wehub-resource-sync
865 lines
30 KiB
Python
865 lines
30 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import re
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from itertools import product
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from .lexer import Token, TokenType
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def macro(name, priority):
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def decorator(func):
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macro_registry.register_macro(name, func, priority)
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return func
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return decorator
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class MacroRegistry:
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_instance = None
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def __new__(cls, *args, **kwargs):
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(self):
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if not hasattr(self, 'macros'):
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self.macros = []
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def register_macro(self, name, func, priority):
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if any(macro['name'] == name for macro in self.macros):
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raise ValueError(f"Macro '{name}' is already registered.")
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self.macros.append({'name': name, 'func': func, 'priority': priority})
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self.macros.sort(key=lambda x: x['priority'], reverse=False)
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macro_registry = MacroRegistry()
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GLOBAL_ATTRIBUTE_KEYWORDS = [
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"axis",
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'fused_ffn',
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'fused_qkv_old',
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'num_heads',
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'num_key_value_groups',
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'permute',
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'dtype',
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'fused_qkv',
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'src_dtype',
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'dst_dtype',
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]
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EXTRA_SUFFIX = [
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"^T",
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]
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def extract_axis_and_clean_tokens(tokens):
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axis = 1
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for idx, tkn in enumerate(tokens):
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if tkn.value == "axis" and idx + 2 < len(tokens):
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axis = int(tokens[idx + 2].value)
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end_idx = idx + 3
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if end_idx < len(tokens) - 1:
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assert tokens[end_idx].value == ",", (
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f"The different attributes must split by a comma, but now the token is {tokens[end_idx].value}."
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)
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end_idx += 1
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tokens = tokens[:idx] + tokens[end_idx:]
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break
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return axis, tokens
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# star_macro must be called after layer_id_macro
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@macro(name='star_macro', priority=3)
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def star_macro(tokens, expression, context):
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STAR_TAG = "*"
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if STAR_TAG not in expression:
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return expression
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def _sort_keys_by_numeric_part(prefix, suffix, allkeys):
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pattern = re.compile(rf"{re.escape(prefix)}(\d+){re.escape(suffix)}")
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filtered_keys = []
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for key in allkeys:
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match = pattern.fullmatch(key)
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if match:
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num = int(match.group(1))
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filtered_keys.append((key, num))
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sorted_keys = sorted(filtered_keys, key=lambda x: x[1])
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return [key for key, _ in sorted_keys]
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pre_rarrow = True
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new_tokens = []
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for token in tokens:
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if token.type == TokenType.RARROW:
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pre_rarrow = False
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if token.type == TokenType.IDENTIFIER and STAR_TAG in token.value:
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prefix, suffix = token.value.split(STAR_TAG)
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allkeys = (
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context.get_all_dst_state_keys()
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if not pre_rarrow
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else context.get_all_src_state_keys()
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)
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assert len(allkeys) != 0, (
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f"No keys found with prefix '{prefix}' and suffix '{suffix}' in "
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f"{'destination_state_shard_info' if not pre_rarrow else 'source_state_shard_info'}, please check!"
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)
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keys = list(_sort_keys_by_numeric_part(prefix, suffix, allkeys))
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for key in keys:
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new_tokens.append(Token(TokenType.IDENTIFIER, key))
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if key != keys[-1]:
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new_tokens.append(Token(TokenType.COMMA, ","))
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else:
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new_tokens.append(token)
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new_expression = "".join([token.value for token in new_tokens])
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return new_expression
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@macro(name='layer_id_offset_macro', priority=1)
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def layer_id_offset_macro(tokens, expression, context):
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LAYER_ID_OFFSET_MACRO_TAG = "$LAYER_ID_OFFSET"
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if LAYER_ID_OFFSET_MACRO_TAG not in expression:
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return expression
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name_with_layer_id_offset = next(
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(
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token.value
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for token in tokens
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if token.type == TokenType.IDENTIFIER
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and LAYER_ID_OFFSET_MACRO_TAG in token.value
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),
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None,
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)
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assert name_with_layer_id_offset, (
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"No $LAYER_ID_OFFSET found in NAME tokens.Please check the aoa_config."
