remove use of vulkaninfo

Update vmfb naming for vulkan devices in order to resolve naming
conflicts in the presence of multiple vulkan devices.

Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>

README.md

@@ -10,7 +10,7 @@ High Performance Machine Learning Distribution
   <summary>Prerequisites - Drivers </summary>
   
 #### Install your Windows hardware drivers
-* [AMD RDNA Users] Download the latest driver [here](https://www.amd.com/en/support/kb/release-notes/rn-rad-win-23-2-1).
+* [AMD RDNA Users] Download the latest driver (23.2.1 is the oldest supported) [here](https://www.amd.com/en/support).
 * [macOS Users] Download and install the 1.3.216 Vulkan SDK from [here](https://sdk.lunarg.com/sdk/download/1.3.216.0/mac/vulkansdk-macos-1.3.216.0.dmg). Newer versions of the SDK will not work. 
 * [Nvidia Users] Download and install the latest CUDA / Vulkan drivers from [here](https://developer.nvidia.com/cuda-downloads)
   

apps/language_models/scripts/vicuna.py

@@ -382,8 +382,7 @@ class VicunaBase(SharkLLMBase):
             if sharded:
                 output = self.shark_model.forward(input_ids, is_first=is_first)
             else:
-                output = self.shark_model("first_vicuna_forward", (input_ids,))
-                out_tensor = torch.tensor(output[1:])
+                output = self.shark_model("first_vicuna_forward", (input_ids,), send_to_host=False)
 
         else:
             token = params["token"]
@@ -402,7 +401,7 @@ class VicunaBase(SharkLLMBase):
                 token = token.to(torch.int64).reshape([1, 1])
                 second_input = (token,) + tuple(past_key_values)
                 output = self.shark_model(
-                    "second_vicuna_forward", second_input
+                    "second_vicuna_forward", second_input, send_to_host=False
                 )
 
         if sharded:
@@ -410,8 +409,8 @@ class VicunaBase(SharkLLMBase):
             _past_key_values = output["past_key_values"]
             _token = int(torch.argmax(_logits[:, -1, :], dim=1)[0])
         else:
-            _logits = torch.tensor(output[0])
-            _past_key_values = torch.tensor(output[1:])
+            _logits = torch.tensor(output[0].to_host())
+            _past_key_values = output[1:]
             _token = torch.argmax(_logits[:, -1, :], dim=1)
 
         _detok = self.tokenizer.decode(_token, skip_special_tokens=False)
@@ -1221,6 +1220,7 @@ class UnshardedVicuna(VicunaBase):
         hf_auth_token: str = None,
         max_num_tokens=512,
         device="cpu",
+        vulkan_target_triple="",
         precision="int8",
         vicuna_mlir_path=None,
         vicuna_vmfb_path=None,
@@ -1230,6 +1230,7 @@ class UnshardedVicuna(VicunaBase):
         download_vmfb=False,
         cache_vicunas=False,
         extra_args_cmd=[],
+        device_id=None,
         debug=False,
     ) -> None:
         super().__init__(
@@ -1248,6 +1249,8 @@ class UnshardedVicuna(VicunaBase):
         print(f"[DEBUG] hf model name: {self.hf_model_path}")
         self.max_sequence_length = 256
         self.device = device
+        self.vulkan_target_triple = vulkan_target_triple.replace("-","_")
+        self.device_id = device_id
         self.precision = precision
         self.download_vmfb = download_vmfb
         self.vicuna_vmfb_path = vicuna_vmfb_path
@@ -1268,8 +1271,9 @@ class UnshardedVicuna(VicunaBase):
         safe_device = self.device.split("-")[0]
         if suffix in ["mlirbc", "mlir"]:
             return Path(f"{self.model_name}_{self.precision}.{suffix}")
+        target_triple = "" if self.vulkan_target_triple=="" else f"_{self.vulkan_target_triple}"
         return Path(
-            f"{self.model_name}_{self.precision}_{safe_device}.{suffix}"
+            f"{self.model_name}_{self.precision}_{safe_device}{target_triple}.{suffix}"
         )
 
     def get_tokenizer(self):
@@ -1410,7 +1414,7 @@ class UnshardedVicuna(VicunaBase):
                 single_file=True,
             )
         self.shark_model = get_vmfb_from_path(
-            self.vicuna_vmfb_path, self.device, "tm_tensor"
+            self.vicuna_vmfb_path, self.device, "tm_tensor", self.device_id
         )
         if self.shark_model is not None:
             print(f"[DEBUG] vmfb found at {self.vicuna_vmfb_path.absolute()}")
@@ -1658,6 +1662,7 @@ class UnshardedVicuna(VicunaBase):
             mlir_module=combined_module,
             device=self.device,
             mlir_dialect="tm_tensor",
+            device_idx=self.device_id
         )
         path = shark_module.save_module(
             self.vicuna_vmfb_path.parent.absolute(),
@@ -1782,14 +1787,26 @@ start_message = {
 def create_prompt(model_name, history):
     global start_message
     system_message = start_message[model_name]
-    conversation = "".join(
-        [
-            "".join(["<|USER|>" + item[0], "<|ASSISTANT|>" + item[1]])
-            for item in history
-        ]
-    )
-    msg = system_message + conversation
-    msg = msg.strip()
+    if "llama2" in model_name:
+        B_INST, E_INST = "[INST]", "[/INST]"
+        B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+        conversation = "".join(
+            [
+                f"{B_INST} {item[0].strip()} {E_INST} {item[1].strip()} "
+                for item in history[1:]
+            ]
+        )
+        msg = f"{B_INST} {B_SYS} {system_message} {E_SYS} {history[0][0]} {E_INST} {history[0][1]} {conversation}"
+
+    else:
+        conversation = "".join(
+            [
+                "".join(["<|USER|>" + item[0], "<|ASSISTANT|>" + item[1]])
+                for item in history
+            ]
+        )
+        msg = system_message + conversation
+        msg = msg.strip()
     return msg
 
