Remove usage of SparseAutoModel.log_model_load

neuralmagic · Apr 9, 2024 · 25c6795 · 25c6795
1 parent d636d35
commit 25c6795
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Showing 7 changed files with 6 additions and 19 deletions.
diff --git a/...als/sentiment-analysis/docs-sentiment-analysis-python-custom-teacher-rottentomatoes.ipynb b/...als/sentiment-analysis/docs-sentiment-analysis-python-custom-teacher-rottentomatoes.ipynb
@@ -364,13 +364,11 @@
     "model_kwargs = {\"config\": model_config}\n",
     "model_kwargs[\"state_dict\"], s_delayed = SparseAutoModel._loadable_state_dict(model_path)\n",
     "model = AutoModelForSequenceClassification.from_pretrained(model_path,**model_kwargs,)\n",
-    "SparseAutoModel.log_model_load(model, model_path, \"student\", s_delayed) # prints metrics on sparsity profile\n",
     "\n",
     "# initialize teacher using familiar HF AutoModel\n",
     "teacher_kwargs = {\"config\": teacher_config}\n",
     "teacher_kwargs[\"state_dict\"], t_delayed = SparseAutoModel._loadable_state_dict(teacher_path)\n",
-    "teacher = AutoModelForSequenceClassification.from_pretrained(teacher_path,**teacher_kwargs,)\n",
-    "SparseAutoModel.log_model_load(teacher, teacher_path, \"teacher\", t_delayed)"
+    "teacher = AutoModelForSequenceClassification.from_pretrained(teacher_path,**teacher_kwargs,)"
    ]
   },
   {

diff --git a/...gface-transformers/tutorials/sentiment-analysis/docs-sentiment-analysis-python-sst2.ipynb b/...gface-transformers/tutorials/sentiment-analysis/docs-sentiment-analysis-python-sst2.ipynb
@@ -440,11 +440,7 @@
     "\n",
     "teacher_kwargs = {'config':teacher_config}\n",
     "teacher_kwargs[\"state_dict\"], t_delayed = SparseAutoModel._loadable_state_dict(teacher_path)\n",
-    "teacher = AutoModelForSequenceClassification.from_pretrained(teacher_path, **teacher_kwargs,)\n",
-    "\n",
-    "# optional - prints metrics about sparsity profiles of the models\n",
-    "SparseAutoModel.log_model_load(model, model_path, \"student\", s_delayed)\n",
-    "SparseAutoModel.log_model_load(teacher, teacher_path, \"teacher\", t_delayed)"
+    "teacher = AutoModelForSequenceClassification.from_pretrained(teacher_path, **teacher_kwargs,)"
    ]
   },
   {

diff --git a/...orials/text-classification/docs-text-classification-python-custom-teacher-tweeteval.ipynb b/...orials/text-classification/docs-text-classification-python-custom-teacher-tweeteval.ipynb
@@ -515,10 +515,7 @@
     "# initialize model using familiar HF AutoModel\n",
     "model_kwargs = {\"config\": config}\n",
     "model_kwargs[\"state_dict\"], s_delayed = SparseAutoModel._loadable_state_dict(model_path)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_path, **model_kwargs,)\n",
-    "\n",
-    "# prints metrics on sparsity profile\n",
-    "SparseAutoModel.log_model_load(model, model_path, \"student\", s_delayed)"
+    "model = AutoModelForSequenceClassification.from_pretrained(model_path, **model_kwargs,)"
    ]
   },
   {

diff --git a/...face-transformers/tutorials/text-classification/docs-text-classification-python-qqp.ipynb b/...face-transformers/tutorials/text-classification/docs-text-classification-python-qqp.ipynb
@@ -375,13 +375,11 @@
     "model_kwargs = {\"config\": model_config}\n",
     "model_kwargs[\"state_dict\"], s_delayed = SparseAutoModel._loadable_state_dict(model_path)\n",
     "model = AutoModelForSequenceClassification.from_pretrained(model_path, **model_kwargs,)\n",
-    "SparseAutoModel.log_model_load(model, model_path, \"student\", s_delayed) # prints metrics on sparsity profile\n",
     "\n",
     "# initialize teacher using familiar HF AutoModel\n",
     "teacher_kwargs = {\"config\": teacher_config}\n",
     "teacher_kwargs[\"state_dict\"], t_delayed = SparseAutoModel._loadable_state_dict(teacher_path)\n",
-    "teacher = AutoModelForSequenceClassification.from_pretrained(teacher_path, **teacher_kwargs,)\n",
-    "SparseAutoModel.log_model_load(teacher, teacher_path, \"teacher\", t_delayed) # prints metrics on sparsity profile"
+    "teacher = AutoModelForSequenceClassification.from_pretrained(teacher_path, **teacher_kwargs,)\n"
    ]
   },
   {

diff --git a/...ace-transformers/tutorials/text-classification/docs-text-classification-python-sick.ipynb b/...ace-transformers/tutorials/text-classification/docs-text-classification-python-sick.ipynb
@@ -361,7 +361,6 @@
     "model_kwargs = {\"config\": config}\n",
     "model_kwargs[\"state_dict\"], s_delayed = SparseAutoModel._loadable_state_dict(model_path)\n",
     "model = AutoModelForSequenceClassification.from_pretrained(model_path,**model_kwargs,)\n",
-    "SparseAutoModel.log_model_load(model, model_path, \"student\", s_delayed) # prints metrics on sparsity profile\n",
     "\n",
     "# FYI: there is a factory function called SparseAutoModel that does the same as above\n",
     "# model, teacher = SparseAutoModel.text_classification_from_pretrained_distil(\n",

diff --git a/...nsformers/tutorials/token-classification/docs-token-classification-python-conll2003.ipynb b/...nsformers/tutorials/token-classification/docs-token-classification-python-conll2003.ipynb
diff --git a/...tutorials/token-classification/docs-token-classification-python-custom-teacher-wnut.ipynb b/...tutorials/token-classification/docs-token-classification-python-custom-teacher-wnut.ipynb
@@ -587,8 +587,7 @@
     "# initialize model\n",
     "model_kwargs = {\"config\": config}\n",
     "model_kwargs[\"state_dict\"], s_delayed = SparseAutoModel._loadable_state_dict(model_path)\n",
-    "model = AutoModelForTokenClassification.from_pretrained(model_path, **model_kwargs,)\n",
-    "SparseAutoModel.log_model_load(model, model_path, \"student\", s_delayed) # prints metrics on sparsity profile"
+    "model = AutoModelForTokenClassification.from_pretrained(model_path, **model_kwargs,)"
    ]
   },
   {