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)
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assert all(
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(t.type != TokenType.IDENTIFIER)
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or (LAYER_ID_OFFSET_MACRO_TAG in t.value)
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or (t.value in GLOBAL_ATTRIBUTE_KEYWORDS)
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for t in tokens
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), (
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f"All IDENTIFIER tokens must contain {LAYER_ID_OFFSET_MACRO_TAG} when a NAME with it is present, except for GLOBAL_ATTRIBUTE_KEYWORDS."
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)
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match_layer_id_offset = context.get_num_hidden_layers(
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name_with_layer_id_offset, LAYER_ID_OFFSET_MACRO_TAG
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)
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expanded_expressions = []
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match_layer_id_offset = sorted(match_layer_id_offset)
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for layer_id in match_layer_id_offset:
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expr = ""
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before_rarrow = True
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for token in tokens:
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if token.type == TokenType.RARROW:
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before_rarrow = False
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if before_rarrow:
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cur_layer_id = layer_id
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else:
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cur_layer_id = layer_id - 1
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if token.type == TokenType.IDENTIFIER:
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if LAYER_ID_OFFSET_MACRO_TAG in token.value:
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expr += token.value.replace(
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LAYER_ID_OFFSET_MACRO_TAG, str(cur_layer_id)
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)
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elif token.value not in GLOBAL_ATTRIBUTE_KEYWORDS:
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expr += f"{token.value}.layer.{cur_layer_id}"
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else:
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expr += token.value
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else:
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expr += token.value
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expanded_expressions.append(expr)
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return expanded_expressions
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@macro(name='array_macro', priority=2)
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def array_macro(tokens, expression, context):
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if "[" not in expression:
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return expression
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new_tokens = []
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idx = 0
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while idx < len(tokens):
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if tokens[idx].type == TokenType.LBRACKET:
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name = tokens[idx - 1].value
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assert (
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tokens[idx + 1].type == TokenType.NUMBER
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and tokens[idx + 2].type == TokenType.COLON
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and tokens[idx + 3].type == TokenType.NUMBER
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and tokens[idx + 4].type == TokenType.RBRACKET
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), (
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f"The array macro format is incorrect which is must be like: NAME[START:END], but now the format is {tokens[idx].value}{tokens[idx + 1].value}:{tokens[idx + 3].value}{tokens[idx + 4].value}."
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)
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new_tokens.pop()
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start = int(tokens[idx + 1].value)
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end = int(tokens[idx + 3].value)
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for i in range(start, end):
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new_tokens.append(
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Token(TokenType.IDENTIFIER, name + "_" + str(i))
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)
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if i != end - 1:
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new_tokens.append(Token(TokenType.COMMA, ","))
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idx += 5
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else:
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new_tokens.append(tokens[idx])
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idx += 1
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new_expression = "".join([token.value for token in new_tokens])
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return new_expression
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@macro(name='fused_qkv_old_macro', priority=6)
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def fused_qkv_old_macro(tokens, expression, context):
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FUSED_QKV_OLD_TAG = "fused_qkv_old"
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if not any(tkn.value == FUSED_QKV_OLD_TAG for tkn in tokens):
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return expression
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axis, tokens = extract_axis_and_clean_tokens(tokens)
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attn_head_num = None
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num_key_value_groups = None
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fused_qkv_old_pos = None
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rarrow_pos = None
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right_var_end_pos = None
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for idx, token in enumerate(tokens):
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if token.type == TokenType.IDENTIFIER:
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if token.value == "num_heads" and idx + 2 < len(tokens):
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attn_head_num = int(tokens[idx + 2].value)
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elif token.value == "num_key_value_groups" and idx + 2 < len(
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tokens
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):
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num_key_value_groups = int(tokens[idx + 2].value)
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elif token.value == FUSED_QKV_OLD_TAG:
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fused_qkv_old_pos = idx
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elif token.type == TokenType.RARROW and rarrow_pos is None:
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rarrow_pos = idx
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if (
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right_var_end_pos is None
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and token.type == TokenType.IDENTIFIER
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and token.value
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in {FUSED_QKV_OLD_TAG, "num_heads", "num_key_value_groups"}
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):
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right_var_end_pos = idx + 1
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assert attn_head_num and attn_head_num > 0, (
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f"num_heads must be positive.(got: {attn_head_num})."
