Update triton to work in master (#517)

* Update triton to work in master

* Move mem_estimate out of runner

accel/triton/ops_triton.py

@@ -16,9 +16,6 @@ from tinygrad.shape import ShapeTracker
 from tinygrad.helpers import prod
 from tinygrad.ast import ASTKernel
 
-from tinygrad.shape import View, ZeroView
-from tinygrad.shape.symbolic import Variable
-
 stream = cuda.Stream()
 
 class TritonASTKernel(ASTKernel):
@@ -31,22 +28,12 @@ class TritonASTKernel(ASTKernel):
   }
   start_for_op = {ReduceOps.SUM: "0.0", ReduceOps.MAX: "float('-inf')"}
 
-  # TODO: move to shapetracker
-  def compute_buf_index_symbolic(self, st, buf_index, offset=0):
-    view = View(self.shapes[buf_index], self.strides[buf_index], self.offsets[buf_index] + offset)
-    idx = view.expr_idxs([f"idx{i}" for i in range(self.shape_len)])
-    valid = Variable.num(1)
-    for v in st.views[0:-1][::-1]:
-      if isinstance(v, ZeroView): valid = v.expr_node(valid, idx)
-      else: idx = v.expr_node(idx)
-    return idx, valid
-
   def ast_parse(self, x:Union[TritonBuffer, LazyOp], acc:str, do_reduce=False) -> str:
     if not isinstance(x, LazyOp):
       # this is a load
       buf_index = self.bufs.index(x)
       if buf_index not in self.loaded:
-        idx, valid = self.compute_buf_index_symbolic(self.bufs[buf_index].st, buf_index)
+        idx, valid = self.sts[buf_index].expr_idxs()
         valid_expr = str(valid).replace("&&", "*1*")
         self.kernel.append(self.kernel_prefix + f"  val{buf_index} = tl.where({valid_expr}, tl.load(data{buf_index} + {idx}, mask={valid_expr}), 0.0)")
         self.loaded.add(buf_index)
@@ -70,7 +57,7 @@ class TritonASTKernel(ASTKernel):
     self.kernel = ["@triton.jit"]
     self.kernel.append("def fxn("+','.join(f"data{i}" for i in range(len(self.bufs)))+"):")
 
-    self.output_shape = list(self.shapes[0][:self.first_reduce])
+    self.output_shape = list(self.sts[0].shape[:self.first_reduce]) 
 
     # copied from ops_gpu
     # TODO CUDA only supports a grid of (2^31-1, 65535, 65535), that results in invalid kernel launches for some shapes, so flattern the grid for now.
@@ -85,8 +72,8 @@ class TritonASTKernel(ASTKernel):
     elif len(self.output_shape) == 0: self.output_shape = [1]
     
     if self.reduceop:
-      full_shape = [x for x in self.shapes if x != self.shapes[0]]
-      full_shape = self.shapes[0] if len(full_shape) == 0 else full_shape[0]
+      full_shape = [st.shape for st in self.sts if st.shape != self.sts[0].shape]
+      full_shape = self.sts[0].shape if len(full_shape) == 0 else full_shape[0]
       self.kernel += [f"  acc = {TritonASTKernel.start_for_op[self.reduceop.op]}"]
       self.kernel += [("  "*(i-self.first_reduce)+f"  for idx{i} in range(0, {full_shape[i]}):") for i in range(self.first_reduce, self.shape_len)]
       self.kernel_prefix =  "  "*(self.shape_len - self.first_reduce)
@@ -96,7 +83,7 @@ class TritonASTKernel(ASTKernel):
     code = self.ast_parse(self.ast, "acc")
 
     # store
-    idx, valid = self.compute_buf_index_symbolic(self.bufs[0].st, 0)
+    idx, valid = self.sts[0].expr_idxs()
     self.kernel.append(f"  tl.store(data0 + {idx}, {code})")
 
     # Torch inductor seems to write out files too!
@@ -108,8 +95,7 @@ class TritonASTKernel(ASTKernel):
     codeObject = compile(kernel, fn, "exec")
     exec(codeObject, globals())
     program = globals()['fxn']
-
-    mem_estimate = sum(prod(x) for x in self.shapes)
+    mem_estimate = sum(prod(x._base_shape) for x in self.bufs)
     def runner(*bufs):
       GlobalCounters.global_ops += self.info.flops
       GlobalCounters.global_mem += mem_estimate
@@ -120,10 +106,10 @@ class TritonASTKernel(ASTKernel):
 class TritonBuffer(ExplicitExecAST):
   def __init__(self, shape:Union[ShapeTracker, Tuple[int, ...]], hostbuf:Optional[TritonBuffer]=None, backing:Optional[np.ndarray]=None, force_create=False):
     super().__init__(shape, hostbuf)
-    if hostbuf is not None and hostbuf._buf is None: hostbuf.torch
-    self._buf : Optional[TritonBuffer] = hostbuf._buf if hostbuf is not None else None
+    self._buf : Optional[TritonWrapper] = hostbuf._buf if hostbuf is not None else None
     self._base_shape : Tuple[int, ...] = hostbuf._base_shape if hostbuf is not None else self.shape
     self._backing : Optional[np.ndarray] = hostbuf._backing if hostbuf is not None else backing
+    if force_create: self.torch
 
   @property
   def torch(self):