Add e2e tests for recipe parsing

tests/e2e/recipe.yaml

@@ -0,0 +1,17 @@
+quant_stage:
+  quant_modifiers:
+    SmoothQuantModifier:
+      smoothing_strength: 0.8
+      mappings:
+      - - ['re:.*q_proj', 're:.*k_proj', 're:.*v_proj']
+        - re:.*input_layernorm
+      - - ['re:.*gate_proj', 're:.*up_proj']
+        - re:.*post_attention_layernorm
+    GPTQModifier:
+      sequential_update: false
+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          weights: {num_bits: 8, type: int, symmetric: true, strategy: channel}
+          input_activations: {num_bits: 8, symmetric: false}
+          targets: [Linear]

tests/e2e/test_recipe_parsing.py

@@ -0,0 +1,94 @@
+from pathlib import Path
+
+import pytest
+
+from llmcompressor.core.session_functions import reset_session
+from llmcompressor.modifiers.quantization.gptq import GPTQModifier
+from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+from llmcompressor.modifiers.smoothquant.base import DEFAULT_SMOOTHQUANT_MAPPINGS
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+from tests.testing_utils import requires_gpu
+
+
+@pytest.fixture
+def common_setup():
+    model_stub = "Xenova/llama2.c-stories15M"
+    model = SparseAutoModelForCausalLM.from_pretrained(
+        model_stub, device_map="auto", torch_dtype="auto"
+    )
+
+    dataset = "ultrachat-200k"
+    output_dir = "./test_output"
+    splits = {"calibration": "train_gen[:5%]"}
+    max_seq_length = 2048
+    pad_to_max_length = False
+    num_calibration_samples = 8
+
+    return (
+        model,
+        dataset,
+        output_dir,
+        splits,
+        max_seq_length,
+        pad_to_max_length,
+        num_calibration_samples,
+    )
+
+
+def recipes():
+    modifier_objects = [
+        SmoothQuantModifier(
+            smoothing_strength=0.8, mappings=DEFAULT_SMOOTHQUANT_MAPPINGS
+        ),
+        GPTQModifier(
+            targets="Linear", scheme="W8A8", ignore=["lm_head"], sequential_update=False
+        ),
+    ]
+
+    recipe_str = """
+DEFAULT_stage:
+  DEFAULT_modifiers:
+    SmoothQuantModifier:
+      smoothing_strength: 0.8
+      mappings:
+      - - ['re:.*q_proj', 're:.*k_proj', 're:.*v_proj']
+        - re:.*input_layernorm
+      - - ['re:.*gate_proj', 're:.*up_proj']
+        - re:.*post_attention_layernorm
+    GPTQModifier:
+      sequential_update: false
+      targets: Linear
+      scheme: W8A8
+"""
+
+    recipe_file = str(Path(__file__).parent / "recipe.yaml")
+
+    return [modifier_objects, recipe_str, recipe_file]
+
+
+@requires_gpu
+@pytest.mark.parametrize("recipe", recipes())
+def test_oneshot(common_setup, recipe):
+    (
+        model,
+        dataset,
+        output_dir,
+        splits,
+        max_seq_length,
+        pad_to_max_length,
+        num_calibration_samples,
+    ) = common_setup
+
+    oneshot(
+        model=model,
+        dataset=dataset,
+        recipe=recipe,
+        output_dir=output_dir,
+        splits=splits,
+        max_seq_length=max_seq_length,
+        pad_to_max_length=pad_to_max_length,
+        num_calibration_samples=num_calibration_samples,
+        save_compressed=True,
+    )
+
+    reset_session()