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)
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assert num_key_value_groups and num_key_value_groups > 0, (
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f"num_key_value_groups must be positive.(got: {num_key_value_groups})."
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)
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assert fused_qkv_old_pos is not None, (
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f"No fused_qkv_old tag found in expression. The tag must be {FUSED_QKV_OLD_TAG}."
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)
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assert rarrow_pos is not None, "No -> found in expression."
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assert attn_head_num % num_key_value_groups == 0, (
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f"num_heads ({attn_head_num}) must be divisible by num_key_value_groups ({num_key_value_groups})."
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)
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results = []
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num_key_value_heads = num_key_value_groups
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if rarrow_pos == 1:
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src_qkv_weight_name = tokens[0].value
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if fused_qkv_old_pos > 4:
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dst_qkv_weight_name = None
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else:
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dst_qkv_weight_name = tokens[2].value
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if context.aoa_config_reverse:
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dst_state_shard_num = context.get_src_state_shard_num(
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dst_qkv_weight_name
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)
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src_state_shard_num = (
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context.get_dst_state_shard_num(src_qkv_weight_name)
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if src_qkv_weight_name is not None
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else 1
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)
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else:
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src_state_shard_num = context.get_src_state_shard_num(
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src_qkv_weight_name
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)
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dst_state_shard_num = (
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context.get_dst_state_shard_num(dst_qkv_weight_name)
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if dst_qkv_weight_name is not None
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else 1
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)
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configs = [
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(src_state_shard_num, src_qkv_weight_name),
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(dst_state_shard_num, dst_qkv_weight_name),
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]
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head_config = [
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("Q", attn_head_num),
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("K", num_key_value_heads),
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("V", num_key_value_heads),
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]
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def gen_expr(tp_degree, num_heads, tp_rank, comp):
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start = tp_rank * num_heads // tp_degree
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count = num_heads // tp_degree
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return ",".join(
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f"fused_qkv_old_tmp.{comp}_{i}"
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for i in range(start, start + count)
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)
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for idx, (tp_degree, qkv_weight_name) in enumerate(configs):
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qkv_parts = [
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gen_expr(tp_degree, n, tp_rank, c)
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for tp_rank in range(tp_degree)
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for c, n in head_config
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]
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if idx == 0:
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mapping = (
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f"{qkv_weight_name} -> {','.join(qkv_parts)}, axis={axis}"
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)
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results.append(mapping)
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elif qkv_weight_name is not None:
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mapping = (
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f"{','.join(qkv_parts)} -> {qkv_weight_name}, axis={axis}"
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)
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results.append(mapping)
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if fused_qkv_old_pos > 4:
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def _generate_expr(prefix, count, target_name):
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elements = ",".join(
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f"fused_qkv_old_tmp.{prefix}_{i}" for i in range(count)
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)
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return f"{elements} -> {target_name}, axis={axis}"
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q_name = tokens[2].value
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k_name = tokens[4].value
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v_name = tokens[6].value
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results.append(_generate_expr("Q", attn_head_num, q_name))
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results.append(_generate_expr("K", num_key_value_heads, k_name))
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results.append(_generate_expr("V", num_key_value_heads, v_name))
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elif rarrow_pos == 5:
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q_name = tokens[0].value
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k_name = tokens[2].value
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v_name = tokens[4].value
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dst_qkv_weight_name = tokens[6].value
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fused_qkv_tmp_name = f"{q_name}.{k_name}.{v_name}.tmp"
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results.append(
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f"{q_name},{k_name},{v_name} -> {fused_qkv_tmp_name}, axis={axis}"
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)
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dst_state_shard_num = context.get_dst_state_shard_num(
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dst_qkv_weight_name
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)
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configs = [
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(1, fused_qkv_tmp_name),
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(dst_state_shard_num, dst_qkv_weight_name),
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]
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head_config = [
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("Q", attn_head_num),
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("K", num_key_value_heads),
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("V", num_key_value_heads),
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]
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def gen_expr(tp_degree, num_heads, tp_rank, comp):
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start = tp_rank * num_heads // tp_degree
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count = num_heads // tp_degree
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return ",".join(
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f"fused_qkv_old_tmp.{comp}_{i}"
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for i in range(start, start + count)
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)
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for idx, (tp_degree, qkv_weight_name) in enumerate(configs):
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qkv_parts = [
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gen_expr(tp_degree, n, tp_rank, c)
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for tp_rank in range(tp_degree)
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for c, n in head_config
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]
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if idx == 0:
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mapping = (
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f"{qkv_weight_name} -> {','.join(qkv_parts)}, axis={axis}"
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)
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else:
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mapping = (
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f"{','.join(qkv_parts)} -> {qkv_weight_name}, axis={axis}"
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)
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results.append(mapping)
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else:
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raise ValueError(
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f"Unsupported fused_qkv_old macro format: {expression}."