 
@@ -1797,11 +1814,37 @@ if __name__ == "__main__":
     args, unknown = parser.parse_known_args()
 
     _extra_args = []
-    # vulkan target triple
-    if args.iree_vulkan_target_triple != "":
+    device_id = None
+    # Process vulkan target triple.
+    # TODO: This feature should just be in a common utils for other LLMs and in general
+    #       any model run via SHARK for Vulkan backend.
+    vulkan_target_triple = args.iree_vulkan_target_triple
+    if vulkan_target_triple != "":
         _extra_args.append(
             f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
         )
+        # Step 1. Fetch the device ID.
+        from shark.iree_utils.vulkan_utils import (
+            get_all_vulkan_devices,
+            get_vulkan_target_triple
+        )
+        vulkaninfo_list = get_all_vulkan_devices()
+        id = 0
+        for device in vulkaninfo_list:
+            target_triple = get_vulkan_target_triple(vulkaninfo_list[id])
+            if target_triple == vulkan_target_triple:
+                device_id = id
+                break
+            id += 1
+        
+        assert device_id, f"no vulkan hardware for target-triple '{vulkan_target_triple}' exists"
+        # Step 2. Add a few flags targetting specific hardwares.
+        if "rdna" in vulkan_target_triple:
+            flags_to_add = [
+                "--iree-spirv-index-bits=64",
+            ]
+            _extra_args = _extra_args + flags_to_add
+        
 
     vic = None
     if not args.sharded:
@@ -1827,6 +1870,7 @@ if __name__ == "__main__":
             download_vmfb=args.download_vmfb,
             cache_vicunas=args.cache_vicunas,
             extra_args_cmd=_extra_args,
+            device_id=device_id
         )
     else:
         if args.config is not None:

apps/language_models/src/pipelines/falcon_pipeline.py

@@ -28,7 +28,9 @@ parser = argparse.ArgumentParser(
     description="runs a falcon model",
 )
 
-parser.add_argument("--falcon_variant_to_use", default="7b", help="7b, 40b")
+parser.add_argument(
+    "--falcon_variant_to_use", default="7b", help="7b, 40b, 180b"
+)
 parser.add_argument(
     "--precision", "-p", default="fp16", help="fp32, fp16, int8, int4"
 )
@@ -49,7 +51,7 @@ parser.add_argument(
 )
 parser.add_argument(
     "--load_mlir_from_shark_tank",
-    default=False,
+    default=True,
     action=argparse.BooleanOptionalAction,
     help="download precompile mlir from shark tank",
 )
@@ -59,13 +61,20 @@ parser.add_argument(
     action=argparse.BooleanOptionalAction,
     help="Run model in cli mode",
 )
+parser.add_argument(
+    "--hf_auth_token",
+    type=str,
+    default=None,
+    help="Specify your own huggingface authentication token for falcon-180B model.",
+)
 
 
 class Falcon(SharkLLMBase):
     def __init__(
         self,
         model_name,
-        hf_model_path,
+        hf_model_path="tiiuae/falcon-7b-instruct",
+        hf_auth_token: str = None,
         max_num_tokens=150,
         device="cuda",
         precision="fp32",
@@ -74,6 +83,15 @@ class Falcon(SharkLLMBase):
         debug=False,
     ) -> None:
         super().__init__(model_name, hf_model_path, max_num_tokens)
+        print("hf_model_path: ", self.hf_model_path)
+
+        if "180b" in self.model_name and hf_auth_token == None:
+            raise ValueError(
+                """ HF auth token required for falcon-180b. Pass it using
+                --hf_auth_token flag. You can ask for the access to the model
+                here: https://huggingface.co/tiiuae/falcon-180B-chat."""
+            )
+        self.hf_auth_token = hf_auth_token
         self.max_padding_length = 100
         self.device = device
         self.precision = precision
@@ -81,12 +99,14 @@ class Falcon(SharkLLMBase):
         self.falcon_mlir_path = falcon_mlir_path
         self.debug = debug
         self.tokenizer = self.get_tokenizer()
-        self.shark_model = self.compile()
         self.src_model = self.get_src_model()
+        self.shark_model = self.compile()
 
     def get_tokenizer(self):
         tokenizer = AutoTokenizer.from_pretrained(
-            self.hf_model_path, trust_remote_code=True
+            self.hf_model_path,
+            trust_remote_code=True,
+            token=self.hf_auth_token,
         )
         tokenizer.padding_side = "left"
         tokenizer.pad_token_id = 11
@@ -94,13 +114,17 @@ class Falcon(SharkLLMBase):
 
     def get_src_model(self):
         print("Loading src model: ", self.model_name)
-        kwargs = {"torch_dtype": torch.float, "trust_remote_code": True}
+        kwargs = {
+            "torch_dtype": torch.float,
+            "trust_remote_code": True,
+            "token": self.hf_auth_token,
+        }
         falcon_model = AutoModelForCausalLM.from_pretrained(
             self.hf_model_path, **kwargs
         )
         return falcon_model
 
-    def compile_falcon(self):
+    def compile(self):
         if args.use_precompiled_model:
             if not self.falcon_vmfb_path.exists():
                 # Downloading VMFB from shark_tank
@@ -122,37 +146,39 @@ class Falcon(SharkLLMBase):
             if vmfb is not None:
                 return vmfb
 
