Revert "[Vulkan] Add cooperative matrix support"

This reverts commit 34ceb8cc210a0dbcbac347b2b1508164ac23a66b.

include/tvm/meta_schedule/task_scheduler.h

@@ -175,7 +175,6 @@ class TaskSchedulerNode : public runtime::Object {
    * \param measure_callbacks The callbacks to be called after each measurement
    * \param database The database used in tuning
    * \param cost_model The cost model used in tuning
-   * \param min_design_space The minimum design space used in tuning
    */
   virtual void Tune(Array<TuneContext> tasks,                  //
                     Array<FloatImm> task_weights,              //
@@ -186,8 +185,7 @@ class TaskSchedulerNode : public runtime::Object {
                     Runner runner,                             //
                     Array<MeasureCallback> measure_callbacks,  //
                     Optional<Database> database,               //
-                    Optional<CostModel> cost_model,            //
-                    int min_design_space);
+                    Optional<CostModel> cost_model);
   /*!
    * \brief Terminate a task
    * \param task_id The id of the task to be terminated
@@ -230,9 +228,7 @@ class PyTaskSchedulerNode : public TaskSchedulerNode {
                                               Runner runner,                             //
                                               Array<MeasureCallback> measure_callbacks,  //
                                               Optional<Database> database,               //
-                                              Optional<CostModel> cost_model,
-
-                                              int min_design_space)>;
+                                              Optional<CostModel> cost_model)>;
 
   /*! \brief The packed function to the `NextTaskId` function. */
   FNextTaskId f_next_task_id;
@@ -253,7 +249,7 @@ class PyTaskSchedulerNode : public TaskSchedulerNode {
   void Tune(Array<TuneContext> tasks, Array<FloatImm> task_weights, int max_trials_global,
             int max_trials_per_task, int num_trials_per_iter, Builder builder, Runner runner,
             Array<MeasureCallback> measure_callbacks, Optional<Database> database,
-            Optional<CostModel> cost_model, int min_design_space) final;
+            Optional<CostModel> cost_model) final;
 
   static constexpr const char* _type_key = "meta_schedule.PyTaskScheduler";
   TVM_DECLARE_FINAL_OBJECT_INFO(PyTaskSchedulerNode, TaskSchedulerNode);

python/tvm/meta_schedule/relay_integration.py

@@ -275,7 +275,6 @@ def tune_relay(
     num_tuning_cores: Union[Literal["physical", "logical"], int] = "physical",
     disabled_pass: Optional[Union[List[str], Set[str], Tuple[str]]] = None,
     instruments: Optional[Sequence[PassInstrument]] = None,
-    min_design_space: int = 1,
 ) -> Database:
     """Tune a Relay program.
 
@@ -329,8 +328,6 @@ def tune_relay(
         The list of disabled passes during tasks extraction
     instruments : Optional[Sequence[PassInstrument]]
         The list of pass instrument implementations.
-    min_design_space : int
-        The minimum design space.
 
     Returns
     -------
@@ -366,7 +363,6 @@ def tune_relay(
         measure_callbacks=measure_callbacks,
         task_scheduler=task_scheduler,
         module_equality=module_equality,
-        min_design_space=min_design_space,
     )
 
 

python/tvm/meta_schedule/task_scheduler/task_scheduler.py

@@ -102,7 +102,6 @@ def tune(
         measure_callbacks: List[MeasureCallback],
         database: Optional[Database],
         cost_model: Optional[CostModel],
-        min_design_space: int = 1,
     ) -> None:
         """Auto-tuning.
 
@@ -128,8 +127,6 @@ def tune(
             The database.
         cost_model : Optional[CostModel]
             The cost model.
-        min_design_space : int
-            THe minimum size of design space.
         """
         task_weights = [float(w) for w in task_weights]
         _ffi_api.TaskSchedulerTune(  # type: ignore # pylint: disable=no-member
@@ -144,7 +141,6 @@ def tune(
             measure_callbacks,
             database,
             cost_model,
-            min_design_space,
         )
 
     def terminate_task(self, task_id: int) -> None:
@@ -247,7 +243,6 @@ def tune(
         measure_callbacks: List[MeasureCallback],
         database: Optional[Database],
         cost_model: Optional[CostModel],
-        min_design_space: int = 1,
     ) -> None:
         """Auto-tuning."""
         # Using self._outer to replace the self pointer
@@ -262,7 +257,6 @@ def tune(
             measure_callbacks,
             database,
             cost_model,
-            min_design_space,
         )
 
     def next_task_id(self) -> int:

python/tvm/meta_schedule/tir_integration.py

@@ -58,7 +58,6 @@ def tune_tir(  # pylint: disable=too-many-locals
     seed: Optional[int] = None,
     module_equality: str = "structural",
     special_space: Optional[Mapping[str, SpaceGenerator.SpaceGeneratorType]] = None,
-    min_design_space: int = 1,
 ) -> Database:
     """Tune a TIR function or an IRModule of TIR functions.
 
