📈 feat: add MemoryObserver for GPU-CPU communication tracking

Implement MemoryObserver to track memory transfers between GPU and CPU:
- H2D (Host to Device): CPU → GPU transfers
- D2H (Device to Host): GPU → CPU transfers
- D2D (Device to Device): GPU buffer copies
- Supports prefill/decode phase separation

Integration points in offload_engine.py:
- load_to_slot_layer: H2D with is_prefill parameter
- offload_slot_layer_to_cpu, offload_prefill_buffer_async: D2H
- write_to_prefill_buffer, write_to_decode_buffer: D2D
- load_block_sample_from_cpu, load_block_full_from_cpu: H2D

Add bench_offload.py integration for memory stats printing.

Benchmark results (Llama-3.1-8B, 64K context):
- Full Policy: Prefill H2D 262.13 GB
- XAttention: Prefill H2D 386.62 GB (1.48x)

Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>

nanovllm/engine/llm_engine.py

@@ -11,6 +11,7 @@ from nanovllm.engine.sequence import Sequence
 from nanovllm.engine.scheduler import Scheduler
 from nanovllm.engine.model_runner import ModelRunner
 from nanovllm.utils.observer import InferenceObserver
+from nanovllm.utils.memory_observer import MemoryObserver
 
 
 class LLMEngine:
@@ -95,6 +96,7 @@ class LLMEngine:
         debug_enabled = log_level.upper() == 'DEBUG'
 
         InferenceObserver.complete_reset()
+        MemoryObserver.complete_reset()
         if use_tqdm:
             pbar = tqdm(total=len(prompts), desc="Generating", dynamic_ncols=True)
         if not isinstance(sampling_params, list):