hidden_states = hidden_states + self.ls2 * self.mlp(self.norm2(hidden_states)) + return hidden_states + + +class InternVisionEncoder(nn.Module): + """ + InternViT-300M vision encoder. + + Takes pixel_values [B, 3, 448, 448], returns [B, 1025, 1024] (with CLS). + """ + + def __init__( + self, + hidden_size=VISION_HIDDEN_SIZE, + num_heads=VISION_NUM_HEADS, + num_layers=VISION_NUM_LAYERS, + intermediate_size=VISION_INTERMEDIATE_SIZE, + patch_size=VISION_PATCH_SIZE, + image_size=VISION_IMAGE_SIZE, + ): + super().__init__() + self.hidden_size = hidden_size + self.num_patches = (image_size // patch_size) ** 2 + + self.patch_embedding = nn.Conv2d( + 3, hidden_size, kernel_size=patch_size, stride=patch_size, bias=True + ) + self.class_embedding = nn.Parameter(torch.zeros(1, 1, hidden_size)) + self.position_embedding = nn.Parameter( + torch.zeros(1, self.num_patches + 1, hidden_size) + ) + + self.layers = nn.ModuleList( + [ + InternVisionLayer(hidden_size, num_heads, intermediate_size) + for _ in range(num_layers) + ] + ) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + batch = pixel_values.shape[0] + # Cast pixel_values to match Conv2d weight dtype to avoid + # "Input type (BFloat16) and bias type (float) should be the same" error + # when HF weights are loaded in float32 but input arrives in bf16. + # The compiler's --auto-cast=matmult handles the actual mixed-precision math. + pixel_values = pixel_values.to(dtype=self.patch_embedding.weight.dtype) + patches = self.patch_embedding(pixel_values) + patches = patches.flatten(2).transpose(1, 2) + cls_tokens = self.class_embedding.expand(batch, -1, -1) + hidden_states = torch.cat([cls_tokens, patches], dim=1) + hidden_states = hidden_states + self.position_embedding + for layer in self.layers: + hidden_states = layer(hidden_states) + return hidden_states + + +# --------------------------------------------------------------------------- +# Full vision pipeline (encoder + pixel shuffle + projector + padding) +# --------------------------------------------------------------------------- + + +class NeuronInternVL3VisionModel(nn.Module): + """ + Full InternVL3 vision pipeline for NxDI tracing. + + Input: pixel_values [B, 3, 448, 448] + Output: [1, text_seq_len, text_hidden_size] (padded for scatter_by_index_put) + + Pipeline: + 1. InternViT encoder -> [B, 1025, 1024] + 2. Strip CLS token -> [B, 1024, 1024] + 3. Pixel shuffle (0.5x) -> [B, 256, 4096] + 4. Projector MLP: LayerNorm -> Linear -> GELU -> Linear -> [B, 256, 3584] + 5. Pad to text seq_len -> [1, seq_len, 3584] + """ + + def __init__(self, config: InferenceConfig): + super().__init__() + self.config = config + + # Extract text seq_len for output padding + self.text_seq_len = config.text_config.neuron_config.seq_len + self.text_hidden_size = config.text_config.hidden_size + self.text_dtype = config.text_config.neuron_config.torch_dtype + + # Vision encoder + self.encoder = InternVisionEncoder( + hidden_size=VISION_HIDDEN_SIZE, + num_heads=VISION_NUM_HEADS, + num_layers=VISION_NUM_LAYERS, + intermediate_size=VISION_INTERMEDIATE_SIZE, + patch_size=VISION_PATCH_SIZE, + image_size=VISION_IMAGE_SIZE, + ) + + # Projector MLP (pixel_shuffle output -> text hidden size) + proj_input_dim = int(VISION_HIDDEN_SIZE * (1.0 / DOWNSAMPLE_RATIO) ** 2) # 4096 + self.proj_norm = nn.LayerNorm(proj_input_dim) # mlp1.0 + self.proj_linear1 = nn.Linear( + proj_input_dim, TEXT_HIDDEN_SIZE, bias=True + ) # mlp1.1 + # mlp1.2 is GELU (no weights) + self.proj_linear2 = nn.Linear( + TEXT_HIDDEN_SIZE, TEXT_HIDDEN_SIZE, bias=True + ) # mlp1.3 + + def pixel_shuffle(self, x: torch.Tensor) -> torch.Tensor: + """ + Pixel shuffle downsampling (downsample_ratio=0.5). + + Input: [B, H*W, C] where H=W=32, C=1024 + Output: [B, (H/2)*(W/2), C*4] = [B, 256, 4096] + """ + batch, n_patches, channels = x.shape + h = w = int(math.sqrt(n_patches)) + + x = x.reshape(batch, h, w, channels) + + ratio = DOWNSAMPLE_RATIO + x = x.reshape(batch, h, int(w * ratio), int(channels / ratio)) + x = x.permute(0, 2, 1, 3).contiguous() + x = x.reshape( + batch, int(h * ratio), int(w * ratio), int(channels / (ratio * ratio)) + ) + x = x.permute(0, 2, 1, 3).contiguous() + + return x.reshape(batch, -1, x.shape[-1]) + + def pad_to_text_seq_len(self, hidden_states: torch.Tensor) -> torch.Tensor: + """ + Pad vision embeddings to text model's sequence length. + + Input: [B, 256, 3584] + Output: [1, text_seq_len, 3584] (zero-padded, batch=1) + """ + hidden_states = hidden_states.to(self.text_dtype) + batch, n_tokens, hidden_size = hidden_states.shape + + # Pad sequence dimension to text seq_len + if n_tokens < self.text_seq_len: + pad = torch.zeros( + batch, + self.text_seq_len - n_tokens, + hidden_size, + dtype=hidden_states.dtype, + device=hidden_states.device, + ) + hidden_states = torch.cat([hidden_states, pad], dim=1) + + # Reshape to [1, seq_len, hidden] (batch=1 for scatter) + hidden_states = hidden_states.view(-1, hidden_size).unsqueeze(0) + return hidden_states + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + """ + Args: + pixel_values: [batch, 3, 448, 448] + + Returns: + vision_embeddings: [1, text_seq_len, text_hidden_size] padded + """ + # Encoder: [B, 1025, 1024] + hidden_states = self.encoder(pixel_values) + + # Strip CLS: [B, 1024, 1024] + hidden_states = hidden_states[:, 1:, :] + + # Pixel shuffle: [B, 256, 4096] + hidden_states = self.pixel_shuffle(hidden_states) + + # Projector MLP: [B, 256, 3584] + hidden_states = self.proj_norm(hidden_states) + hidden_states = F.gelu(self.proj_linear1(hidden_states)) + hidden_states = self.proj_linear2(hidden_states) + + # Pad to text seq_len: [1, seq_len, 3584] + hidden_states = self.pad_to_text_seq_len(hidden_states) + + return hidden_states + + +# --------------------------------------------------------------------------- +# Vision model wrapper (for NxDI tracing) +# --------------------------------------------------------------------------- + + +class InternVL3VisionModelWrapper(ModelWrapper): + """ + Wrapper for tracing the InternVL3 vision encoder on Neuron. + + Uses EncoderModelInstance (no KV cache). + Vision buckets represent number of images (always 1 for InternVL3). + """ + + def __init__( + self, + config: InferenceConfig, + model_cls, + tag="", + compiler_args=None, + priority_model_idx=None, + pipeline_execution=True, + return_ranked_to_cpu=False, + model_init_kwargs={}, + ) -> None: + super().__init__( + config, + model_cls, + tag, + compiler_args, + priority_model_idx, + pipeline_execution, + return_ranked_to_cpu, + model_init_kwargs, + ) + + def input_generator(self) -> List[Tuple[torch.Tensor]]: + """ + Generate example inputs for vision encoder tracing. + + InternVL3 processes one 448x448 image at a time (no dynamic patching + at this stage). Single bucket with batch=1. + """ + inputs = [] + # Vision buckets = [1] (single image) + for bucket in self.config.vision_config.neuron_config.buckets: + pixel_values = torch.ones( + [bucket, 3, VISION_IMAGE_SIZE, VISION_IMAGE_SIZE], + dtype=self.config.vision_config.neuron_config.torch_dtype, + ) + inputs.append((pixel_values,)) + return inputs + + def get_model_instance(self): + return EncoderModelInstance(model_cls=self.model_cls, config=self.config) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + """ + Run vision encoder on Neuron. + + Args: + pixel_values: [batch, 3, 448, 448] + + Returns: + vision_embeddings: [1, text_seq_len, text_hidden_size] + """ + if self.model is None: + raise RuntimeError( + "Forward called before load. Run load() or load_state_dict() first." + ) + output = self._forward(pixel_values) + return output + + +# --------------------------------------------------------------------------- +# Vision weight conversion +# --------------------------------------------------------------------------- + + +def convert_vision_hf_to_neuron_state_dict(state_dict: dict) -> dict: + """ + Convert InternVL3 vision + projector weights from HF to NxDI format. + + HF keys: + vision_model.embeddings.class_embedding + vision_model.embeddings.patch_embedding.{weight,bias} + vision_model.embeddings.position_embedding + vision_model.encoder.layers.{i}.attn.qkv.{weight,bias} + vision_model.encoder.layers.{i}.attn.proj.{weight,bias} + vision_model.encoder.layers.{i}.ls1 + vision_model.encoder.layers.{i}.ls2 + vision_model.encoder.layers.{i}.norm1.{weight,bias} + vision_model.encoder.layers.{i}.norm2.{weight,bias} + vision_model.encoder.layers.{i}.mlp.fc1.{weight,bias} + vision_model.encoder.layers.{i}.mlp.fc2.{weight,bias} + mlp1.0.{weight,bias} -> proj_norm + mlp1.1.{weight,bias} -> proj_linear1 + mlp1.3.{weight,bias} -> proj_linear2 + + NxDI keys: + encoder.class_embedding + encoder.patch_embedding.{weight,bias} + encoder.position_embedding + encoder.layers.{i}.* + proj_norm.{weight,bias} + proj_linear1.{weight,bias} + proj_linear2.{weight,bias} + """ + neuron_state_dict = {} + + # Projector key mapping + PROJECTOR_MAP = { + "mlp1.0.weight": "proj_norm.weight", + "mlp1.0.bias": "proj_norm.bias", + "mlp1.1.weight": "proj_linear1.weight", + "mlp1.1.bias": "proj_linear1.bias", + "mlp1.3.weight": "proj_linear2.weight", + "mlp1.3.bias": "proj_linear2.bias", + } + + for key, tensor in state_dict.items(): + # Projector weights + if key in PROJECTOR_MAP: + neuron_state_dict[PROJECTOR_MAP[key]] = tensor.detach().clone() + continue + + # Vision encoder embeddings + if key.startswith("vision_model.embeddings."): + suffix = key[len("vision_model.embeddings.") :] + neuron_state_dict[f"encoder.{suffix}"] = tensor.detach().clone() + continue + + # Vision encoder layers + if key.startswith("vision_model.encoder.layers."): + suffix = key[len("vision_model.encoder.") :] + neuron_state_dict[f"encoder.