@@ -100,8 +99,6 @@ def tune_tir(  # pylint: disable=too-many-locals
         A string to specify the module equality testing and hashing method.
     special_space : Optional[Mapping[str, SpaceGenerator.SpaceGeneratorType]]
         A mapping from task name to a special space generator for that task.
-    min_design_space : int
-        The minimum design space.
 
     Returns
     -------
@@ -157,7 +154,6 @@ def tune_tir(  # pylint: disable=too-many-locals
         measure_callbacks=measure_callbacks,
         task_scheduler=task_scheduler,
         module_equality=module_equality,
-        min_design_space=min_design_space,
     )
 
 

python/tvm/meta_schedule/tune.py

@@ -41,7 +41,6 @@ def tune_tasks(
     measure_callbacks: MeasureCallback.CallbackListType = "default",
     task_scheduler: TaskScheduler.TaskSchedulerType = "gradient",
     module_equality: str = "structural",
-    min_design_space: int = 1,
 ) -> Database:
     """Tune a list of tasks. Using a task scheduler.
 
@@ -74,15 +73,14 @@ def tune_tasks(
     module_equality : Optional[str]
         A string to specify the module equality testing and hashing method.
         It must be one of the followings:
+
             - "structural": Use StructuralEqual/Hash
             - "ignore-ndarray": Same as "structural", but ignore ndarray raw data during equality
                 testing and hashing.
             - "anchor-block": Apply equality testing and hashing on the anchor block extracted from
                 a given module. The "ignore-ndarray" varint is used for the extracted blocks or in
                 case no anchor block is found. For the definition of the anchor block, see
                 tir/analysis/analysis.py.
-    min_design_space : int
-        Minimum design space.
 
     Returns
     -------
@@ -128,6 +126,5 @@ def tune_tasks(
         measure_callbacks=measure_callbacks,
         database=database,
         cost_model=cost_model,
-        min_design_space=min_design_space,
     )
     return database

python/tvm/relay/op/strategy/cuda.py

@@ -145,13 +145,9 @@ def conv2d_strategy_cuda(attrs, inputs, out_type, target):
     kernel_layout = attrs.kernel_layout
     if dilation_h < 1 or dilation_w < 1:
         raise ValueError("dilation should be positive value")
-
     if groups == 1:
         if layout == "NCHW":
             assert kernel_layout == "OIHW"
-            do_im2col = topi.nn.use_im2col(
-                data, kernel, stride_h, stride_w, dilation_h, dilation_w, padding
-            )
             if (
                 (target.kind.name in ["cuda", "vulkan", "rocm"])
                 and data.dtype in ("int8", "uint8")
@@ -163,14 +159,6 @@ def conv2d_strategy_cuda(attrs, inputs, out_type, target):
                     wrap_topi_schedule(topi.cuda.schedule_conv2d_nchw_int8),
                     name="conv2d_nchw_int8.cuda",
                 )
-            elif do_im2col:
-                assert data.dtype == kernel.dtype
-                strategy.add_implementation(
-                    wrap_compute_conv2d(topi.cuda.conv2d_nchw_mma),
-                    naive_schedule,
-                    name="conv2d_nchw_mma.cuda",
-                    plevel=15,
-                )
             else:
                 strategy.add_implementation(
                     wrap_compute_conv2d(topi.cuda.conv2d_nchw),

python/tvm/target/target.py

@@ -173,25 +173,11 @@ def max_num_threads(self):
         """Returns the max_num_threads from the target if it exists."""
         return int(self.attrs["max_num_threads"])
 
-    @property
-    def max_block_size_x(self):
-        """Returns the max block size in x-dimension from the target if it exists."""
-        return int(self.attrs["max_block_size_x"])
-
-    @property
-    def max_block_size_y(self):
-        """Returns the max block size in y-dimension from the target if it exists."""
-        return int(self.attrs["max_block_size_y"])
-
     @property
     def thread_warp_size(self):
         """Returns the thread_warp_size from the target if it exists."""
         return int(self.attrs["thread_warp_size"])
 
-    @property
-    def max_shared_memory_per_block(self):
-        return int(self.attrs["max_shared_memory_per_block"])
-
     @property
     def max_function_args(self):
         return int(self.attrs.get("max_function_args", -1))
@@ -233,13 +219,6 @@ def supports_integer_dot_product(self):
     def libs(self):
         return list(self.attrs.get("libs", []))
 
-    @property
-    def supports_cooperative_matrix(self):
-        if self.attrs.get("supports_cooperative_matrix", []):
-            return bool(self.attrs["supports_cooperative_matrix"])
-        else:
-            return False
-
     @property
     def features(self):
         return TargetFeatures(self)