Add vLLM e2e tests

tests/e2e/vLLM/configs/FP8/fp8_dynamic_per_token.yaml

@@ -0,0 +1,4 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+scheme: FP8_DYNAMIC

tests/e2e/vLLM/configs/FP8/fp8_static_per_tensor.yaml

@@ -0,0 +1,6 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+scheme: FP8
+dataset_id: HuggingFaceH4/ultrachat_200k
+dataset_split: train_sft

tests/e2e/vLLM/configs/FP8/fp8_weight_only_channel.yaml

@@ -0,0 +1,5 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+recipe: tests/e2e/vLLM/recipes/FP8/recipe_fp8_weight_only_channel.yaml
+scheme: FP8A16_channel

tests/e2e/vLLM/configs/FP8/fp8_weight_only_tensor.yaml

@@ -0,0 +1,5 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+recipe: tests/e2e/vLLM/recipes/FP8/recipe_fp8_weight_only_per_tensor.yaml
+scheme: FP8A16_tensor

tests/e2e/vLLM/configs/INT8/int8_channel_weight_static_per_tensor_act.yaml

@@ -0,0 +1,7 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+recipe: tests/e2e/vLLM/recipes/INT8/recipe_int8_channel_weight_static_per_tensor_act.yaml
+dataset_id: HuggingFaceH4/ultrachat_200k
+dataset_split: train_sft
+scheme: W8A8_channel_weight_static_per_tensor

tests/e2e/vLLM/configs/INT8/int8_dynamic_per_token.yaml

@@ -0,0 +1,6 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+scheme: W8A8
+dataset_id: HuggingFaceH4/ultrachat_200k
+dataset_split: train_sft

tests/e2e/vLLM/configs/INT8/int8_tensor_weight_static_per_tensor_act.yaml

@@ -0,0 +1,7 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+recipe: tests/e2e/vLLM/recipes/INT8/recipe_int8_tensor_weight_static_per_tensor_act.yaml
+dataset_id: HuggingFaceH4/ultrachat_200k
+dataset_split: train_sft
+scheme: W8A8_tensor_weight_static_per_tensor_act

tests/e2e/vLLM/configs/WNA16/w4a16_channel_quant.yaml

@@ -0,0 +1,7 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+scheme: W4A16_channel
+dataset_id: HuggingFaceH4/ultrachat_200k
+dataset_split: train_sft
+recipe: tests/e2e/vLLM/recipes/WNA16/recipe_w4a16_channel_quant.yaml

tests/e2e/vLLM/configs/WNA16/w4a16_grouped_quant.yaml

@@ -0,0 +1,6 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+scheme: W4A16
+dataset_id: HuggingFaceH4/ultrachat_200k
+dataset_split: train_sft

tests/e2e/vLLM/configs/WNA16/w8a16_channel_quant.yaml

@@ -0,0 +1,7 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+scheme: W8A16_channel
+dataset_id: HuggingFaceH4/ultrachat_200k
+dataset_split: train_sft
+recipe: tests/e2e/vLLM/recipes/WNA16/recipe_w8a16_channel_quant.yaml

tests/e2e/vLLM/configs/WNA16/w8a16_grouped_quant.yaml

@@ -0,0 +1,6 @@
+cadence: "nightly"
+test_type: "regression"
+model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+scheme: W8A16
+dataset_id: HuggingFaceH4/ultrachat_200k
+dataset_split: train_sft

tests/e2e/vLLM/recipes/FP8/recipe_fp8_weight_only_channel.yaml

@@ -0,0 +1,9 @@
+quant_stage:
+  quant_modifiers:
+    QuantizationModifier:
+      sequential_update: false
+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          weights: {num_bits: 8, type: float, symmetric: true, strategy: channel, dynamic: false}
+          targets: [Linear]

tests/e2e/vLLM/recipes/FP8/recipe_fp8_weight_only_per_tensor.yaml

@@ -0,0 +1,9 @@
+quant_stage:
+  quant_modifiers:
+    QuantizationModifier:
+      sequential_update: false
+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          weights: {num_bits: 8, type: float, symmetric: true, strategy: tensor, dynamic: false}
+          targets: [Linear]

tests/e2e/vLLM/recipes/INT8/recipe_int8_channel_weight_static_per_tensor_act.yaml

