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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/03/19 19:25:36 UTC

[GitHub] [tvm] tqchen commented on a change in pull request #7630: [TensorIR] TVMScript Parser/Printer

tqchen commented on a change in pull request #7630:
URL: https://github.com/apache/tvm/pull/7630#discussion_r597925017



##########
File path: python/tvm/script/context_maintainer.py
##########
@@ -16,59 +16,179 @@
 # under the License.
 """TVM Script Context Maintainer for TIR"""
 
-from tvm.te import schedule
+from typing import List, Mapping, Union, Optional, Dict, Callable
+import synr
+
+
+import tvm
+from tvm.ir import Span
+from tvm.tir import Var, Buffer, PrimExpr, Stmt, MatchBufferRegion
+from tvm.runtime import Object
+from .node import BufferSlice
+
+
+class BlockInfo:
+    """Information for block and block_realize signature"""
+
+    alloc_buffers: List[Buffer] = []
+    """List[Buffer]: list of tir.alloc_buffer statements in the block signature"""
+    match_buffers: List[MatchBufferRegion] = []
+    """List[MatchBufferRegion]: list of tir.match_buffer_region statements in the block signature"""
+    iter_bindings: Mapping[Var, PrimExpr] = {}
+    """Mapping[Var, PrimExpr]: map of block iter var to its values"""
+    reads: Optional[List[BufferSlice]] = None
+    """Optional[List[BufferSlice]]:
+    list of tir.reads statements in the block signature, None for not-visited"""
+    writes: Optional[List[BufferSlice]] = None
+    """Optional[List[BufferSlice]]:
+    list of tir.writes statements in the block signature, None for not-visited"""
+    annotations: Optional[Mapping[str, Object]] = None
+    """Optional[Mapping[str, Object]]:
+    list of tir.block_attr statements in the block signature, None for not-visited"""
+    predicate: Optional[PrimExpr] = None
+    """Optional[PrimExpr]: block realize predicate, None for not-visited"""
+    init: Optional[Stmt] = None
+    """Optional[Stmt]: init part of the block, None for not-visited"""
+
+    def __init__(self):
+        self.alloc_buffers = []
+        self.match_buffers = []
+        self.iter_bindings = {}
+        self.reads = None
+        self.writes = None
+        self.annotations = None
+        self.predicate = None
+        self.init = None
 
 
 class ContextMaintainer:
-    """Maintain all the necessary context info"""
+    """Maintain all the necessary context info
+    Parameters
+    ----------
+    _report_error : Callable[[str, Union[Span, synr.ast.Span]], None]
+        The report error function handle
+    """
 
-    def __init__(self, parser):
+    # scope context
+    node_stack: List[List[synr.ast.Node]] = []
+    """List[List[synr.ast.Node]]: The ast nodes insides the current scope"""
+    block_info_stack: List[BlockInfo] = []
+    """List[BlockInfo]: The block info for the current block scope"""
+    loop_stack: List[List[Var]] = []
+    """List[List[Var]]: List of loop vars inside the current block scope"""
+    symbols: List[Dict[str, Union[Var, Buffer]]] = []
+    """List[Dict[str, Union[Var, Buffer]]]: Symbol map from name to object for the current scope"""
+
+    # function context
+    func_params: List[Var] = []
+    """List[Var]: The function parameters"""
+    func_buffer_map: Mapping[Var, Buffer] = {}
+    """Mapping[Var, Buffer]: The function buffer map"""
+    func_dict_attr: Mapping[str, Object] = {}
+    """Mapping[str, Object]: The function attrs"""
+    func_var_env_dict: Mapping[Var, str] = {}
+    """Mapping[Var, str]: The map from var to env thread"""
+
+    # parser and analyzer
+    analyzer: tvm.arith.Analyzer = tvm.arith.Analyzer()
+    """tvm.arith.Analyzer: The analyzer for simplifying"""
+    _report_error: Callable[[str, Union[Span, synr.ast.Span]], None]
+    """Callable[[str, Union[Span, synr.ast.Span]], None]: The report error function handle"""
+
+    def __init__(self, _report_error: Callable[[str, Union[Span, synr.ast.Span]], None]):
         # scope context
-        self.node_stack = []  # AST nodes of scopes
-        self.symbols = []  # symbols of scopes
+        self.node_stack = []
+        self.block_info_stack = []
+        self.loop_stack = []
+        self.symbols = []
         # function context
-        self.func_params = []  # parameter list of function
-        self.func_buffer_map = {}  # buffer_map of function
-        self.func_dict_attr = {}  # func_attr of function
-        self.func_var_env_dict = {}  # map from var to env_name
-        # parser
-        self.parser = parser
-
-    def pop_scope(self):
-        """Pop the inner most scope"""
-        self.symbols.pop()
-        self.node_stack.pop()
+        self.func_params = []
+        self.func_buffer_map = {}
+        self.func_dict_attr = {}
+        self.func_var_env_dict = {}
+        # parser and analyzer
+        self._report_error = _report_error
+        self.analyzer = tvm.arith.Analyzer()
 
-    def new_scope(self, nodes=None):
-        """Creating a new scope"""
+    def enter_scope(self, nodes: Optional[List[synr.ast.Node]] = None):
+        """Creates a new scope
+
+        Note
+        ----
+        This function is used for normal scopes that do not involve 
+        a `with block` scope. Use `enter_block_scope`
+        for block scope cases.
+
+        Parameters
+        ----------
+        nodes : Optional[List[synr.ast.Node]]
+            The synr AST nodes in new scope
+        """
         if nodes is None:
             nodes = []
         self.node_stack.append(list(reversed(nodes)))
         self.symbols.append(dict())
 
-    def update_symbol(self, name, symbol):
+    def enter_block_scope(self, nodes: Optional[List[synr.ast.Node]] = None):
+        """Creates a new block scope, the function will call `enter_scope` implicitly
+        Besides the behaviors of `enter_scope`, it will update loop_stack and block_info_stack
+        to maintain block info.
+
+        Note
+        ----
+        This function should be used to handle a block scope,
+        aka the blocks that involve a `with block` scope.

Review comment:
       The block structure is clearly described in https://discuss.tvm.apache.org/t/rfc-tensorir-a-schedulable-ir-for-tvm/7872, 




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