-        print(
-            f"[DEBUG] vmfb not found at {self.falcon_vmfb_path.absolute()}. Trying to work with"
-            f"[DEBUG] mlir path { self.falcon_mlir_path} {'exists' if self.falcon_mlir_path.exists() else 'does not exist'}"
-        )
+        print(f"[DEBUG] vmfb not found at {self.falcon_vmfb_path.absolute()}")
         if self.falcon_mlir_path.exists():
+            print(f"[DEBUG] mlir found at {self.falcon_mlir_path.absolute()}")
             with open(self.falcon_mlir_path, "rb") as f:
                 bytecode = f.read()
         else:
             mlir_generated = False
-            # Downloading MLIR from shark_tank
-            download_public_file(
-                "gs://shark_tank/falcon/"
-                + "falcon_"
-                + args.falcon_variant_to_use
-                + "_"
-                + self.precision
-                + ".mlir",
-                self.falcon_mlir_path.absolute(),
-                single_file=True,
+            print(
+                f"[DEBUG] mlir not found at {self.falcon_mlir_path.absolute()}"
             )
-            if self.falcon_mlir_path.exists():
-                with open(self.falcon_mlir_path, "rb") as f:
-                    bytecode = f.read()
-                mlir_generated = True
-            else:
-                raise ValueError(
-                    f"MLIR not found at {self.falcon_mlir_path.absolute()}"
-                    " after downloading! Please check path and try again"
+            if args.load_mlir_from_shark_tank:
+                # Downloading MLIR from shark_tank
+                print(f"[DEBUG] Trying to download mlir from shark_tank")
+                download_public_file(
+                    "gs://shark_tank/falcon/"
+                    + "falcon_"
+                    + args.falcon_variant_to_use
+                    + "_"
+                    + self.precision
+                    + ".mlir",
+                    self.falcon_mlir_path.absolute(),
+                    single_file=True,
                 )
+                if self.falcon_mlir_path.exists():
+                    print(
+                        f"[DEBUG] mlir found at {self.falcon_mlir_path.absolute()}"
+                    )
+                    with open(self.falcon_mlir_path, "rb") as f:
+                        bytecode = f.read()
+                    mlir_generated = True
 
             if not mlir_generated:
+                print(f"[DEBUG] generating MLIR locally")
                 compilation_input_ids = torch.randint(
                     low=1, high=10000, size=(1, 100)
                 )
@@ -191,10 +217,9 @@ class Falcon(SharkLLMBase):
                 bytecode = bytecode_stream.getvalue()
                 del module
 
-                print(f"[DEBUG] writing mlir to file")
-                with open(f"{self.model_name}.mlir", "wb") as f_:
-                    with redirect_stdout(f_):
-                        print(module.operation.get_asm())
+                f_ = open(self.falcon_mlir_path, "wb")
+                f_.write(bytecode)
+                print("Saved falcon mlir at ", str(self.falcon_mlir_path))
                 f_.close()
 
         shark_module = SharkInference(
@@ -204,11 +229,9 @@ class Falcon(SharkLLMBase):
             self.falcon_vmfb_path.parent.absolute(),
             self.falcon_vmfb_path.stem,
             extra_args=[
-                "--iree-hal-dump-executable-sources-to=ies",
                 "--iree-vm-target-truncate-unsupported-floats",
                 "--iree-codegen-check-ir-before-llvm-conversion=false",
                 "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-                "--iree-spirv-index-bits=64",
             ],
             debug=self.debug,
         )
@@ -217,10 +240,6 @@ class Falcon(SharkLLMBase):
 
         return shark_module
 
-    def compile(self):
-        falcon_shark_model = self.compile_falcon()
-        return falcon_shark_model
-
     def generate(self, prompt):
         model_inputs = self.tokenizer(
             prompt,
@@ -469,11 +488,16 @@ if __name__ == "__main__":
         else Path(args.falcon_vmfb_path)
     )
 
+    if args.falcon_variant_to_use == "180b":
+        hf_model_path_value = "tiiuae/falcon-180B-chat"
+    else:
+        hf_model_path_value = (
+            "tiiuae/falcon-" + args.falcon_variant_to_use + "-instruct"
+        )
+
     falcon = Falcon(
-        "falcon_" + args.falcon_variant_to_use,
-        hf_model_path="tiiuae/falcon-"
-        + args.falcon_variant_to_use
-        + "-instruct",
+        model_name="falcon_" + args.falcon_variant_to_use,
+        hf_model_path=hf_model_path_value,
         device=args.device,
         precision=args.precision,
         falcon_mlir_path=falcon_mlir_path,

apps/language_models/utils.py

@@ -8,7 +8,7 @@ from shark.shark_downloader import download_public_file
 
 # expects a Path / str as arg
 # returns None if path not found or SharkInference module
-def get_vmfb_from_path(vmfb_path, device, mlir_dialect):
+def get_vmfb_from_path(vmfb_path, device, mlir_dialect, device_id=None):
     if not isinstance(vmfb_path, Path):
         vmfb_path = Path(vmfb_path)
 
@@ -20,7 +20,7 @@ def get_vmfb_from_path(vmfb_path, device, mlir_dialect):
     print("Loading vmfb from: ", vmfb_path)
     print("Device from get_vmfb_from_path - ", device)
     shark_module = SharkInference(
-        None, device=device, mlir_dialect=mlir_dialect
+        None, device=device, mlir_dialect=mlir_dialect, device_idx=device_id
     )
     shark_module.load_module(vmfb_path)
     print("Successfully loaded vmfb")
@@ -28,7 +28,13 @@ def get_vmfb_from_path(vmfb_path, device, mlir_dialect):
 
 
 def get_vmfb_from_config(
-    shark_container, model, precision, device, vmfb_path, padding=None
+    shark_container,
+    model,
+    precision,
+    device,
+    vmfb_path,
+    padding=None,
+    device_id=None,
 ):
     vmfb_url = (
         f"gs://shark_tank/{shark_container}/{model}_{precision}_{device}"
@@ -37,4 +43,6 @@ def get_vmfb_from_config(
         vmfb_url = vmfb_url + f"_{padding}"
     vmfb_url = vmfb_url + ".vmfb"
     download_public_file(vmfb_url, vmfb_path.absolute(), single_file=True)
-    return get_vmfb_from_path(vmfb_path, device, "tm_tensor")
+    return get_vmfb_from_path(
+        vmfb_path, device, "tm_tensor", device_id=device_id
+    )

