diff --git a/dace/transformation/semantic_preserving/HeapToStack.py b/dace/transformation/semantic_preserving/HeapToStack.py new file mode 100644 index 0000000000..422536c058 --- /dev/null +++ b/dace/transformation/semantic_preserving/HeapToStack.py @@ -0,0 +1,130 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the HeapToStack transformation. + +""" + +import dace + +class HeapToStack(): + """ + This class implements the HeapToStack transformation. It changes the storage type of the array to the stack. + + Inputs: + - hard_limit (int): The hard limit of the stack size. If the stack size exceeds this limit, the transformation is not applied. Default is None (no hard limit). + - stack_size_factor (int): The factor to calculate the stack size. Default is 1. Which means the stack size is 1 * 1MB (minus a small amount). This is just a heuristic. + + + Attributes: + check_limit (bool): If True, the transformation checks if the stack size is exceeded. + stack_size (int): The size of the stack. Default is 1MB. This can be changed by the user. This is just a heuristic. + hard_limit (int): The hard limit of the stack size. If the stack size exceeds this limit, the transformation is not applied. Default is None (no hard limit). + """ + + def __init__(self, check_limit = True, hard_limit=None, stack_size_factor=1.0): + self.__name = 'HeapToStack' + self.checked = False + # check if stack size is exceeded + self.check_limit = check_limit + # Default assumption: stack size is 1MB = 1 * 1'048'576 bytes + self.stack_size = stack_size_factor * 1048576 * 0.95 #1MB * 0.95 + self.hard_limit = hard_limit #65536 #(64KB) + + @property + def name(self): + return self.__name + + def get_stack_size(self, sdfg, limit='soft'): + import os + if os.name == 'nt': #windows + self.stack_size = None + else: #unix like systems + import resource + soft_limit, hard_limit = resource.getrlimit(resource.RLIMIT_STACK) + if limit == 'soft': + self.stack_size = soft_limit + else: + self.stack_size = hard_limit + + def current_stack_size(self, sdfg): + current_size = 0 + all_nodes = set() + # we want only the arrays that are represented in the SDFG + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.AccessNode): + all_nodes.add(node.label) + + for name, data in sdfg.arrays.items(): + if not name in all_nodes: + continue + if data.storage == dace.StorageType.Register: + current_size += data.total_size * data.dtype.bytes + return current_size + + def check_current_stack_usage(self): + """Check the current stack size usage in bytes.""" + import sys + frame = sys._getframe() + stack_usage = 0 + while frame is not None: + # Each frame has its own local variables and other information + stack_usage += sum(sys.getsizeof(v) for v in frame.f_locals.values()) + frame = frame.f_back # Move to the previous frame + return stack_usage + + def stack_size_exceeded(self, sdfg, data, safety_factor=1.0): + size = data.total_size * data.dtype.bytes + available_stack = self.stack_size - self.current_stack_size(sdfg) + if safety_factor*size > available_stack: + return True + return False + + def find(self, sdfg): + """ + This method searches for arrays in the SDFG that are not yet stored on the stack (Register). + + This transformation is only applied if the stack size is not exceeded. + + We try not to exceed the stack size. + Args: + sdfg (dace.SDFG): The SDFG to search for arrays. + + Returns: + list: A list of name of arrays that are not stored on the stack ordered by array size * type size (ascending order). + """ + result = [] + for name, data in sdfg.arrays.items(): + if data.storage == dace.StorageType.Register: + continue + if self.hard_limit: + if self.hard_limit < data.total_size * data.dtype.bytes: + continue + if self.check_limit and self.stack_size_exceeded(sdfg, data): + continue + result.append(name) + self.checked = True + return sorted(result, key=lambda x: (sdfg.arrays[x].total_size*sdfg.arrays[x].dtype.bytes, sdfg.arrays[x].total_size)) + + def apply(self, sdfg, name): + """ + This method applies the HeapToStack transformation to the given SDFG. It changes the storage type of the array to the stack. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + name (String): The array to apply the transformation to. + """ + if not self.checked and not name in self.find(sdfg): + return + self.checked = False + data = sdfg.arrays[name] + data.storage = dace.StorageType.Register \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/JoinMapScopes.py b/dace/transformation/semantic_preserving/JoinMapScopes.py new file mode 100644 index 0000000000..74bcd20593 --- /dev/null +++ b/dace/transformation/semantic_preserving/JoinMapScopes.py @@ -0,0 +1,360 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the JoinMapScopes transformation. + +""" + +import dace + +class JoinMapScopes(): + """ + This class implements the JoinMapScopes transformation. It searches for two map scopes in the SDFG and joins them. The transformation is only applied if the map scopes are joinable. + """ + + def __init__(self): + self.__name = 'JoinMapScopes' + self.checked = False + + @property + def name(self): + return self.__name + + def find_entries(self, state): + result = [] + for node in state.nodes(): + if isinstance(node, dace.nodes.MapEntry): + result.append(node) + return result + + def find(self, sdfg): + """ + This method searches for two map scopes in the SDFG and returns a list of tuples containing the two map scopes. + + Two map scopes are joinable if the following conditions are met: + - The two map scopes have the same entry node + - The two maps have the same map range + - The two maps have the same map parameters + - The second map scope comes directly after the first map scope and is the only map scope that follows the first map scope + - Nodes that are output of the first map scope and input of the second map scope use the same indexing + - If an output node of is involved in a WCR (reduction), the shape of the output node is equal to the range of the first map scope + + Args: + sdfg (dace.SDFG): The SDFG to search for joinable map scopes. + + Returns: + list: A list of tuples, where each tuple contains two joinable map scopes and the state they are in. + """ + candidates = [] + for state in sdfg.nodes(): + entries = self.find_entries(state) + for i in range(len(entries)): + for j in range(len(entries)): + if i == j: + continue + entry1, entry2 = entries[i], entries[j] + if not state.entry_node(entry1) == state.entry_node(entry2): + continue + if not entry1.map.range == entry2.map.range: + continue + #TODO: allow but rename the map params ?! + if not entry1.map.params == entry2.map.params: + continue + out_nodes = [edge.dst for edge in state.out_edges(state.exit_node(entry1))] + next_maps = {edge.dst for node in out_nodes for edge in state.out_edges(node) if isinstance(edge.dst, dace.nodes.MapEntry)} + if not entry2 in next_maps or len(next_maps) > 1: + continue + candidates.append((state, entry1, entry2)) + + result = [] + for (state, entry1, entry2) in candidates: + if not self.check(state, entry1, entry2): + continue + result.append((state, entry1, entry2)) + self.checked = True + return result + + def check(self, state, entry1, entry2): + reject = False + entry1_out = [edge.dst for edge in state.out_edges(state.exit_node(entry1))] + entry2_in = [edge.src for edge in state.in_edges(entry2)] + entry1_params = entry1.map.params + entry2_params = entry2.map.params + for elem in set([node.label for node in entry1_out]) & set([node.label for node in entry2_in]): + nodes_1 = [node for node in entry1_out if node.label == elem] + nodes_2 = [node for node in entry2_in if node.label == elem] + idxs_1 = [edge.data for edge in state.in_edges(nodes_1[0])] + idxs_2 = [edge.data for edge in state.out_edges(nodes_2[0])] + for idx1 in idxs_1: + for idx2 in idxs_2: + if not idx1.subset == idx2.subset: + reject = True + + for node in nodes_1: + in_edge = state.in_edges(node)[0] + if in_edge.data.wcr: + above_maps = self.get_entry_maps(state, entry1) + above_idxs = [map_entry.map.params[0] for map_entry in above_maps] if above_maps else [] + above_idxs.append(entry1.map.params[0]) + write_op = self.get_write_op(state, node) + reduce_indices = set() + for in_edge in state.in_edges(write_op): + for subset in in_edge.data.subset: + reduce_indices.add(str(subset[0])) + for out_edge in state.out_edges(write_op): + for subset in out_edge.data.subset: + reduce_indices.discard(str(subset[0])) + #dst_shape = in_edge.data.dst_subset.size() + #print(f'above_idxs: {above_idxs}, reduce_indices: {reduce_indices}') + for idxs in reduce_indices: + if idxs in above_idxs: + reject = True + break + """ + if not dst_shape == entry1.map.range.size(): + reject = True + """ + if not reject: + return True + return False + + def get_write_op(self, state, node): + data_name = node.data + + def traverse(node, visited=None): + if visited is None: + visited = set() + visited.add(node) + if isinstance(node, dace.nodes.Tasklet): + if state.out_edges(node)[0].data.data == data_name: + return node + for edge in state.in_edges(node): + if edge.src in visited: + continue + res = traverse(edge.src) + if res: + return res + return None + + return traverse(node) + + + def get_entry_maps(self, state, node): + dims = [] + entry = state.entry_node(node) + if not entry: + return dims + while True: + dims.append(entry) + entry = state.entry_node(entry) + if not entry: + break + return dims + + # not used currently + def find_2(self, sdfg): + transform = dace.transformation.dataflow.map_fusion.MapFusion() + candidates = [] + for state in sdfg.nodes(): + entries = self.find_entries(state) + for i in range(len(entries)): + next = False + for j in range(i+1, len(entries)): + node1, node2 = entries[i], entries[j] + if not state.entry_node(node1) == state.entry_node(node2): + continue + if next: + break + next = True + if not node1.map.range == node2.map.range: + continue + if not len(node1.map.params) == len(node2.map.params): + continue + transform.first_map_exit = state.exit_node(node1) + transform.second_map_entry = node2 + if transform.can_be_applied(state, 0, sdfg): + candidates.append((state, (node1, node2))) + result = [] + for state,(entry1, entry2) in candidates: + reject = False + entry1_out = [edge.dst for edge in state.out_edges(state.exit_node(entry1))] + entry2_in = [edge.src for edge in state.in_edges(entry2)] + entry1_params = entry1.map.params + entry2_params = entry2.map.params + for elem in set([node.label for node in entry1_out]) & set([node.label for node in entry2_in]): + nodes_1 = [node for node in entry1_out if node.label == elem] + nodes_2 = [node for node in entry2_in if node.label == elem] + idxs_1 = [edge.data for edge in state.in_edges(nodes_1[0])] + idxs_2 = [edge.data for edge in state.out_edges(nodes_2[0])] + for idx1 in idxs_1: + for idx2 in idxs_2: + if not idx1.subset == idx2.subset: + reject = True + break + + for node in nodes_1: + in_edge = state.in_edges(node)[0] + if in_edge.data.wcr: + dst_shape = in_edge.data.dst_subset.size() + if not dst_shape == entry1.map.range.size(): + reject = True + break + if not reject: + result.append((state, entry1, entry2)) + print(result) + return [] + + def apply(self, sdfg, state, entry1, entry2): + """ + This method applies the JoinMapScopes transformation to the given SDFG. It joins the two given map scopes. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + state (dace.SDFGState): The state the map scopes are in. + entry1 (dace.MapEntry): The first map scope to join. + entry2 (dace.MapEntry): The second map scope to join. + """ + if not self.checked and not (state, entry1, entry2) in self.find(sdfg): + return + self.checked = False + exit1 = state.exit_node(entry1) + exit2 = state.exit_node(entry2) + input_nodes = set() + input_nodes = {edge.src for edge in state.in_edges(entry1)} + input_labels = {node.label for node in input_nodes} + output_nodes = {edge.dst for edge in state.out_edges(exit1)} + inbetweeners = set() + fuse_nodes = set() + input_prop = set() + # redirect nodes that are inbetween the first exit and second entry + # exit1 + for in_edge in state.in_edges(exit1): + for out_edge in state.out_edges(exit1): + if not in_edge.data.data == out_edge.data.data: + continue + if not in_edge.dst_conn[3:] == out_edge.src_conn[4:]: + continue + # output nodes of first and second map are same node + if out_edge.dst in {edge.src for edge in state.in_edges(entry2)}: + inbetweeners.add(out_edge.dst) + state.add_memlet_path(in_edge.src, out_edge.dst, memlet=in_edge.data, src_conn=in_edge.src_conn, dst_conn=out_edge.dst_conn) + # output is used elsewhere + else: + state.add_memlet_path(in_edge.src, exit2, out_edge.dst, memlet=in_edge.data, src_conn=in_edge.src_conn, dst_conn=out_edge.dst_conn) + state.remove_edge(in_edge) + state.remove_edge(out_edge) + + # entry2 + for in_edge in state.in_edges(entry2): + for out_edge in state.out_edges(entry2): + if not in_edge.data.data == out_edge.data.data: + continue + if in_edge.dst_conn is None or out_edge.src_conn is None: + continue + if not in_edge.dst_conn[3:] == out_edge.src_conn[4:]: + continue + """ + Case 1: Input Node of Entry1 is input node of Entry2 + => Redirect the edge through Entry1 + Case 2: Same data input for Entry1 and Entry2, but two separate nodes + => Remove one input node and redirect the edge through Entry1 + Case 3: Output of Entry1 is input of Entry2 + => Redirect the edge + Case 4: Input Node of Entry2 is not input entry2 and is transient + 4.1 if written to and read in entry1 scope (happens if you use split transformation) + => fuse the node and redirect the edge + Case 5: Else: Input of Entry2 + => Redirect the edge through Entry1 + """ + # input nodes of first and second map are same node + if in_edge.src in input_nodes: + # moved this to further in the code since the where errors with leftover nodes + input_prop.add((in_edge.src, out_edge.dst, out_edge.data, out_edge.dst_conn, in_edge.src_conn)) + state.remove_edge(in_edge) + state.remove_edge(out_edge) + #state.add_memlet_path(src, entry1, dst, memlet=mem, dst_conn=dst_conn) + # input nodes of first and second map are different nodes, but same data potentialy + elif in_edge.src.label in input_labels and not state.in_edges(in_edge.src): + target = [node for node in input_nodes if node.label == in_edge.src.label][0] + if not target: + state.add_memlet_path(in_edge.src, entry1, out_edge.dst, memlet=out_edge.data, src_conn=in_edge.src_conn, dst_conn=out_edge.dst_conn) + state.remove_edge(in_edge) + state.remove_edge(out_edge) + else: + state.add_memlet_path(target, entry1, out_edge.dst, memlet=out_edge.data, dst_conn=out_edge.dst_conn, src_conn=in_edge.src_conn) + state.remove_edge(in_edge) + # remove second identical input node + state.remove_node(in_edge.src) + state.remove_edge(out_edge) + # output of first map is input of second map + elif state.in_edges(in_edge.src) and in_edge.src in inbetweeners: + state.add_memlet_path(in_edge.src, out_edge.dst, memlet=out_edge.data, dst_conn=out_edge.dst_conn, src_conn=in_edge.src_conn) + state.remove_edge(out_edge) + state.remove_edge(in_edge) + # input of second map, but produced and used in first map too + elif not state.in_edges(in_edge.src) and sdfg.arrays[in_edge.data.data].transient: + t = self.find_node(state, entry1, in_edge.src) + #if entry1.label == 'map_1_inv_idx' and entry2.label == 'map_1_max_idx': + # print(f'edge: {in_edge.data.data}, src: {in_edge.src}, dst: {out_edge.dst}, in_Edge_src: {in_edge.src_conn}, out_edge_dst: {out_edge.dst_conn} ({t}))') + if t: + # happens especially when using split transformation, some nodes get created because redrection is not possible + fuse_nodes.add(in_edge.src) + state.add_memlet_path(in_edge.src, out_edge.dst, memlet=out_edge.data, src_conn=in_edge.src_conn, dst_conn=out_edge.dst_conn) + else: + state.add_memlet_path(in_edge.src, entry1, out_edge.dst, memlet=in_edge.data, src_conn=in_edge.src_conn, dst_conn=out_edge.dst_conn) + state.remove_edge(in_edge) + state.remove_edge(out_edge) + # input of second map, but not used in first map + else: + state.add_memlet_path(in_edge.src, entry1, out_edge.dst, memlet=out_edge.data, src_conn=in_edge.src_conn, dst_conn=out_edge.dst_conn) + state.remove_edge(in_edge) + state.remove_edge(out_edge) + + #fuse the nodes + for node in fuse_nodes: + target = self.find_node(state, entry1, node) + if target: + for edge in state.out_edges(node): + state.add_memlet_path(target, edge.dst, memlet=edge.data, dst_conn=edge.dst_conn) + state.remove_node(node) + + # propagate input nodes + for src, dst, mem, dst_conn, src_conn in input_prop: + state.add_memlet_path(src, entry1, dst, memlet=mem, dst_conn=dst_conn, src_conn=src_conn) + + # replace entry2 with entry1 + entry2.in_connectors = entry1.in_connectors + entry2.out_connectors = entry1.out_connectors + for in_edge in state.in_edges(entry1): + dace.transformation.helpers.redirect_edge(state, in_edge, new_dst = entry2, new_dst_conn = in_edge.dst_conn) + for out_edge in state.out_edges(entry1): + dace.transformation.helpers.redirect_edge(state, out_edge, new_src = entry2, new_src_conn = out_edge.src_conn) + + # remove entry1 and exit1 + state.remove_node(entry1) + state.remove_node(exit1) + + def find_node(self, state, cur_node, target, traversed=None): + if traversed is None: + traversed = set() + traversed.add(cur_node) + for edge in state.out_edges(cur_node): + if isinstance(edge.dst, dace.nodes.AccessNode) and edge.dst.label == target.label and not edge.dst == target: + return edge.dst + elif isinstance(edge.dst, dace.nodes.MapExit): + continue + elif not edge.dst in traversed: + tmp = self.find_node(state, edge.dst, target, traversed) + if tmp: + return tmp + else: + continue + return None \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/SplitMapScopes.py b/dace/transformation/semantic_preserving/SplitMapScopes.py new file mode 100644 index 0000000000..e4a04cd209 --- /dev/null +++ b/dace/transformation/semantic_preserving/SplitMapScopes.py @@ -0,0 +1,387 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the SplitMapScopes transformation. + +""" + +import copy +import dace + +class SplitMapScopes(): + """ + This class implements the SplitMapScopes transformation. It searches for map scopes in the SDFG and splits them. The transformation is only applied if the map scope is splittable. + """ + + def __init__(self): + self.__name = 'SplitMapScopes' + self.checked = False + + @property + def name(self): + return self.__name + + def isInnermost(self, state, map_entry, traversed=None): + if traversed is None: + traversed = set() + for edge in state.out_edges(map_entry): + if isinstance(edge.dst, dace.nodes.MapEntry): + return False + if isinstance(edge.dst, dace.nodes.MapExit): + continue + if not edge.dst in traversed: + if not self.isInnermost(state, edge.dst, traversed): + return False + return True + + def traverse(self, state, map_entry, tasklets=None, visited=None): + if tasklets is None: + tasklets = set() + if visited is None: + visited = set() + + visited.add(map_entry) + next_edges = state.out_edges(map_entry) + for edge in next_edges: + if isinstance(edge.dst, dace.nodes.Tasklet) and not edge.dst in tasklets: + tasklets.add((state, edge.dst)) + elif isinstance(edge.dst, dace.nodes.MapExit) or isinstance(edge.dst, dace.nodes.Tasklet): + continue + if not edge.dst in visited: + self.traverse(state, edge.dst, tasklets, visited) + + return list(tasklets) + + def find_2(self, sdfg): + splitable_targets = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapEntry): + if not self.isInnermost(state, node): + continue + splitable_targets.extend(self.traverse(state, node)) + result = [] + for state, target_tasklet in splitable_targets: + for edge in state.in_edges(target_tasklet): + if not isinstance(edge.src, dace.nodes.MapEntry): + result.append((state, target_tasklet)) + break + self.checked = True + return result + + def find(self, sdfg): + """ + This method searches for map scopes in the SDFG and returns a list of tuples containing the map scope and the tasklet to split. + + The map scope is splittable if the following conditions are met: + - The map scope is the innermost map scope + - The map scope contains more than one tasklet + + Args: + sdfg (dace.SDFG): The SDFG to search for splittable map scopes. + + Returns: + list: A list of tuples, where each tuple contains a the state and the tasklet to split. + """ + + splitable_targets = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapEntry): + if not self.isInnermost(state, node): + continue + splitable_targets.extend(self.traverse(state, node)) + + result = [] + + # for tigthening the application space + for state, node in splitable_targets: + scope_nodes = [] + reject = False + self.find_scope_nodes(state, node, scope_nodes) + for scope_node in scope_nodes[1:]: + inputs = [edge.src for edge in state.in_edges(scope_node)] + for ins in inputs: + if isinstance(ins, dace.nodes.MapEntry): + reject = True + break + if ins not in scope_nodes: + reject = True + break + + if not reject: + result.append(node) + self.checked = True + return result + + def apply(self, sdfg, state, target_tasklet): + """ + This method applies the SplitMapScopes transformation to the given SDFG. It splits the given map scope at the given tasklet. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + state (dace.SDFGState): The state the map scope is in. + target_tasklet (dace.nodes.Tasklet): The tasklet to split the map scope at. + """ + if not self.checked and not (state, target_tasklet) in self.find(sdfg): + return + self.checked = False + + map_entry = state.entry_node(target_tasklet) + map1_input1 = [] + map1_input2 = [] + map1_input1 = [edge for edge in state.in_edges(map_entry)] + map1_input2 = [edge for edge in state.out_edges(map_entry)] + + #add a new map + new_map_entry, new_map_exit = state.add_map(f'{map_entry.label}', {map_entry.map.params[0]: f'0:{map_entry.map.range.size()[0]}'}) + + # reconnect old map entry edges to new one + for edge in map1_input1: + in_conn = edge.dst_conn + if in_conn: + map_entry.remove_in_connector(in_conn) + new_map_entry.add_in_connector(in_conn) + dace.transformation.helpers.redirect_edge(state, edge, new_dst = new_map_entry, new_dst_conn = in_conn) + for edge in map1_input2: + out_conn = edge.src_conn + if out_conn: + map_entry.remove_out_connector(out_conn) + new_map_entry.add_out_connector(out_conn) + dace.transformation.helpers.redirect_edge(state, edge, new_src = new_map_entry, new_src_conn = out_conn) + + # find the paths that have to be redirected because of the new map + inbetween_nodes = [] + inbetween_nodes = list(set([edge.src for edge in state.in_edges(target_tasklet)])) + path_nodes = [] + for node in inbetween_nodes: + if isinstance(node, dace.nodes.AccessNode): + in_edges = state.in_edges(node) + out_edges = state.out_edges(node) + for edge in in_edges: + for edge2 in out_edges: + if not edge2.dst == target_tasklet: + continue + path_nodes.append((edge, node, edge2)) + if isinstance(node, dace.nodes.MapEntry): + in_edges = {edge:False for edge in state.in_edges(node)} + out_edges = {edge:False for edge in state.out_edges(node)} + for edge, used in in_edges.items(): + if used: + continue + for edge2, used2 in out_edges.items(): + if not edge2.dst == target_tasklet: + continue + if used2: + continue + if edge.dst_conn[3:] == edge2.src_conn[4:]: + in_edges[edge] = True + out_edges[edge2] = True + path_nodes.append((edge, node, edge2)) + break + + # redirect the edges of the input nodes of the tasklet + # make a copy if input is used somewhere else aswell + already_mapped = set() + keep = set() + for (edge1, btw_node, edge2) in path_nodes: + if isinstance(btw_node, dace.nodes.AccessNode): + out_edges = {edge.dst for edge in state.out_edges(btw_node) if not isinstance(edge.dst, dace.nodes.MapEntry)} + if len(out_edges) == 1: + memlet = edge1.data + memlet2 = copy.deepcopy(edge1.data) + src = edge1.src + dst = edge2.dst + if isinstance(src, dace.nodes.Tasklet) and not src in already_mapped: + already_mapped.add(src) + state.add_memlet_path(src, new_map_exit, btw_node, memlet=memlet, src_conn=edge1.src_conn, dst_conn=edge1.dst_conn) + state.add_memlet_path(btw_node, map_entry, dst, memlet=memlet2, dst_conn=edge2.dst_conn, src_conn=edge2.src_conn) + else: + new_node = copy.deepcopy(btw_node) + memlet = copy.deepcopy(edge1.data) + keep.add(edge1) + state.add_memlet_path(new_node, map_entry, edge2.dst, memlet=memlet, dst_conn=edge2.dst_conn) + + #add in and out connector manually if not already doen by add_memlet_path + out_edge = state.out_edges(new_node)[0] + if not out_edge.dst_conn in map_entry.in_connectors: + map_entry.add_in_connector(out_edge.dst_conn) + in_edges = state.in_edges(new_node) + in_edge = None + for edge in in_edges: + if edge.src_conn[4:] == out_edge.dst_conn[3:]: + in_edge = edge + break + if in_edge and not in_edge.src_conn in map_entry.out_connectors: + new_node.add_out_connector(in_edge.src_conn) + + elif isinstance(btw_node, dace.nodes.MapEntry): + memlet = edge1.data + tmp = edge1.dst_conn + if tmp: + btw_node.remove_in_connector(tmp) + tmp = edge2.src_conn + if tmp: + map_entry.remove_out_connector(tmp) + in_conn = edge1.src_conn + dst_conn = edge2.dst_conn + new_map_entry.remove_out_connector(edge2.src_conn) + new_map_entry.remove_in_connector(edge1.dst_conn) + state.add_memlet_path(edge1.src, map_entry, edge2.dst, memlet=memlet, src_conn=in_conn, dst_conn=dst_conn) + + # remove the edges that are not needed anymore + for edge1, btw_node, edge2 in path_nodes: + try: + if not edge1 in keep: + state.remove_edge(edge1) + except: + pass + try: + state.remove_edge(edge2) + except: + pass + + # add empty edge if first map exit is not connected anymore + if len(state.in_edges(new_map_exit)) == 0: + if keep: + dangeling_node = keep.pop().dst + state.add_nedge(dangeling_node, new_map_exit, dace.memlet.Memlet()) + # enforce first map scope is executed before the second one + if len(state.out_edges(map_entry)) == 0: + in_nodes = [edge.src for edge in state.in_edges(map_entry)] + for node in in_nodes: + if node.label == dangeling_node.label: + state.add_nedge(new_map_exit, node, dace.memlet.Memlet()) + break + + # find paths in current sdfg that have to be redirected + scope_nodes = [] + self.find_scope_nodes(state, map_entry, scope_nodes) + fixable_paths = [] + redirection_maps = [] + for node in scope_nodes[1:]: + in_edges = state.in_edges(node) + for edge in in_edges: + if edge.src not in scope_nodes: + if isinstance(edge.src, dace.nodes.MapEntry): + redirection_maps.append((edge, node)) + else: + fixable_paths.append((edge, node)) + + fixable_maps = [] + for edge, node in redirection_maps: + in_edges = state.in_edges(edge.src) + for edge2 in in_edges: + if edge2.dst_conn[3:] == edge.src_conn[4:]: + fixable_maps.append((edge2, edge, node)) + + # tmp variables that are produced in first map scope and used in second map scope have to be redirected + # if node has to outgoing edges, node is copied, else redirected + for edge, node in fixable_paths: + if isinstance(edge.src, dace.nodes.AccessNode): + input_edge = state.in_edges(edge.src)[0] + src_conn = input_edge.src_conn + dst_conn = edge.dst_conn + new_node = copy.deepcopy(edge.src) + out_set = {edge.dst for edge in state.out_edges(edge.src)} + if len(out_set) == 1: + state.add_memlet_path(input_edge.src, new_map_exit, new_node, memlet=input_edge.data, src_conn=src_conn, dst_conn=dst_conn) + state.remove_node(edge.src) + state.add_memlet_path(new_node, map_entry, node, memlet=edge.data, src_conn=edge.src_conn, dst_conn=edge.dst_conn) + try: + state.remove_edge(edge) + except: + pass + elif isinstance(edge.src, dace.nodes.Tasklet): + # should not happen + pass + + # output edges of previous map scope that are only used in first map scope have to be redirected to new map exit + exit_node = state.exit_node(map_entry) + exit_input = [(edge, edge.src) for edge in state.in_edges(exit_node)] + for edge, node in exit_input: + if not node in scope_nodes: + memlet = edge.data + dst_edge = None + for edge2 in state.out_edges(exit_node): + if edge2.src_conn[4:] == edge.dst_conn[3:]: + dst_edge = edge2 + break + if dst_edge: + exit_node.remove_in_connector(edge.dst_conn) + exit_node.remove_out_connector(dst_edge.src_conn) + state.add_memlet_path(node, new_map_exit, dst_edge.dst, memlet=memlet, src_conn=edge.src_conn, dst_conn=dst_edge.dst_conn) + state.remove_edge(edge) + state.remove_edge(dst_edge) + + # input of previous map scope that are only used in second new map scope can directly be redirected to the new map scope + for edge2, edge, node in fixable_maps: + new_map_entry.remove_in_connector(edge2.dst_conn) + new_map_entry.remove_out_connector(edge.src_conn) + state.add_memlet_path(edge2.src, map_entry, node, memlet=edge.data, src_conn=edge2.src_conn, dst_conn=edge.dst_conn) + state.remove_edge(edge2) + state.remove_edge(edge) + + # maybe fuse input nodes of second map if the are the same + input_nodes = [edge.src for edge in state.in_edges(map_entry)] + in_map = {} + for node1 in input_nodes: + if node1.label not in in_map: + in_map[node1.label] = set() + in_map[node1.label].add(node1) + for key, value in in_map.items(): + if len(value) > 1: + chosen_node = None + for node in value: + if state.in_edges(node): + chosen_node = node + break + if not chosen_node: + chosen_node = value.pop() + for node in value: + if node == chosen_node: + continue + out_edges = state.out_edges(node) + for edge in out_edges: + dace.transformation.helpers.redirect_edge(state, edge, new_src = chosen_node) + state.remove_node(node) + + # some nodes from the first map scope that are hanging without any connection can be reconnect potentially + scope_1_nodes = [] + self.find_scope_nodes(state, new_map_entry, scope_1_nodes) + for node in scope_1_nodes[1:]: + if isinstance(node, dace.nodes.AccessNode): + out_edges = state.out_edges(node) + if not out_edges or (out_edges[0] and not out_edges[0].data.data): + dst_node = [edge.src for edge in state.in_edges(map_entry) if