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)
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return results
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@macro(name='fused_ffn_macro', priority=6)
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def fused_ffn_macro(tokens, expression, context):
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FUSED_FFN_TAG = "fused_ffn"
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if not any(tkn.value == FUSED_FFN_TAG for tkn in tokens):
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return expression
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axis, tokens = extract_axis_and_clean_tokens(tokens)
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rarrow_pos = None
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fused_ffn_pos = None
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for idx, token in enumerate(tokens):
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if token.type == TokenType.RARROW and rarrow_pos is None:
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rarrow_pos = idx
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elif (
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token.type == TokenType.IDENTIFIER and token.value == FUSED_FFN_TAG
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):
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fused_ffn_pos = idx
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assert rarrow_pos is not None, "No -> found in expression."
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assert fused_ffn_pos is not None, (
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f"No fused_ffn tag found in expression. The tag must be {FUSED_FFN_TAG}."
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)
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results = []
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if rarrow_pos == 1:
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src_ffn_weight_name = tokens[0].value
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if fused_ffn_pos == 4:
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dst_ffn_weight_name = tokens[2].value
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else:
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dst_ffn_weight_name = None
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if context.aoa_config_reverse:
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dst_state_shard_num = context.get_src_state_shard_num(
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dst_ffn_weight_name
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)
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src_state_shard_num = (
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context.get_dst_state_shard_num(src_ffn_weight_name)
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if src_ffn_weight_name is not None
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else 1
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)
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else:
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src_state_shard_num = context.get_src_state_shard_num(
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src_ffn_weight_name
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)
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dst_state_shard_num = (
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context.get_dst_state_shard_num(dst_ffn_weight_name)
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if dst_ffn_weight_name is not None
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else 1
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)
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splited_num = math.lcm(src_state_shard_num, dst_state_shard_num)
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configs = [
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(src_state_shard_num, src_ffn_weight_name),
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(dst_state_shard_num, dst_ffn_weight_name),
