Correct naming for model name property #1049 (#1050)

Currently, we're storing invalid test property for our model tests. As
consequence, model names aren't properly tracked as part of our CI and
DB. This change should correct it.

Fix #1049

forge/test/models/pytorch/audio/stereo/test_stereo.py

@@ -28,7 +28,7 @@ def test_stereo(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="stereo", variant=variant)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     framework_model, processor = load_model(variant)
 

forge/test/models/pytorch/audio/whisper/test_whisper_0.py

@@ -104,7 +104,7 @@ def test_whisper(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="whisper", variant=variant)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     framework_model, inputs = generate_model_whisper_congen_hf_pytorch(variant)
 
@@ -124,7 +124,7 @@ def test_whisper_pipeline(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="whisper", variant=variant, suffix="pipeline")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Configurations
     compiler_cfg = forge.config._get_global_compiler_config()
@@ -187,7 +187,7 @@ def test_whisper_encoder(record_forge_property, test_device, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="whisper", variant=variant, suffix="encoder")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Configurations
     compiler_cfg = _get_global_compiler_config()

forge/test/models/pytorch/audio/whisper/test_whisper_1.py

@@ -48,7 +48,7 @@ def test_whisper_dec_past_cache(record_forge_property, test_device, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="whisper", variant=variant, suffix="pipeline")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     model, inputs, other = generate_model_whisper_decoder_past_cache(variant)
     compile_inputs = other["compile_inputs"]
@@ -87,7 +87,7 @@ def test_whisper_enc_dec(record_forge_property, test_device, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="whisper", variant=variant, suffix="enc_dec")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     compiler_cfg = _get_global_compiler_config()
     compiler_cfg.enable_tvm_cpu_fallback = False  # Run full model on silicon
@@ -325,7 +325,7 @@ def test_whisper_enc_dec_pipeline(record_forge_property, test_device, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     compiler_cfg = _get_global_compiler_config()
     compiler_cfg.enable_tvm_cpu_fallback = False  # Run full model on silicon

forge/test/models/pytorch/audio/whisper/test_whisper_3.py

@@ -48,7 +48,7 @@ def test_whisper_large_v3_speech_translation(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     processor = WhisperProcessor.from_pretrained(variant)
     framework_model = WhisperForConditionalGeneration.from_pretrained(variant)

forge/test/models/pytorch/multimodal/clip/test_clip.py

@@ -21,7 +21,7 @@ def test_clip_pytorch(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="clip", variant=variant, suffix="text")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load processor and model from HuggingFace
     model = download_model(CLIPModel.from_pretrained, variant, torchscript=True)

forge/test/models/pytorch/multimodal/stable_diffusion/test_stable_diffusion.py

@@ -19,7 +19,7 @@ def test_stable_diffusion_pytorch(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="stable_diffusion", variant=variant)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     batch_size = 1
 

forge/test/models/pytorch/multimodal/vilt/test_vilt.py

@@ -64,7 +64,7 @@ def test_vilt_question_answering_hf_pytorch(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     framework_model, inputs, _ = generate_model_vilt_question_answering_hf_pytorch(variant)
 
@@ -113,7 +113,7 @@ def test_vilt_maskedlm_hf_pytorch(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     framework_model, inputs, _ = generate_model_vilt_maskedlm_hf_pytorch(variant)
 

forge/test/models/pytorch/text/albert/test_albert.py

@@ -32,7 +32,7 @@ def test_albert_masked_lm_pytorch(record_forge_property, size, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     model_ckpt = f"albert-{size}-{variant}"
 
@@ -77,7 +77,7 @@ def test_albert_token_classification_pytorch(record_forge_property, size, varian
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # NOTE: These model variants are pre-trined only. They need to be fine-tuned
     # on a downstream task. Code is for demonstration purposes only.

forge/test/models/pytorch/text/bart/test_bart.py

@@ -33,7 +33,7 @@ def test_pt_bart_classifier(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     model = download_model(BartForSequenceClassification.from_pretrained, variant, torchscript=True)
     tokenizer = download_model(BartTokenizer.from_pretrained, variant, pad_to_max_length=True)

forge/test/models/pytorch/text/bert/test_bert.py

@@ -45,7 +45,7 @@ def test_bert_masked_lm_pytorch(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="bert", variant=variant, task=Task.MASKED_LM)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     framework_model, inputs, _ = generate_model_bert_maskedlm_hf_pytorch(variant)
 
@@ -95,7 +95,7 @@ def test_bert_question_answering_pytorch(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="bert", variant=variant, task=Task.QA)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     framework_model, inputs, _ = generate_model_bert_qa_hf_pytorch(variant)
 
@@ -137,7 +137,7 @@ def test_bert_sequence_classification_pytorch(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     framework_model, inputs, _ = generate_model_bert_seqcls_hf_pytorch(variant)
 