edge.src.label == node.label][0] + if dst_node and state.in_edges(dst_node) and state.in_edges(dst_node)[0].data.data: + continue + elif dst_node and (not state.in_edges(dst_node) or (state.in_edges(dst_node)[0] and not state.in_edges(dst_node)[0].data.data)): + src_edge = state.in_edges(node)[0] + state.add_memlet_path(src_edge.src, new_map_exit, dst_node, memlet=src_edge.data, src_conn=src_edge.src_conn, dst_conn=src_edge.dst_conn) + state.remove_node(node) + if state.in_edges(dst_node)[0] and not state.in_edges(dst_node)[0].data.data: + state.remove_edge(state.in_edges(dst_node)[0]) + continue + + def find_scope_nodes(self, state, node, scope_nodes, visited=None): + if visited is None: + visited = set() + + visited.add(node) + scope_nodes.append(node) + next_edges = [edge for edge in state.out_edges(node)] + for edge in next_edges: + if isinstance(edge.dst, dace.nodes.MapExit): + continue + elif not edge.dst in visited: + self.find_scope_nodes(state, edge.dst, scope_nodes, visited) + return \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/StackToHeap.py b/dace/transformation/semantic_preserving/StackToHeap.py new file mode 100644 index 0000000000..aa684aaeb2 --- /dev/null +++ b/dace/transformation/semantic_preserving/StackToHeap.py @@ -0,0 +1,59 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the StackToHeap transformation. + +""" + +import dace + +class StackToHeap(): + """ + This class implements the StackToHeap transformation. It changes the storage type of the array to the heap. + """ + def __init__(self): + self.__name = 'StackToHeap' + self.checked = False + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for arrays in the SDFG that are not yet stored on the heap (CPU_HEAP). + + Args: + sdfg (dace.SDFG): The SDFG to search for arrays. + + Returns: + list: A list of data arrays that are not stored on the heap. + """ + result = [] + for name, data in sdfg.arrays.items(): + if not data.storage == dace.StorageType.CPU_Heap: + result.append(name) + self.checked = True + return result + + def apply(self, sdfg, name): + """ + This method applies the StackToHeap transformation to the given SDFG. It changes the storage type of the array to the heap. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + name (String): The array to apply the transformation to. + """ + if not self.checked and not name in self.find(sdfg): + return + self.checked = False + data = sdfg.arrays[name] + data.storage = dace.StorageType.CPU_Heap \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/SwapLoopMap.py b/dace/transformation/semantic_preserving/SwapLoopMap.py new file mode 100644 index 0000000000..6da99a7351 --- /dev/null +++ b/dace/transformation/semantic_preserving/SwapLoopMap.py @@ -0,0 +1,125 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the SwapLoopMap transformation. + +""" + +import dace + +class SwapLoopMap(): + """ + This class implements the SwapLoopMap transformation. It searches for two consecutive maps in the SDFG and swaps them. The transformation is only applied if the maps are swappable. + """ + def __init__(self): + self.__name = 'SwapLoopMap' + self.checked = False + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for two consecutive maps in the SDFG and returns a list of tuples containing the two maps. + + Two maps are swappable if the following conditions are met: + - The maps are consecutive + - The outer map contains exactly one child: the inner map + + Args: + sdfg (dace.SDFG): The SDFG to search for swappable maps. + + + Returns: + set: A list of tuples, where each tuple contains two consecutive swappable maps. + """ + swappable_maps = set() + for state in sdfg.nodes(): + for edges in state.edges(): + if isinstance(edges.src, dace.nodes.MapEntry) and isinstance(edges.dst, dace.nodes.MapEntry): + if (edges.src, edges.dst) in swappable_maps: + continue + out_edges = state.out_edges(edges.src) + dst_entry = edges.dst + not_swapable = False + for edge in out_edges: + if not isinstance(edge.dst, dace.nodes.MapEntry): + not_swapable = True + break + if isinstance(edge.dst, dace.nodes.MapEntry) and not dst_entry == edge.dst: + not_swapable = True + break + if not_swapable: + continue + src = edges.src.map + dst = edges.dst.map + for i, param in enumerate(src.params): + if param in dst.params: + continue + swappable_maps.add((edges.src, edges.dst)) + self.checked = True + return list(swappable_maps) + + def find_exit_maps(self, sdfg, map1, map2): + map1 = map1.map + map2 = map2.map + exit1 = None + exit2 = None + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapExit): + if node.map == map1: + exit1 = node + if node.map == map2: + exit2 = node + return exit1, exit2 + + def apply(self, sdfg, map_entry1, map_entry2): + """ + This method applies the SwapLoopMap transformation to the given SDFG. It swaps the two given maps. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + map_entry1 (dace.MapEntry): The first (outer) map to swap. + map_entry2 (dace.MapEntry): The second (inner) map to swap. + """ + assert isinstance(map_entry1, dace.sdfg.nodes.MapEntry) + assert isinstance(map_entry2, dace.sdfg.nodes.MapEntry) + if not self.checked and not (map_entry1, map_entry2) in self.find(sdfg): + return + self.checked = False + + exit1, exit2 = self.find_exit_maps(sdfg, map_entry1, map_entry2) + assert exit1 is not None + assert exit2 is not None + + state = find_state(sdfg, map_entry2) + for edge in state.in_edges(map_entry1): + dace.transformation.helpers.redirect_edge(state, edge, new_dst = map_entry2) + for edge in state.out_edges(map_entry2): + dace.transformation.helpers.redirect_edge(state, edge, new_src = map_entry1) + for edge in state.out_edges(map_entry1): + if edge.dst == map_entry2: + dace.transformation.helpers.redirect_edge(state, edge, new_src = map_entry2, new_dst = map_entry1) + for edge in state.in_edges(exit2): + dace.transformation.helpers.redirect_edge(state, edge, new_dst = exit1) + for edge in state.out_edges(exit1): + dace.transformation.helpers.redirect_edge(state, edge, new_src = exit2) + for edge in state.out_edges(exit2): + if edge.dst == exit1: + dace.transformation.helpers.redirect_edge(state, edge, new_src = exit1, new_dst = exit2) + +def find_state(sdfg, node): + for sdfg_state in sdfg.nodes(): + if node in sdfg_state.nodes(): + return sdfg_state + \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/SwapMapScope.py b/dace/transformation/semantic_preserving/SwapMapScope.py new file mode 100644 index 0000000000..762bbde4bc --- /dev/null +++ b/dace/transformation/semantic_preserving/SwapMapScope.py @@ -0,0 +1,100 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the SwapMapScope transformation. + +""" + +import dace + +class SwapMapScope(): + """ + This class implements the SwapMapScope transformation. It searches for two map scopes in the SDFG and swaps them, i.e. inducing a order between two map scopes. The transformation is only applied if the map scopes are swappable. + """ + + def __init__(self): + self.__name = 'SwapMapScope' + self.checked = False + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for two map scopes in the SDFG and returns a list of tuples containing the two map scopes. + + Two map scopes are swappable if the following conditions are met: + - The two maps scopes have the same entry node (None if they are the outtermost maps) + - The map scopes are in the same state + - The input of the second map scope is not used in the output of the first map scope, i.e. the scopes are independent/parallel + - Map Scopes should be consecutive + + Args: + sdfg (dace.SDFG): The SDFG to search for swappable map scopes. + + Returns: + list: A list of tuples, where each tuple contains two swappable map scopes. + """ + result = [] + map_scopes = self.find_map_scopes(sdfg) + for i in range(len(map_scopes)): + entry1, exit1, state1 = map_scopes[i] + next = False + for j in range(i+1, len(map_scopes)): + entry2, exit2, state2 = map_scopes[j] + if not state1.entry_node(entry1) == state2.entry_node(entry2): + continue + if not state1 == state2: + continue + if next: + break + next = True + output_1 = {edge.data.data for edge in state1.out_edges(exit1)} + input_2 = {edge.data.data for edge in state2.in_edges(entry2)} + if input_2 & output_1: + continue + result.append((entry1, entry2)) + self.checked = True + return result + + def find_map_scopes(self, sdfg): + map_scopes = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapEntry): + #map_scopes.append(dace.sdfg.scope._scope_subgraph(state, node, True, True)) + map_scopes.append((node, state.exit_node(node), state)) + + return map_scopes + + def apply(self, sdfg, entry1, entry2): + """ + This method applies the SwapMapScope transformation to the given SDFG. It adds an empty edge from the output of the second map scope to the MapEntry of the first map scope. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + entry1 (dace.MapEntry): The first map scope to swap. + entry2 (dace.MapEntry): The second map scope to swap. + """ + if not self.checked and not (entry1, exit2) in self.find(sdfg): + return + self.checked = False + state = find_state(sdfg, entry2) + exit2 = state.exit_node(entry2) + dst_node = [edge.dst for edge in state.out_edges(exit2)][0] + state.add_nedge(dst_node, entry1, dace.memlet.Memlet()) + +def find_state(sdfg, node): + for sdfg_state in sdfg.nodes(): + if node in sdfg_state.nodes(): + return sdfg_state + \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/SwapOperations.py b/dace/transformation/semantic_preserving/SwapOperations.py new file mode 100644 index 0000000000..79c79ccab1 --- /dev/null +++ b/dace/transformation/semantic_preserving/SwapOperations.py @@ -0,0 +1,113 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the SwapOperations transformation. + +""" + +import dace + +class SwapOperations(): + """ + This class implements the SwapOperations transformation. It searches for two tasklets in the SDFG and swaps their execution order. The transformation is only applied if the tasklets are swappable. + """ + + def __init__(self): + self.