{suffix}"] = tensor.detach().clone() + continue + + # Skip other vision_model keys and all non-vision keys + # (text weights handled by text conversion) + + return neuron_state_dict diff --git a/contrib/models/InternVL3-8B-Instruct/test/__init__.py b/contrib/models/InternVL3-8B-Instruct/test/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/contrib/models/InternVL3-8B-Instruct/test/integration/__init__.py b/contrib/models/InternVL3-8B-Instruct/test/integration/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/contrib/models/InternVL3-8B-Instruct/test/integration/test_model.py b/contrib/models/InternVL3-8B-Instruct/test/integration/test_model.py new file mode 100644 index 00000000..eafb79ad --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/test/integration/test_model.py @@ -0,0 +1,242 @@ +#!/usr/bin/env python3 +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Integration tests for InternVL3-8B-Instruct NxDI contrib model. + +Validates Neuron model accuracy against CPU reference using logit_validation. + +Usage: + pytest test_model.py -v --tb=short + +Prerequisites: + - Model downloaded to MODEL_PATH + - Compiled model at COMPILED_MODEL_PATH (run compile_internvl3_vlm.py first) + - Neuron runtime available (trn2.3xlarge, LNC=2, TP=4) +""" + +import json +import math +import os +import sys +from pathlib import Path + +import pytest +import torch +from torch_neuronx.testing.validation import logit_validation +from transformers import AutoTokenizer, GenerationConfig + +from neuronx_distributed_inference.models.config import NeuronConfig +from neuronx_distributed_inference.utils.accuracy import generate_expected_logits +from neuronx_distributed_inference.utils.hf_adapter import HuggingFaceGenerationAdapter + +# Import from src directory +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) +from modeling_internvl3 import ( + NeuronInternVL3ForCausalLM, + InternVL3InferenceConfig, +) + + +# Test configuration — override via environment variables if needed +MODEL_PATH = os.environ.get( + "INTERNVL3_MODEL_PATH", "/mnt/models/InternVL3-8B-Instruct/" +) +COMPILED_MODEL_PATH = os.environ.get( + "INTERNVL3_COMPILED_PATH", "/mnt/models/neuron_models/InternVL3-8B-Instruct/" +) +NUM_TOKENS_TO_CHECK = 16 +TOKEN_DIVERGENCE_ATOL = 0.02 + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def load_compiled_model(model_path: str, compiled_path: str): + """Load pre-compiled InternVL3 model for inference.""" + config_path = Path(compiled_path) / "neuron_config.json" + if not config_path.exists(): + raise FileNotFoundError(f"neuron_config.json not found at {config_path}") + + with open(config_path) as f: + config_data = json.load(f) + nc_dict = config_data.get("neuron_config", config_data) + + dtype_str = nc_dict.get("torch_dtype", "torch.bfloat16") + if isinstance(dtype_str, str): + dtype = ( + getattr(torch, dtype_str.split(".")[-1]) + if "torch" in dtype_str + else torch.bfloat16 + ) + else: + dtype = dtype_str + + text_neuron_config = NeuronConfig( + tp_degree=nc_dict.get("tp_degree", 4), + max_batch_size=nc_dict.get("batch_size", nc_dict.get("max_batch_size", 1)), + seq_len=nc_dict.get("seq_len", 2048), + torch_dtype=dtype, + on_device_sampling_config=None, + save_sharded_checkpoint=True, + ) + + vision_neuron_config = NeuronConfig( + tp_degree=nc_dict.get("tp_degree", 4), + max_batch_size=1, + seq_len=256, + torch_dtype=dtype, + on_device_sampling_config=None, + buckets=[1], + fused_qkv=True, + save_sharded_checkpoint=True, + ) + + config = InternVL3InferenceConfig.from_pretrained( + model_path, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + ) + + model = NeuronInternVL3ForCausalLM(model_path, config=config) + model.load(compiled_path) + return model + + +def get_tp_aligned_vocab_size(vocab_size: int, tp_degree: int) -> int: + """Get the largest vocab index that's valid (not TP padding). + + When vocab_size is not divisible by tp_degree, lm_head pads up to the + next multiple of tp_degree. The padded positions contain -FLT_MAX in + the Neuron output. Truncate to this boundary to avoid false failures. + """ + return (vocab_size // tp_degree) * tp_degree + + +# --------------------------------------------------------------------------- +# Integration tests (requires pre-compiled Neuron model) +# --------------------------------------------------------------------------- + + +class TestInternVL3Integration: + """Integration tests for InternVL3-8B-Instruct on Neuron.""" + + @pytest.fixture(scope="class") + def neuron_model(self): + """Load pre-compiled Neuron model (shared across tests in this class).""" + compiled_path = Path(COMPILED_MODEL_PATH) + if not compiled_path.exists(): + pytest.skip(f"Compiled model not found at {compiled_path}") + return load_compiled_model(MODEL_PATH, COMPILED_MODEL_PATH) + + @pytest.fixture(scope="class") + def tokenizer(self): + """Load tokenizer.""" + return AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) + + def test_config(self, neuron_model): + """Validate InternVL3 config matches expected Qwen2.5-7B architecture.""" + config = neuron_model.config + assert config.hidden_size == 3584 + assert config.num_attention_heads == 28 + assert config.num_key_value_heads == 4 + assert config.num_hidden_layers == 28 + assert config.intermediate_size == 18944 + assert config.vocab_size == 151674 + assert config.rope_theta == 1000000.0 + + def test_text_logit_validation(self, neuron_model, tokenizer): + """ + Validate text-only logit accuracy: Neuron vs CPU reference. + + Uses generate_expected_logits() for CPU golden logits and + logit_validation() for multi-tier logit comparison. + + InternVL3 vocab_size (151674) is not divisible by TP degree (4), + so lm_head pads to the next multiple (151676). The 2 padding + positions get -FLT_MAX in Neuron output. We truncate both CPU + and Neuron logits to the TP-aligned boundary (151672) to avoid + false failures from these padding artifacts. + """ + prompt = "The capital of France is" + input_ids = tokenizer(prompt, return_tensors="pt").input_ids + attention_mask = torch.ones_like(input_ids, dtype=torch.int32) + + generation_config = GenerationConfig( + do_sample=False, + max_new_tokens=NUM_TOKENS_TO_CHECK, + eos_token_id=tokenizer.eos_token_id, + pad_token_id=tokenizer.pad_token_id or 0, + ) + + # 1. Generate CPU reference logits + expected_logits = generate_expected_logits( + neuron_model=neuron_model, + input_ids=input_ids, + inputs_attention_mask=attention_mask, + generation_config=generation_config, + num_tokens=NUM_TOKENS_TO_CHECK, + ) + + # 2. Truncate expected logits to TP-aligned vocab boundary + tp_degree = neuron_model.config.neuron_config.tp_degree + vocab_size = neuron_model.config.vocab_size + aligned_vocab = get_tp_aligned_vocab_size(vocab_size, tp_degree) + expected_logits = expected_logits[..., :aligned_vocab] + + # 3. Build generate_fn that returns Neuron logits (also truncated) + adapter = HuggingFaceGenerationAdapter(neuron_model) + + expected_attention_mask = torch.ones( + (attention_mask.shape[0], expected_logits.shape[0]), + dtype=torch.int32, + ) + extrapolated_attention_mask = torch.cat( + (attention_mask, expected_attention_mask), dim=1 + ) + + def generate_fn(input_ids_tensor): + neuron_model.reset() + input_length = input_ids_tensor.shape[1] + attn_mask = extrapolated_attention_mask[:, :input_length] + new_tokens = NUM_TOKENS_TO_CHECK + input_ids.shape[1] - input_length + + outputs = adapter.generate( + input_ids=input_ids_tensor, + attention_mask=attn_mask, + max_new_tokens=new_tokens, + min_new_tokens=new_tokens, + do_sample=False, + return_dict_in_generate=True, + output_scores=True, + generation_config=generation_config, + ) + actual_logits = torch.stack(outputs.scores) + # Truncate to TP-aligned vocab boundary + actual_logits = actual_logits[..., :aligned_vocab] + return actual_logits + + # 4. Run logit_validation with BF16-appropriate tolerances. + # Default tolerances (top-5: 0.01, top-50: 0.02) are calibrated for + # FP16/FP32 models. BF16 has lower mantissa precision (7 bits vs 10), + # so normalized logit errors of 0.03-0.06 are expected. + bf16_tol_map = { + 5: (1e-5, 0.05), + 50: (1e-5, 0.06), + 1000: (1e-5, 0.06), + None: (1e-5, 0.08), + } + + passed, results, status_msg = logit_validation( + input_ids=input_ids, + generate_fn=generate_fn, + expected_logits=expected_logits, + tol_map=bf16_tol_map, + divergence_difference_tol=TOKEN_DIVERGENCE_ATOL, + ) + + print(f"\n{status_msg}") + assert passed, f"Logit validation failed:\n{status_msg}" diff --git a/contrib/models/InternVL3-8B-Instruct/test/unit/__init__.py b/contrib/models/InternVL3-8B-Instruct/test/unit/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/contrib/models/InternVL3-8B-Instruct/tp2_nki_sweep.py b/contrib/models/InternVL3-8B-Instruct/tp2_nki_sweep.py new file mode 100644 index 00000000..c388ed58 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/tp2_nki_sweep.py @@ -0,0 +1,407 @@ +#!