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]
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split_config = [("GATE", splited_num), ("UP", splited_num)]
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def gen_expr(tp_degree, splited_num, tp_rank, comp):
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return ",".join(
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f"fused_ffn_tmp.{comp}_{tp_rank * splited_num // tp_degree + idx}"
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for idx in range(splited_num // tp_degree)
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)
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for idx, (tp_degree, ffn_weight_name) in enumerate(configs):
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ffn_parts = [
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gen_expr(tp_degree, n, tp_rank, c)
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for tp_rank in range(tp_degree)
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for c, n in split_config
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]
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if idx == 0:
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results.append(
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f"{ffn_weight_name} -> {','.join(ffn_parts)}, axis={axis}"
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)
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elif ffn_weight_name is not None:
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results.append(
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f"{','.join(ffn_parts)} -> {ffn_weight_name}, axis={axis}"
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)
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if fused_ffn_pos > 4:
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|
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def _generate_expr(prefix, count, target_name):
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elements = ",".join(
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f"fused_ffn_tmp.{prefix}_{i}" for i in range(count)
|
|
)
|
|
return f"{elements} -> {target_name}, axis={axis}"
|
|
|
|
gate_name = tokens[2].value
|
|
up_name = tokens[4].value
|
|
|
|
results.append(_generate_expr("GATE", splited_num, gate_name))
|
|
results.append(_generate_expr("UP", splited_num, up_name))
|
|
|
|
elif rarrow_pos == 3:
|
|
gate_name = tokens[0].value
|
|
up_name = tokens[2].value
|
|
dst_ffn_weight_name = tokens[4].value
|
|
|
|
fused_gate_up_tmp_name = f"{gate_name}.{up_name}.tmp"
|
|
results.append(
|
|
f"{gate_name},{up_name} -> {fused_gate_up_tmp_name}, axis={axis}"
|
|
)
|
|
dst_state_shard_num = context.get_dst_state_shard_num(
|
|
dst_ffn_weight_name
|
|
)
|
|
|
|
configs = [
|
|
(1, fused_gate_up_tmp_name),
|
|
(dst_state_shard_num, dst_ffn_weight_name),
|
|
]
|
|
|
|
split_config = [
|
|
("GATE", dst_state_shard_num),
|
|
("UP", dst_state_shard_num),
|
|
]
|
|
|
|
def gen_expr(tp_degree, splited_num, tp_rank, comp):
|
|
return ",".join(
|
|
f"fused_ffn_tmp.{comp}_{tp_rank * splited_num // tp_degree + idx}"
|
|
for idx in range(splited_num // tp_degree)
|
|
)
|
|
|
|
for idx, (tp_degree, ffn_weight_name) in enumerate(configs):
|
|
ffn_parts = [
|
|
gen_expr(tp_degree, n, tp_rank, c)
|
|
for tp_rank in range(tp_degree)
|
|
for c, n in split_config
|
|
]
|
|
if idx == 0:
|
|
results.append(
|
|
f"{ffn_weight_name} -> {','.join(ffn_parts)}, axis={axis}"
|
|
)
|
|
else:
|
|
results.append(
|
|
f"{','.join(ffn_parts)} -> {ffn_weight_name}, axis={axis}"
|
|
)
|
|
else:
|
|
raise ValueError(f"Unsupported fused_ffn macro format: {expression}.")
|
|
return results
|
|
|
|
|
|
@macro(name='transpose_macro', priority=5)
|
|
def transpose_macro(tokens, expression, context):
|
|
TRANSPOSE_TAG = "^T"
|
|
|
|
if TRANSPOSE_TAG not in expression:
|
|
return expression
|
|
|
|
transpose_vars = set()
|
|
new_expression = ""
|
|
rarrow_pos = None
|
|
|
|
for idx, token in enumerate(tokens):
|
|
