docs: add more info on HCQProgram (#5683)

* docs: add more info on HCQProgram

* linter

* linter2

* one more type

tinygrad/device.py

@@ -416,28 +416,48 @@ class HCQProgram:
   def __init__(self, device:HCQCompiled, name:str, kernargs_alloc_size:int, kernargs_args_offset:int=0):
     self.device, self.name, self.kernargs_alloc_size, self.kernargs_args_offset = device, name, kernargs_alloc_size, kernargs_args_offset
 
-  def fill_kernargs(self, bufs:Tuple[Any, ...], vals:Tuple[int, ...]=(), kernargs_ptr:Optional[int]=None):
+  def fill_kernargs(self, bufs:Tuple[HCQBuffer, ...], vals:Tuple[int, ...]=(), kernargs_ptr:Optional[int]=None) -> int:
     """
-    Fills arguments for the kernel, optionally allocating space from device if kernargs_ptr is not set.
+    Fills arguments for the kernel, optionally allocating space from the device if `kernargs_ptr` is not provided.
+
+    Args:
+      bufs: Buffers to be written to kernel arguments.
+      vals: Values to be written to kernel arguments.
+      kernargs_ptr: Optional pointer to pre-allocated kernel arguments memory.
+
+    Returns:
+      Pointer to the filled kernel arguments.
     """
     self._fill_kernargs(ptr:=(kernargs_ptr or self.device._alloc_kernargs(self.kernargs_alloc_size)), bufs, vals)
     return ptr
-  def _fill_kernargs(self, kernargs_ptr:int, bufs:Tuple[Any, ...], vals:Tuple[int, ...]=()): raise NotImplementedError("need fill_kernargs")
+  def _fill_kernargs(self, kernargs_ptr:int, bufs:Tuple[HCQBuffer, ...], vals:Tuple[int, ...]=()): raise NotImplementedError("need fill_kernargs")
 
-  def __call__(self, *args, global_size:Tuple[int,int,int]=(1,1,1), local_size:Tuple[int,int,int]=(1,1,1), vals:Tuple[int, ...]=(), wait=False):
-    kernargs_ptr = self.fill_kernargs(args, vals)
+  def __call__(self, *bufs:HCQBuffer, global_size:Tuple[int,int,int]=(1,1,1), local_size:Tuple[int,int,int]=(1,1,1),
+               vals:Tuple[int, ...]=(), wait:bool=False) -> Optional[float]:
+    """
+    Enqueues the program for execution with the given arguments and dimensions.
+
+    Args:
+      bufs: Buffer arguments to execute the kernel with.
+      global_size: Specifies the global work size for kernel execution (equivalent to CUDA's grid size).
+      local_size: Specifies the local work size for kernel execution (equivalent to CUDA's block size).
+      vals: Value arguments to execute the kernel with.
+      wait: If True, waits for the kernel to complete execution.
+
+    Returns:
+      Execution time of the kernel if 'wait' is True, otherwise None.
+    """
 
     q = self.device.hw_compute_queue_t().wait(self.device.timeline_signal, self.device.timeline_value - 1).memory_barrier()
 
     with hcq_profile(self.device, queue=q, desc=self.name, enabled=wait or PROFILE) as (sig_st, sig_en):
-      q.exec(self, kernargs_ptr, global_size, local_size)
+      q.exec(self, self.fill_kernargs(bufs, vals), global_size, local_size)
 
     q.signal(self.device.timeline_signal, self.device.timeline_value).submit(self.device)
     self.device.timeline_value += 1
 
-    if wait:
-      self.device.timeline_signal.wait(self.device.timeline_value - 1)
-      return (sig_en.timestamp - sig_st.timestamp) / 1e6
+    if wait: self.device.timeline_signal.wait(self.device.timeline_value - 1)
+    return ((sig_en.timestamp - sig_st.timestamp) / 1e6) if wait else None
 
 class HCQCompiled(Compiled):
   """

tinygrad/runtime/ops_nv.py

@@ -301,11 +301,11 @@ class NVProgram(HCQProgram):
     kernargs = [arg_half for arg in bufs for arg_half in data64_le(arg.va_addr)] + list(vals)
     to_mv(kernargs_ptr, (len(self.constbuffer_0) + len(kernargs)) * 4).cast('I')[:] = array.array('I', self.constbuffer_0 + kernargs)
 
-  def __call__(self, *args, global_size:Tuple[int,int,int]=(1,1,1), local_size:Tuple[int,int,int]=(1,1,1), vals:Tuple[int, ...]=(), wait=False):
+  def __call__(self, *bufs, global_size:Tuple[int,int,int]=(1,1,1), local_size:Tuple[int,int,int]=(1,1,1), vals:Tuple[int, ...]=(), wait=False):
     if prod(local_size) > 1024 or self.max_threads < prod(local_size): raise RuntimeError("Too many resources requsted for launch")
     if any(cur > mx for cur,mx in zip(global_size, [2147483647, 65535, 65535])) or any(cur > mx for cur,mx in zip(local_size, [1024, 1024, 64])):
       raise RuntimeError(f"Invalid global/local dims {global_size=}, {local_size=}")
-    return super().__call__(*args, global_size=global_size, local_size=local_size, vals=vals, wait=wait)
+    return super().__call__(*bufs, global_size=global_size, local_size=local_size, vals=vals, wait=wait)
 
 class NVAllocator(HCQAllocator):
   def __init__(self, device:NVDevice): super().__init__(device)