__name = 'SwapOperations' + self.checked = False + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for two tasklets in the SDFG and returns a list of tuples containing the two tasklets. + + Two tasklets are swappable if the following conditions are met: + - The two tasklets are in the same state + - The two tasklets lie in the same scope + - The output of the first tasklet is not used in the input of the second tasklet + - There is no path from the first tasklet to the second tasklet + + Args: + sdfg (dace.SDFG): The SDFG to search for swappable tasklets. + + Returns: + list: A list of tuples, where each tuple contains two swappable tasklets. + """ + result = [] + tasklets = self.find_tasklets(sdfg) + for tasklet1, state1 in tasklets: + for tasklet2, state2 in tasklets: + if tasklet1 == tasklet2: + continue + if not state1 == state2: + continue + if not state1.entry_node(tasklet1) == state2.entry_node(tasklet2): + continue + """ + output_1 = {edge.data.data for edge in state1.out_edges(tasklet1)} + input_2 = {edge.data.data for edge in state2.in_edges(tasklet2)} + if input_2 & output_1: + continue + """ + if not self.check(state1, tasklet1, tasklet2) or not self.check(state2, tasklet2, tasklet1): + continue + result.append((tasklet1, tasklet2)) + self.checked = True + return result + + def check(self, state, tasklet1, tasklet2): + """ + This method checks if there is a path from first tasklet to second tasklet. + Returns True if there is no path, False otherwise. + """ + def traverse(node, t2, visited=None): + if visited is None: + visited = set() + visited.add(node) + #if isinstance(node, dace.nodes.MapExit): + # return False + for edge in state.out_edges(node): + if edge.dst == t2: + return True + if not edge.dst in visited: + if traverse(edge.dst, t2, visited): + return True + return False + + return not traverse(tasklet1, tasklet2) + + def find_tasklets(self, sdfg): + result = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + result.append((node, state)) + return result + + def apply(self, sdfg, op1, op2): + """ + This method applies the SwapOperations transformation to the given SDFG. It swaps the order of execution of the two given tasklets by adding an empty edge from the output of the second tasklet to the first tasklet. + op1 -> op2 + """ + if not self.checked and not (op1, op2) in self.find(sdfg): + return + self.checked = False + state = find_state(sdfg, op1) + out_node = [edge.dst for edge in state.out_edges(op1)][0] + state.add_nedge(out_node, op2, dace.memlet.Memlet()) + +def find_state(sdfg, node): + for sdfg_state in sdfg.nodes(): + if node in sdfg_state.nodes(): + return sdfg_state diff --git a/dace/transformation/semantic_preserving/TileMapScope.py b/dace/transformation/semantic_preserving/TileMapScope.py new file mode 100644 index 0000000000..06bf711a74 --- /dev/null +++ b/dace/transformation/semantic_preserving/TileMapScope.py @@ -0,0 +1,157 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the TileMapScope transformation. + +""" + +import dace +import sympy +import sp_opts.SwapLoopMap as SwapLoopMap + +class TileMapScope(): + """ + This class implements the TileMapScope transformation. It searches for map scopes in the SDFG and tiles them. The transformation is only applied if the map scope is tileable. + + Attributes: + name (str): The name of the transformation. + checked (bool): A flag to indicate whether the transformation has been checked. + put_last (bool): If set, the tiled map is put as innermost map. + only_innermost (bool): If set, only the innermost map is tiled. + max_tile (int): The maximum tile size. + tile_sizes (list): A list of tile sizes. Can be None. + """ + + def __init__(self, put_last = True, tile_sizes = [16]): + self.__name = 'TileMapScope' + self.checked = False + # if set, the tiled map is put as innermost map + self.put_last = put_last + # if set, only the innermost map is tiled + self.only_innermost = False + self.not_innermost = False + # maximum tile size + self.max_tile = 1024 + # for symbolic loop bounds + self.tile_sizes = tile_sizes + # dont tile maps where + + @property + def name(self): + return self.__name + + def isInnermost(self, state, map_entry, traversed=None): + if traversed is None: + traversed = set() + for edge in state.out_edges(map_entry): + if isinstance(edge.dst, dace.nodes.MapEntry): + return False + if isinstance(edge.dst, dace.nodes.MapExit): + continue + if not edge.dst in traversed: + if not self.isInnermost(state, edge.dst, traversed): + return False + return True + + def find(self, sdfg): + """ + This method searches for map scopes in the SDFG and returns a list of tuples containing the map scope and the tile size. + + The map scope is tileable if the following conditions are met: + - The map scope has a size greater than 1 if it is a constant loop bound + - The tile size is a divisor of the map scope size + + Args: + sdfg (dace.SDFG): The SDFG to search for tileable map scopes. + + Returns: + list: A list of tuples, where each tuple contains a tileable map scope and a tile size. + """ + tileable_maps = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapEntry): + if self.only_innermost and not self.isInnermost(state, node): + continue + if self.not_innermost and self.isInnermost(state, node): + continue + map = node.map + dim = map.range.size()[0] + if isinstance(dim, sympy.core.numbers.Integer) and int(dim) > 1: + dim = int(dim) + if self.tile_sizes: + divisors = [i for i in self.tile_sizes if dim % i == 0 ] + tileable_maps.extend([(node, tile_size) for tile_size in divisors]) + else: + divisors = [i for i in range(2, min(math.floor(math.sqrt(dim))+1, self.max_tile)) if dim % i == 0] + tileable_maps.extend([(node, tile_size) for tile_size in divisors]) + elif isinstance(dim, sympy.core.symbol.Symbol): + tileable_maps.extend([(node, tile_size) for tile_size in self.tile_sizes]) + self.checked = True + return tileable_maps + + def apply(self, sdfg, map_entry, tile_size): + """ + This method applies the TileMapScope transformation to the given SDFG. It tiles the given map scope with the given tile size. + This method uses the dace.transformation.helpers.tile method to tile the map scope. If the map scope has a symbolic loop bound, the method uses the dace.transformation.helpers.tile method with the divides_evenly set to False. + If the put_last attribute is set, the tiled map is put as the innermost map. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + map_entry (dace.MapEntry): The map scope to tile. + tile_size (int): The tile size. + """ + if not self.checked and not (map_entry, tile_size) in self.find(sdfg): + return + self.checked = False + assert isinstance(map_entry, dace.sdfg.nodes.MapEntry) + assert len(map_entry.map.params) == 1 + paras = map_entry.map.params[0] + tile_dict = {paras: tile_size} + if isinstance(map_entry.range.size()[0], sympy.core.symbol.Symbol): + dace.transformation.helpers.tile(sdfg=sdfg, map_entry=map_entry, divides_evenly=False, skew=True, **tile_dict) + else: + dace.transformation.helpers.tile(sdfg=sdfg, map_entry=map_entry, divides_evenly=True, skew=True, **tile_dict) + + if self.put_last: + prop_map = map_entry + state = find_state(sdfg, map_entry) + # all idx are in the form 'i0', 'i1', ... (or 'tiled_i0', 'tiled_i1', ...) + prop_map_idx = int(prop_map.map.params[0][-1]) + if prop_map: + while True: + cant_swap = False + swap_map = None + for out_edge in state.out_edges(prop_map): + if not isinstance(out_edge.dst, dace.nodes.MapEntry): + cant_swap = True + break + else: + if swap_map is None: + swap_map = out_edge.dst + else: + if not swap_map == out_edge.dst: + cant_swap = True + break + if cant_swap or swap_map is None: + break + # try to have the same order of maps as before + print(f'swap_map: {swap_map.map.params[0]}/{int(swap_map.map.params[0][-1])} and prop_map: {prop_map.map.params[0]}') + if not swap_map.range.size()[2] > 1: + if int(swap_map.map.params[0][-1]) > prop_map_idx: + break + opt = SwapLoopMap.SwapLoopMap() + opt.apply(sdfg, prop_map, swap_map) + +def find_state(sdfg, node): + for sdfg_state in sdfg.nodes(): + if node in sdfg_state.nodes(): + return sdfg_state \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/UnrollMapScope.py b/dace/transformation/semantic_preserving/UnrollMapScope.py new file mode 100644 index 0000000000..ed2ec51b5b --- /dev/null +++ b/dace/transformation/semantic_preserving/UnrollMapScope.py @@ -0,0 +1,129 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the UnrollMapScope transformation. + +""" + +import sympy +import dace + +class UnrollMapScope(): + """ + This class implements the UnrollMapScope transformation. It searches for map scopes in the SDFG and unrolls them. The transformation is only applied if the map scope is unrollable. + Only applicable to constant number of iterations. + + Attributes: + max_unroll_factor (int): The maximum unroll factor. + explicit_factor (bool): A flag that indicates whether the unroll factor is explicitly given. + + Args: + unroll_factor (int): The maximum unroll factor (default is 2). + """ + + def __init__(self, unroll_factor = 4): + self.__name = 'UnrollMapScope' + self.checked = False + self.max_unroll_factor = unroll_factor + self.explicit_factor = True + self.no_reduction = False + + @property + def name(self): + return self.