if token.type == TokenType.RARROW:
|
|
rarrow_pos = idx
|
|
break
|
|
|
|
assert rarrow_pos is not None, "No -> found in expression."
|
|
|
|
for token in tokens[rarrow_pos + 1 :]:
|
|
if token.type == TokenType.IDENTIFIER and token.value.endswith(
|
|
TRANSPOSE_TAG
|
|
):
|
|
raise ValueError(
|
|
"Cannot assign to transpose (e.g., 'A -> B^T').\n"
|
|
"B^T is not a real variable, just a view.\n"
|
|
"Assign first: A -> B\n"
|
|
"Then transpose: B^T -> B"
|
|
)
|
|
for token in tokens:
|
|
if token.type == TokenType.IDENTIFIER and token.value.endswith(
|
|
TRANSPOSE_TAG
|
|
):
|
|
var_name = token.value[: -len(TRANSPOSE_TAG)]
|
|
transpose_vars.add(var_name)
|
|
new_expression += var_name + "_transpose_tmp"
|
|
else:
|
|
new_expression += token.value
|
|
|
|
results = [
|
|
f'{var} -> {var}_transpose_tmp, permute = "[]"'
|
|
for var in transpose_vars
|
|
]
|
|
results.append(new_expression)
|
|
return results
|
|
|
|
|
|
@macro(name='fused_qkv_macro', priority=6)
|
|
def fused_qkv_macro(tokens, expression, context):
|
|
FUSED_QKV_TAG = "fused_qkv"
|
|
if not any(tkn.value == FUSED_QKV_TAG for tkn in tokens):
|
|
return expression
|
|
|
|
axis, tokens = extract_axis_and_clean_tokens(tokens)
|
|
|
|
attn_head_num = num_heads = None
|
|
num_key_value_groups = None
|
|
fused_qkv_pos = None
|
|
rarrow_pos = None
|
|
|
|
for idx, token in enumerate(tokens):
|
|
if token.type == TokenType.IDENTIFIER:
|
|
if token.value == "num_heads" and idx + 2 < len(tokens):
|
|
attn_head_num = int(tokens[idx + 2].value)
|
|
elif token.value == "num_key_value_groups" and idx + 2 < len(
|
|
tokens
|
|
):
|
|
num_key_value_groups = int(tokens[idx + 2].value)
|
|
elif token.value == FUSED_QKV_TAG:
|
|
fused_qkv_pos = idx
|
|
elif token.type == TokenType.RARROW and rarrow_pos is None:
|
|
rarrow_pos = idx
|
|
|
|
assert attn_head_num and attn_head_num > 0, (
|
|
f"num_heads must be positive (got: {attn_head_num})"
|
|
)
|
|
assert num_key_value_groups and num_key_value_groups > 0, (
|
|
f"num_key_value_groups must be positive (got: {num_key_value_groups})"
|
|
)
|
|
assert fused_qkv_pos is not None, (
|
|
f"No fused_qkv tag found in expression. The tag must be {FUSED_QKV_TAG}."
|
|
)
|
|
assert rarrow_pos is not None, "No -> found in expression."
|
|
assert rarrow_pos == 1 or rarrow_pos == 5, (
|
|
"Only support q,k,v -> fused_qkv or fused_qkv -> q,k,v patterns"
|
|
)
|
|
assert attn_head_num % num_key_value_groups == 0, (
|
|
f"num_heads ({attn_head_num}) must be divisible by num_key_value_groups ({num_key_value_groups})."
|
|
)
|
|
|
|
num_key_value_heads = attn_head_num // num_key_value_groups
|
|
|
|
def make_names(base, n):
|
|
return [f"{base}{i}" for i in range(n)]
|
|
|
|
results = []
|
|
|
|
if rarrow_pos == 1:
|
|
fused_qkv_var = tokens[0].value
|
|
q_var = tokens[rarrow_pos + 1].value
|
|
k_var = tokens[rarrow_pos + 3].value
|
|
v_var = tokens[rarrow_pos + 5].value
|
|
|
|
q_names = make_names(q_var, attn_head_num)
|
|
k_names = make_names(k_var, num_key_value_groups)
|
|
v_names = make_names(v_var, num_key_value_groups)
|
|
|
|
fused_qkv_order = []
|
|
for g in range(num_key_value_groups):
|
|
fused_qkv_order.extend(
|
|
q_names[g * num_key_value_heads : (g + 1) * num_key_value_heads]
|
|
)
|
|
fused_qkv_order.append(k_names[g])
|
|
fused_qkv_order.append(v_names[g])
|
|
results.append(
|
|
f"{fused_qkv_var} -> {','.join(fused_qkv_order)}, axis={axis}"
|
|
)
|
|
|
|
results.append(f"{','.join(q_names)} -> {q_var}, axis={axis}")
|
|
results.append(f"{','.join(k_names)} -> {k_var}, axis={axis}")
|
|
results.append(f"{','.join(v_names)} -> {v_var}, axis={axis}")
|
|
|
|
return results
|
|
|
|
elif rarrow_pos == 5:
|
|
q_var = tokens[0].value
|
|
k_var = tokens[2].value
|
|
v_var = tokens[4].value
|
|
fused_qkv_var = tokens[rarrow_pos + 1].value
|
|
|
|
q_names = make_names(q_var, attn_head_num)
|
|
k_names = make_names(k_var, num_key_value_groups)
|
|
v_names = make_names(v_var, num_key_value_groups)
|
|
|
|
results.append(f"{q_var} -> {','.join(q_names)}, axis={axis}")
|
|
results.append(f"{k_var} -> {','.join(k_names)}, axis={axis}")