/usr/bin/env python3 +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +TP=2 NKI kernel sweep for InternVL3-8B-Instruct. + +Tests all NKI kernel combinations at TP=2 on trn2.3xlarge LNC=2: + A) baseline — no NKI kernels + B) qkv+mlp — qkv_kernel_enabled + mlp_kernel_enabled + C) attn_block_tkg — attn_block_tkg_nki_kernel_enabled (was NCC_ISTP902 at TP=4) + D) all_kernels — qkv + mlp + attn_block_tkg + +Each config: compile → load → accuracy check → TTFT (5 runs) → TKG (3 runs, 64 tok). + +Usage: + python tp2_nki_sweep.py # Run all configs + python tp2_nki_sweep.py --configs baseline qkv_mlp # Run specific configs + python tp2_nki_sweep.py --skip-compile # Skip compilation (load from disk) + +Output: /mnt/models/tp2_nki_sweep_results.json +""" + +import argparse +import gc +import json +import os +import sys +import time +import traceback +from pathlib import Path + +import torch + +sys.path.insert(0, str(Path(__file__).parent / "src")) + +from modeling_internvl3 import InternVL3InferenceConfig, NeuronInternVL3ForCausalLM +from neuronx_distributed_inference.models.config import NeuronConfig +from neuronx_distributed_inference.utils.hf_adapter import HuggingFaceGenerationAdapter + +MODEL_PATH = "/mnt/models/InternVL3-8B-Instruct/" +BASE_COMPILED_DIR = "/mnt/models/neuron_models" +RESULTS_PATH = "/mnt/models/tp2_nki_sweep_results.json" + +TP_DEGREE = 2 +SEQ_LEN = 2048 +BATCH_SIZE = 1 + +# NKI kernel configurations to test +CONFIGS = { + "baseline": { + "label": "Baseline (no NKI)", + "fused_qkv": False, + "qkv_kernel_enabled": False, + "mlp_kernel_enabled": False, + "attn_block_tkg_nki_kernel_enabled": False, + }, + "qkv_mlp": { + "label": "QKV + MLP kernels", + "fused_qkv": True, + "qkv_kernel_enabled": True, + "mlp_kernel_enabled": True, + "attn_block_tkg_nki_kernel_enabled": False, + }, + "attn_block": { + "label": "Attn Block TKG kernel", + "fused_qkv": True, + "qkv_kernel_enabled": True, # Required by attn_block_tkg + "mlp_kernel_enabled": False, + "attn_block_tkg_nki_kernel_enabled": True, + }, + "all_kernels": { + "label": "All NKI kernels", + "fused_qkv": True, + "qkv_kernel_enabled": True, + "mlp_kernel_enabled": True, + "attn_block_tkg_nki_kernel_enabled": True, + }, +} + + +def compiled_path_for(config_name): + return f"{BASE_COMPILED_DIR}/InternVL3-8B-tp2-{config_name}" + + +def create_config(config_name): + """Create inference config for a given NKI kernel combination.""" + cfg = CONFIGS[config_name] + + text_neuron_config = NeuronConfig( + tp_degree=TP_DEGREE, + max_batch_size=BATCH_SIZE, + seq_len=SEQ_LEN, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + save_sharded_checkpoint=True, + fused_qkv=cfg["fused_qkv"], + qkv_kernel_enabled=cfg["qkv_kernel_enabled"], + mlp_kernel_enabled=cfg["mlp_kernel_enabled"], + attn_block_tkg_nki_kernel_enabled=cfg["attn_block_tkg_nki_kernel_enabled"], + ) + + vision_neuron_config = NeuronConfig( + tp_degree=TP_DEGREE, + max_batch_size=1, + seq_len=256, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + buckets=[1], + fused_qkv=True, # Vision encoder always has fused QKV + save_sharded_checkpoint=True, + ) + + config = InternVL3InferenceConfig.from_pretrained( + MODEL_PATH, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + ) + + return config + + +def compile_and_load(config_name, config, skip_compile=False): + """Compile (or skip) and load model. Returns (model, compile_time) or raises.""" + cpath = compiled_path_for(config_name) + compile_time = None + + if not skip_compile: + print(f"\n--- Compiling: {CONFIGS[config_name]['label']} ---") + nc = config.text_config.neuron_config + print(f" tp_degree={nc.tp_degree}, fused_qkv={nc.fused_qkv}") + print( + f" qkv_kernel={nc.qkv_kernel_enabled}, mlp_kernel={nc.mlp_kernel_enabled}" + ) + print(f" attn_block_tkg={nc.attn_block_tkg_nki_kernel_enabled}") + print(f" Output: {cpath}") + + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + start = time.time() + model.compile(cpath) + compile_time = time.time() - start + print(f" Compilation: {compile_time:.1f}s ({compile_time / 60:.1f} min)") + + # Load compiled NEFFs and save sharded weights + model.load(cpath) + else: + print(f"\n--- Loading (skip-compile): {CONFIGS[config_name]['label']} ---") + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + model.load(cpath) + + return model, compile_time + + +def measure_ttft(model, tokenizer, n_runs=5): + """Measure TTFT for text-only CTE.""" + messages = [{"role": "user", "content": "What is the capital of France?"}] + templated = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = tokenizer(templated, return_tensors="pt") + input_ids = inputs.input_ids + seq_len = input_ids.shape[-1] + position_ids = torch.arange(seq_len, dtype=torch.int32).unsqueeze(0) + seq_ids = torch.zeros(1, dtype=torch.int32) + + # Warmup + model.reset() + with torch.no_grad(): + model(input_ids=input_ids, position_ids=position_ids, seq_ids=seq_ids) + + # Measure + times = [] + for _ in range(n_runs): + model.reset() + start = time.perf_counter() + with torch.no_grad(): + model(input_ids=input_ids, position_ids=position_ids, seq_ids=seq_ids) + times.append(time.perf_counter() - start) + + return sum(times) / len(times) * 1000 # ms + + +def measure_tkg(model, tokenizer, num_tokens=64, n_runs=3): + """Measure TKG throughput via HuggingFaceGenerationAdapter.""" + adapter = HuggingFaceGenerationAdapter(model) + messages = [ + {"role": "user", "content": "Tell me a detailed story about a brave knight."} + ] + templated = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = tokenizer(templated, return_tensors="pt") + input_ids = inputs.input_ids + attention_mask = torch.ones_like(input_ids) + eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>") + + # Warmup + with torch.no_grad(): + adapter.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=num_tokens, + do_sample=False, + eos_token_id=eos_token_id, + ) + + # Measure + results = [] + for _ in range(n_runs): + start = time.perf_counter() + with torch.no_grad(): + out = adapter.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=num_tokens, + do_sample=False, + eos_token_id=eos_token_id, + ) + elapsed = time.perf_counter() - start + generated = out.shape[-1] - input_ids.shape[-1] + results.append((generated, elapsed)) + + avg_tok = sum(r[0] for r in results) / len(results) + avg_time = sum(r[1] for r in results) / len(results) + tok_s = avg_tok / avg_time if avg_time > 0 else 0.0 + return tok_s, avg_tok, avg_time + + +def run_accuracy_check(model, tokenizer): + """Quick accuracy sanity check — returns (pass, details).""" + adapter = HuggingFaceGenerationAdapter(model) + prompts = [ + ("france", "What is the capital of France?"), + ("math", "What is 2 + 2? Answer with just the number:"), + ] + eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>") + details = {} + for name, text in prompts: + messages = [{"role": "user", "content": text}] + templated = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + ids = tokenizer(templated, return_tensors="pt") + input_ids = ids.input_ids + with torch.no_grad(): + out = adapter.generate( + input_ids=input_ids, + attention_mask=torch.ones_like(input_ids), + max_new_tokens=32, + do_sample=False, + eos_token_id=eos_token_id, + ) + gen = tokenizer.decode(out[0, input_ids.shape[-1] :], skip_special_tokens=True) + details[name] = gen + print(f" {name}: {gen!r}") + + ok = "paris" in details.get("france", "").lower() and "4" in details.get("math", "") + return ok, details + + +def unload_model(model): + """Best-effort cleanup of a loaded Neuron model.""" + try: + del model + except Exception: + pass + gc.collect() + # Short sleep to let the runtime release NC resources + time.sleep(3) + + +def main(): + parser = argparse.ArgumentParser(description="TP=2 NKI kernel sweep") + parser.add_argument( + "--configs", + nargs="+", + choices=list(CONFIGS.keys()), + default=list(CONFIGS.keys()), + help="Which configs to test (default: all)", + ) + parser.add_argument("--skip-compile", action="store_true", help="Skip compilation") + args = parser.parse_args() + + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) + + all_results = {} + + for config_name in args.configs: + cfg_info = CONFIGS[config_name] + print("\n" + "=" * 70) + print(f"CONFIG: {config_name} — {cfg_info['label']}") + print(f" TP={TP_DEGREE}, seq_len={SEQ_LEN}, batch={BATCH_SIZE}") + print("=" * 70) + + result = { + "label": cfg_info["label"], + "tp_degree": TP_DEGREE, + "seq_len": SEQ_LEN, + "batch_size": BATCH_SIZE, + "fused_qkv": cfg_info["fused_qkv"], + "qkv_kernel_enabled": cfg_info["qkv_kernel_enabled"], + "mlp_kernel_enabled": cfg_info["mlp_kernel_enabled"], + "attn_block_tkg_nki_kernel_enabled": cfg_info[ + "attn_block_tkg_nki_kernel_enabled" + ], + } + + try: + config = create_config(config_name) + model, compile_time = compile_and_load( + config_name, config, args.skip_compile + ) + result["compile_time_s"] = round(compile_time, 1) if compile_time else None + result["compile_status"] = "PASS" + + # Accuracy + print(f"\n Accuracy check:") + acc_ok, acc_details = run_accuracy_check(model, tokenizer) + result["accuracy"] = "PASS" if acc_ok else "FAIL" + result["accuracy_details"] = acc_details + print(f" Accuracy: {result['accuracy']}") + + # TTFT + print(f"\n TTFT benchmark (5 runs):") + ttft = measure_ttft(model, tokenizer, n_runs=5) + result["ttft_ms"] = round(ttft, 1) + print(f" TTFT: {ttft:.1f} ms") + + # TKG + print(f"\n TKG benchmark (3 runs, 64 tok):") + tok_s, avg_tok, avg_time = measure_tkg( + model, tokenizer, num_tokens=64, n_runs=3 + ) + result["tkg_tok_s"] = round(tok_s, 1) + result["tkg_avg_tokens"] = round(avg_tok, 1) + result["tkg_avg_time_s"] = round(avg_time, 3) + print(f" TKG: {tok_s:.1f} tok/s ({avg_tok:.0f} tokens in {avg_time:.3f}s)") + + result["status"] = "PASS" + + except Exception as e: + result["status"] = "FAIL" + result["error"] = str(e) + result["traceback"] = traceback.format_exc() + print(f"\n FAILED: {e}") + # Print full traceback for debugging + traceback.print_exc() + + all_results[config_name] = result + + # Attempt to unload before next config + try: + unload_model(model) + except Exception: + pass + + # Save intermediate results after each config + with open(RESULTS_PATH, "w") as f: + json.dump(all_results, f, indent=2) + print(f"\n Intermediate results saved to {RESULTS_PATH}") + + # Final summary + print("\n\n" + "=" * 70) + print(f"TP={TP_DEGREE} NKI KERNEL SWEEP — FINAL SUMMARY") + print("=" * 70) + + # Find baseline for delta computation + baseline_ttft = all_results.get("baseline", {}).get("ttft_ms") + baseline_tkg = all_results.get("baseline", {}).get("tkg_tok_s") + + header = f"{'Config':<20} {'Status':<8} {'Accuracy':<8} {'TTFT(ms)':<10} {'TKG(tok/s)':<12} {'TTFT Δ':<10} {'TKG Δ':<10}" + print(header) + print("-" * len(header)) + + for name in args.configs: + r = all_results.get(name, {}) + status = r.get("status", "N/A") + acc = r.get("accuracy", "N/A") + ttft = r.get("ttft_ms", None) + tkg = r.get("tkg_tok_s", None) + + ttft_str = f"{ttft:.1f}" if ttft else "N/A" + tkg_str = f"{tkg:.1f}" if tkg else "N/A" + + if baseline_ttft and ttft and name != "baseline": + ttft_d = ((ttft - baseline_ttft) / baseline_ttft) * 100 + ttft_delta = f"{ttft_d:+.1f}%" + else: + ttft_delta = "—" + + if baseline_tkg and tkg and name != "baseline": + tkg_d = ((tkg - baseline_tkg) / baseline_tkg) * 100 + tkg_delta = f"{tkg_d:+.1f}%" + else: + tkg_delta = "—" + + print( + f"{name:<20} {status:<8} {acc:<8} {ttft_str:<10} {tkg_str:<12} {ttft_delta:<10} {tkg_delta:<10}" + ) + + print(f"\nFull results: {RESULTS_PATH}") + + +if __name__ == "__main__": + main() diff --git a/contrib/models/InternVL3-8B-Instruct/validate_cte.py b/contrib/models/InternVL3-8B-Instruct/validate_cte.py new file mode 100644 index 00000000..5feae62e --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/validate_cte.py @@ -0,0 +1,617 @@ +#!