__name + + def isInnermost(self, state, map_entry, traversed=None): + if traversed is None: + traversed = set() + for edge in state.out_edges(map_entry): + if isinstance(edge.dst, dace.nodes.MapEntry): + return False + if isinstance(edge.dst, dace.nodes.MapExit): + continue + if not edge.dst in traversed: + if not self.isInnermost(state, edge.dst, traversed): + return False + return True + + def find(self, sdfg): + """ + This method searches for map scopes in the SDFG and returns a list of tuples containing the map scope and the unroll factor. + + The map scope is unrollable if the following conditions are met: + - The loop bounds are constant + - The map scope is the innermost map scope + - The unroll factor is less than the maximum unroll factor + - The unroll factor is a divisor of the map range size + - The map is not already unrolled + + Args: + sdfg (dace.SDFG): The SDFG to search for unrollable map scopes. + + Returns: + list: A list of tuples, where each tuple contains a the map scope and the unroll factor. + """ + unrollable_maps = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapEntry): + if node.map.schedule == dace.ScheduleType.Unrolled or node.map.unroll: + continue + if not self.isInnermost(state, node): + continue + if self.no_reduction: + br_cond = False + exit = state.exit_node(node) + in_edges = state.in_edges(exit) + for edge in in_edges: + if edge.data.wcr: + br_cond = True + break + if br_cond: + continue + dim = node.map.range.size()[0] + if isinstance(dim, sympy.core.numbers.Integer) and int(dim) > 1: + if self.explicit_factor: + divisors = [self.max_unroll_factor] if dim % self.max_unroll_factor == 0 else [i for i in range(2, min(self.max_unroll_factor+1, int(dim))) if dim % i == 0] + unrollable_maps.extend([(node, div) for div in divisors]) + else: + divisors = [i for i in range(2, min(self.max_unroll_factor+1, int(dim))) if dim % i == 0] + unrollable_maps.extend([(node, div) for div in divisors]) + self.checked = True + return unrollable_maps + + def apply(self, sdfg, map_entry, unroll_factor): + """ + This method applies the UnrollMapScope transformation to the given SDFG. It unrolls the given map scope with the given unroll factor. + + It uses the dace.transformation.helpers.tile method to unroll the map scope. It creates a new map scope with the given unroll factor and sets the schedule type to unrolled. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + map_entry (dace.nodes.MapEntry): The map scope to unroll. + unroll_factor (int): The unroll factor. + """ + if not self.checked and not (map_entry, unroll_factor) in self.find(sdfg): + return + self.checked = False + assert isinstance(map_entry, dace.sdfg.nodes.MapEntry) + para = map_entry.map.params[0] + label = map_entry.map.label + unroll_dict = {para: unroll_factor} + dace.transformation.helpers.tile(sdfg=sdfg, map_entry=map_entry, divides_evenly=True, skew=True, **unroll_dict) + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapEntry) and node.map.label == label: + if node.map.params[0] == para and node.map.range.size()[0] == unroll_factor: + new_map = node.map + new_map.schedule = dace.ScheduleType.Unrolled + new_map.unroll = True + new_map.unroll_factor = unroll_factor + break diff --git a/dace/transformation/semantic_preserving/addArrayCopies.py b/dace/transformation/semantic_preserving/addArrayCopies.py new file mode 100644 index 0000000000..d08a565978 --- /dev/null +++ b/dace/transformation/semantic_preserving/addArrayCopies.py @@ -0,0 +1,196 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the addArrayCopies transformation. +""" + +import dace + +class addArrayCopies(): + """ + This class implements the addArrayCopies transformation. It searches for arrays in the SDFG and adds copies of them. The transformation is only applied if the array is copyable. + """ + + def __init__(self): + self.__name = 'addArrayCopies' + self.checked = False + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for arrays in the SDFG and returns a list of copyable arrays. + + This method is applicable to all arrays that are used (as Nodes) in the SDFG. + + Args: + sdfg (dace.SDFG): The SDFG to search for copyable arrays. + + Returns: + list: A list of copyable arrays. + """ + copyable_arrays = set() + for state in sdfg.nodes(): + if not state.label == 'state_0': + continue + for node in state.nodes(): + # TODO: Not nodes that are also used as loop bounds, e.g. N + if isinstance(node, dace.nodes.AccessNode): + # copy source nodes only once, otherwise gets messy with states + if node in state.source_nodes(): + if 'copy' in node.data: + continue + copyable_arrays.add(node.data) + self.checked = True + return sorted(list(copyable_arrays)) + + def apply(self, sdfg, arr_name): + """ + This method applies the addArrayCopies transformation to the given SDFG. It adds a copy of the given array. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + arr_name (str): The array to add a copy of. + """ + if not self.checked and not arr_name in self.find(sdfg): + return + self.checked = False + + state = state_0(sdfg) + data = sdfg.arrays[arr_name] + input_nodes = state.source_nodes() + adj_state = state + + # check if array is input of main state, if so, create a new state to copy it + if arr_name in [node.data for node in input_nodes]: + s = len(sdfg.nodes()) + new_state = sdfg.add_state(f'state_{s}') + sdfg.add_edge(new_state, sdfg.nodes()[s-1], dace.InterstateEdge()) + adj_state = new_state + + # get a new name for the array + new_name = self.new_name(sdfg, arr_name) + + # add the array copy + if data.shape: + sdfg.add_array(name=new_name, shape=data.shape, dtype=data.dtype, storage=data.storage, transient=True, lifetime=data.lifetime) + else: + sdfg.add_scalar(name=new_name, dtype=data.dtype, transient=True, lifetime=data.lifetime) + # the move instruction + tasklet = adj_state.add_tasklet("move", {"in_1"}, {"out"}, "out = in_1") + + # binding all the elements together + output_node = adj_state.add_access(new_name) + if arr_name in [node.data for node in input_nodes]: + # input nodes + new_input_node = new_state.add_read(arr_name) + loop_entry, loop_exit, idxs = self.add_maps(adj_state, data.shape) + adj_state.add_memlet_path(new_input_node, *loop_entry, tasklet, dst_conn='in_1', memlet=dace.Memlet.simple(new_input_node, idxs)) + adj_state.add_memlet_path(tasklet, *loop_exit, output_node, src_conn='out', memlet=dace.Memlet.simple(output_node, idxs)) + state.replace(arr_name, new_name) + else: + input_node = None + for edge in state.edges(): + if isinstance(edge.dst, dace.nodes.AccessNode) and edge.dst.data == arr_name: + input_node = edge.dst + break + out_edges = state.out_edges(input_node) + entry = state.entry_node(input_node) + if not entry and out_edges: + # map exit nodes + loop_entry, loop_exit, idxs = self.add_maps(adj_state, data.shape) + state.add_memlet_path(input_node, *loop_entry, tasklet, dst_conn='in_1', memlet=dace.Memlet.simple(input_node, idxs)) + state.add_memlet_path(tasklet, *loop_exit, output_node, src_conn='out', memlet=dace.Memlet.simple(output_node, idxs)) + for edge in out_edges: + dace.transformation.helpers.redirect_edge(state, edge, new_src = output_node) + self.traverse_n_replace(state, output_node, arr_name, new_name) + elif not entry: + #output node => to keep naming consistent, we switch temporary and original array + in_edges = state.in_edges(input_node) + loop_entry, loop_exit, idxs = self.add_maps(adj_state, data.shape) + state.add_memlet_path(output_node, *loop_entry, tasklet, dst_conn='in_1', memlet=dace.Memlet.simple(output_node, idxs)) + state.add_memlet_path(tasklet, *loop_exit, input_node, src_conn='out', memlet=dace.Memlet.simple(input_node, idxs)) + for edge in in_edges: + dace.transformation.helpers.redirect_edge(state, edge, new_dst = output_node) + self.traverse_n_replace(state, output_node, arr_name, new_name, backwards=True) + else: + # nodes that are only used inside a scope + entries = [entry] + while True: + tmp = entries[-1] + e = state.entry_node(tmp) + if not e: + break + entries.append(e) + dims = list(reversed([entry.map.range.size()[0] for entry in entries])) + idxs = '' + for shape in data.shape: + if not shape in dims: + #not implemented: if map operates not over all of the shape dimension + assert False + else: + idx = dims.index(shape) + idxs += f'i{idx},' + idxs = idxs[:-1] + state.add_edge(input_node, None, tasklet, 'in_1', dace.Memlet.simple(input_node, out_edges[0].data.subset)) + state.add_edge(tasklet, 'out', output_node, None, dace.Memlet.simple(output_node, idxs)) + for edge in out_edges: + dace.transformation.helpers.redirect_edge(state, edge, new_src = output_node) + self.traverse_n_replace(state, output_node, arr_name, new_name) + + def new_name(self, sdfg, arr_name): + new_name = '' + i = 0 + while True: + new_name = f'{arr_name}_copy_{i}' + if not new_name in sdfg.arrays: + break + i += 1 + return new_name + + def add_maps(self, state, shape): + loop_entry, loop_exit = [], [] + idxs = '' + for i,s in enumerate(shape): + dim = str(s) + index = f'i{i}' + idxs += f'{index},' + entry, exit = state.add_map(f'copy_map_{i}', {index: f'0:{dim}'}) + loop_entry.append(entry) + loop_exit.append(exit) + idxs = idxs[:-1] + return loop_entry, loop_exit, idxs + + def traverse_n_replace(self, state, node, old_name, new_name, start=True, backwards=False): + if not start: + if not isinstance(node, dace.nodes.MapEntry) and not isinstance(node, dace.nodes.MapExit): + return + + next_edges = None + if backwards: + next_edges = state.in_edges(node) + else: + next_edges = state.out_edges(node) + + for edge in next_edges: + if edge.data.data == old_name: + edge.data.data = new_name + if backwards: + self.traverse_n_replace(state, edge.src, old_name, new_name, False, True) + else: + self.traverse_n_replace(state, edge.dst, old_name, new_name, False) + +def state_0(sdfg): + for state in sdfg.nodes(): + if state.label == 'state_0': + return state \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/addArrayDims.py b/dace/transformation/semantic_preserving/addArrayDims.py new file mode 100644 index 0000000000..81a6431953 --- /dev/null +++ b/dace/transformation/semantic_preserving/addArrayDims.py @@ -0,0 +1,518 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the addArrayDims transformation. + +""" +import dace + +class addArrayDims(): + """ + This class implements the addArrayDims transformation. It searches for arrays in the SDFG and adds an additional scope of iteration to the write operation. The transformation is only applied if the array is extendable. + + Transformation example (read to write, write is target): + Before: After: + M x[m]=z[m] M N x[m,n]=z[m] + M N y[m,n]=x[m] M N y[m,n]=x[m,n] + + Transformation example (write to read with reduction propagation, read is target): + Before: After: + M N x[m]+=y[m,n] M N x[m,n]=y[m,n] + M z[m]=x[m] M N z[m]+=x[m,n] + + Read to write: + - The write operation is the target of the transformation (new scope added for write scope) + -> mode 0 + + Write to read: + - The read operations are the target of the transformation (new scope added for read scopes) + -> mode 1 + + Assumptions: + - No reusing of arrays + - No joined scopes, every scope contains exactly one scope or tasklet + + Requirements: + - All reads should be enclosed in the scope equivalent to the referenced scope. + """ + + def __init__(self): + self.