|
|
results.append(f"{v_var} -> {','.join(v_names)}, axis={axis}")
|
|
|
|
fused_qkv_order = []
|
|
for g in range(num_key_value_groups):
|
|
fused_qkv_order.extend(
|
|
q_names[g * num_key_value_heads : (g + 1) * num_key_value_heads]
|
|
)
|
|
fused_qkv_order.append(k_names[g])
|
|
fused_qkv_order.append(v_names[g])
|
|
results.append(
|
|
f"{','.join(fused_qkv_order)} -> {fused_qkv_var}, axis={axis}"
|
|
)
|
|
return results
|
|
|
|
else:
|
|
return expression
|
|
|
|
|
|
class IDMatcher:
|
|
def __init__(
|
|
self,
|
|
source_keys: list[str],
|
|
extra_suffixes: list[str],
|
|
allowed_placeholders: list[str],
|
|
):
|
|
self.source_keys = set(source_keys)
|
|
self.allowed_placeholders = allowed_placeholders
|
|
# Dynamically build regex pattern from allowed placeholders
|
|
placeholder_pattern = '|'.join(
|
|
re.escape(ph) for ph in self.allowed_placeholders
|
|
)
|
|
self._placeholder_pattern = re.compile(f'({placeholder_pattern})')
|
|
self.extra_suffixes = sorted(extra_suffixes, key=lambda x: (-len(x), x))
|
|
|
|
def _remove_extra_suffixes(self, key: str) -> str:
|
|
for sfx in self.extra_suffixes:
|
|
if key.endswith(sfx):
|
|
key = key[: -len(sfx)]
|
|
break
|
|
return key
|
|
|
|
def _pattern_to_regex(self, pattern: str) -> tuple[re.Pattern, list[str]]:
|
|
placeholders = sorted(set(self._placeholder_pattern.findall(pattern)))
|
|
regex_str = re.escape(pattern)
|
|
for ph in placeholders:
|
|
group_name = ph[1:]
|
|
regex_str = regex_str.replace(
|
|
re.escape(ph), f'(?P<{group_name}>\\d+)'
|
|
)
|
|
return re.compile(f'^{regex_str}$'), [ph[1:] for ph in placeholders]
|
|
|
|
def _substitute_ids(self, pattern: str, id_dict: dict[str, int]) -> str:
|
|
key = pattern
|
|
for ph, value in id_dict.items():
|
|
key = key.replace(f'${ph}', str(value))
|
|
return key
|
|
|
|
def find_matches(self, pattern: str) -> dict[str, list[int]]:
|
|
pattern = self._remove_extra_suffixes(pattern)
|
|
regex, ph_names = self._pattern_to_regex(pattern)
|
|
id_values = {ph: set() for ph in ph_names}
|
|
for key in self.source_keys:
|
|
match = regex.match(key)
|
|
if match:
|
|
for k, v in match.groupdict().items():
|
|
id_values[k].add(int(v))
|
|
return {k: sorted(vs) for k, vs in id_values.items()}
|
|
|
|
|
|
# Global registry for allowed_placeholders
|
|
_REGISTERED_PLACEHOLDERS = ['$EXPERT_ID', '$LAYER_ID']
|
|
|
|
|
|
# TODO: need to adapt the scene of temp_layers.\$LAYER_ID.weight -> dst_layers.\$LAYER_ID.weight
|
|
@macro(name='id_macro', priority=1)
|
|
def id(tokens, expression, context):
|
|
allowed_placeholders = _REGISTERED_PLACEHOLDERS
|
|
has_allowed_placeholder = any(
|
|
ph in expression for ph in allowed_placeholders
|
|
)
|
|
if not has_allowed_placeholder:
|
|
return expression
|
|
|
|
if not context.aoa_config_reverse:
|
|
name_with_id = next(
|
|
(
|
|
token.value
|
|
for token in tokens
|
|
if token.type == TokenType.IDENTIFIER
|
|
and any(ph in token.value for ph in allowed_placeholders)
|
|
),
|
|
None,
|
|
)
|
|
else:
|
|
flag_right_var = False
|
|
for token in tokens:
|
|
if token.type == TokenType.RARROW:
|
|
flag_right_var = True
|
|
if token.type == TokenType.IDENTIFIER and any(
|
|
ph in token.value for ph in allowed_placeholders
|
|
):
|
|
if flag_right_var:
|
|
name_with_id = token.value
|
|
break
|
|
|
|
assert name_with_id is not None, "No $ID found in NAME tokens"
|
|
all_src_state_keys = context.get_all_src_state_keys()
|
|
id_matcher = IDMatcher(
|
|
all_src_state_keys, EXTRA_SUFFIX, allowed_placeholders
|
|
)
|
|
valid_id_combos = id_matcher.find_matches(name_with_id)
|
|
valid_keys = list(valid_id_combos.keys())
|
|
IDENTIFIER_tokens = []
|
|
for token in tokens:
|
|
if token.value in GLOBAL_ATTRIBUTE_KEYWORDS:
|
|
break
|
|
if token.type == TokenType.IDENTIFIER:
|
|
IDENTIFIER_tokens.append(token)
|
|
|
|
for token in IDENTIFIER_tokens:
|
|
assert all(k in token.value for k in valid_keys), (
|
|
f"The token: {token.value} must contain all of the following keys: {valid_keys}.When use the id macro all IDENTIFIER tokens must contain the same ID placeholders."
