Add phi-3-medium model

forge/test/models/pytorch/text/phi3/test_phi3_medium.py

@@ -0,0 +1,137 @@
+# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+import pytest
+from transformers import (
+    AutoTokenizer,
+    Phi3Config,
+    Phi3ForCausalLM,
+    Phi3ForSequenceClassification,
+    Phi3ForTokenClassification,
+)
+
+import forge
+
+from test.models.utils import Framework, Source, Task, build_module_name
+
+variants = ["microsoft/Phi-3-medium-128k-instruct", "microsoft/Phi-3-mini-128k-instruct"]
+
+
+@pytest.mark.nightly
+@pytest.mark.parametrize("variant", variants)
+def test_phi3_causal_lm(record_forge_property, variant):
+
+    # Build Module Name
+    module_name = build_module_name(variant, Source.HUGGINGFACE, Framework.PYTORCH, Task.CAUSAL_LM)
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Phi3Config from pretrained variant, disable return_dict and caching.
+    config = Phi3Config.from_pretrained(variant)
+    config_dict = config.to_dict()
+    config_dict["return_dict"] = False
+    config_dict["use_cache"] = False
+    config = Phi3Config(**config_dict)
+
+    # Load tokenizer and model from HuggingFace
+    tokenizer = AutoTokenizer.from_pretrained(variant)
+    framework_model = Phi3ForCausalLM.from_pretrained(variant, config=config).to("cpu")
+    framework_model.eval()
+
+    # input_prompt
+    input_prompt = "Africa is an emerging economy because"
+
+    # Tokenize input
+    inputs = tokenizer(input_prompt, return_tensors="pt").to("cpu")
+
+    sample_inputs = [inputs["input_ids"], inputs["attention_mask"]]
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs, module_name)
+
+
+@pytest.mark.nightly
+@pytest.mark.parametrize("variant", variants)
+def test_phi3_token_classification(record_forge_property, variant):
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="phi3",
+        variant=variant,
+        task=Task.TOKEN_CLASSIFICATION,
+        source=Source.HUGGINGFACE,
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Phi3Config from pretrained variant, disable return_dict and caching.
+    config = Phi3Config.from_pretrained(variant)
+    config_dict = config.to_dict()
+    config_dict["return_dict"] = False
+    config_dict["use_cache"] = False
+    config = Phi3Config(**config_dict)
+
+    # Tokenize input
+    tokenizer = AutoTokenizer.from_pretrained(variant, trust_remote_code=True)
+    framework_model = Phi3ForTokenClassification.from_pretrained(variant, trust_remote_code=True, config=config)
+    framework_model.eval()
+
+    # input_prompt
+    input_prompt = "HuggingFace is a company based in Paris and New York"
+
+    # Tokenize input
+    inputs = tokenizer(input_prompt, return_tensors="pt")
+    inputs = [inputs["input_ids"]]
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, inputs, module_name)
+
+
+@pytest.mark.nightly
+@pytest.mark.parametrize("variant", variants)
+def test_phi3_sequence_classification(record_forge_property, variant):
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="phi3",
+        variant=variant,
+        task=Task.SEQUENCE_CLASSIFICATION,
+        source=Source.HUGGINGFACE,
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Phi3Config from pretrained variant, disable return_dict and caching.
+    config = Phi3Config.from_pretrained(variant)
+    config_dict = config.to_dict()
+    config_dict["return_dict"] = False
+    config_dict["use_cache"] = False
+    config_dict["pad_token_id"] = None
+    config = Phi3Config(**config_dict)
+
+    # Load tokenizer and model from HuggingFace
+    tokenizer = AutoTokenizer.from_pretrained(variant, return_tensors="pt", trust_remote_code=True)
+    framework_model = Phi3ForSequenceClassification.from_pretrained(variant, trust_remote_code=True, config=config)
+    framework_model.eval()
+
+    # input_prompt
+    input_prompt = "the movie was great!"
+
+    # Tokenize input
+    inputs = tokenizer(
+        input_prompt,
+        return_tensors="pt",
+        max_length=256,
+        pad_to_max_length=True,
+        truncation=True,
+    )
+
+    inputs = [inputs["input_ids"]]
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, inputs, module_name)