Add stable-diffusion in dicp tops ci.

.github/workflows/_runs-on-topsrider.yml

@@ -35,10 +35,13 @@ jobs:
                        pip uninstall dicp -y && \
                        cd dipu && python setup.py install --user && \
                        cd ../dicp && python setup.py install --user && \
-                       cd .. && source dicp/scripts/ci/tops/ci_tops_test_env.sh /mnt/models/llama_models && \
+                       cd .. && source dicp/scripts/ci/tops/ci_tops_test_env.sh \
+                       /mnt/models/llama_models /mnt/models/stable_diffusion_models && \
                        export TEST_DIR=$(pwd)/dicp/test && echo ${TEST_DIR} && \
                        bash ${TEST_DIR}/tops_scripts/ops/run_test_ops.sh false && \
-                       bash ${TEST_DIR}/tops_scripts/models/run_test_models.sh false
+                       bash ${TEST_DIR}/model/run_test_model.sh llama topsgraph false && \
+                       bash ${TEST_DIR}/model/run_test_model.sh resnet50 topsgraph false && \
+                       bash ${TEST_DIR}/model/run_test_model.sh stable_diffusion topsgraph false
                        "
           job_name: "build_test"
           cover_job: "0"

dicp/scripts/ci/tops/ci_tops_test_env.sh

@@ -4,5 +4,6 @@ LLAMA_MODEL_DIR=$1
 STABLE_DIFFUSION_MODEL_DIR=$2
 
 export DIPU_MOCK_CUDA=false
+export ENFLAME_LOG_LEVEL=ERROR
 export LLAMA_MODEL_DIR=$1
 export STABLE_DIFFUSION_MODEL_DIR=$2

dicp/test/model/test_stable_diffusion.py

@@ -0,0 +1,46 @@
+import pytest
+import torch
+import os
+import torch._dynamo as dynamo
+from ..common import utils
+import torch_dipu
+from diffusers import StableDiffusionPipeline
+dynamo.config.cache_size_limit = 128
+utils.update_dynamo_config(False)
+device = utils.get_device()
+torch_dipu.dipu.set_device(device)
+models_dir = os.environ.get("STABLE_DIFFUSION_MODEL_DIR")
+assert models_dir is not None
+
+
+class TestLlama():
+    @pytest.mark.parametrize("model_path", [f"{models_dir}/stable-diffusion-2"])
+    @pytest.mark.parametrize("num_inference_steps", [50])
+    def test_inference(
+        self,
+        model_path: str,
+        backend: str,
+        dynamic: bool,
+        num_inference_steps: int
+    ):
+        prompt = "A photo of an astronaut riding a horse on mars."
+        utils.update_dynamo_config(dynamic=dynamic)
+        torch_dipu.dipu.set_device(device)
+
+        dicp_pipe = StableDiffusionPipeline.from_pretrained(model_path).to(device)
+        dicp_pipe.text_encoder = torch.compile(dicp_pipe.text_encoder, backend=backend)
+        dicp_pipe.unet = torch.compile(dicp_pipe.unet, backend=backend)
+        dicp_image = dicp_pipe(prompt, num_inference_steps=num_inference_steps).images[0]
+        if backend == "ascendgraph":
+            with open("stable_diffusion/topsgraph_output.txt", "r") as f:
+                standard_output = eval(f.read())
+        elif backend == "topsgraph":
+            with open("stable_diffusion/topsgraph_output.txt", "r") as f:
+                standard_output = eval(f.read())
+        else:
+            raise ValueError("backend should in (ascendgrap, topsgraph)")
+        dicp_output = list(dicp_image.getdata())
+        same = 0
+        for i, item in enumerate(standard_output):
+            same += 1 if dicp_output[i] == item else 0
+        assert len(standard_output) == len(dicp_output) and same / len(standard_output) >= 0.999