refactor: report cache usage directly

[lvhan028]

Motivation

ScheduleMetrics.total_blocks and free_blocks expose allocator-specific details to the upper metrics layer.

Having the upper layer derive cache utilization from these fields couples it to a particular allocation strategy. Each engine should instead calculate cache utilization according to its own memory model and report a backend-independent value.

Modification

• Removed total_blocks and free_blocks from ScheduleMetrics.
• Changed cache_usage to a concrete float with a default of 0.0.
• Updated the PyTorch scheduler to calculate and report cache usage directly, including handling zero GPU-cache capacity.

[Copilot]

Pull request overview

This PR refactors scheduler cache reporting to expose cache_usage directly (as a normalized 0.0–1.0 fraction) and removes the older total_blocks/free_blocks fields from schedule-metrics across TurboMind (C++/pybind) and Python.

Changes:

• Remove total_blocks/free_blocks from TurboMind ScheduleMetrics (struct, logging, and pybind exposure).
• Compute and expose cache_usage directly in the PyTorch paging scheduler, with added unit coverage including a zero-GPU-blocks edge case.
• Simplify metrics aggregation to always consume ScheduleMetrics.cache_usage (no fallback derivation).

Reviewed changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
tests/pytorch/paging/test_scheduler.py	Adds assertions/tests validating schedule_metrics.cache_usage, including num_gpu_blocks=0.
src/turbomind/utils/metrics.h	Removes block-count fields from ScheduleMetrics and stops printing them.
src/turbomind/python/bind.cpp	Stops exporting total_blocks/free_blocks to Python bindings.
src/turbomind/engine/engine.cc	Stops populating removed block-count fields; retains cache_usage.
lmdeploy/turbomind/turbomind.py	Stops reading removed TurboMind fields when constructing Python ScheduleMetrics.
lmdeploy/pytorch/paging/scheduler.py	Computes cache_usage directly from block-manager utilization.
lmdeploy/metrics/stats.py	Uses scheduled_metrics.cache_usage directly for gpu_cache_usage.
lmdeploy/messages.py	Updates the shared ScheduleMetrics dataclass to drop block-count fields and make cache_usage non-optional.

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