apps/stable_diffusion/shark_studio_imports.py

@@ -15,8 +15,8 @@ pathex = [
 
 # datafiles for pyinstaller
 datas = []
-datas += collect_data_files("torch")
 datas += copy_metadata("torch")
+datas += copy_metadata("tokenizers")
 datas += copy_metadata("tqdm")
 datas += copy_metadata("regex")
 datas += copy_metadata("requests")
@@ -31,18 +31,17 @@ datas += copy_metadata("Pillow")
 datas += copy_metadata("sentencepiece")
 datas += copy_metadata("pyyaml")
 datas += copy_metadata("huggingface-hub")
+datas += collect_data_files("torch")
 datas += collect_data_files("tokenizers")
 datas += collect_data_files("tiktoken")
 datas += collect_data_files("accelerate")
 datas += collect_data_files("diffusers")
 datas += collect_data_files("transformers")
 datas += collect_data_files("pytorch_lightning")
-datas += collect_data_files("opencv_python")
 datas += collect_data_files("skimage")
 datas += collect_data_files("gradio")
 datas += collect_data_files("gradio_client")
 datas += collect_data_files("iree")
-datas += collect_data_files("google_cloud_storage")
 datas += collect_data_files("shark", include_py_files=True)
 datas += collect_data_files("timm", include_py_files=True)
 datas += collect_data_files("tqdm")
@@ -53,6 +52,7 @@ datas += collect_data_files("jsonschema")
 datas += collect_data_files("jsonschema_specifications")
 datas += collect_data_files("cpuinfo")
 datas += collect_data_files("langchain")
+datas += collect_data_files("cv2")
 datas += [
     ("src/utils/resources/prompts.json", "resources"),
     ("src/utils/resources/model_db.json", "resources"),
@@ -81,4 +81,4 @@ hiddenimports += [
     if not any(kw in x for kw in blacklist)
 ]
 hiddenimports += [x for x in collect_submodules("iree") if "tests" not in x]
-hiddenimports += ["iree._runtime", "iree._runtime_libs"]
+hiddenimports += ["iree._runtime", "iree.compiler._mlir_libs._mlir.ir"]

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_stencil.py

@@ -273,6 +273,7 @@ class StencilPipeline(StableDiffusionPipeline):
         cpu_scheduling,
         max_embeddings_multiples,
         use_stencil,
+        resample_type,
     ):
         # Control Embedding check & conversion
         # TODO: 1. Change `num_images_per_prompt`.

apps/stable_diffusion/src/utils/sd_annotation.py

@@ -158,9 +158,9 @@ def load_lower_configs(base_model_id=None):
                 f"{spec}.json"
             )
 
-    full_gs_url = config_bucket + config_name
     lowering_config_dir = os.path.join(WORKDIR, "configs", config_name)
     print("Loading lowering config file from ", lowering_config_dir)
+    full_gs_url = config_bucket + config_name
     download_public_file(full_gs_url, lowering_config_dir, True)
     return lowering_config_dir
 
@@ -281,13 +281,9 @@ def sd_model_annotation(mlir_model, model_name, base_model_id=None):
         if "rdna2" not in args.iree_vulkan_target_triple.split("-")[0]:
             use_winograd = True
             winograd_config_dir = load_winograd_configs()
-            winograd_model = annotate_with_winograd(
+            tuned_model = annotate_with_winograd(
                 mlir_model, winograd_config_dir, model_name
             )
-            lowering_config_dir = load_lower_configs(base_model_id)
-            tuned_model = annotate_with_lower_configs(
-                winograd_model, lowering_config_dir, model_name, use_winograd
-            )
         else:
             tuned_model = mlir_model
     else:

apps/stable_diffusion/src/utils/utils.py

@@ -470,7 +470,18 @@ def get_available_devices():
     set_iree_runtime_flags()
 
     available_devices = []
-    vulkan_devices = get_devices_by_name("vulkan")
+    from shark.iree_utils.vulkan_utils import (
+        get_all_vulkan_devices,
+    )
+
+    vulkaninfo_list = get_all_vulkan_devices()
+    vulkan_devices = []
+    id = 0
+    for device in vulkaninfo_list:
+        vulkan_devices.append(f"{device.strip()} => vulkan://{id}")
+        id += 1
+    if id != 0:
+        print(f"vulkan devices are available.")
     available_devices.extend(vulkan_devices)
     metal_devices = get_devices_by_name("metal")
     available_devices.extend(metal_devices)
@@ -577,7 +588,7 @@ def preprocessCKPT(custom_weights, is_inpaint=False):
     )
     num_in_channels = 9 if is_inpaint else 4
     pipe = download_from_original_stable_diffusion_ckpt(
-        checkpoint_path=custom_weights,
+        checkpoint_path_or_dict=custom_weights,
         extract_ema=extract_ema,
         from_safetensors=from_safetensors,
         num_in_channels=num_in_channels,
@@ -827,6 +838,8 @@ def clear_all():
     elif os.name == "unix":
         shutil.rmtree(os.path.join(home, ".cache/AMD/VkCache"))
         shutil.rmtree(os.path.join(home, ".local/shark_tank"))
+    if args.local_tank_cache != "":
+        shutil.rmtree(args.local_tank_cache)
 
 
 def get_generated_imgs_path() -> Path:

apps/stable_diffusion/web/ui/stablelm_ui.py

@@ -69,25 +69,28 @@ start_message = {
 def create_prompt(model_name, history):
     system_message = start_message[model_name]
 
-    if model_name in [
-        "vicuna",
-        "llama2_7b",
-        "llama2_13b",
-        "llama2_70b",
-    ]:
+    if "llama2" in model_name:
+        B_INST, E_INST = "[INST]", "[/INST]"
+        B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+        conversation = "".join(
+            [f"{B_INST} {item[0]} {E_INST} {item[1]} " for item in history[1:]]
+        )
+        msg = f"{B_INST} {B_SYS} {system_message} {E_SYS} {history[0][0]} {E_INST} {history[0][1]} {conversation}"
+    elif model_name in ["vicuna"]:
         conversation = "".join(
             [
                 "".join(["<|USER|>" + item[0], "<|ASSISTANT|>" + item[1]])
                 for item in history
             ]
         )
+        msg = system_message + conversation
+        msg = msg.strip()
     else:
         conversation = "".join(
             ["".join([item[0], item[1]]) for item in history]
         )
-
-    msg = system_message + conversation
-    msg = msg.strip()
+        msg = system_message + conversation
+        msg = msg.strip()
     return msg
 
 
@@ -140,6 +143,7 @@ def chat(
     global model_vmfb_key
     global vicuna_model
 
+    device_id = None
     model_name, model_path = list(map(str.strip, model.split("=>")))
     if "cuda" in device:
         device = "cuda"
@@ -148,6 +152,7 @@ def chat(
     elif "task" in device:
         device = "cpu-task"
     elif "vulkan" in device:
+        device_id = int(device.split("://")[1])
         device = "vulkan"
     elif "rocm" in device:
         device = "rocm"
@@ -166,10 +171,45 @@ def chat(
         # get iree flags that need to be overridden, from commandline args
         _extra_args = []
         # vulkan target triple
-        if args.iree_vulkan_target_triple != "":
+        vulkan_target_triple = args.iree_vulkan_target_triple
+        from shark.iree_utils.vulkan_utils import (
+            get_all_vulkan_devices,
+            get_vulkan_target_triple,
+        )
+
+        if device == "vulkan":
+            vulkaninfo_list = get_all_vulkan_devices()
+            if vulkan_target_triple == "":
+                # We already have the device_id extracted via WebUI, so we directly use
+                # that to find the target triple.
+                vulkan_target_triple = get_vulkan_target_triple(
+                    vulkaninfo_list[device_id]
+                )
             _extra_args.append(
-                f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+                f"-iree-vulkan-target-triple={vulkan_target_triple}"
             )
+            if "rdna" in vulkan_target_triple:
+                flags_to_add = [
+                    "--iree-spirv-index-bits=64",
+                ]
+                _extra_args = _extra_args + flags_to_add
+
+            if device_id is None:
+                id = 0
+                for device in vulkaninfo_list:
+                    target_triple = get_vulkan_target_triple(
+                        vulkaninfo_list[id]
+                    )
+                    if target_triple == vulkan_target_triple:
+                        device_id = id
+                        break
+                    id += 1
+
+                assert (
+                    device_id
+                ), f"no vulkan hardware for target-triple '{vulkan_target_triple}' exists"
+
+        print(f"Will use target triple : {vulkan_target_triple}")
 
         if model_name == "vicuna4":
             vicuna_model = ShardedVicuna(
@@ -188,11 +228,13 @@ def chat(
                 hf_model_path=model_path,
                 hf_auth_token=args.hf_auth_token,
                 device=device,
+                vulkan_target_triple=vulkan_target_triple,
                 precision=precision,
                 max_num_tokens=max_toks,
                 download_vmfb=download_vmfb,
                 load_mlir_from_shark_tank=True,
                 extra_args_cmd=_extra_args,
+                device_id=device_id,
             )
 
     prompt = create_prompt(model_name, history)
@@ -251,6 +293,7 @@ def llm_chat_api(InputData: dict):
         UnshardedVicuna,
     )
 
+    device_id = None
     if vicuna_model == 0:
         if "cuda" in device:
             device = "cuda"
@@ -259,6 +302,7 @@ def llm_chat_api(InputData: dict):
         elif "task" in device:
             device = "cpu-task"
         elif "vulkan" in device:
+            device_id = int(device.split("://")[1])
             device = "vulkan"
         else:
             print("unrecognized device")
@@ -271,6 +315,7 @@ def llm_chat_api(InputData: dict):
             max_num_tokens=max_toks,
             download_vmfb=True,
             load_mlir_from_shark_tank=True,
+            device_id=device_id,
         )
 