@@ -0,0 +1,10 @@
+quant_stage:
+  quant_modifiers:
+    QuantizationModifier:
+      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, type: int, symmetric: true, strategy: tensor}
+          targets: [Linear]

tests/e2e/vLLM/recipes/INT8/recipe_int8_tensor_weight_static_per_tensor_act.yaml

@@ -0,0 +1,10 @@
+quant_stage:
+  quant_modifiers:
+    QuantizationModifier:
+      sequential_update: false
+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          weights: {num_bits: 8, type: int, symmetric: true, strategy: tensor}
+          input_activations: {num_bits: 8, type: int, symmetric: true, strategy: tensor}
+          targets: [Linear]

tests/e2e/vLLM/recipes/WNA16/recipe_w4a16_channel_quant.yaml

@@ -0,0 +1,9 @@
+quant_stage:
+  quant_modifiers:
+    QuantizationModifier:
+      sequential_update: false
+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          weights: {num_bits: 4, type: int, symmetric: true, strategy: channel, dynamic: false}
+          targets: [Linear]

tests/e2e/vLLM/recipes/WNA16/recipe_w8a16_channel_quant.yaml

@@ -0,0 +1,9 @@
+quant_stage:
+  quant_modifiers:
+    QuantizationModifier:
+      sequential_update: false
+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          weights: {num_bits: 8, type: int, symmetric: true, strategy: channel, dynamic: false}
+          targets: [Linear]

tests/e2e/vLLM/test_vllm.py

@@ -0,0 +1,144 @@
+import shutil
+import unittest
+
+import pytest
+from datasets import load_dataset
+from parameterized import parameterized_class
+from transformers import AutoTokenizer
+
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+from tests.testing_utils import parse_params, requires_gpu, requires_torch
+
+try:
+    from vllm import LLM, SamplingParams
+
+    vllm_installed = True
+except ImportError:
+    vllm_installed = False
+
+# Defines the file paths to the directories containing the test configs
+# for each of the quantization schemes
+WNA16 = "tests/e2e/vLLM/configs/WNA16"
+FP8 = "tests/e2e/vLLM/configs/FP8"
+INT8 = "tests/e2e/vLLM/configs/INT8"
+CONFIGS = [WNA16, FP8, INT8]
+
+
+@requires_gpu
+@requires_torch
+@pytest.mark.skipif(not vllm_installed, reason="vLLM is not installed, skipping test")
+@parameterized_class(parse_params(CONFIGS))
+class TestvLLM(unittest.TestCase):
+    """
+    The following test quantizes a model using a preset scheme or recipe,
+    runs the model using vLLM, and then pushes the model to the hub for
+    future use. Each test case is focused on a specific quantization type
+    (e.g W4A16 with grouped quantization, W4N16 with channel quantization).
+    To add a new test case, a new config has to be added to one of the folders
+    listed in the `CONFIGS` folder. If the test case is for a data type not listed
+    in `CONFIGS`, a new folder can be created and added to the list. The tests
+    run on a cadence defined by the `cadence` field. Each config defines the model
+    to quantize. Optionally, a dataset id and split can be provided for calibration.
+    Finally, all config files must list a scheme. The scheme can be a preset scheme
+    from https://github.com/neuralmagic/compressed-tensors/blob/main/src/compressed_tensors/quantization/quant_scheme.py # noqa: E501
+    or another identifier which can be used for the particular test case. If a recipe
+    is not provided, it is assumed that the scheme provided is a preset scheme and will
+    be used for quantization. Otherwise, the recipe will always be used if given.
+    """
+
+    model = None
+    scheme = None
+    dataset_id = None
+    dataset_split = None
+    recipe = None
+
+    def setUp(self):
+        print("========== RUNNING ==============")
+        print(self.scheme)
+
+        self.save_dir = None
+        self.device = "cuda:0"
+        self.oneshot_kwargs = {}
+        self.num_calibration_samples = 256
+        self.max_seq_length = 1048
+        self.prompts = [
+            "The capital of France is",
+            "The president of the US is",
+            "My name is",
+        ]
+
+    def test_vllm(self):
+        # Load model.
+        loaded_model = SparseAutoModelForCausalLM.from_pretrained(
+            self.model, device_map=self.device, torch_dtype="auto"
+        )
+        tokenizer = AutoTokenizer.from_pretrained(self.model)
+
+        def preprocess(example):
+            return {
+                "text": tokenizer.apply_chat_template(
+                    example["messages"],
+                    tokenize=False,
+                )
+            }
+
+        def tokenize(sample):
+            return tokenizer(
+                sample["text"],
+                padding=False,
+                max_length=self.max_seq_length,
+                truncation=True,
+                add_special_tokens=False,
+            )
+
+        if self.dataset_id:
+            ds = load_dataset(self.dataset_id, split=self.dataset_split)
+            ds = ds.shuffle(seed=42).select(range(self.num_calibration_samples))
+            ds = ds.map(preprocess)
+            ds = ds.map(tokenize, remove_columns=ds.column_names)
+            self.oneshot_kwargs["dataset"] = ds
+            self.oneshot_kwargs["max_seq_length"] = self.max_seq_length
+            self.oneshot_kwargs["num_calibration_samples"] = (
+                self.num_calibration_samples
+            )
+
+        self.save_dir = self.model.split("/")[1] + f"-{self.scheme}"
+        self.oneshot_kwargs["model"] = loaded_model
+        if self.recipe:
+            self.oneshot_kwargs["recipe"] = self.recipe
+        else:
+            # Test assumes that if a recipe was not provided, using
+            # a compatible preset sceme
+            self.oneshot_kwargs["recipe"] = QuantizationModifier(
+                targets="Linear", scheme=self.scheme, ignore=["lm_head"]
+            )
+
+        # Apply quantization.
+        print("ONESHOT KWARGS", self.oneshot_kwargs)
+        oneshot(
+            **self.oneshot_kwargs,
+            clear_sparse_session=True,
+            oneshot_device=self.device,
+        )
+        self.oneshot_kwargs["model"].save_pretrained(self.save_dir)
+        tokenizer.save_pretrained(self.save_dir)
+        # Run vLLM with saved model
+        print("================= RUNNING vLLM =========================")
+        sampling_params = SamplingParams(temperature=0.80, top_p=0.95)
+        llm = LLM(model=self.save_dir)
+        outputs = llm.generate(self.prompts, sampling_params)
+        print("================= vLLM GENERATION ======================")
+        for output in outputs:
+            assert output
+            prompt = output.prompt
+            generated_text = output.outputs[0].text
+            print("PROMPT", prompt)
+            print("GENERATED TEXT", generated_text)
+
+        print("================= UPLOADING TO HUB ======================")
+        self.oneshot_kwargs["model"].push_to_hub(f"nm-testing/{self.save_dir}-e2e")
+        tokenizer.push_to_hub(f"nm-testing/{self.save_dir}-e2e")
+
+    def tearDown(self):
+        shutil.rmtree(self.save_dir)

