Documentation/more model verification (#1207)

* Setup architecture adapters for the 3 Granite Architectures

* Additional Model verification

* CI checks

Author: jlarson4

transformer_lens/benchmarks/main_benchmark.py

@@ -658,7 +658,6 @@ def run_benchmark_suite(
     test_weight_processing_individually: bool = False,
     phases: list[int] | None = None,
     trust_remote_code: bool = False,
-    conserve_memory: bool = False,
     scoring_model: PreTrainedModel | None = None,
     scoring_tokenizer: PreTrainedTokenizerBase | None = None,
 ) -> List[BenchmarkResult]:
@@ -691,11 +690,6 @@ def run_benchmark_suite(
             tests that check each processing flag individually (default: False)
         phases: Optional list of phase numbers to run (e.g., [1, 2, 3]). If None, runs all phases.
         trust_remote_code: Whether to trust remote code for custom architectures.
-        conserve_memory: When True, Phase 1 avoids loading a separate HF model
-            and instead uses bridge.original_model for component benchmarks and
-            forward pass comparison. This halves Phase 1 peak memory (1.0x vs 2.0x)
-            at the cost of losing the independent HF loading cross-check (~5%
-            weakening). Default is False (full dual-load for maximum test coverage).
         scoring_model: Optional pre-loaded GPT-2 scoring model for Phase 4. When
             provided with scoring_tokenizer, avoids reloading for each model in batch.
         scoring_tokenizer: Optional pre-loaded tokenizer for Phase 4 scoring model.
@@ -1024,24 +1018,10 @@ def cleanup_model(model, model_name_str: str):
                 if verbose:
                     print(f"⚠ Could not apply architecture patches: {patch_err}")
 
-    # ----------------------------------------------------------------
-    # Phase 1 memory strategy (controlled by conserve_memory flag):
-    #
-    # conserve_memory=False (default):
-    #   Load separate HF model, capture logits to CPU, load Bridge,
-    #   run component benchmark with both models (brief 2.0x), delete
-    #   HF immediately after, forward pass uses saved logits (1.0x).
-    #
-    # conserve_memory=True:
-    #   Skip separate HF model entirely.  Load Bridge only (1.0x
-    #   throughout).  Component benchmark uses bridge.original_model
-    #   as the HF reference.  Forward pass compares bridge output
-    #   against bridge.original_model logits.
-    # ----------------------------------------------------------------
     hf_saved_logits = None
     hf_saved_loss = None
 
-    if use_hf_reference and not conserve_memory and should_run_phase(1):
+    if use_hf_reference and should_run_phase(1):
         try:
             if verbose:
                 print("Loading HuggingFace reference model...")
@@ -1146,28 +1126,12 @@ def cleanup_model(model, model_name_str: str):
     # Run Phase 1 benchmarks
     if should_run_phase(1) and bridge_unprocessed:
         if verbose:
-            mode_label = " [conserve-memory]" if conserve_memory else ""
-            print(f"Running Phase 1 benchmarks{mode_label}...\n")
+            print("Running Phase 1 benchmarks...\n")
 
         # Component-level benchmarks
         if verbose:
             print("1. Component-Level Benchmarks")
-        if conserve_memory:
-            # conserve_memory mode: use bridge.original_model as the HF
-            # reference (no separate HF load, 1.0x peak throughout).
-            try:
-                component_result = benchmark_all_components(
-                    bridge_unprocessed, bridge_unprocessed.original_model
-                )
-                add_result(component_result)
-                if verbose:
-                    status = "✓" if component_result.passed else "✗"
-                    print(f"{status} {component_result.message}")
-                    print("  (reference: bridge.original_model)\n")
-            except Exception as e:
-                if verbose:
-                    print(f"✗ Component benchmark failed: {e}\n")
-        elif hf_model is not None:
+        if hf_model is not None:
             # Full mode: component benchmark with independent HF model (brief 2.0x)
             try:
                 component_result = benchmark_all_components(bridge_unprocessed, hf_model)
@@ -1242,27 +1206,7 @@ def cleanup_model(model, model_name_str: str):
         # matmul non-determinism can exceed the float32 default of 1e-3
         p1_atol = 1e-3 if dtype == torch.float32 else 5e-3
 
-        if conserve_memory:
-            # conserve_memory mode: capture reference logits from
-            # bridge.original_model (same tokenization as bridge).
-            try:
-                tokens = bridge_unprocessed.to_tokens(test_text)
-                with torch.no_grad():
-                    hf_out = bridge_unprocessed.original_model(tokens)
-                    ref_logits = hf_out.logits.detach()
-                add_result(
-                    benchmark_forward_pass(
-                        bridge_unprocessed,
-                        test_text,
-                        reference_logits=ref_logits,
-                        atol=p1_atol,
-                    )
-                )
-                del ref_logits
-            except Exception as e:
-                if verbose:
-                    print(f"✗ Forward pass benchmark failed: {e}\n")
-        elif hf_saved_logits is not None:
+        if hf_saved_logits is not None:
             # Full mode: use pre-captured HF logits (bridge only, 1.0x)
             try:
                 add_result(
@@ -2028,13 +1972,6 @@ def main():
         action="store_true",
         help="Trust remote code for custom architectures (e.g., OpenELM)",
     )
-    parser.add_argument(
-        "--conserve-memory",
-        action="store_true",
-        help="Reduce Phase 1 peak memory from 2.0x to 1.0x by using "
-        "bridge.original_model instead of loading a separate HF model",
-    )
-
     args = parser.parse_args()
 
     results = run_benchmark_suite(
@@ -2045,7 +1982,6 @@ def main():
         enable_compatibility_mode=not args.no_compat,
         verbose=not args.quiet,
         trust_remote_code=args.trust_remote_code,
-        conserve_memory=args.conserve_memory,
     )
 
     if args.update_registry: