Update links after repository renamings

SHARK was renamed to SHARK-Studio and SHARK-Turbine was renamed to
SHARK-ModelDev.

.github/workflows/test_models.yml

@@ -30,12 +30,12 @@ jobs:
           python-version: 3.11
       - name: "Checkout This Repo"
         uses: actions/checkout@v4
-      - name: "Checkout SHARK-Turbine repo"
+      - name: "Checkout SHARK-ModelDev repo"
         uses: actions/checkout@v4
         with:
-          repository: nod-ai/SHARK-Turbine
+          repository: nod-ai/SHARK-ModelDev
           # TODO: Let the ref be passed as a parameter to run integration tests.
-          path: SHARK-Turbine
+          path: SHARK-ModelDev
       - name: Sync source deps
         # build IREE from source with -DIREE_BUILD_TRACY=ON if getting tracy profile
         run: |
@@ -48,7 +48,7 @@ jobs:
           pip install --no-compile -r pytorch-cpu-requirements.txt
           pip install --no-compile --pre --upgrade -r requirements.txt
           pip install --no-compile --pre -e .[testing]
-          pip install --no-compile --pre --upgrade -e ${{ github.workspace }}/SHARK-Turbine/models -r ${{ github.workspace }}/SHARK-Turbine/models/requirements.txt
+          pip install --no-compile --pre --upgrade -e ${{ github.workspace }}/SHARK-ModelDev/models -r ${{ github.workspace }}/SHARK-ModelDev/models/requirements.txt
           pip install \
             --find-links https://iree.dev/pip-release-links.html \
             --upgrade \
@@ -60,23 +60,23 @@ jobs:
       - name: Run stateless_llama tests
         run: |
           source turbine_venv/bin/activate
-          pytest -v ${{ github.workspace }}/SHARK-Turbine/models/turbine_models/tests/stateless_llama_test.py
+          pytest -v ${{ github.workspace }}/SHARK-ModelDev/models/turbine_models/tests/stateless_llama_test.py
       - name: Run sd tests
         run: |
           source turbine_venv/bin/activate
-          pytest -v ${{ github.workspace }}/SHARK-Turbine/models/turbine_models/tests/sd_test.py
-          pytest -v ${{ github.workspace }}/SHARK-Turbine/models/turbine_models/tests/sdxl_test.py --device cpu --rt_device local-task --iree_target_triple x86_64-linux-gnu
+          pytest -v ${{ github.workspace }}/SHARK-ModelDev/models/turbine_models/tests/sd_test.py
+          pytest -v ${{ github.workspace }}/SHARK-ModelDev/models/turbine_models/tests/sdxl_test.py --device cpu --rt_device local-task --iree_target_triple x86_64-linux-gnu
   test-turbine-models-gpu:
     runs-on: nodai-amdgpu-mi250-x86-64
     steps:
       - name: "Checkout This Repo"
         uses: actions/checkout@v4
-      - name: "Checkout SHARK-Turbine repo"
+      - name: "Checkout SHARK-ModelDev repo"
         uses: actions/checkout@v4
         with:
-          repository: nod-ai/SHARK-Turbine
+          repository: nod-ai/SHARK-ModelDev
           # TODO: Let the ref be passed as a parameter to run integration tests.
-          path: SHARK-Turbine
+          path: SHARK-ModelDev
       - name: Sync source deps
         # build IREE from source with -DIREE_BUILD_TRACY=ON if getting tracy profile
         run: |
@@ -89,7 +89,7 @@ jobs:
           pip install --no-compile -r pytorch-cpu-requirements.txt
           pip install --no-compile --pre --upgrade -r requirements.txt
           pip install --no-compile --pre -e .[testing]
-          pip install --no-compile --pre --upgrade -e ${{ github.workspace }}/SHARK-Turbine/models -r ${{ github.workspace }}/SHARK-Turbine/models/requirements.txt
+          pip install --no-compile --pre --upgrade -e ${{ github.workspace }}/SHARK-ModelDev/models -r ${{ github.workspace }}/SHARK-ModelDev/models/requirements.txt
           pip install \
             --find-links https://iree.dev/pip-release-links.html \
             --upgrade \
@@ -101,5 +101,5 @@ jobs:
       - name: Run sdxl gpu tests
         run: |
           source turbine_venv/bin/activate
-          pytest -v ${{ github.workspace }}/SHARK-Turbine/models/turbine_models/tests/sdxl_test.py --device vulkan --rt_device vulkan --iree_target_triple rdna3-unknown-linux
-          pytest -v ${{ github.workspace }}/SHARK-Turbine/models/turbine_models/tests/sdxl_test.py --device rocm --rt_device hip --iree_target_triple gfx90a --precision fp16
+          pytest -v ${{ github.workspace }}/SHARK-ModelDev/models/turbine_models/tests/sdxl_test.py --device vulkan --rt_device vulkan --iree_target_triple rdna3-unknown-linux
+          pytest -v ${{ github.workspace }}/SHARK-ModelDev/models/turbine_models/tests/sdxl_test.py --device rocm --rt_device hip --iree_target_triple gfx90a --precision fp16

.github/workflows/test_shark.yml

@@ -31,10 +31,10 @@ jobs:
       - name: "Checkout SHARK"
         uses: actions/checkout@v4
         with:
-          repository: "nod-ai/SHARK"
+          repository: "nod-ai/SHARK-Studio"
           path: SHARK
 
-      - name: "Install SHARK"
+      - name: "Install SHARK Studio"
         run: |
           cd $GITHUB_WORKSPACE/SHARK
           python${{ matrix.version }} -m venv shark.venv

examples/eager_mlp/mlp_eager_simple.py

@@ -14,7 +14,7 @@
 import torchvision.datasets as datasets
 
 torch._dynamo.config.dynamic_shapes = (
-    False  # TODO: https://github.com/nod-ai/SHARK-Turbine/issues/93
+    False  # TODO: https://github.com/nod-ai/SHARK-ModelDev/issues/93
 )
 
 

examples/resnet-18/README.md

@@ -5,8 +5,8 @@ This example AOT-compiles a Resnet-18 module for performing inference on a dynam
 To run this example (with Python3.11), you should clone the repository to your local device and install the requirements in a virtual environment.
 
 ```bash
-git clone https://github.com/nod-ai/SHARK-Turbine.git
-cd SHARK-Turbine/examples/resnet-18
+git clone https://github.com/nod-ai/SHARK-ModelDev.git
+cd SHARK-ModelDev/examples/resnet-18
 python -m venv rn18_venv
 source ./rn18_venv/bin/activate
 pip install -r requirements.txt

iree/turbine/aot/compiled_module.py

@@ -273,7 +273,7 @@ def def_export_proc(self, name, f) -> ExportProcDef:
         input_sig = []
         parameter_list = list(sig.parameters.values())
         # TODO: Reconstitute a pytree so as to handle kwargs?
-        # See: https://github.com/nod-ai/SHARK-Turbine/issues/128
+        # See: https://github.com/nod-ai/SHARK-ModelDev/issues/128
         for param in parameter_list[1:]:
             if (
                 param.kind != inspect.Parameter.POSITIONAL_ONLY
@@ -285,7 +285,7 @@ def def_export_proc(self, name, f) -> ExportProcDef:
             param_desc = param.default
             if param_desc is inspect.Parameter.empty:
                 # TODO: Merge from a decorator?
-                # See: https://github.com/nod-ai/SHARK-Turbine/issues/126
+                # See: https://github.com/nod-ai/SHARK-ModelDev/issues/126
                 raise TypeError(
                     f"export function {name} missing required default value annotation "
                     f"for '{param.name}'"
@@ -542,7 +542,7 @@ def run_pass_pipeline(
                 pm.run(module_op)
             except MLIRError:
                 # TODO: Better error handling.
-                # See: https://github.com/nod-ai/SHARK-Turbine/issues/127
+                # See: https://github.com/nod-ai/SHARK-ModelDev/issues/127
                 print(module_op, file=sys.stderr)
                 raise
 
@@ -638,7 +638,7 @@ def __new__(
         # Instantiate procs.
         # TODO: This should be done in two phases, first binding the symbols
         # and then defining them, enabling dependence.
-        # See: https://github.com/nod-ai/SHARK-Turbine/issues/129
+        # See: https://github.com/nod-ai/SHARK-ModelDev/issues/129
         for key, proc_def in info.class_info.export_procs:
 
             def do_export(proc_def: ExportProcDef):

iree/turbine/aot/support/ir_utils.py

@@ -206,7 +206,7 @@ def unique_auto_symbol(self, requested_name: str) -> str:
 
     def handle_mlir_error(self, op: Operation, e: MLIRError, message: str):
         # TODO: Replace with a real dumping facility.
-        # See: https://github.com/nod-ai/SHARK-Turbine/issues/136
+        # See: https://github.com/nod-ai/SHARK-ModelDev/issues/136
         dump_path = Path(tempfile.gettempdir()) / "turbine_module_builder_error.mlir"
         logger.exception(f"{message} (dumping to {dump_path})")
         try:

iree/turbine/aot/support/procedural/iree_emitter.py

@@ -173,7 +173,7 @@ def tensor_empty(
         """Constructs a tensor with uninitialized values.
 
         TODO: Support an IREE/raw element type in addition to the torch dtype.
-        See: https://github.com/nod-ai/SHARK-Turbine/issues/130
+        See: https://github.com/nod-ai/SHARK-ModelDev/issues/130
         """
         dim_decls, dyn_dim_values = cast_tensor_dim_decl(dims)
         try:
@@ -325,7 +325,7 @@ def tensor_splat(
         dtype: torch.dtype,
     ) -> "IrTensor":
         # TODO: Type infer the dtype if missing.
-        # See: https://github.com/nod-ai/SHARK-Turbine/issues/125
+        # See: https://github.com/nod-ai/SHARK-ModelDev/issues/125