@@ -185,7 +185,7 @@ def test_bert_token_classification_pytorch(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     framework_model, inputs, _ = generate_model_bert_tkcls_hf_pytorch(variant)
 

forge/test/models/pytorch/text/codegen/test_codegen.py

@@ -30,7 +30,7 @@ def test_codegen(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="codegen", variant=variant, task=Task.CAUSAL_LM)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load model (with tokenizer)
     tokenizer = download_model(AutoTokenizer.from_pretrained, variant)

forge/test/models/pytorch/text/distilbert/test_distilbert.py

@@ -31,7 +31,7 @@ def test_distilbert_masked_lm_pytorch(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load DistilBert tokenizer and model from HuggingFace
     # Variants: distilbert-base-uncased, distilbert-base-cased,
@@ -71,7 +71,7 @@ def test_distilbert_question_answering_pytorch(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="distilbert", variant=variant, task=Task.QA)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load Bert tokenizer and model from HuggingFace
     tokenizer = download_model(DistilBertTokenizer.from_pretrained, variant)
@@ -119,7 +119,7 @@ def test_distilbert_sequence_classification_pytorch(record_forge_property, varia
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load DistilBert tokenizer and model from HuggingFace
     tokenizer = download_model(DistilBertTokenizer.from_pretrained, variant)
@@ -157,7 +157,7 @@ def test_distilbert_token_classification_pytorch(record_forge_property, variant)
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load DistilBERT tokenizer and model from HuggingFace
     tokenizer = download_model(DistilBertTokenizer.from_pretrained, variant)

forge/test/models/pytorch/text/dpr/test_dpr.py

@@ -31,7 +31,7 @@ def test_dpr_context_encoder_pytorch(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="dpr", variant=variant, suffix="context_encoder")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load Bert tokenizer and model from HuggingFace
     # Variants: facebook/dpr-ctx_encoder-single-nq-base, facebook/dpr-ctx_encoder-multiset-base
@@ -74,7 +74,7 @@ def test_dpr_question_encoder_pytorch(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load Bert tokenizer and model from HuggingFace
     # Variants: facebook/dpr-question_encoder-single-nq-base, facebook/dpr-question_encoder-multiset-base
@@ -120,7 +120,7 @@ def test_dpr_reader_pytorch(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="dpr", variant=variant, suffix="reader")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load Bert tokenizer and model from HuggingFace
     # Variants: facebook/dpr-reader-single-nq-base, facebook/dpr-reader-multiset-base

forge/test/models/pytorch/text/falcon/test_falcon.py

@@ -20,7 +20,7 @@ def test_falcon(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="falcon", variant=variant)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     tokenizer = AutoTokenizer.from_pretrained(variant)
     model = FalconForCausalLM.from_pretrained(variant)

forge/test/models/pytorch/text/fuyu/test_fuyu_8b.py

@@ -38,7 +38,7 @@ def test_fuyu8b(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="fuyu", variant=variant)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     config = FuyuConfig.from_pretrained(variant)
     config_dict = config.to_dict()
@@ -95,7 +95,7 @@ def test_fuyu8b_past_cache(record_forge_property, variant, test_device):
     module_name = build_module_name(framework=Framework.PYTORCH, model="fuyu", variant=variant)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()

forge/test/models/pytorch/text/gemma/test_gemma_2b.py

@@ -62,7 +62,7 @@ def test_gemma_2b_rotary_embedding(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Random see for reproducibility
     torch.manual_seed(42)
@@ -108,7 +108,7 @@ def test_gemma_2b_rms_norm(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="gemma", variant=variant, suffix="rms_norm")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Random see for reproducibility
     torch.manual_seed(42)
@@ -153,7 +153,7 @@ def test_gemma_2b_attention(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="gemma", variant=variant, suffix="attention")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Random see for reproducibility
     torch.manual_seed(42)
@@ -200,7 +200,7 @@ def test_gemma_2b_mlp(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="gemma", variant=variant, suffix="mlp")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Random see for reproducibility
     torch.manual_seed(42)
@@ -246,7 +246,7 @@ def test_gemma_2b_single_decoder(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Random see for reproducibility
     torch.manual_seed(42)
@@ -291,7 +291,7 @@ def test_gemma_2b(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="gemma", variant=variant)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Random see for reproducibility
     torch.manual_seed(42)
@@ -340,7 +340,7 @@ def test_gemma_2b_gen(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="gemma", variant=variant, suffix="gen")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Random seed for reproducibility
     torch.manual_seed(42)
@@ -405,7 +405,7 @@ def test_gemma_2b_1x1_gen(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="gemma", variant=variant, suffix="gen_1x1")
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Random seed for reproducibility
     torch.manual_seed(42)

forge/test/models/pytorch/text/gpt2/test_gpt2.py

@@ -21,7 +21,7 @@ def test_gpt2_text_gen(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load tokenizer and model from HuggingFace
     config = GPT2Config.from_pretrained(variant)
@@ -80,7 +80,7 @@ def test_gpt2_past_cache(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     compiler_cfg = forge.config._get_global_compiler_config()
     compiler_cfg.compile_subgraphs = True

forge/test/models/pytorch/text/gptneo/test_gptneo.py

@@ -33,7 +33,7 @@ def test_gptneo_causal_lm(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="gptneo", variant=variant, task=Task.CAUSAL_LM)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Set random seed for repeatability
     torch.manual_seed(42)
@@ -95,7 +95,7 @@ def test_gptneo_sequence_classification(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load tokenizer and model from HuggingFace
     # Variants: # EleutherAI/gpt-neo-125M, EleutherAI/gpt-neo-1.3B,

forge/test/models/pytorch/text/llama/test_llama3.py

@@ -137,7 +137,7 @@ def test_llama3_causal_lm(record_forge_property, variant):
     module_name = build_module_name(framework=Framework.PYTORCH, model="llama3", variant=variant, task=Task.CAUSAL_LM)
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load model (with tokenizer)
     tokenizer = download_model(AutoTokenizer.from_pretrained, variant)
@@ -185,7 +185,7 @@ def test_llama3_sequence_classification(record_forge_property, variant):
     )
 
     # Record Forge Property
-    record_forge_property("module_name", module_name)
+    record_forge_property("model_name", module_name)
 
     # Load model (with tokenizer)
     tokenizer = download_model(AutoTokenizer.from_pretrained, variant)