Add model support unit test

tests/test_model_support.py

@@ -0,0 +1,97 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import pytest
+
+import deepspeed
+import torch
+from deepspeed.inference.v2.checkpoint import (
+    CheckpointEngineBase,
+    HuggingFaceCheckpointEngine,
+)
+from transformers import AutoConfig, AutoModel, GenerationConfig
+from typing import Iterable, Tuple
+
+
+class RandomWeightsCheckpointEngine(CheckpointEngineBase):
+
+    # When using AutoModel.from_config() to load the model, the layer names are
+    # often missing a prefix. We default to adding "model." as the prefix, but
+    # others can be specified here.
+    layer_prefix_map = {"falcon": "transformer."}
+
+    # When using AutoModel.from_config() to load the model, the lm_head layer is
+    # not generated. We default to populating this with the
+    # "embed_tokens.weight" layer, but others can be specified here.
+    lm_head_layer_map = {"falcon": "word_embeddings.weight"}
+
+    def __init__(self, model_name_or_path: str, auth_token: str = None) -> None:
+        self.model_name_or_path = model_name_or_path
+        self.model_config = AutoConfig.from_pretrained(self.model_name_or_path)
+        if hasattr(self.model_config, "max_position_embeddings"):
+            self.model_config.max_seq_length = self.model_config.max_position_embeddings
+        else:
+            try:
+                generation_config = GenerationConfig.from_pretrained(
+                    self.model_name_or_path)
+                self.model_config.max_seq_length = generation_config.max_length
+            except OSError:
+                self.model_config.max_seq_length = 2048
+
+    def _get_layer_prefix(self) -> str:
+        for model_type, prefix in self.layer_prefix_map.items():
+            if model_type in self.model_name_or_path.lower():
+                return prefix
+        return "model."
+
+    def _get_lm_head_layer(self) -> str:
+        for model_type, layer in self.lm_head_layer_map.items():
+            if model_type in self.model_name_or_path.lower():
+                return layer
+        return "embed_tokens.weight"
+
+    def parameters(self) -> Iterable[Tuple[str, torch.Tensor]]:
+        layer_prefix = self._get_layer_prefix()
+        lm_head_layer = self._get_lm_head_layer()
+
+        # Load with meta device is faster
+        with deepspeed.OnDevice(dtype=torch.float16, device="meta"):
+            model = AutoModel.from_config(self.model_config)
+
+        for param_name, param in model.state_dict().items():
+            yield layer_prefix + param_name, torch.zeros(param.shape)
+            if param_name == lm_head_layer:
+                yield "lm_head.weight", torch.zeros(param.shape)
+
+
+@pytest.fixture(scope="module", autouse=True)
+def inject_checkpoint_engine():
+    # Inject the random weihts checkpoint engine
+    deepspeed.inference.v2.engine_factory.HuggingFaceCheckpointEngine = (
+        RandomWeightsCheckpointEngine)
+    yield None
+    # Restore the original checkpoint engine
+    deepspeed.inference.v2.engine_factory.HuggingFaceCheckpointEngine = (
+        HuggingFaceCheckpointEngine)
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "tiiuae/falcon-7b",
+        "NousResearch/Llama-2-7b-hf",
+        "mistralai/Mistral-7B-v0.1",
+        "cloudyu/Mixtral_11Bx2_MoE_19B",
+        "facebook/opt-125m",
+    ],
+    ids=["falcon",
+         "llama",
+         "mistral",
+         "mixtral",
+         "opt"],
+)
+def test_model(pipeline, query):
+    outputs = pipeline(query, max_new_tokens=16)
+    assert outputs[0], "output is empty"