|
|
)
|
|
|
|
def dict_cartesian_tuples(d: dict[str, list[int]]):
|
|
keys = list(d.keys())
|
|
value_lists = [d[k] for k in keys]
|
|
for prod in product(*value_lists):
|
|
yield tuple(zip(keys, prod))
|
|
|
|
results = []
|
|
id_combs = dict_cartesian_tuples(valid_id_combos)
|
|
id_combs = sorted(id_combs)
|
|
for id_comb in id_combs:
|
|
cur_statement = ""
|
|
for tkn in tokens:
|
|
tkn_val = tkn.value
|
|
if tkn.type == TokenType.IDENTIFIER and any(
|
|
ph in tkn.value for ph in allowed_placeholders
|
|
):
|
|
for id_tag, id_val in id_comb:
|
|
tkn_val = tkn_val.replace("$" + id_tag, str(id_val))
|
|
cur_statement += tkn_val
|
|
else:
|
|
cur_statement += tkn_val
|
|
results.append(cur_statement)
|
|
|
|
return results
|
|
|
|
|
|
# This macro processes variable mappings between source and destination states,
|
|
# but it requires that all expansion macros (layer_id_macro, expert_id_macro,
|
|
# star_macro, array_macro, etc.) have already been executed to expand template
|
|
# variables into concrete variable names.
|
|
@macro(name='get_var_mapping_chain_macro', priority=4)
|
|
def get_var_mapping_chain_macro(tokens, expression, context):
|
|
flag_left_var = True
|
|
left_var_list = []
|
|
right_var_list = []
|
|
for tkn in tokens:
|
|
if tkn.value in GLOBAL_ATTRIBUTE_KEYWORDS:
|
|
break
|
|
if tkn.type == TokenType.RARROW:
|
|
flag_left_var = False
|
|
if tkn.type == TokenType.IDENTIFIER:
|
|
extra_suffix_removed_value = tkn.value
|
|
for sfx in EXTRA_SUFFIX:
|
|
extra_suffix_removed_value = (
|
|
extra_suffix_removed_value.removesuffix(sfx)
|
|
)
|
|
if flag_left_var:
|
|
left_var_list.append(extra_suffix_removed_value)
|
|
else:
|
|
right_var_list.append(extra_suffix_removed_value)
|
|
assert len(left_var_list) == 1 or len(right_var_list) == 1, (
|
|
"Left or right variable must have the only one element,the aoa_statements not support 'multiple var -> multiple var' pattern."
|
|
)
|
|
if len(left_var_list) == 1:
|
|
context.left_var_to_right_var_mapping[left_var_list[0]] = right_var_list
|
|
for right_var in right_var_list:
|
|
context.right_var_from_left_var_mapping[right_var] = left_var_list
|
|
else:
|
|
context.right_var_from_left_var_mapping[right_var_list[0]] = (
|
|
left_var_list
|
|
)
|
|
for left_var in left_var_list:
|
|
context.left_var_to_right_var_mapping[left_var] = right_var_list
|
|
return expression
|