     # TODO: add role dict for different models

inference/CMakeLists.txt

@@ -1,192 +0,0 @@
-# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#  * Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-#  * Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in the
-#    documentation and/or other materials provided with the distribution.
-#  * Neither the name of NVIDIA CORPORATION nor the names of its
-#    contributors may be used to endorse or promote products derived
-#    from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
-# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
-# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-cmake_minimum_required(VERSION 3.17)
-
-project(sharkbackend LANGUAGES C CXX)
-
-#
-# Options
-#
-
-option(TRITON_ENABLE_GPU "Enable GPU support in backend" ON)
-option(TRITON_ENABLE_STATS "Include statistics collections in backend" ON)
-
-set(TRITON_COMMON_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/common repo")
-set(TRITON_CORE_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/core repo")
-set(TRITON_BACKEND_REPO_TAG "main" CACHE STRING "Tag for triton-inference-server/backend repo")
-
-if(NOT CMAKE_BUILD_TYPE)
-  set(CMAKE_BUILD_TYPE Release)
-endif()
-
-#
-# Dependencies
-#
-# FetchContent requires us to include the transitive closure of all
-# repos that we depend on so that we can override the tags.
-#
-include(FetchContent)
-
-FetchContent_Declare(
-  repo-common
-  GIT_REPOSITORY https://github.com/triton-inference-server/common.git
-  GIT_TAG ${TRITON_COMMON_REPO_TAG}
-  GIT_SHALLOW ON
-)
-FetchContent_Declare(
-  repo-core
-  GIT_REPOSITORY https://github.com/triton-inference-server/core.git
-  GIT_TAG ${TRITON_CORE_REPO_TAG}
-  GIT_SHALLOW ON
-)
-FetchContent_Declare(
-  repo-backend
-  GIT_REPOSITORY https://github.com/triton-inference-server/backend.git
-  GIT_TAG ${TRITON_BACKEND_REPO_TAG}
-  GIT_SHALLOW ON
-)
-FetchContent_MakeAvailable(repo-common repo-core repo-backend)
-
-#
-# The backend must be built into a shared library. Use an ldscript to
-# hide all symbols except for the TRITONBACKEND API.
-#
-configure_file(src/libtriton_dshark.ldscript libtriton_dshark.ldscript COPYONLY)
-
-add_library(
-  triton-dshark-backend SHARED
-  src/dshark.cc
-  #src/dshark_driver_module.c
-)
-
-add_library(
-  SharkBackend::triton-dshark-backend ALIAS triton-dshark-backend
-)
-
-target_include_directories(
-  triton-dshark-backend
-  PRIVATE
-    ${CMAKE_CURRENT_SOURCE_DIR}/src
-)
-
-list(APPEND CMAKE_MODULE_PATH "${PROJECT_BINARY_DIR}/lib/cmake/mlir")
-
-add_subdirectory(thirdparty/srt EXCLUDE_FROM_ALL)
-
-target_link_libraries(triton-dshark-backend PRIVATE iree_base_base
-  iree_hal_hal
-  iree_hal_cuda_cuda
-  iree_hal_cuda_registration_registration
-  iree_hal_vmvx_registration_registration
-  iree_hal_dylib_registration_registration
-  iree_modules_hal_hal
-  iree_vm_vm
-  iree_vm_bytecode_module
-  iree_hal_local_loaders_system_library_loader
-  iree_hal_local_loaders_vmvx_module_loader
-  )
-
-target_compile_features(triton-dshark-backend PRIVATE cxx_std_11)
-
-
-target_link_libraries(
-  triton-dshark-backend
-  PRIVATE
-    triton-core-serverapi   # from repo-core
-    triton-core-backendapi  # from repo-core
-    triton-core-serverstub  # from repo-core
-    triton-backend-utils    # from repo-backend
-)
-
-if(WIN32)
-  set_target_properties(
-    triton-dshark-backend PROPERTIES
-    POSITION_INDEPENDENT_CODE ON
-    OUTPUT_NAME triton_dshark
-  )
-else()
-  set_target_properties(
-    triton-dshark-backend PROPERTIES
-    POSITION_INDEPENDENT_CODE ON
-    OUTPUT_NAME triton_dshark
-    LINK_DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/libtriton_dshark.ldscript
-    LINK_FLAGS "-Wl,--version-script libtriton_dshark.ldscript"
-  )
-endif()
-
-
-
-#
-# Install
-#
-include(GNUInstallDirs)
-set(INSTALL_CONFIGDIR ${CMAKE_INSTALL_LIBDIR}/cmake/SharkBackend)
-
-install(
-  TARGETS
-    triton-dshark-backend
-  EXPORT
-    triton-dshark-backend-targets
-  LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/backends/dshark
-  RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/backends/dshark
-)
-
-install(
-  EXPORT
-    triton-dshark-backend-targets
-  FILE
-    SharkBackendTargets.cmake
-  NAMESPACE
-    SharkBackend::
-  DESTINATION
-    ${INSTALL_CONFIGDIR}
-)
-
-include(CMakePackageConfigHelpers)
-configure_package_config_file(
-  ${CMAKE_CURRENT_LIST_DIR}/cmake/SharkBackendConfig.cmake.in
-  ${CMAKE_CURRENT_BINARY_DIR}/SharkBackendConfig.cmake
-  INSTALL_DESTINATION ${INSTALL_CONFIGDIR}
-)
-
-install(
-  FILES
-  ${CMAKE_CURRENT_BINARY_DIR}/SharkBackendConfig.cmake
-  DESTINATION ${INSTALL_CONFIGDIR}
-)
-
-#
-# Export from build tree
-#
-export(
-  EXPORT triton-dshark-backend-targets
-  FILE ${CMAKE_CURRENT_BINARY_DIR}/SharkBackendTargets.cmake
-  NAMESPACE SharkBackend::
-)
-
-export(PACKAGE SharkBackend)
-

inference/README.md

@@ -1,100 +0,0 @@
-# SHARK Triton Backend
-
-The triton backend for shark.
-
-# Build
-
-Install SHARK
-
-```
-git clone https://github.com/nod-ai/SHARK.git
-# skip above step if dshark is already installed
-cd SHARK/inference
-```
-
-install dependancies
-
-```
-apt-get install patchelf rapidjson-dev python3-dev
-git submodule update --init
-```
-
-update the submodules of iree
-
-```
-cd thirdparty/srt
-git submodule update --init
-```
-
-Next, make the backend and install it
-
-```
-cd ../..
-mkdir build && cd build
-cmake -DTRITON_ENABLE_GPU=ON \
--DIREE_HAL_DRIVER_CUDA=ON \
--DIREE_TARGET_BACKEND_CUDA=ON \
--DMLIR_ENABLE_CUDA_RUNNER=ON \
--DCMAKE_INSTALL_PREFIX:PATH=`pwd`/install \
--DTRITON_BACKEND_REPO_TAG=r22.02 \
--DTRITON_CORE_REPO_TAG=r22.02 \
--DTRITON_COMMON_REPO_TAG=r22.02 ..
-make install
-```
-
-# Incorporating into Triton
-
-There are much more in depth explenations for the following steps in triton's documentation:
-https://github.com/triton-inference-server/server/blob/main/docs/compose.md#triton-with-unsupported-and-custom-backends
-
-There should be a file at /build/install/backends/dshark/libtriton_dshark.so.  You will need to copy it into your triton server image.  
-More documentation is in the link above, but to create the docker image, you need to run the compose.py command in the triton-backend server repo
-
-
-To first build your image, clone the tritonserver repo.
-
-```
-git clone https://github.com/triton-inference-server/server.git
-```
-
-then run `compose.py` to build a docker compose file 
-```
-cd server
-python3 compose.py --repoagent checksum --dry-run
-```
-
-Because dshark is a third party backend, you will need to manually modify the `Dockerfile.compose` to include the dshark backend.  To do this, in the Dockerfile.compose file produced, copy this line.
-the dshark backend will be located in the build folder from earlier under `/build/install/backends`
-
-```
-COPY /path/to/build/install/backends/dshark /opt/tritonserver/backends/dshark
-```
-
-Next run 
-```
-docker build -t tritonserver_custom -f Dockerfile.compose .
-docker run -it --gpus=1 --net=host -v/path/to/model_repos:/models  tritonserver_custom:latest tritonserver --model-repository=/models
-```
-
-where `path/to/model_repos` is where you are storing the models you want to run
-
-if your not using gpus, omit `--gpus=1`
-
-```
-docker run -it  --net=host -v/path/to/model_repos:/models  tritonserver_custom:latest tritonserver --model-repository=/models
-```
-
-# Setting up a model
-
-to include a model in your backend, add a directory with your model name to your model repository directory.  examples of models can be seen here: https://github.com/triton-inference-server/backend/tree/main/examples/model_repos/minimal_models
-
-make sure to adjust the input correctly in the config.pbtxt file, and save a vmfb file under 1/model.vmfb
-
-# CUDA
-
-if you're having issues with cuda, make sure your correct drivers are installed, and that `nvidia-smi` works, and also make sure that the nvcc compiler is on the path.
-
-
-
-
-