tests/testing_utils.py

@@ -68,39 +68,49 @@ def _validate_test_config(config: dict):
 # Set cadence in the config. The environment must set if nightly, weekly or commit
 # tests are running
 def parse_params(
-    configs_directory: str, type: Optional[str] = None
+    configs_directory: Union[list, str], type: Optional[str] = None
 ) -> List[Union[dict, CustomTestConfig]]:
-    # parses the config file provided
-    assert os.path.isdir(
-        configs_directory
-    ), f"Config_directory {configs_directory} is not a directory"
+    # parses the config files provided
 
     config_dicts = []
-    for file in os.listdir(configs_directory):
-        config = _load_yaml(configs_directory, file)
-        if not config:
-            continue
-
-        cadence = os.environ.get("CADENCE", "commit")
-        expected_cadence = config.get("cadence")
-
-        if not isinstance(expected_cadence, list):
-            expected_cadence = [expected_cadence]
-        if cadence in expected_cadence:
-            if type == "custom":
-                config = CustomTestConfig(**config)
+
+    def _parse_configs_dir(current_config_dir):
+        assert os.path.isdir(
+            current_config_dir
+        ), f"Config_directory {current_config_dir} is not a directory"
+
+        for file in os.listdir(current_config_dir):
+            config = _load_yaml(current_config_dir, file)
+            if not config:
+                continue
+
+            cadence = os.environ.get("CADENCE", "commit")
+            expected_cadence = config.get("cadence")
+
+            if not isinstance(expected_cadence, list):
+                expected_cadence = [expected_cadence]
+            if cadence in expected_cadence:
+                if type == "custom":
+                    config = CustomTestConfig(**config)
+                else:
+                    if not _validate_test_config(config):
+                        raise ValueError(
+                            "The config provided does not comply with the expected "
+                            "structure. See tests.data.TestConfig for the expected "
+                            "fields."
+                        )
+                config_dicts.append(config)
             else:
-                if not _validate_test_config(config):
-                    raise ValueError(
-                        "The config provided does not comply with the expected "
-                        "structure. See tests.data.TestConfig for the expected "
-                        "fields."
-                    )
-            config_dicts.append(config)
-        else:
-            logging.info(
-                f"Skipping testing model: {file} for cadence: {config['cadence']}"
-            )
+                logging.info(
+                    f"Skipping testing model: {file} for cadence: {config['cadence']}"
+                )
+
+    if isinstance(configs_directory, list):
+        for config in configs_directory:
+            _parse_configs_dir(config)
+    else:
+        _parse_configs_dir(configs_directory)
+
     return config_dicts