/usr/bin/env python3 +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Task 009: CTE (Prefill) Validation for InternVL3-8B-Instruct on Neuron. + +Compares Neuron VLM CTE logits against CPU reference outputs. + +Tests: +1. Full logit tensor comparison: "I believe the meaning of life is" + - Per-position top-1 match + - Cosine similarity per position + - Max absolute error +2. Text-only top-1 match: "What is the capital of France?" +3. Multimodal: "\nDescribe this image briefly." with test_image.png + +Usage: + python validate_cte.py [--skip-compile] +""" + +import json +import sys +import time +from pathlib import Path + +import torch + +# Add contrib src to path +sys.path.insert(0, str(Path(__file__).parent / "src")) + +from modeling_internvl3 import InternVL3InferenceConfig, NeuronInternVL3ForCausalLM +from neuronx_distributed_inference.models.config import NeuronConfig + +MODEL_PATH = "/mnt/models/InternVL3-8B-Instruct/" +COMPILED_PATH = "/mnt/models/neuron_models/InternVL3-8B-VLM/" + +# Reference files from Task 006 +REF_LOGITS_PT = "/mnt/models/reference_logits.pt" +REF_LOGITS_JSON = "/mnt/models/reference_logits.json" +REF_TEXT_ONLY_JSON = "/mnt/models/reference_text_only.json" +REF_IMAGE_JSON = "/mnt/models/reference_image.json" +TEST_IMAGE = "/mnt/models/test_image.png" + + +def create_config(): + """Create InternVL3 VLM inference config.""" + text_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=2048, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + save_sharded_checkpoint=True, + ) + vision_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=256, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + buckets=[1], + fused_qkv=True, + save_sharded_checkpoint=True, + ) + config = InternVL3InferenceConfig.from_pretrained( + MODEL_PATH, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + ) + return config + + +def load_model(skip_compile=False): + """Load compiled VLM model.""" + config = create_config() + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + + if not skip_compile: + print("Compiling model...") + model.compile(COMPILED_PATH) + + print("Loading model...") + start = time.time() + model.load(COMPILED_PATH) + print(f"Load completed in {time.time() - start:.1f}s") + return model + + +def run_cte(model, input_ids, pixel_values=None): + """Run CTE (prefill) and return raw logits from the NEFF output.""" + seq_len = input_ids.shape[-1] + position_ids = torch.arange(seq_len, dtype=torch.int32).unsqueeze(0) + seq_ids = torch.zeros(1, dtype=torch.int32) + + with torch.no_grad(): + outputs = model( + input_ids=input_ids, + position_ids=position_ids, + seq_ids=seq_ids, + pixel_values=pixel_values, + ) + return outputs + + +def validate_logits(test_name, neuron_logits, ref_logits, input_ids, tokenizer): + """ + Compare Neuron CTE logits against CPU reference. + + Returns dict of metrics and pass/fail status. + """ + results = {"test": test_name, "pass": True, "details": []} + n_positions = input_ids.shape[-1] + + # Neuron output may only have last-position logits (on-device sampling disabled) + # or full sequence logits depending on CTE implementation + neuron_f = neuron_logits.float() + ref_f = ref_logits.float() + + # Check shapes + results["neuron_shape"] = list(neuron_f.shape) + results["ref_shape"] = list(ref_f.shape) + + # NxDI CTE typically returns [batch, 1, vocab] (last position only) + # Reference has [batch, seq_len, vocab] (all positions) + # Detect this case and compare last positions + neuron_seq_len = neuron_f.shape[1] if neuron_f.dim() == 3 else 1 + ref_seq_len = ref_f.shape[1] if ref_f.dim() == 3 else 1 + + if neuron_seq_len < ref_seq_len: + # Neuron returns fewer positions (typically just the last one) + # Compare against the last position of reference + print( + f" Note: Neuron returns {neuron_seq_len} positions, reference has {ref_seq_len}. Comparing last position only." + ) + + neuron_last = neuron_f[0, -1] if neuron_f.dim() == 3 else neuron_f[-1] + ref_last = ref_f[0, -1] if ref_f.dim() == 3 else ref_f[-1] + + # Neuron CTE may pad unused logit positions with -FLT_MAX (~-3.4e38). + # Mask these out for cosine similarity computation. + valid_mask = neuron_last > -1e30 + neuron_valid = neuron_last[valid_mask] + ref_valid = ref_last[valid_mask] + n_valid = valid_mask.sum().item() + n_total = neuron_last.numel() + results["n_valid_logits"] = n_valid + results["n_total_logits"] = n_total + if n_valid < n_total: + print( + f" Note: {n_total - n_valid} logit positions masked (padding -FLT_MAX)" + ) + + # Top-1 match + neuron_top1 = neuron_last.argmax().item() + ref_top1 = ref_last.argmax().item() + match = neuron_top1 == ref_top1 + results["last_pos_top1_match"] = match + results["neuron_top1"] = neuron_top1 + results["ref_top1"] = ref_top1 + results["neuron_top1_token"] = tokenizer.decode([neuron_top1]) + results["ref_top1_token"] = tokenizer.decode([ref_top1]) + if not match: + results["pass"] = False + results["details"].append( + f"Top-1 mismatch at last pos: neuron={neuron_top1} " + f"({tokenizer.decode([neuron_top1])!r}) vs " + f"ref={ref_top1} ({tokenizer.decode([ref_top1])!r})" + ) + + # Cosine similarity at last position (using valid logits only) + if n_valid > 0: + cos_sim = torch.nn.functional.cosine_similarity( + neuron_valid.unsqueeze(0), ref_valid.unsqueeze(0) + ).item() + else: + cos_sim = 0.0 + results["last_pos_cosine_sim"] = cos_sim + if cos_sim < 0.95: + results["pass"] = False + results["details"].append(f"Cosine sim too low at last pos: {cos_sim:.6f}") + + # Top-5 overlap + neuron_top5 = set(torch.topk(neuron_last, 5).indices.tolist()) + ref_top5 = set(torch.topk(ref_last, 5).indices.tolist()) + overlap = len(neuron_top5 & ref_top5) + results["last_pos_top5_overlap"] = f"{overlap}/5" + + # Max abs error on valid (non-padding) logits + if n_valid > 0: + max_abs_err = (neuron_valid - ref_valid).abs().max().item() + results["max_abs_error_valid"] = max_abs_err + + # Top-10 overlap + neuron_top10 = set(torch.topk(neuron_last, 10).indices.tolist()) + ref_top10 = set(torch.topk(ref_last, 10).indices.tolist()) + overlap10 = len(neuron_top10 & ref_top10) + results["last_pos_top10_overlap"] = f"{overlap10}/10" + + print( + f" [Last pos] top1_match={match}, cosine={cos_sim:.6f}, " + f"top5_overlap={overlap}/5, top10_overlap={overlap10}/10" + ) + if n_valid > 0: + print(f" max_abs_error (valid logits): {max_abs_err:.4f}") + + elif neuron_seq_len == ref_seq_len and neuron_f.dim() == 3 and ref_f.dim() == 3: + # Full sequence logits available — compare per-position + n_compare = min(neuron_f.shape[1], ref_f.shape[1]) + top1_matches = 0 + cos_sims = [] + + for pos in range(n_compare): + n_pos = neuron_f[0, pos] + r_pos = ref_f[0, pos] + + # Top-1 match + n_top1 = n_pos.argmax().item() + r_top1 = r_pos.argmax().item() + if n_top1 == r_top1: + top1_matches += 1 + + # Cosine similarity + cos = torch.nn.functional.cosine_similarity( + n_pos.unsqueeze(0), r_pos.unsqueeze(0) + ).item() + cos_sims.append(cos) + + # Detailed per-position log + n_token = tokenizer.decode([n_top1]) + r_token = tokenizer.decode([r_top1]) + match_str = "MATCH" if n_top1 == r_top1 else "MISMATCH" + print( + f" pos {pos}: {match_str} | neuron={n_top1} ({n_token!r}) " + f"ref={r_top1} ({r_token!r}) | cosine={cos:.6f}" + ) + + top1_rate = top1_matches / n_compare + avg_cos = sum(cos_sims) / len(cos_sims) + min_cos = min(cos_sims) + + results["top1_match_rate"] = f"{top1_matches}/{n_compare} ({top1_rate:.1%})" + results["avg_cosine_sim"] = avg_cos + results["min_cosine_sim"] = min_cos + + # Max absolute error + max_abs_err = ( + (neuron_f[0, :n_compare] - ref_f[0, :n_compare]).abs().max().item() + ) + results["max_abs_error"] = max_abs_err + + print( + f"\n Summary: top1={top1_matches}/{n_compare} ({top1_rate:.1%}), " + f"avg_cos={avg_cos:.6f}, min_cos={min_cos:.6f}, max_abs_err={max_abs_err:.4f}" + ) + + if top1_rate < 0.7: + results["pass"] = False + results["details"].append(f"Top-1 match rate too low: {top1_rate:.1%}") + if min_cos < 0.90: + results["pass"] = False + results["details"].append(f"Min cosine sim too low: {min_cos:.6f}") + else: + # Shape mismatch — just compare what we can + results["details"].append( + f"Shape mismatch: neuron={neuron_f.shape} vs ref={ref_f.shape}" + ) + # Fall back to comparing last-position argmax + if neuron_f.dim() >= 2: + neuron_last = neuron_f[0, -1] if neuron_f.dim() == 3 else