inference/cmake/SharkBackendConfig.cmake.in

@@ -1,39 +0,0 @@
-# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#  * Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-#  * Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in the
-#    documentation and/or other materials provided with the distribution.
-#  * Neither the name of NVIDIA CORPORATION nor the names of its
-#    contributors may be used to endorse or promote products derived
-#    from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
-# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
-# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-include(CMakeFindDependencyMacro)
-
-get_filename_component(
-  SHARKBACKEND_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH
-)
-
-list(APPEND CMAKE_MODULE_PATH ${SHARKBACKEND_CMAKE_DIR})
-
-if(NOT TARGET SharkBackend::triton-dshark-backend)
-  include("${SHARKBACKEND_CMAKE_DIR}/SharkBackendTargets.cmake")
-endif()
-
-set(SHARKBACKEND_LIBRARIES SharkBackend::triton-dshark-backend)

inference/src/libtriton_dshark.ldscript

@@ -1,30 +0,0 @@
-# Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#  * Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-#  * Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in the
-#    documentation and/or other materials provided with the distribution.
-#  * Neither the name of NVIDIA CORPORATION nor the names of its
-#    contributors may be used to endorse or promote products derived
-#    from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
-# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
-# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-{
-  global:
-    TRITONBACKEND_*;
-  local: *;
-};

requirements-importer.txt

@@ -16,7 +16,7 @@ iree-tools-tf
 # TensorFlow and JAX.
 gin-config
 tf-nightly
-keras
+keras-nightly
 #tf-models-nightly
 #tensorflow-text-nightly
 transformers

requirements.txt

@@ -25,7 +25,7 @@ diffusers
 accelerate
 scipy
 ftfy
-gradio
+gradio==3.44.3
 altair
 omegaconf
 # 0.3.2 doesn't have binaries for arm64

shark/iree_utils/compile_utils.py

@@ -300,6 +300,7 @@ def compile_module_to_flatbuffer(
     args += get_iree_common_args(debug=debug)
     args += get_model_specific_args()
     args += extra_args
+    args += shark_args.additional_compile_args
 
     if frontend in ["tensorflow", "tf"]:
         input_type = "auto"
@@ -497,9 +498,9 @@ def export_iree_module_to_vmfb(
         )
         module_name = f"{mlir_dialect}_{device_name}"
     filename = os.path.join(directory, module_name + ".vmfb")
-    print(f"Saved vmfb in {filename}.")
     with open(filename, "wb") as f:
         f.write(flatbuffer_blob)
+    print(f"Saved vmfb in {filename}.")
     return filename
 
 

shark/iree_utils/vulkan_target_env_utils.py

@@ -57,11 +57,8 @@ def get_version(triple):
 @functools.cache
 def get_extensions(triple):
     def make_ext_list(ext_list):
-        res = ""
-        for e in ext_list:
-            res += e + ", "
-        res = f"[{res[:-2]}]"
-        return res
+        res = ", ".join(ext_list)
+        return f"[{res}]"
 
     arch, product, os = triple
     if arch == "m1":
@@ -119,7 +116,7 @@ def get_extensions(triple):
     ]
 
     if get_vendor(triple) == "NVIDIA" or arch == "rdna3":
-        ext.append("VK_NV_cooperative_matrix")
+        ext.append("VK_KHR_cooperative_matrix")
     if get_vendor(triple) == ["NVIDIA", "AMD", "Intel"]:
         ext.append("VK_KHR_shader_integer_dot_product")
     return make_ext_list(ext_list=ext)
@@ -247,7 +244,7 @@ def get_vulkan_target_capabilities(triple):
         if arch == "rdna3":
             # TODO: Get scope value
             cap["coopmatCases"] = [
-                "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f16, resultType = f16, scope = #vk.scope<Subgroup>"
+                "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f16, resultType = f16, accSat = false, scope = #vk.scope<Subgroup>"
             ]
 
         if product == "rx5700xt":
@@ -468,9 +465,9 @@ def get_vulkan_target_capabilities(triple):
         cap["variablePointersStorageBuffer"] = True
 
         cap["coopmatCases"] = [
-            "mSize = 8, nSize = 8, kSize = 32, aType = i8, bType = i8, cType = i32, resultType = i32, scope = #vk.scope<Subgroup>",
-            "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f16, resultType = f16, scope = #vk.scope<Subgroup>",
-            "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f32, resultType = f32, scope = #vk.scope<Subgroup>",
+            "mSize = 8, nSize = 8, kSize = 32, aType = i8, bType = i8, cType = i32, resultType = i32, accSat = false, scope = #vk.scope<Subgroup>",
+            "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f16, resultType = f16, accSat = false, scope = #vk.scope<Subgroup>",
+            "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f32, resultType = f32, accSat = false, scope = #vk.scope<Subgroup>",
         ]
 
     elif arch == "adreno":
@@ -531,7 +528,7 @@ def get_vulkan_target_capabilities(triple):
                 cmc = ""
                 for case in v:
                     cmc += f"#vk.coop_matrix_props<{case}>, "
-                res += f"cooperativeMatrixPropertiesNV = [{cmc[:-2]}], "
+                res += f"cooperativeMatrixPropertiesKHR = [{cmc[:-2]}], "
             else:
                 res += f"{k} = {get_comma_sep_str(v)}, "
         else:

shark/iree_utils/vulkan_utils.py

@@ -23,11 +23,19 @@ from shark.iree_utils.vulkan_target_env_utils import get_vulkan_target_env_flag
 from shark.parser import shark_args
 
