Update nightly.yml

.github/workflows/nightly.yml

@@ -123,7 +123,7 @@ jobs:
       continue-on-error: true
       run: |
         cd $GITHUB_WORKSPACE
-        USE_IREE=1 VENV_DIR=iree.venv ./setup_venv.sh
+        USE_IREE=1 VENV_DIR=iree.venv BENCHMARK=1 ./setup_venv.sh
         source iree.venv/bin/activate
         package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
         SHARK_PACKAGE_VERSION=${package_version} \

generate_sharktank.py

@@ -41,9 +41,12 @@ def create_hash(file_name):
 
 
 def save_torch_model(torch_model_list):
-    from tank.model_utils import get_hf_model
-    from tank.model_utils import get_vision_model
-    from tank.model_utils import get_hf_img_cls_model
+    from tank.model_utils import (
+        get_hf_model,
+        get_vision_model,
+        get_hf_img_cls_model,
+        get_fp16_model,
+    )
 
     with open(torch_model_list) as csvfile:
         torch_reader = csv.reader(csvfile, delimiter=",")
@@ -65,7 +68,8 @@ def save_torch_model(torch_model_list):
                 model, input, _ = get_hf_model(torch_model_name)
             elif model_type == "hf_img_cls":
                 model, input, _ = get_hf_img_cls_model(torch_model_name)
-
+            elif model_type == "fp16":
+                model, input, _ = get_fp16_model(torch_model_name)
             torch_model_name = torch_model_name.replace("/", "_")
             torch_model_dir = os.path.join(
                 WORKDIR, str(torch_model_name) + "_torch"

shark/shark_downloader.py

@@ -169,9 +169,12 @@ def download_model(
                 os.path.join(model_dir, "upstream_hash.npy"),
                 single_file=True,
             )
-            upstream_hash = str(
-                np.load(os.path.join(model_dir, "upstream_hash.npy"))
-            )
+            try:
+                upstream_hash = str(
+                    np.load(os.path.join(model_dir, "upstream_hash.npy"))
+                )
+            except FileNotFoundError:
+                upstream_hash = None
             if local_hash != upstream_hash:
                 print(
                     "Hash does not match upstream in gs://shark_tank/latest. If you want to use locally generated artifacts, this is working as intended. Otherwise, run with --update_tank."

tank/all_models.csv

@@ -17,6 +17,7 @@ albert-base-v2,linalg,torch,1e-2,1e-3,default,None,True,True,True,"issue with at
 alexnet,linalg,torch,1e-2,1e-3,default,None,False,False,True,"Assertion Error: Zeros Output"
 bert-base-cased,linalg,torch,1e-2,1e-3,default,None,False,False,False,""
 bert-base-uncased,linalg,torch,1e-2,1e-3,default,None,False,False,False,""
+bert-base-uncased_fp16,linalg,torch,1e-2,1e-2,default,None,True,True,True,""
 facebook/deit-small-distilled-patch16-224,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"Fails during iree-compile."
 google/vit-base-patch16-224,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"https://github.com/nod-ai/SHARK/issues/311"
 microsoft/beit-base-patch16-224-pt22k-ft22k,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"https://github.com/nod-ai/SHARK/issues/390"
@@ -28,6 +29,7 @@ nvidia/mit-b0,linalg,torch,1e-2,1e-3,default,None,True,True,True,"https://github
 resnet101,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"Vulkan Numerical Error (mostly conv)"
 resnet18,linalg,torch,1e-2,1e-3,default,None,True,True,True,""
 resnet50,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"Vulkan Numerical Error (mostly conv)"
+resnet50_fp16,linalg,torch,1e-2,1e-2,default,nhcw-nhwc,True,False,True,""
 squeezenet1_0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"https://github.com/nod-ai/SHARK/issues/388"
 wide_resnet50_2,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"Vulkan Numerical Error (mostly conv)"
 efficientnet-v2-s,mhlo,tf,1e-02,1e-3,default,nhcw-nhwc,False,False,True,"https://github.com/nod-ai/SHARK/issues/575"

tank/model_metadata.csv

@@ -2,12 +2,14 @@ model_name, use_tracing, dynamic, param_count, tags, notes
 microsoft/MiniLM-L12-H384-uncased,True,True,66M,"nlp;bert-variant;transformer-encoder","Large version has 12 layers; 384 hidden size; Smaller than BERTbase (66M params vs 109M params)"
 albert-base-v2,True,True,11M,"nlp;bert-variant;transformer-encoder","12 layers; 128 embedding dim; 768 hidden dim; 12 attention heads; Smaller than BERTbase (11M params vs 109M params); Uses weight sharing to reduce # params but computational cost is similar to BERT."
 bert-base-uncased,True,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
+bert-base-uncased_fp16,True,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
 bert-base-cased,True,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
 distilbert-base-uncased,True,True,66M,"nlp;bert-variant;transformer-encoder","Smaller and faster than BERT with 97percent retained accuracy."
 google/mobilebert-uncased,True,True,25M,"nlp,bert-variant,transformer-encoder,mobile","24 layers, 512 hidden size, 128 embedding"
 alexnet,False,True,61M,"cnn,parallel-layers","The CNN that revolutionized computer vision (move away from hand-crafted features to neural networks),10 years old now and probably no longer used in prod."
 resnet18,False,True,11M,"cnn,image-classification,residuals,resnet-variant","1 7x7 conv2d and the rest are 3x3 conv2d"
 resnet50,False,True,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
+resnet50_fp16,False,True,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
 resnet101,False,True,29M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
 squeezenet1_0,False,True,1.25M,"cnn,image-classification,mobile,parallel-layers","Parallel conv2d (1x1 conv to compress -> (3x3 expand | 1x1 expand) -> concat)"
 wide_resnet50_2,False,True,69M,"cnn,image-classification,residuals,resnet-variant","Resnet variant where model depth is decreased and width is increased."