neuron_f[-1] + else: + neuron_last = neuron_f + ref_last = ref_f[0, -1] if ref_f.dim() == 3 else ref_f[-1] + + neuron_top1 = neuron_last.argmax().item() + ref_top1 = ref_last.argmax().item() + match = neuron_top1 == ref_top1 + results["last_pos_top1_match"] = match + results["neuron_top1"] = neuron_top1 + results["ref_top1"] = ref_top1 + print(f" Shape mismatch fallback: top1_match={match}") + + return results + + +def test_1_full_logits(model, tokenizer): + """Test 1: Full logit tensor comparison.""" + print("\n" + "=" * 60) + print("TEST 1: Full logit tensor comparison") + print("=" * 60) + + # Load reference + with open(REF_LOGITS_JSON) as f: + ref_meta = json.load(f) + ref_logits = torch.load(REF_LOGITS_PT, weights_only=True) + + prompt = ref_meta["prompt"] + ref_input_ids = ref_meta["input_ids"] + print(f"Prompt: {prompt!r}") + print(f"Reference input_ids: {ref_input_ids}") + print(f"Reference logits shape: {ref_logits.shape}") + + # Tokenize and verify + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs.input_ids + print(f"Neuron input_ids: {input_ids[0].tolist()}") + + if input_ids[0].tolist() != ref_input_ids: + print("WARNING: Input IDs differ from reference!") + print(f" Neuron: {input_ids[0].tolist()}") + print(f" Ref: {ref_input_ids}") + + # Run CTE + outputs = run_cte(model, input_ids) + + # Extract logits + if hasattr(outputs, "logits"): + neuron_logits = outputs.logits + elif isinstance(outputs, tuple): + neuron_logits = outputs[0] + else: + neuron_logits = outputs + + print(f"Neuron output shape: {neuron_logits.shape}") + + return validate_logits( + "full_logits", neuron_logits, ref_logits, input_ids, tokenizer + ) + + +def test_2_text_only(model, tokenizer): + """Test 2: Text-only top-1 validation.""" + print("\n" + "=" * 60) + print("TEST 2: Text-only top-1 validation") + print("=" * 60) + + with open(REF_TEXT_ONLY_JSON) as f: + ref = json.load(f) + + prompt = ref["prompt"] + ref_top5 = ref["top5_tokens"] # [[id, logit], ...] + ref_top1_id = ref_top5[0][0] + ref_top1_logit = ref_top5[0][1] + + print(f"Prompt: {prompt!r}") + print( + f"Reference top-1: id={ref_top1_id} ({tokenizer.decode([ref_top1_id])!r}), logit={ref_top1_logit}" + ) + + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs.input_ids + print(f"Input IDs shape: {input_ids.shape}") + + outputs = run_cte(model, input_ids) + + if hasattr(outputs, "logits"): + logits = outputs.logits + elif isinstance(outputs, tuple): + logits = outputs[0] + else: + logits = outputs + + # Get last position logits + if logits.dim() == 3: + last_logits = logits[0, -1].float() + elif logits.dim() == 2: + last_logits = logits[-1].float() + else: + last_logits = logits.float() + + neuron_top5 = torch.topk(last_logits, 5) + neuron_top1_id = neuron_top5.indices[0].item() + neuron_top1_logit = neuron_top5.values[0].item() + + print(f"\nNeuron top-5:") + for i, (tid, tval) in enumerate(zip(neuron_top5.indices, neuron_top5.values)): + token = tokenizer.decode([tid.item()]) + ref_match = " ← MATCH" if tid.item() == ref_top5[i][0] else "" + print(f" {i}: id={tid.item()} ({token!r}), logit={tval.item():.4f}{ref_match}") + + match = neuron_top1_id == ref_top1_id + result = { + "test": "text_only_top1", + "pass": match, + "prompt": prompt, + "neuron_top1": neuron_top1_id, + "neuron_top1_token": tokenizer.decode([neuron_top1_id]), + "neuron_top1_logit": neuron_top1_logit, + "ref_top1": ref_top1_id, + "ref_top1_token": tokenizer.decode([ref_top1_id]), + "ref_top1_logit": ref_top1_logit, + "details": [], + } + + if not match: + result["details"].append( + f"Top-1 mismatch: neuron={neuron_top1_id} ({tokenizer.decode([neuron_top1_id])!r}) " + f"vs ref={ref_top1_id} ({tokenizer.decode([ref_top1_id])!r})" + ) + + # Top-5 overlap + neuron_top5_set = set(neuron_top5.indices.tolist()) + ref_top5_set = set(t[0] for t in ref_top5) + overlap = len(neuron_top5_set & ref_top5_set) + result["top5_overlap"] = f"{overlap}/5" + + status = "PASS" if match else "FAIL" + print(f"\n Result: {status} | top1_match={match}, top5_overlap={overlap}/5") + + return result + + +def test_3_multimodal(model, tokenizer): + """Test 3: Multimodal CTE validation.""" + print("\n" + "=" * 60) + print("TEST 3: Multimodal CTE validation") + print("=" * 60) + + with open(REF_IMAGE_JSON) as f: + ref = json.load(f) + + # Check for test image + import os + + if not os.path.exists(TEST_IMAGE): + print( + f"WARNING: Test image not found at {TEST_IMAGE}, using random pixel values" + ) + pixel_values = torch.randn(1, 3, 448, 448) + has_real_image = False + else: + # Load the test image through the proper preprocessing pipeline + try: + from PIL import Image + from torchvision import transforms + + img = Image.open(TEST_IMAGE).convert("RGB") + # InternVL3 uses 448x448 with ImageNet normalization + transform = transforms.Compose( + [ + transforms.Resize((448, 448)), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] + ), + ] + ) + pixel_values = transform(img).unsqueeze(0) + has_real_image = True + print(f"Loaded test image: {TEST_IMAGE}") + except Exception as e: + print(f"WARNING: Failed to load image ({e}), using random pixel values") + pixel_values = torch.randn(1, 3, 448, 448) + has_real_image = False + + print(f"Pixel values shape: {pixel_values.shape}") + + # Build input with image tokens + IMG_CONTEXT_ID = 151667 + IMG_START_ID = 151665 + IMG_END_ID = 151666 + + text_before = "Describe this image briefly." + text_ids = tokenizer(text_before, return_tensors="pt").input_ids[0] + + img_tokens = torch.full((256,), IMG_CONTEXT_ID, dtype=torch.long) + full_ids = torch.cat( + [ + text_ids, + torch.tensor([IMG_START_ID]), + img_tokens, + torch.tensor([IMG_END_ID]), + ] + ).unsqueeze(0) + + print(f"Input IDs shape: {full_ids.shape}") + print(f"IMG_CONTEXT tokens: {(full_ids == IMG_CONTEXT_ID).sum().item()}") + + outputs = run_cte(model, full_ids, pixel_values=pixel_values) + + if hasattr(outputs, "logits"): + logits = outputs.logits + elif isinstance(outputs, tuple): + logits = outputs[0] + else: + logits = outputs + + # Get last position + if logits.dim() == 3: + last_logits = logits[0, -1].float() + elif logits.dim() == 2: + last_logits = logits[-1].float() + else: + last_logits = logits.float() + + top5 = torch.topk(last_logits, 5) + print(f"\nNeuron multimodal top-5:") + for i, (tid, tval) in enumerate(zip(top5.indices, top5.values)): + token = tokenizer.decode([tid.item()]) + print(f" {i}: id={tid.item()} ({token!r}), logit={tval.item():.4f}") + + top1_id = top5.indices[0].item() + top1_token = tokenizer.decode([top1_id]) + + result = { + "test": "multimodal_cte", + "pass": True, # No exact reference for multimodal -- check sanity + "has_real_image": has_real_image, + "neuron_top1": top1_id, + "neuron_top1_token": top1_token, + "pixel_values_shape": list(pixel_values.shape), + "input_ids_shape": list(full_ids.shape), + "details": [], + } + + # Sanity checks + # 1. Output should not be degenerate (all same logit) + # Filter out padding values (-FLT_MAX) before computing std + valid_logits = last_logits[last_logits > -1e30] + logit_std = valid_logits.std().item() if valid_logits.numel() > 0 else 0.0 + result["logit_std"] = logit_std + if logit_std < 0.1: + result["pass"] = False + result["details"].append(f"Degenerate logits: std={logit_std:.6f}") + + # 2. Top-1 should be a reasonable token (not padding or special) + if top1_id in [0, 1, 2]: + result["pass"] = False + result["details"].append(f"Top-1 is special token: {top1_id}") + + # 3. If real image, compare with reference response start + if has_real_image: + ref_response = ref.get("response", "") + ref_first_word = ref_response.split()[0] if ref_response else "" + result["ref_first_word"] = ref_first_word + # Check if top-1 token is consistent with reference response start + if ref_first_word and top1_token.strip().lower() == ref_first_word.lower(): + result["first_word_match"] = True + else: + result["first_word_match"] = False + result["details"].append( + f"First word mismatch: neuron={top1_token!r} vs ref={ref_first_word!r} " + f"(may be OK due to pixel value preprocessing differences)" + ) + + status = "PASS" if result["pass"] else "FAIL" + print(f"\n Result: {status} | top1={top1_token!r}, logit_std={logit_std:.4f}") + + return result + + +def main(): + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "--skip-compile", + action="store_true", + help="Skip compilation (use existing compiled model)", + ) + args = parser.parse_args() + + print("=" * 60) + print("Task 009: CTE Validation - InternVL3-8B-Instruct") + print("=" * 60) + + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) + + model = load_model(skip_compile=args.skip_compile) + + all_results = [] + + # Test 1: Full logit tensor comparison + r1 = test_1_full_logits(model, tokenizer) + all_results.append(r1) + + # Test 2: Text-only top-1 + r2 = test_2_text_only(model, tokenizer) + all_results.append(r2) + + # Test 3: Multimodal + r3 = test_3_multimodal(model, tokenizer) + all_results.append(r3) + + # Summary + print("\n" + "=" * 60) + print("VALIDATION SUMMARY") + print("=" * 60) + all_pass = True + for r in all_results: + status = "PASS" if r["pass"] else "FAIL" + print(f" {r['test']}: {status}") + if r["details"]: + for d in r["details"]: + print(f" - {d}") + if not r["pass"]: + all_pass = False + + print() + if all_pass: + print("OVERALL: ALL TESTS PASSED") + else: + print("OVERALL: SOME TESTS FAILED") + + # Save results + results_path = "/mnt/models/validation_cte_results.json" + with open(results_path, "w") as f: + json.dump(all_results, f, indent=2, default=str) + print(f"\nResults saved to {results_path}") + + return 0 if all_pass else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/contrib/models/InternVL3-8B-Instruct/validate_tkg.py b/contrib/models/InternVL3-8B-Instruct/validate_tkg.py new file mode 100644 index 00000000..a3fb46db --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/validate_tkg.py @@ -0,0 +1,501 @@ +#!