__name = 'addArrayDims' + self.checked = False + + @property + def name(self): + return self.__name + + def get_entry_maps(self, state, node): + dims = [] + entry = state.entry_node(node) + if not entry: + return dims + + while True: + dims.append(entry) + entry = state.entry_node(entry) + if not entry: + break + return dims + + def only_one_child(self, state, entry): + out_edges = state.out_edges(entry) + outs = set() + for edge in out_edges: + outs.add(edge.dst) + if not len(outs) == 1: + return False + + out = list(outs)[0] + if isinstance(out, dace.nodes.MapEntry): + return True + elif isinstance(out, dace.nodes.Tasklet): + out_edge = state.out_edges(out)[0] + if isinstance(out_edge.dst, dace.nodes.MapExit): + return True + else: + return False + + def single_map_scope(self,state, node): + if not state.entry_node(node): + return True + + for edge in state.in_edges(node): + if isinstance(edge.src, dace.nodes.AccessNode): + return False + for out_edge in state.out_edges(node): + if isinstance(out_edge.dst, dace.nodes.AccessNode): + return False + + return True + + + def find(self, sdfg): + """ + This method searches for arrays in the SDFG and returns a list of tuples containing the tasklet and the mode of the transformation. + Mode 0: read to write access, write is target + Mode 1: write to read access, read is target + + This method is applicable if the following conditions are met: + - The array is transient + - All reads should be enclosed in the scope equivalent to the referenced scope + - The write and read tasklets are the only nodes in their scope + Write to read: + - The write operation is a reduction operation + + Args: + sdfg (dace.SDFG): The SDFG to search for arrays. + + Returns: + list: A list of tuples, where each tuple contains a the tasklet and the mode of the transformation + """ + + result = [] + # --- read to write access, write is target + read_dims = {} + for state in sdfg.nodes(): + if not state.label == 'state_0': + continue + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + in_edges = state.in_edges(node) + for edge in in_edges: + data_name = edge.data.data + if not data_name in sdfg.arrays: + continue + data_node = sdfg.arrays[data_name] + if not data_node.transient: + continue + if not self.single_map_scope(state, node): + continue + tmp = set(edge.data.subset) + subset = set() + for subs in tmp: + for idx in subs: + subset.add(str(idx)) + + entry_maps = self.get_entry_maps(state, node) + map_params = {m.map.params[0] for m in entry_maps} + unused_params = map_params - subset + unused_dims = set() + + for entry in entry_maps: + + for param in unused_params: + if param in entry.map.params: + unused_dims.add(entry.map.range.size()[0]) + + if data_name in read_dims: + read_dims[data_name] &= unused_dims + else: + read_dims[data_name] = unused_dims + + #print(f'read_dims {read_dims}') + for state in sdfg.nodes(): + if not state.label == 'state_0': + continue + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + out_edges = state.out_edges(node) + for edge in out_edges: + if not edge.data.data in sdfg.arrays: + continue + data_node = sdfg.arrays[edge.data.data] + if not data_node.transient: + continue + if not read_dims.get(edge.data.data, set()): + continue + result.append((node, 0)) + + # --- write to read access, read is target + write_dims = {} + for state in sdfg.nodes(): + if not state.label == 'state_0': + continue + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + out_edges = state.out_edges(node) + for edge in out_edges: + if not edge.data.data in sdfg.arrays: + continue + data_node = sdfg.arrays[edge.data.data] + if not data_node.transient: + continue + if not edge.data.wcr: + continue + if not self.single_map_scope(state, node): + continue + tmp = set(edge.data.subset) + subset = set() + for subs in tmp: + for idx in subs: + subset.add(str(idx)) + entry_maps = self.get_entry_maps(state, node) + map_params = {m.map.params[0] for m in entry_maps} + unused_params = map_params - subset + unused_dims = set() + for entry in entry_maps: + for param in unused_params: + if param in entry.map.params: + unused_dims.add(entry.map.range.size()[0]) + + write_dims[edge.data.data] = (unused_dims, node) + + #print(f'write_dims {write_dims}') + candidates = set() + for state in sdfg.nodes(): + if not state.label == 'state_0': + continue + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + in_edges = state.in_edges(node) + for edge in in_edges: + if not edge.data.data in sdfg.arrays: + continue + data_node = sdfg.arrays[edge.data.data] + if not data_node.transient: + continue + if not edge.data.data in write_dims: + continue + unused_dims, write_node = write_dims[edge.data.data] + if not "reduce" in node.label or ("reduce" in node.label and not node.label == write_node.label) or not node.label in ["move", "copy_temp"]: + continue + candidates.add(edge.data.data) + + #print(f'candidates {candidates}') + for state in sdfg.nodes(): + if not state.label == 'state_0': + continue + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + in_edges = state.in_edges(node) + for edge in in_edges: + if edge.data.data in candidates: + result.append((node, 1)) + candidates.remove(edge.data.data) + + self.checked = True + return result + + def apply(self, sdfg, op, mode): + """ + This method applies the addArrayDims transformation to the given SDFG. It adds an additional scope of iteration to the write operation. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + op (dace.nodes.Tasklet): The write tasklet to apply the transformation to. + mode (int): The mode of the transformation. 0: read to write access, write is target. 1: write to read access, read is target. + """ + if not self.checked and not (op, mode) in self.find(sdfg): + return + self.checked = False + + # mode 0: read to write access + if mode == 0: + target = state_0(sdfg).out_edges(op)[0].data.data + + read_tasklet = self.find_read_tasklet(sdfg, target) + write_tasklet = self.find_write_tasklet(sdfg, target) + + common_unused = None + for i_state, node in read_tasklet: + if not i_state.out_edges(node)[0].data.data == target: + continue + in_edge = None + for edge in i_state.in_edges(node): + if edge.data.data == target: + in_edge = edge + break + map_scopes = self.get_entry_maps(i_state, node) + edge_subset = [str(idx[0]) for idx in in_edge.data.subset] + map_params = sorted({m.map.params[0] for m in map_scopes} - set(edge_subset)) + + unused_dims = [] + for entry in map_scopes: + for param in map_params: + if param in entry.map.params: + unused_dims.append(entry.map.range.size()[0]) + + if common_unused is None: + common_unused = unused_dims + else: + tmp = common_unused & unused_dims + common_unused = [dim for dim in common_unused if dim in tmp] + + unused_dim = common_unused[0] + + self.adjust_data_array(sdfg, target, unused_dim) + + for i_state, op_node in write_tasklet: + self.add_new_map(i_state, op_node, unused_dim) + + for i,(i_state, node) in enumerate(write_tasklet): + self.replace_write_memlet(i_state, node, target, unused_dim) + + for i_state, node in read_tasklet: + self.replace_read_memlet(i_state, node, target, unused_dim) + + # mode 1: write to read access + elif mode == 1: + target = state_0(sdfg).out_edges(op)[0].data.data + wcr = state_0(sdfg).out_edges(op)[0].data.wcr + unused_dims = set() + write_tasklet = self.find_write_tasklet(sdfg, target) + unused_dims = self.find_unused_dims(sdfg, write_tasklet, write=True) + unused_dim = unused_dims[0] + + self.adjust_data_array(sdfg, target, unused_dim) + + for i_state, tasklet_node in write_tasklet: + self.replace_write_memlet(i_state, tasklet_node, target, unused_dim) + + for i_state, tasklet_node in read_tasklet: + if not i_state.out_edges(tasklet_node)[0].data.data == target: + self.add_new_map(i_state, tasklet_node, unused_dim) + + for i_state, tasklet_node in read_tasklet: + self.replace_read_memlet(i_state, tasklet_node, target, unused_dim) + self.add_reduction(i_state, tasklet_node, target, wcr) + + for i_state, tasklet_node in write_tasklet: + self.delete_reduction(i_state, tasklet_node) + + def find_write_tasklet(self, sdfg, target): + result = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + out_edges = state.out_edges(node) + for edge in out_edges: + if edge.data.data == target: + result.append((state, node)) + return result + + def find_read_tasklet(self, sdfg, target): + result = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + in_edges = state.in_edges(node) + for edge in in_edges: + if edge.data.data == target: + result.append((state, node)) + return result + + def find_unused_dims(self, sdfg, tasklets, write=True): + result = [] + for state, node in tasklets: + edges = [] + if write: + edges = state.out_edges(node) + else: + edges = state.in_edges(node) + for edge in edges: + entry_maps = self.get_entry_maps(state, node) + unacc_indices = {entry.map.params[0] for entry in entry_maps} + unacc_indices -= set(edge.data.subset) + for entry in entry_maps: + if entry.map.params[0] in unacc_indices: + result.append(entry.map.range.size()[0]) + return result + + def adjust_data_array(self, sdfg, target, unused_dim): + # shape adjustment + shape = list(sdfg.arrays[target].shape) + shape.append(unused_dim) + sdfg.arrays[target].shape = tuple(shape) + # total size adjustment + sdfg.arrays[target].total_size *= unused_dim + offset = list(sdfg.arrays[target].offset) + offset.append(0) + # offset adjustment + sdfg.arrays[target].offset = tuple(offset) + # strides adjustment + strides = sdfg.arrays[target].strides + new_strides = [] + for stride in strides: + new_strides.append(stride*unused_dim) + new_strides.append(1) + sdfg.arrays[target].strides = tuple(new_strides) + + def add_new_map(self, state, op_node, unused_dim): + entries = self.get_entry_maps(state, op_node) + new_idx = f'i{len(entries)}' + #idxs.append(new_idx) + new_entry, new_exit = state.add_map(f'map_{len(entries)}_{op_node.label}', {new_idx: f'0:{unused_dim}'}) + + # incorporate new map + for edge in state.in_edges(op_node): + #memlet = copy.deepcopy(edge.data) + memlet = None + if edge.data.data: + memlet = edge.data.