         dim_decls, dyn_dim_values = cast_tensor_dim_decl(dims)
         try:
             element_type = TORCH_DTYPE_TO_IREE_TYPE[dtype]()

iree/turbine/aot/support/procedural/primitives.py

@@ -92,7 +92,7 @@ def __add__(self, other):
             # Type check and promotion.
             # TODO: Add more comprehensive type promotion hiearchy as seen in
             # https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html
-            # See: https://github.com/nod-ai/SHARK-Turbine/issues/132
+            # See: https://github.com/nod-ai/SHARK-ModelDev/issues/132
             lhs = self.ir_value
             if isinstance(other, IrScalar):
                 # Assumes when both are Value, they have same type.
@@ -241,7 +241,7 @@ def get_dim_value(
             # TODO: Add MLIR API support for creating an insertion point after
             # an operation and use that to set the InsertionPoint to the
             # earliest point.
-            # See: https://github.com/nod-ai/SHARK-Turbine/issues/133
+            # See: https://github.com/nod-ai/SHARK-ModelDev/issues/133
             dim_value = build_tensor_dim_value(
                 resolved_ir_value, index, constant_cache=constant_cache
             )
@@ -294,7 +294,7 @@ def _get_meta_tensor_constraints(self) -> tuple[torch.Tensor, list[Constraint]]:
         # cannot be dynamic, and we must use a >= 2 dimension value to represent
         # a dynamic quantity. We therefore adjust the shape in this way and
         # add a dynamic_dim constraint.
-        # See: https://github.com/nod-ai/SHARK-Turbine/issues/134
+        # See: https://github.com/nod-ai/SHARK-ModelDev/issues/134
         extents = [2 if d < 0 else d for d in shape]
         mt = self._meta_tensor = torch.empty(extents, dtype=self.dtype)
         # Generate constraints that are aligned with any dynamic dimensions or None

iree/turbine/aot/support/procedural/tracer.py

@@ -118,7 +118,7 @@ def define_func(
     def trace_py_func(self, py_f: Callable):
         with new_ir_trace_scope(self) as t:
             # TODO: Create IR proxies for python arguments.
-            # See: https://github.com/nod-ai/SHARK-Turbine/issues/135
+            # See: https://github.com/nod-ai/SHARK-ModelDev/issues/135
             return_py_value = _unproxy(py_f(*self.proxy_posargs, **self.proxy_kwargs))
             if return_py_value is None:
                 self.emit_return()

iree/turbine/dynamo/tensor.py

@@ -474,8 +474,8 @@ def _get_device_state() -> DeviceState:
     return DeviceState(driver="local-task")
 
 
-# Inspiration from https://github.com/nod-ai/SHARK-Turbine/blob/8293de5414889c72ff5cd10bf33c43fb0a3ea3ee/python/iree/turbine/aot/builtins/jittable.py#L212-L237
-# and https://github.com/nod-ai/SHARK-Turbine/blob/main/python/iree/turbine/dynamo/backends/cpu.py
+# Inspiration from https://github.com/nod-ai/SHARK-ModelDev/blob/8293de5414889c72ff5cd10bf33c43fb0a3ea3ee/python/iree/turbine/aot/builtins/jittable.py#L212-L237
+# and https://github.com/nod-ai/SHARK-ModelDev/blob/main/python/iree/turbine/dynamo/backends/cpu.py
 # TODO: Try to generalize for other devices.
 def compute_method(super_fn, *args, **kwargs):
     # Compute factory fns reserve the last arg as src_op

iree/turbine/dynamo/type_conversion.py

@@ -86,7 +86,7 @@ def torch_type_to_native(self, torch_type: IrType, signless: bool = True) -> IrT
                         IrType.parse(dtype_str), signless=signless
                     )
                     # TODO: Eliminate RankedTensorType dependence on Location.
-                    # See: https://github.com/nod-ai/SHARK-Turbine/issues/145
+                    # See: https://github.com/nod-ai/SHARK-ModelDev/issues/145
                     with Location.unknown():
                         return RankedTensorType.get(dim_list, dtype)
         raise TypeError(f"Unsupported torch type conversion for {torch_type}")

tests/dynamo/llama_test.py

@@ -315,7 +315,7 @@ def main():
     opt(example_tokens, start_pos)
 
 
-@pytest.mark.xfail(reason="https://github.com/nod-ai/SHARK-Turbine/issues/221")
+@pytest.mark.xfail(reason="https://github.com/nod-ai/SHARK-ModelDev/issues/221")
 class ModelTests(unittest.TestCase):
     def testLLama(self):
         main()

tests/dynamo/mninst_test.py

@@ -17,7 +17,7 @@
 import torch._dynamo.config
 
 torch._dynamo.config.dynamic_shapes = (
-    False  # TODO: https://github.com/nod-ai/SHARK-Turbine/issues/93
+    False  # TODO: https://github.com/nod-ai/SHARK-ModelDev/issues/93
 )