/usr/bin/env python3 +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +""" +Task 010: TKG (Token Generation) Validation for InternVL3-8B-Instruct on Neuron. + +Validates the full CTE→TKG autoregressive generation loop using +HuggingFaceGenerationAdapter for clean multi-token decoding. + +Tests: +1. Text-only generation: "What is the capital of France?" → compare first 20 tokens +2. Text-only generation: "I believe the meaning of life is" → compare first 20 tokens +3. Multimodal generation: image + "Describe this image briefly." → coherence check +4. State reset: consecutive generations produce correct output +5. EOS handling: generation stops at EOS token + +Usage: + python validate_tkg.py [--skip-compile] [--max-new-tokens 32] +""" + +import json +import sys +import time +from pathlib import Path + +import torch + +# Add contrib src to path +sys.path.insert(0, str(Path(__file__).parent / "src")) + +from modeling_internvl3 import InternVL3InferenceConfig, NeuronInternVL3ForCausalLM +from neuronx_distributed_inference.models.config import NeuronConfig +from neuronx_distributed_inference.utils.hf_adapter import HuggingFaceGenerationAdapter + +MODEL_PATH = "/mnt/models/InternVL3-8B-Instruct/" +COMPILED_PATH = "/mnt/models/neuron_models/InternVL3-8B-VLM/" + +# Reference files +REF_TEXT_ONLY_JSON = "/mnt/models/reference_text_only.json" +REF_LOGITS_JSON = "/mnt/models/reference_logits.json" +REF_IMAGE_JSON = "/mnt/models/reference_image.json" +TEST_IMAGE = "/mnt/models/test_image.png" + + +def create_config(): + """Create InternVL3 VLM inference config.""" + text_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=2048, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + save_sharded_checkpoint=True, + ) + vision_neuron_config = NeuronConfig( + tp_degree=4, + max_batch_size=1, + seq_len=256, + torch_dtype=torch.bfloat16, + on_device_sampling_config=None, + buckets=[1], + fused_qkv=True, + save_sharded_checkpoint=True, + ) + config = InternVL3InferenceConfig.from_pretrained( + MODEL_PATH, + text_neuron_config=text_neuron_config, + vision_neuron_config=vision_neuron_config, + ) + return config + + +def load_model(skip_compile=False): + """Load compiled VLM model.""" + config = create_config() + model = NeuronInternVL3ForCausalLM(MODEL_PATH, config=config) + + if not skip_compile: + print("Compiling model...") + model.compile(COMPILED_PATH) + + print("Loading model...") + start = time.time() + model.load(COMPILED_PATH) + print(f"Load completed in {time.time() - start:.1f}s") + return model + + +def generate_text( + adapter, tokenizer, input_ids, attention_mask, max_new_tokens=32, **kwargs +): + """Generate tokens using HuggingFaceGenerationAdapter.""" + with torch.no_grad(): + output_ids = adapter.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=max_new_tokens, + do_sample=False, # greedy decoding for deterministic output + **kwargs, + ) + return output_ids + + +def compute_token_match_rate(neuron_ids, ref_text, tokenizer, n_compare=None): + """Compute token-level match rate between Neuron output and reference text.""" + ref_ids = tokenizer(ref_text, return_tensors="pt").input_ids[0] + + if n_compare is None: + n_compare = min(len(neuron_ids), len(ref_ids)) + + matches = 0 + details = [] + for i in range(n_compare): + n_id = neuron_ids[i].item() if i < len(neuron_ids) else -1 + r_id = ref_ids[i].item() if i < len(ref_ids) else -1 + match = n_id == r_id + if match: + matches += 1 + n_tok = tokenizer.decode([n_id]) if n_id >= 0 else "" + r_tok = tokenizer.decode([r_id]) if r_id >= 0 else "" + details.append( + { + "pos": i, + "match": match, + "neuron_id": n_id, + "neuron_token": n_tok, + "ref_id": r_id, + "ref_token": r_tok, + } + ) + + rate = matches / n_compare if n_compare > 0 else 0 + return rate, matches, n_compare, details + + +def test_1_text_generation(adapter, tokenizer, max_new_tokens): + """Test 1: Text-only generation - capital of France.""" + print("\n" + "=" * 60) + print("TEST 1: Text-only generation (capital of France)") + print("=" * 60) + + with open(REF_TEXT_ONLY_JSON) as f: + ref = json.load(f) + + prompt = ref["prompt"] + ref_response = ref["response"] + print(f"Prompt: {prompt!r}") + print(f"Reference response: {ref_response[:100]!r}...") + + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs.input_ids + attention_mask = inputs.attention_mask + prompt_len = input_ids.shape[-1] + + print(f"Input IDs: {input_ids[0].tolist()}") + print(f"Generating {max_new_tokens} tokens (greedy)...") + + start = time.time() + output_ids = generate_text( + adapter, tokenizer, input_ids, attention_mask, max_new_tokens + ) + gen_time = time.time() - start + + generated_ids = output_ids[0, prompt_len:] + generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True) + full_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) + + print(f"\nGenerated ({gen_time:.2f}s, {len(generated_ids)} tokens):") + print(f" {generated_text!r}") + + # Compare against reference + rate, matches, n_compare, details = compute_token_match_rate( + generated_ids, ref_response, tokenizer, n_compare=min(20, len(generated_ids)) + ) + + print(f"\nToken match rate (first {n_compare}): {matches}/{n_compare} ({rate:.1%})") + for d in details[:20]: + match_str = "MATCH" if d["match"] else "MISS " + print( + f" pos {d['pos']:2d}: {match_str} | neuron={d['neuron_token']!r:10s} ref={d['ref_token']!r:10s}" + ) + + result = { + "test": "text_generation_france", + "pass": True, + "prompt": prompt, + "generated_text": generated_text[:200], + "ref_response": ref_response[:200], + "n_generated": len(generated_ids), + "gen_time_s": round(gen_time, 2), + "token_match_rate": f"{matches}/{n_compare} ({rate:.1%})", + "details": [], + } + + # Check coherence + if "Paris" not in full_text and "paris" not in full_text.lower(): + result["pass"] = False + result["details"].append("Response doesn't mention Paris") + + if rate < 0.5: + result["details"].append(f"Token match rate below 50%: {rate:.1%}") + # Don't fail on match rate alone — bf16 can diverge after a few tokens + + status = "PASS" if result["pass"] else "FAIL" + print(f"\n Result: {status}") + return result + + +def test_2_text_generation_life(adapter, tokenizer, max_new_tokens): + """Test 2: Text-only generation - meaning of life.""" + print("\n" + "=" * 60) + print("TEST 2: Text-only generation (meaning of life)") + print("=" * 60) + + with open(REF_LOGITS_JSON) as f: + ref = json.load(f) + + prompt = ref["prompt"] + ref_response = ref["full_response"] + print(f"Prompt: {prompt!r}") + print(f"Reference response: {ref_response[:100]!r}...") + + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs.input_ids + attention_mask = inputs.attention_mask + prompt_len = input_ids.shape[-1] + + print(f"Generating {max_new_tokens} tokens (greedy)...") + + start = time.time() + output_ids = generate_text( + adapter, tokenizer, input_ids, attention_mask, max_new_tokens + ) + gen_time = time.time() - start + + generated_ids = output_ids[0, prompt_len:] + generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True) + + print(f"\nGenerated ({gen_time:.2f}s, {len(generated_ids)} tokens):") + print(f" {generated_text!r}") + + # Token match + rate, matches, n_compare, details = compute_token_match_rate( + generated_ids, ref_response, tokenizer, n_compare=min(20, len(generated_ids)) + ) + + print(f"\nToken match rate (first {n_compare}): {matches}/{n_compare} ({rate:.1%})") + for d in details[:10]: + match_str = "MATCH" if d["match"] else "MISS " + print( + f" pos {d['pos']:2d}: {match_str} | neuron={d['neuron_token']!r:10s} ref={d['ref_token']!r:10s}" + ) + + result = { + "test": "text_generation_life", + "pass": True, + "prompt": prompt, + "generated_text": generated_text[:200], + "ref_response": ref_response[:200], + "n_generated": len(generated_ids), + "gen_time_s": round(gen_time, 2), + "token_match_rate": f"{matches}/{n_compare} ({rate:.1%})", + "details": [], + } + + # Check the output is coherent English (not garbage) + if len(generated_text.strip()) < 10: + result["pass"] = False + result["details"].append("Generated text too short or empty") + + status = "PASS" if result["pass"] else "FAIL" + print(f"\n Result: {status}") + return result + + +def test_3_multimodal_generation(adapter, tokenizer, max_new_tokens): + """Test 3: Multimodal generation with real image.""" + print("\n" + "=" * 60) + print("TEST 3: Multimodal generation") + print("=" * 60) + + import os + + if not os.path.exists(TEST_IMAGE): + print(f"WARNING: Test image not found at {TEST_IMAGE}, using random pixels") + pixel_values = torch.randn(1, 3, 448, 448) + else: + try: + from PIL import Image + from torchvision import transforms + + img = Image.open(TEST_IMAGE).convert("RGB") + transform = transforms.Compose( + [ + transforms.Resize((448, 448)), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] + ), + ] + ) + pixel_values = transform(img).unsqueeze(0) + print(f"Loaded test image: {TEST_IMAGE}") + except Exception as e: + print(f"WARNING: Failed to load image ({e}), using random pixels") + pixel_values = torch.randn(1, 3, 448, 448) + + # Build multimodal input + IMG_CONTEXT_ID = 151667 + IMG_START_ID = 151665 + IMG_END_ID = 151666 + + text_prompt = "Describe this image briefly." + text_ids = tokenizer(text_prompt, return_tensors="pt").input_ids[0] + + img_tokens = torch.full((256,), IMG_CONTEXT_ID, dtype=torch.long) + full_ids = torch.cat( + [ + text_ids, + torch.tensor([IMG_START_ID]), + img_tokens, + torch.tensor([IMG_END_ID]), + ] + ).unsqueeze(0) + + prompt_len = full_ids.shape[-1] + attention_mask = torch.ones_like(full_ids) + + print(f"Input IDs shape: {full_ids.shape}") + print(f"Generating {max_new_tokens} tokens with image...") + + start = time.time() + output_ids = generate_text( + adapter, + tokenizer, + full_ids, + attention_mask, + max_new_tokens, + pixel_values=pixel_values, + ) + gen_time = time.time() - start + + generated_ids = output_ids[0, prompt_len:] + generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True) + + print(f"\nGenerated ({gen_time:.2f}s, {len(generated_ids)} tokens):") + print(f" {generated_text!r}") + + result = { + "test": "multimodal_generation", + "pass": True, + "prompt": text_prompt, + "generated_text": generated_text[:300], + "n_generated": len(generated_ids), + "gen_time_s": round(gen_time, 2), + "details": [], + } + + # Coherence checks + if len(generated_text.strip()) < 5: + result["pass"] = False + result["details"].append("Generated text too short or empty") + + # Check not degenerate (repeating same token) + if len(generated_ids) > 5: + unique_tokens = len(set(generated_ids.tolist())) + unique_ratio = unique_tokens / len(generated_ids) + result["unique_token_ratio"] = round(unique_ratio, 3) + if unique_ratio < 0.1: + result["pass"] = False + result["details"].append( + f"Degenerate output: only {unique_tokens} unique tokens in {len(generated_ids)}" + ) + + status = "PASS" if result["pass"] else "FAIL" + print(f"\n Result: {status}") + return result + + +def test_4_state_reset(adapter, tokenizer, max_new_tokens): + """Test 4: State reset - consecutive generations produce correct output.""" + print("\n" + "=" * 60) + print("TEST 4: State reset (consecutive generations)") + print("=" * 60) + + prompt = "The capital of France is" + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs.input_ids + attention_mask = inputs.attention_mask + prompt_len = input_ids.shape[-1] + + # Run twice + print("Run 1...") + output_ids_1 = generate_text(adapter, tokenizer, input_ids, attention_mask, 10) + text_1 = tokenizer.decode(output_ids_1[0, prompt_len:], skip_special_tokens=True) + print(f" Output 1: {text_1!r}") + + print("Run 2...") + output_ids_2 = generate_text(adapter, tokenizer, input_ids, attention_mask, 10) + text_2 = tokenizer.decode(output_ids_2[0, prompt_len:], skip_special_tokens=True) + print(f" Output 2: {text_2!r}") + + # Check they match (deterministic greedy decoding) + match = torch.equal(output_ids_1, output_ids_2) + + result = { + "test": "state_reset", + "pass": match, + "output_1": text_1, + "output_2": text_2, + "deterministic": match, + "details": [], + } + + if not match: + result["details"].append( + f"Non-deterministic: run1={text_1!r} vs run2={text_2!r}" + ) + + status = "PASS" if result["pass"] else "FAIL" + print(f"\n Result: {status} (deterministic={match})") + return result + + +def main(): + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--skip-compile", action="store_true") + parser.add_argument("--max-new-tokens", type=int, default=32) + args = parser.parse_args() + + print("=" * 60) + print("Task 010: TKG Validation - InternVL3-8B-Instruct") + print("=" * 60) + + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) + + model = load_model(skip_compile=args.skip_compile) + + # Wrap with HuggingFaceGenerationAdapter + adapter = HuggingFaceGenerationAdapter(model) + + all_results = [] + + # Test 1: Text generation - France + r1 = test_1_text_generation(adapter, tokenizer, args.max_new_tokens) + all_results.append(r1) + + # Test 2: Text generation - meaning of life + r2 = test_2_text_generation_life(adapter, tokenizer, args.max_new_tokens) + all_results.append(r2) + + # Test 3: Multimodal generation + r3 = test_3_multimodal_generation(adapter, tokenizer, args.max_new_tokens) + all_results.append(r3) + + # Test 4: State reset + r4 = test_4_state_reset(adapter, tokenizer, args.max_new_tokens) + all_results.append(r4) + + # Summary + print("\n" + "=" * 60) + print("VALIDATION SUMMARY") + print("=" * 60) + all_pass = True + for r in all_results: + status = "PASS" if r["pass"] else "FAIL" + print(f" {r['test']}: {status}") + if r.get("token_match_rate"): + print(f" token match: {r['token_match_rate']}") + if r.get("gen_time_s"): + print( + f" gen time: {r['gen_time_s']}s ({r.get('n_generated', '?')} tokens)" + ) + if r["details"]: + for d in r["details"]: + print(f" - {d}") + if not r["pass"]: + all_pass = False + + print() + if all_pass: + print("OVERALL: ALL TESTS PASSED") + else: + print("OVERALL: SOME TESTS FAILED") + + # Save results + results_path = "/mnt/models/validation_tkg_results.json" + with open(results_path, "w") as f: + json.dump(all_results, f, indent=2, default=str) + print(f"\nResults saved to {results_path}") + + return 0 if all_pass else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/contrib/models/InternVL3-8B-Instruct/vllm/apply_internvl_patches.py b/contrib/models/InternVL3-8B-Instruct/vllm/apply_internvl_patches.py new file mode 100644 index 00000000..d2e02723 --- /dev/null +++ b/contrib/models/InternVL3-8B-Instruct/vllm/apply_internvl_patches.py @@ -0,0 +1,456 @@ +#!/usr/bin/env python3 +""" +Apply 6 vLLM patches for InternVL3-8B-Instruct on Neuron. + +Patches vllm-neuron 0.5.0 to support InternVL3 via NxDI contrib model. + +Requires: + - NxDI contrib files at /home/ubuntu/internvl3_contrib/src/ + - vLLM-neuron installed at /vllm/ (editable install) + +Usage: + source /opt/aws_neuronx_venv_pytorch_inference_vllm_0_16/bin/activate + python apply_internvl_patches.py [--dry-run] [--revert] +""" + +import argparse +import os +import re +import shutil +import sys + +VLLM_NEURON_DIR = "/vllm/vllm_neuron/worker" +CONSTANTS_FILE = os.path.join(VLLM_NEURON_DIR, "constants.py") +LOADER_FILE = os.path.join(VLLM_NEURON_DIR, "neuronx_distributed_model_loader.py") +RUNNER_FILE = os.path.join(VLLM_NEURON_DIR, "neuronx_distributed_model_runner.py") + +CONTRIB_DIR = "/home/ubuntu/internvl3_contrib/src" + + +def backup_file(filepath): + """Create .orig backup if it doesn't exist yet.""" + backup = filepath + ".orig" + if not os.path.exists(backup): + shutil.copy2(filepath, backup) + print(f" Backed up: {filepath} -> {backup}") + else: + print(f" Backup exists: {backup}") + + +def revert_file(filepath): + """Revert from .orig backup.""" + backup = filepath + ".orig" + if os.path.exists(backup): + shutil.copy2(backup, filepath) + print(f" Reverted: {filepath}") + else: + print(f" No backup found for: {filepath}") + + +def read_file(filepath): + with open(filepath, "r") as f: + return f.read() + + +def write_file(filepath, content): + with open(filepath, "w") as f: + f.write(content) + + +def apply_patch_1_constants(dry_run=False): + """Patch 1: Add InternVLChatModel to NEURON_MULTI_MODAL_MODELS.""" + print("\n--- Patch 1: Register InternVLChatModel in constants.py ---") + + content = read_file(CONSTANTS_FILE) + + if "InternVLChatModel" in content: + print(" Already applied.") + return True + + # Add after last entry in NEURON_MULTI_MODAL_MODELS list + old = ' "Qwen3VLForConditionalGeneration",\n]' + new = ' "Qwen3VLForConditionalGeneration",\n "InternVLChatModel",\n]' + + if old not in content: + print(" ERROR: Cannot find insertion point in constants.py") + return False + + if not dry_run: + backup_file(CONSTANTS_FILE) + content = content.replace(old, new) + write_file(CONSTANTS_FILE, content) + + print(" Applied: Added 'InternVLChatModel' to NEURON_MULTI_MODAL_MODELS") + return True + + +def apply_patch_2_llm_config_fallback(dry_run=False): + """Patch 2: Handle llm_config fallback in _get_model_configs().""" + print("\n--- Patch 2: llm_config fallback in _get_model_configs ---") + + content = read_file(LOADER_FILE) + + if "llm_config" in content and 'getattr(config, "llm_config"' in content: + print(" Already applied.") + return True + + # Find the line: config = getattr(config, "text_config", None) + # Replace with fallback that also checks llm_config + old = ' if architecture in NEURON_MULTI_MODAL_MODELS:\n config = getattr(config, "text_config", None)' + new = ' if architecture in NEURON_MULTI_MODAL_MODELS:\n config = getattr(config, "text_config", None)\n if config is None:\n config = getattr(config, "llm_config", None)' + + if old not in content: + # Try alternative: maybe the indentation is different + print(" ERROR: Cannot find insertion point for llm_config fallback") + return False + + # Wait, the second getattr uses the already-None config. + # We need: try text_config first, if None try llm_config from the ORIGINAL config. + # Let me fix: store original config first. + old = ' if architecture in NEURON_MULTI_MODAL_MODELS:\n config = getattr(config, "text_config", None)' + new = ( + " if architecture in NEURON_MULTI_MODAL_MODELS:\n" + " # InternVL uses llm_config instead of text_config\n" + ' config = getattr(config, "text_config", None) or getattr(config, "llm_config", None)' + ) + + if old not in content: + print(" ERROR: Cannot find insertion point for llm_config fallback") + return False + + if not dry_run: + backup_file(LOADER_FILE) + content = content.replace(old, new) + write_file(LOADER_FILE, content) + + print(" Applied: Added llm_config fallback in _get_model_configs()") + return True + + +def apply_patch_3_internvl_class(dry_run=False): + """Patch 3: Add NeuronInternVL3ForCausalLM class with dynamic import from contrib.""" + print("\n--- Patch 3: Add NeuronInternVL3ForCausalLM class ---") + + content = read_file(LOADER_FILE) + + if "NeuronInternVL3ForCausalLM" in content: + print(" Already applied.") + return True + + # Insert the class right before the get_neuron_model function + class_code = ''' +class NeuronInternVL3ForCausalLM(NeuronMultiModalCausalLM): + """InternVL3-8B-Instruct via NxDI contrib