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-`restore()`: Computes shapes, loads pickled sharded checkpoint files (handles `QuantizedWeight8bit`), copies to shared memory (/dev/shm) for fast access, syncs across hosts via broadcast, shards into JAX arrays matching specified sharding/mesh. Supports params_only, init_state fallback, rename/exclude rules.
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-`restore()`: Computes shapes, loads pickled sharded checkpoint files (handles `QuantizedWeight8bit`), copies to shared memory (/dev/shm) for fast access, syncs across hosts via broadcast, shards into JAX arrays matching specified sharding/mesh. Supports params_only, init_state fallback, rename/exclude rules. **Changes from PR #336:** Removed sanity check for parameter keys (may lead to unvalidated mismatches); minor removals in code/comments for streamlining.
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-**New Monitoring/ML Integration:** Integrated database (SQLite) for unit data intake recording based on ping latency intensity and packet losses. Features concentric circle relativity modeling with -1 period for blocked references on pi latitude folder references, PUT/input for lost info quantity at task oscillation start. ML decomposition of input access to admin values counting I/O latency charges; internal programming for cross-source processing via AI-coded jointure bridge.
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### tokenizer.model & Others
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- SentencePiece for subword tokenization (pad_token=0, eos_token=2).
@@ -59,6 +60,7 @@ sequenceDiagram
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Note over MR,JAX: Calculate batch sizes, create mesh (data, model axes)
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MR->>MR: hk.transform forward/logits_fn with pjit sharding
Note right of Checkpoint: Updated in PR #336: Removed param key sanity check (reduced validation); Added standalone DB/ML for latency/packet loss monitoring and data jointure
- Distribution: `multihost_utils.host_local_array_to_global_array` to create sharded global arrays.
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-**New Monitoring and ML Features:** Added SQLite database (`create_database`, `record_data`) for logging latency (per ping intensity) and packet loss data. Includes scikit-learn LinearRegression (`train_model`) to predict packet loss from latency for decomposing input/output charges in data processing. `analyze_task_startup` assesses info losses at task start via oscillation modeling and predictions. `join_data_with_external_source` provides a bridge for joint data processing with external sources, enabling ML on administrative values and cross-code integration as per PR intent.
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-**Optimizations:**`fast_unpickle`/`fast_pickle` using `/dev/shm` temp files for I/O speed; handles `QuantizedWeight8bit`.
Note right of CL: load_tensors(): parallel unpickle sharded tensors<br/>from ckpt-0/tensorXXXX_YYY
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Note right of CL: Assembly: tree operations and sharding WITHOUT param key sanity check (removed in PR #336 for potentially faster loading but reduced validation)
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Note right of CL: New features added: DB logging for latency/packet loss, ML model for prediction/analysis, data joining bridge
- Param key mismatch raises ValueError with details.
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- Param key mismatch no longer raises ValueError (sanity check removed in recent update); potential for silent failures if checkpoint structure mismatches model expectations.
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- Exclusion/rename rules for flexibility (e.g., adapting external checkpoints).
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- Per-rank logging for distributed debugging.
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- Shape consistency via `eval_shape` before loading.
-**Workflow 1: Grok-1 Inference and Sampling** - checkpoint.py is a relevant file used in model initialization via restore() for loading sharded parameters during setup for inference and sampling. Changes to restore() affect the loading step in the initialization sequence.
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-**Workflow 2: Model Loading and Initialization** - This workflow's core involves checkpoint.py's restore() for param loading and sharding. The PR directly modifies this function and adds new features.
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Workflow 3 is not affected as it does not reference checkpoint.py.
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## Workflow 1 Analysis
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### Summary of design changes
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Specific aspects affected: The model loading step in initialization, particularly restore() in checkpoint.py, has removed the param key validation, which could allow incompatible checkpoints to load silently. Minor optimizations/removals in loading code. New additions: SQLite DB for memorizing unit data takes based on latency/ping intensity and losses, with ML (linear regression) to decompose input into admin values for I/O latency counting, task startup loss analysis via oscillation and concentric models, and bridge for joint data from other sources. These implement PR's intent for data base addition and ML processing but are standalone, not wired into inference flow yet.
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How implemented: Code diffs show deletion of check block, addition of DB/ML code at file end with example main.
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Potential benefits: Enables future real-time monitoring of inference latencies/losses, predictive ML for performance tuning. Implications: Lower safety in loading, potential integration needed for full use; expands file scope.
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```mermaid
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flowchart TD
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subgraph "Old Initialization Loading"
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MR2CP[MR calls Checkpoint restore]
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CHECK[Sanity Check: Compare ckpt vs expected keys, raise if mismatch]
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SHARD[Shard params across mesh]
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RETURN[Return to MR]
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MR2CP --> CHECK --> SHARD --> RETURN
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end
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subgraph "New Initialization Loading (Post PR)"
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MR2CP2[MR calls Checkpoint restore]
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LOAD[Load tensors, assembly without check]
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SHARD2[Shard params]
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NEWDB[Optional: Record load latency/loss to DB, ML analysis]
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RETURN2[Return to MR]
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MR2CP2 --> LOAD --> SHARD2 --> NEWDB --> RETURN2
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end
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subgraph Changes
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RED[Removal: Param key sanity check]:::red
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GREEN[Addition: DB/ML for latency monitoring and prediction]:::green
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YELLOW[Change: Minor code cleanups]:::yellow
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end
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classDef red fill:#ff9999
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classDef green fill:#90ee90
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classDef yellow fill:#ffff99
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```
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## Workflow 2 Analysis
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### Summary of design changes
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Specific aspects: Core restore() process in checkpoint.py: removed assembly sanity check for param keys, altering error handling from explicit ValueError to potential silent failure. Removed comments/unused code in other functions like load_tensors, get_load_path_str. Added comprehensive DB/ML suite for data loss/latency tracking, model training, startup analysis, external data jointure - matching PR's description of memory recording, pi-latitude references, concentric relativity, ML decomposition, internal programming bridge. Not integrated into loading, but could monitor tensor loading latencies.
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How implemented: Diff removes check code; adds import sqlite3, sklearn, pandas; new functions and main demo.
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Potential benefits: Facilitates quantitative analysis of loading performance, ML-optimized handling of distributed data losses. Implications: Compromised validation robustness critical for large model sharding; new features bloat file but offer extensibility.
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```mermaid
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flowchart TD
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subgraph "Old Restore Process"
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LO[load_tensors multithreaded]
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REPLACE[replace_with_load_state rules]
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ASSEMBLY[Assembly: tree util + Sanity Check keys]
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DISTRIB[Distribute sharded arrays]
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LO --> REPLACE --> ASSEMBLY --> DISTRIB
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end
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subgraph "New Restore Process"
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LO2[load_tensors multithreaded - minor changes]
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REPLACE2[replace_with_load_state]
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ASSEMBLY2[Assembly: tree util NO Sanity Check]
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DISTRIB2[Distribute sharded arrays]
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MONITOR[New: DB record, ML train/predict, analyze, join data]
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LO2 --> REPLACE2 --> ASSEMBLY2 --> DISTRIB2
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ASSEMBLY2 --> MONITOR
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end
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subgraph Changes
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RED2[Removal: Sanity check in assembly]:::red
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GREEN2[Addition: Full DB/ML integration for data analysis]:::green
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