tank/model_utils.py

@@ -12,6 +12,7 @@ vision_models = [
     "resnet101",
     "resnet18",
     "resnet50",
+    "resnet50_fp16",
     "squeezenet1_0",
     "wide_resnet50_2",
     "mobilenet_v3_small",
@@ -31,6 +32,8 @@ def get_torch_model(modelname):
         return get_vision_model(modelname)
     elif modelname in hf_img_cls_models:
         return get_hf_img_cls_model(modelname)
+    elif "fp16" in modelname:
+        return get_fp16_model(modelname)
     else:
         return get_hf_model(modelname)
 
@@ -114,7 +117,6 @@ class HuggingFaceLanguage(torch.nn.Module):
 def get_hf_model(name):
     from transformers import (
         BertTokenizer,
-        TFBertModel,
     )
 
     model = HuggingFaceLanguage(name)
@@ -146,6 +148,7 @@ def get_vision_model(torch_model):
         "alexnet": models.alexnet(weights="DEFAULT"),
         "resnet18": models.resnet18(weights="DEFAULT"),
         "resnet50": models.resnet50(weights="DEFAULT"),
+        "resnet50_fp16": models.resnet50(weights="DEFAULT"),
         "resnet101": models.resnet101(weights="DEFAULT"),
         "squeezenet1_0": models.squeezenet1_0(weights="DEFAULT"),
         "wide_resnet50_2": models.wide_resnet50_2(weights="DEFAULT"),
@@ -153,10 +156,25 @@ def get_vision_model(torch_model):
         "mnasnet1_0": models.mnasnet1_0(weights="DEFAULT"),
     }
     if isinstance(torch_model, str):
+        if "fp16" in torch_model:
+            fp16_model = True
         torch_model = vision_models_dict[torch_model]
     model = VisionModule(torch_model)
     test_input = torch.randn(1, 3, 224, 224)
     actual_out = model(test_input)
+    if fp16_model == True:
+        test_input_fp16 = test_input.to(
+            device=torch.device("cuda"), dtype=torch.half
+        )
+        model_fp16 = model.half()
+        model_fp16.eval()
+        model_fp16.to("cuda")
+        actual_out_fp16 = model_fp16(test_input_fp16)
+        model, test_input, actual_out = (
+            model_fp16,
+            test_input_fp16,
+            actual_out_fp16,
+        )
     return model, test_input, actual_out
 
 
@@ -164,6 +182,52 @@ def get_vision_model(torch_model):
 
 ####################### Other PyTorch HF Models ###############################
 
+
+class BertHalfPrecisionModel(torch.nn.Module):
+    def __init__(self, hf_model_name):
+        super().__init__()
+        from transformers import AutoModelForMaskedLM
+        import transformers as trf
+
+        transformers_path = trf.__path__[0]
+        hf_model_path = f"{transformers_path}/models/{hf_model_name}"
+        self.model = AutoModelForMaskedLM.from_pretrained(
+            hf_model_name,  # The pretrained model.
+            num_labels=2,  # The number of output labels--2 for binary classification.
+            output_attentions=False,  # Whether the model returns attentions weights.
+            output_hidden_states=False,  # Whether the model returns all hidden-states.
+            torchscript=True,
+        )
+
+    def forward(self, tokens):
+        return self.model.forward(tokens)[0]
+
+
+def get_fp16_model(torch_model):
+    from transformers import AutoTokenizer
+
+    modelname = torch_model.replace("_fp16", "")
+    model = BertHalfPrecisionModel(modelname)
+    tokenizer = AutoTokenizer.from_pretrained(modelname)
+    text = "Replace me by any text you like."
+    encoded_input = tokenizer(
+        text,
+        truncation=True,
+        max_length=128,
+        return_tensors="pt",
+    )
+    for key in encoded_input:
+        encoded_input[key] = (
+            encoded_input[key].detach().numpy().astype(np.half)
+        )
+
+    model_fp16 = model.half()
+    model_fp16.eval()
+    model_fp16.to("cuda")
+    actual_out_fp16 = model_fp16(encoded_input)
+    return model_fp16, encoded_input, actual_out_fp16
+
+
 # Utility function for comparing two tensors (torch).
 def compare_tensors(torch_tensor, numpy_tensor, rtol=1e-02, atol=1e-03):
     # torch_to_numpy = torch_tensor.detach().numpy()

tank/torch_model_list.csv

@@ -16,3 +16,4 @@ facebook/deit-small-distilled-patch16-224,True,hf_img_cls,False,22M,"image-class
 microsoft/beit-base-patch16-224-pt22k-ft22k,True,hf_img_cls,False,86M,"image-classification,transformer-encoder,bert-variant,vision-transformer",N/A
 nvidia/mit-b0,True,hf_img_cls,False,3.7M,"image-classification,transformer-encoder",SegFormer
 mnasnet1_0,False,vision,True,-,"cnn, torchvision, mobile, architecture-search","Outperforms other mobile CNNs on Accuracy vs. Latency"
+resnet50_fp16,False,vision,True,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"