 
+@functools.cache
+def get_all_vulkan_devices():
+    from iree.runtime import get_driver
+
+    driver = get_driver("vulkan")
+    device_list_src = driver.query_available_devices()
+    device_list_src.sort(key=lambda d: d["path"])
+    return [d["name"] for d in device_list_src]
+
+
 @functools.cache
 def get_vulkan_device_name(device_num=0):
-    vulkaninfo_dump, _ = run_cmd("vulkaninfo")
-    vulkaninfo_dump = vulkaninfo_dump.split(linesep)
-    vulkaninfo_list = [s.strip() for s in vulkaninfo_dump if "deviceName" in s]
+    vulkaninfo_list = get_all_vulkan_devices()
     if len(vulkaninfo_list) == 0:
         raise ValueError("No device name found in VulkanInfo!")
     if len(vulkaninfo_list) > 1:
@@ -178,9 +186,7 @@ def get_iree_vulkan_args(device_num=0, extra_args=[]):
 @functools.cache
 def get_iree_vulkan_runtime_flags():
     vulkan_runtime_flags = [
-        f"--vulkan_large_heap_block_size={shark_args.vulkan_large_heap_block_size}",
         f"--vulkan_validation_layers={'true' if shark_args.vulkan_validation_layers else 'false'}",
-        f"--vulkan_vma_allocator={'true' if shark_args.vulkan_vma_allocator else 'false'}",
     ]
     return vulkan_runtime_flags
 

shark/parser.py

@@ -14,8 +14,21 @@
 
 import argparse
 import os
+import shlex
 import subprocess
 
+
+class SplitStrToListAction(argparse.Action):
+    def __init__(self, option_strings, dest, *args, **kwargs):
+        super(SplitStrToListAction, self).__init__(
+            option_strings=option_strings, dest=dest, *args, **kwargs
+        )
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        del parser, option_string
+        setattr(namespace, self.dest, shlex.split(values[0]))
+
+
 parser = argparse.ArgumentParser(description="SHARK runner.")
 
 parser.add_argument(
@@ -24,6 +37,13 @@ parser.add_argument(
     default="cpu",
     help="Device on which shark_runner runs. options are cpu, cuda, and vulkan",
 )
+parser.add_argument(
+    "--additional_compile_args",
+    default=list(),
+    nargs=1,
+    action=SplitStrToListAction,
+    help="Additional arguments to pass to the compiler. These are appended as the last arguments.",
+)
 parser.add_argument(
     "--enable_tf32",
     type=bool,
@@ -133,13 +153,6 @@ parser.add_argument(
     help="Profiles vulkan device and collects the .rdc info.",
 )
 
-parser.add_argument(
-    "--vulkan_large_heap_block_size",
-    default="2073741824",
-    help="Flag for setting VMA preferredLargeHeapBlockSize for "
-    "vulkan device, default is 4G.",
-)
-
 parser.add_argument(
     "--vulkan_validation_layers",
     default=False,
@@ -147,11 +160,4 @@ parser.add_argument(
     help="Flag for disabling vulkan validation layers when benchmarking.",
 )
 
-parser.add_argument(
-    "--vulkan_vma_allocator",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="Flag for enabling / disabling Vulkan VMA Allocator.",
-)
-
 shark_args, unknown = parser.parse_known_args()

tank/examples/opt/opt_perf_comparison.py

@@ -168,7 +168,7 @@ def save_json(data, filename):
 
 
 def collect_huggingface_logits(
-    model_name: str, max_seq_len: int, save_json: bool
+    model_name: str, max_seq_len: int, to_save_json: bool
 ) -> Tuple[float, float]:
     # Load
     t0 = time.time()
@@ -194,11 +194,11 @@ def collect_huggingface_logits(
     for idx, tokens in enumerate(tokenized_prompts):
         print("prompt: {}".format(PROMPTS[idx]))
         logits = run_huggingface_model(model_wrapper, tokens)
-        if save_json:
+        if to_save_json:
             results.append([PROMPTS[idx], logits[0].tolist()])
     run_time = time.time() - t0
     print("--- Took {} seconds to run Huggingface.".format(run_time))
-    if save_json:
+    if to_save_json:
         save_json(results, "/tmp/huggingface.json")
     run_memory_info = get_memory_info()
     return {
@@ -215,7 +215,10 @@ def collect_huggingface_logits(
 
 
 def collect_shark_logits(
-    model_name: str, max_seq_len: int, recompile_shark: bool, save_json: bool
+    model_name: str,
+    max_seq_len: int,
+    recompile_shark: bool,
+    to_save_json: bool,
 ) -> Tuple[float, float]:
     # Load
     t0 = time.time()
@@ -246,11 +249,11 @@ def collect_shark_logits(
         print("prompt: {}".format(PROMPTS[idx]))
         logits = run_shark_model(model_wrapper, tokens)
         lst = [e.tolist() for e in logits]
-        if save_json:
+        if to_save_json:
             results.append([PROMPTS[idx], lst])
     run_time = time.time() - t0
     print("--- Took {} seconds to run Shark.".format(run_time))
-    if save_json:
+    if to_save_json:
         save_json(results, "/tmp/shark.json")
     platform_postfix = "-compile" if recompile_shark else "-precompiled"
     run_memory_info = get_memory_info()