model.""" + + def _save_pretrained_model(self, model_name: str): + # InternVL3 uses trust_remote_code; save locally if needed + from transformers import AutoModel + hf_model = AutoModel.from_pretrained(model_name, trust_remote_code=True) + saved_path = os.path.join("local-models", model_name) + hf_model.save_pretrained(saved_path) + return saved_path + + def load_weights(self, model_name_or_path: str, architecture: str, **kwargs): + """Override to dynamically import contrib model class.""" + import sys + contrib_path = "/home/ubuntu/internvl3_contrib/src" + if contrib_path not in sys.path: + sys.path.insert(0, contrib_path) + + from modeling_internvl3 import NeuronInternVL3ForCausalLM as ContribModel + from neuronx_distributed_inference.utils.constants import MODEL_TYPES + + # Register the contrib model in MODEL_TYPES so _get_neuron_model_cls finds it + if "internvl3" not in MODEL_TYPES: + MODEL_TYPES["internvl3"] = {} + MODEL_TYPES["internvl3"]["causal-lm"] = ContribModel + + return super().load_weights(model_name_or_path, architecture, **kwargs) + + def execute_model(self, model_input, **kwargs): + """Forward multimodal inputs for InternVL3.""" + pixel_values = None + if ( + model_input.multi_modal_kwargs is not None + and model_input.multi_modal_kwargs.get("pixel_values") is not None + ): + pixel_values = model_input.multi_modal_kwargs["pixel_values"] + + # Build vision_mask from image token IDs + # InternVL3 uses IMG_CONTEXT token (id=151667) for image placeholders + IMG_CONTEXT_ID = 151667 + vision_mask = (model_input.input_tokens == IMG_CONTEXT_ID).unsqueeze(-1) + + hidden_states = self.forward( + input_ids=model_input.input_tokens, + positions=model_input.position_ids, + input_block_ids=model_input.input_block_ids, + sampling_params=model_input.sampling_params, + pixel_values=pixel_values, + vision_mask=vision_mask, + **kwargs, + ) + return hidden_states + + def forward( + self, + input_ids, + positions, + input_block_ids, + sampling_params, + pixel_values=None, + vision_mask=None, + **kwargs, + ): + """Forward pass with multimodal support for InternVL3.""" + # Cast vision tensors to the configured dtype + if pixel_values is not None: + dtype = self.model.config.vision_config.neuron_config.torch_dtype + if isinstance(pixel_values, torch.Tensor): + pixel_values = pixel_values.to(dtype) + elif isinstance(pixel_values, list): + pixel_values = [p.to(dtype) for p in pixel_values] + + with self._reordered( + input_block_ids, + input_ids=input_ids, + positions=positions, + sampling_params=sampling_params, + pixel_values=pixel_values, + vision_mask=vision_mask, + ) as (sorted_ids, inputs, restore): + output = self.model( + inputs["input_ids"].to(torch.int32), + attention_mask=None, + position_ids=inputs["positions"].to(torch.int32), + seq_ids=sorted_ids.flatten().to(torch.int32), + pixel_values=inputs.get("pixel_values"), + vision_mask=inputs.get("vision_mask"), + sampling_params=inputs["sampling_params"], + **kwargs, + ) + + if self.model.config.neuron_config.on_device_sampling_config: + output = output.hidden_states + else: + output = output.logits[:, -1, :] + + return restore(output) + + +''' + + # Insert before: def _get_model_configs + anchor = "\ndef _get_model_configs(config: PretrainedConfig) -> str:" + if anchor not in content: + print(" ERROR: Cannot find insertion anchor for NeuronInternVL3ForCausalLM") + return False + + if not dry_run: + # backup already done in patch 2, but just in case + backup_file(LOADER_FILE) + content = content.replace(anchor, class_code + anchor) + write_file(LOADER_FILE, content) + + print(" Applied: Added NeuronInternVL3ForCausalLM class") + return True + + +def apply_patch_4_get_neuron_model(dry_run=False): + """Patch 4: Add InternVLChatModel branch in get_neuron_model().""" + print("\n--- Patch 4: Add InternVLChatModel in get_neuron_model() ---") + + content = read_file(LOADER_FILE) + + if 'architecture == "InternVLChatModel"' in content: + print(" Already applied.") + return True + + # Add after Qwen3VL branch + old = ( + ' elif architecture == "Qwen3VLForConditionalGeneration":\n' + " model = NeuronQwen3VLForCausalLM(model_config.hf_config)\n" + " else:\n" + " model = NeuronCausalLM(model_config.hf_config)" + ) + new = ( + ' elif architecture == "Qwen3VLForConditionalGeneration":\n' + " model = NeuronQwen3VLForCausalLM(model_config.hf_config)\n" + ' elif architecture == "InternVLChatModel":\n' + " model = NeuronInternVL3ForCausalLM(model_config.hf_config)\n" + " else:\n" + " model = NeuronCausalLM(model_config.hf_config)" + ) + + if old not in content: + print(" ERROR: Cannot find insertion point for InternVLChatModel branch") + return False + + if not dry_run: + backup_file(LOADER_FILE) + content = content.replace(old, new) + write_file(LOADER_FILE, content) + + print(" Applied: Added InternVLChatModel branch in get_neuron_model()") + return True + + +def apply_patch_5_multimodal_processor(dry_run=False): + """Patch 5: Add internvl_chat to multimodal data processor.""" + print("\n--- Patch 5: Add internvl_chat in multimodal processor ---") + + content = read_file(RUNNER_FILE) + + if "internvl_chat" in content: + print(" Already applied.") + return True + + # Add after llama4 branch + old = ( + ' elif self.model.model.config.model_type == "llama4":\n' + " pass # llama4 doesn't require special processing\n" + " else:" + ) + new = ( + ' elif self.model.model.config.model_type == "llama4":\n' + " pass # llama4 doesn't require special processing\n" + ' elif self.model.model.config.model_type == "internvl_chat":\n' + " pass # InternVL3 handles vision processing internally via NxDI contrib\n" + " else:" + ) + + if old not in content: + print(" ERROR: Cannot find insertion point for internvl_chat") + return False + + if not dry_run: + backup_file(RUNNER_FILE) + content = content.replace(old, new) + write_file(RUNNER_FILE, content) + + print(" Applied: Added internvl_chat to multimodal processor") + return True + + +def apply_patch_6_model_cls(dry_run=False): + """Patch 6: Special-case InternVLChatModel in _get_neuron_model_cls().""" + print("\n--- Patch 6: Handle InternVLChatModel in _get_neuron_model_cls ---") + + content = read_file(LOADER_FILE) + + # Check if already applied: look for our special case comment in the function + func_start = content.find("def _get_neuron_model_cls(") + if func_start != -1 and "Special case: InternVLChatModel" in content[func_start:]: + print(" Already applied.") + return True + + # The issue: InternVLChatModel doesn't follow *ForConditionalGeneration pattern. + # The parser splits on "For" and lowercases. "InternVLChatModel".split("For", 1) + # gives ["InternVLChatModel"] (no "For"), hitting the KeyError path. + # We need to add a special case BEFORE the split logic. + + old = ' try:\n if "For" in architecture:' + new = ( + " # Special case: InternVLChatModel does not follow *For* naming convention\n" + ' if architecture == "InternVLChatModel":\n' + " import sys\n" + ' contrib_path = "/home/ubuntu/internvl3_contrib/src"\n' + " if contrib_path not in sys.path:\n" + " sys.path.insert(0, contrib_path)\n" + " from modeling_internvl3 import NeuronInternVL3ForCausalLM\n" + " return NeuronInternVL3ForCausalLM\n" + "\n" + ' try:\n if "For" in architecture:' + ) + + if old not in content: + print(" ERROR: Cannot find insertion point in _get_neuron_model_cls") + return False + + if not dry_run: + backup_file(LOADER_FILE) + content = content.replace(old, new) + write_file(LOADER_FILE, content) + + print(" Applied: Added InternVLChatModel special case in _get_neuron_model_cls()") + return True + + +def main(): + parser = argparse.ArgumentParser(description="Apply InternVL3 vLLM patches") + parser.add_argument( + "--dry-run", action="store_true", help="Show patches without applying" + ) + parser.add_argument( + "--revert", action="store_true", help="Revert all patches from backups" + ) + args = parser.parse_args() + + if args.revert: + print("Reverting all patches...") + for f in [CONSTANTS_FILE, LOADER_FILE, RUNNER_FILE]: + revert_file(f) + print("\nDone. Reverted to original files.") + return + + # Verify contrib files exist + if not os.path.exists(CONTRIB_DIR): + print(f"ERROR: Contrib directory not found: {CONTRIB_DIR}") + sys.exit(1) + for f in [ + "modeling_internvl3.py", + "modeling_internvl3_text.py", + "modeling_internvl3_vision.py", + ]: + if not os.path.exists(os.path.join(CONTRIB_DIR, f)): + print(f"ERROR: Missing contrib file: {f}") + sys.exit(1) + + print("=" * 60) + print("Applying InternVL3-8B vLLM patches") + print("=" * 60) + print(f"vLLM-neuron dir: {VLLM_NEURON_DIR}") + print(f"Contrib dir: {CONTRIB_DIR}") + if args.dry_run: + print("*** DRY RUN -- no files will be modified ***") + + results = [] + results.append(("Patch 1: constants.py", apply_patch_1_constants(args.dry_run))) + results.append( + ( + "Patch 2: llm_config fallback", + apply_patch_2_llm_config_fallback(args.dry_run), + ) + ) + results.append( + ("Patch 3: NeuronInternVL3 class", apply_patch_3_internvl_class(args.dry_run)) + ) + results.append( + ("Patch 4: get_neuron_model", apply_patch_4_get_neuron_model(args.dry_run)) + ) + results.append( + ( + "Patch 5: multimodal processor", + apply_patch_5_multimodal_processor(args.dry_run), + ) + ) + results.append( + ("Patch 6: _get_neuron_model_cls", apply_patch_6_model_cls(args.dry_run)) + ) + + print("\n" + "=" * 60) + print("Summary:") + for name, success in results: + status = "OK" if success else "FAILED" + print(f" [{status}] {name}") + + failed = sum(1 for _, s in results if not s) + if failed: + print(f"\n{failed} patch(es) failed!") + sys.exit(1) + else: + print(f"\nAll {len(results)} patches applied successfully.") + + +if __name__ == "__main__": + main()