__deepcopy__({}) + state.add_memlet_path(edge.src, new_entry, op_node, memlet=memlet, dst_conn=edge.dst_conn, src_conn=edge.src_conn) + else: + state.add_nedge(edge.src, new_entry, data=dace.Memlet()) + state.add_nedge(new_entry, op_node, data=dace.Memlet()) + state.remove_edge(edge) + + for edge in state.out_edges(op_node): + memlet = edge.data.__deepcopy__({}) + state.add_memlet_path(op_node, new_exit, edge.dst, memlet=memlet, src_conn=edge.src_conn, dst_conn=edge.dst_conn) + state.remove_edge(edge) + + def replace_write_memlet(self, state, node, target, unused_dim): + entries = self.get_entry_maps(state, node) + idx = None + for entry in entries: + if str(unused_dim) == str(entry.map.range.size()[0]): + idx = entry.map.params[0] + break + + write_edges = [] + self.get_write_memlet(state, node, target, write_edges) + subset = list(write_edges[0].data.subset.ranges) + subset.append((idx, idx, 1)) + new_subset = dace.subsets.Range(subset) + + path_nodes = [edge.src for edge in write_edges] + path_nodes.append(write_edges[-1].dst) + out_conn = write_edges[0].src_conn + wcr = write_edges[0].data.wcr + for edge in write_edges: + src_conn = edge.src_conn + if src_conn: + edge.src.remove_out_connector(src_conn) + dst_conn = edge.dst_conn + if dst_conn: + edge.dst.remove_in_connector(dst_conn) + state.remove_edge(edge) + path_nodes[0].add_out_connector(out_conn) + state.add_memlet_path(*path_nodes, memlet=dace.Memlet(data=target, subset= new_subset, wcr=wcr), src_conn=out_conn) + + def get_write_memlet(self, state, node, dst, result, traverse=None): + if traverse is None: + traverse = set() + for edge in state.out_edges(node): + if edge.data.data == dst: + #subset = list(edge.data.subset.ranges) + #subset.append((new_idx, new_idx, 1)) + #edge.data.subset = dace.subsets.Range(subset) + result.append(edge) + if not edge.dst in traverse: + if not (isinstance(edge.dst, dace.nodes.AccessNode) and edge.dst.data == dst): + traverse.add(edge.dst) + self.get_write_memlet(state, edge.dst, dst, result, traverse) + + def delete_reduction(self, state, node, visited=None): + if visited is None: + visited = set() + out_edges = state.out_edges(node) + for edge in out_edges: + edge.data.wcr = None + if not edge.dst in visited and not isinstance(edge.dst, dace.nodes.AccessNode): + visited.add(edge.dst) + self.delete_reduction(state, edge.dst, visited) + + def add_reduction(self, state, node, wcr, visited=None): + if visited is None: + visited = set() + out_edges = state.out_edges(node) + for edge in out_edges: + edge.data.wcr = wcr + if not edge.dst in visited and not isinstance(edge.dst, dace.nodes.AccessNode): + visited.add(edge.dst) + self.add_reduction(state, edge.dst, wcr, visited) + + def replace_read_memlet(self, state, node, target, unused_dim): + entries = self.get_entry_maps(state, node) + idx = None + for entry in entries: + if str(unused_dim) == str(entry.map.range.size()[0]): + idx = entry.map.params[0] + break + + read_edges = [] + self.get_read_memlet(state, node, target, read_edges) + subset = list(read_edges[0].data.subset.ranges) + subset.append((idx, idx, 1)) + #subset.append(idx) + new_subset = dace.subsets.Range(subset) + + path_nodes = [edge.src for edge in reversed(read_edges)] + path_nodes.append(read_edges[0].dst) + out_conn = read_edges[0].dst_conn + for edge in read_edges: + src_conn = edge.src_conn + if src_conn: + edge.src.remove_out_connector(src_conn) + dst_conn = edge.dst_conn + if dst_conn: + edge.dst.remove_in_connector(dst_conn) + state.remove_edge(edge) + + path_nodes[-1].add_in_connector(out_conn) + state.add_memlet_path(*path_nodes, memlet=dace.Memlet(data=target, subset= new_subset), dst_conn=out_conn) + + def get_read_memlet(self, state, node, target, result, traverse=None): + if traverse is None: + traverse = set() + for edge in state.in_edges(node): + if edge.data.data == target: + result.append(edge) + if not edge.src in traverse: + traverse.add(edge.src) + if not (isinstance(edge.src, dace.nodes.AccessNode) and edge.src.data == target): + self.get_read_memlet(state, edge.src, target, result, traverse) + +def state_0(sdfg): + for state in sdfg.nodes(): + if state.label == 'state_0': + return state diff --git a/dace/transformation/semantic_preserving/changeMemoryLayout.py b/dace/transformation/semantic_preserving/changeMemoryLayout.py new file mode 100644 index 0000000000..b8212736f1 --- /dev/null +++ b/dace/transformation/semantic_preserving/changeMemoryLayout.py @@ -0,0 +1,85 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the changeMemoryLayout transformation. + +""" + +import dace + +class changeMemoryLayout(): + """ + This class implements the changeMemoryLayout transformation. It changes the memory layout of the array. + """ + + def __init__(self): + self.__name = 'changeMemoryLayout' + self.checked = False + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for arrays in the SDFG on which the changeMemeoryLayout Transformation can be applied. + + Appicable to all data arrays which are not used as input or output to the kernel. + + Args: + sdfg (dace.SDFG): The SDFG to search for arrays. + + Returns: + list: A list of data arrays on which the transformation can be applied. + """ + changeable_arrays = set() + for state in sdfg.nodes(): + source, sink = state.source_nodes(), state.sink_nodes() + for node in state.nodes(): + if isinstance(node, dace.nodes.AccessNode): + if not node in source and not node in sink: + changeable_arrays.add(node) + self.checked = True + return (list(changeable_arrays)) + + def apply(self, sdfg, node, dim_order): + """ + This method applies the changeMemoryLayout transformation to the given SDFG. It changes the memory layout of the array by chaning the strides of the array. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + node (dace.nodes.AccessNode): The array to apply the transformation to. + dim_order (list): The new order of the dimensions. E.g. [2, 0, 1] for a 3D array. + """ + if not self.checked and not node in self.find(sdfg): + return + self.checked = False + + data = sdfg.arrays[node.data] + shape = data.shape + + # check if order of dimension is correct + assert len(shape) == len(dim_order) + assert max(dim_order) == len(shape) - 1 + assert min(dim_order) == 0 + + m = dict(zip(shape, dim_order)) + new_strides = list() + for dim in shape: + stride = 1 + for dim2 in shape: + if m[dim2] > m[dim]: + stride *= dim2 + new_strides.append(stride) + + # set new stride + data.strides = tuple(new_strides) + return \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/removeArrayCopies.py b/dace/transformation/semantic_preserving/removeArrayCopies.py new file mode 100644 index 0000000000..f54ea27965 --- /dev/null +++ b/dace/transformation/semantic_preserving/removeArrayCopies.py @@ -0,0 +1,159 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the removeArrayCopies transformation. + +""" + +import dace + +class removeArrayCopies(): + """ + This class implements the removeArrayCopies transformation. It searches for array copies in the SDFG and removes them. The transformation is only applied if the array copy is removable. + This class reverses the addArrayCopies transformation. + """ + + def __init__(self): + self.__name = 'removeArrayCopies' + self.checked = False + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for array copies in the SDFG and returns a list of removable array copies. + + This method is applicable to arrays/scalars in the sdfg that + - are not output nodes of a state + - are coppied/moved to another array/scalar in the same state + + Args: + sdfg (dace.SDFG): The SDFG to search for removable array copies. + + Returns: + list: A list of removable array copies. + """ + deleteable_arrays = set() + for state in sdfg.nodes(): + out = [node.data for node in state.sink_nodes()] + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + if node.label.startswith('copy_temp') or node.label.startswith('move'): + out_node = state.out_edges(node)[0].data + if out_node.data in out: + continue + in_node = state.in_edges(node)[0].data + in_data = sdfg.arrays[in_node.data] + out_data = sdfg.arrays[out_node.data] + if in_node.subset == out_node.subset and in_data.shape == out_data.shape and in_data.dtype == out_data.dtype: + deleteable_arrays.add(out_node.data) + self.checked = True + return sorted(list(deleteable_arrays)) + + def apply(self, sdfg, arr_name): + """ + This method applies the removeArrayCopies transformation to the given SDFG. It removes the given array copy. + Reverses the addArrayCopies transformation. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + arr_name (str): The array copy to remove. + """ + if not self.checked and not arr_name in self.find(sdfg): + return + self.checked = False + + data = sdfg.arrays[arr_name] + arr_node = None + state = None + for tstate in sdfg.nodes(): + for edge in tstate.edges(): + if isinstance(edge.dst, dace.nodes.AccessNode) and edge.dst.data == arr_name: + arr_node = edge.dst + state = tstate + break + + out_edges = state.out_edges(arr_node) + entry = state.entry_node(arr_node) + + if entry: + in_edge = state.in_edges(arr_node)[0] + tasklet = in_edge.src + orig_node = state.in_edges(tasklet)[0].src + orig_name = orig_node.data + for edge in out_edges: + dace.transformation.helpers.redirect_edge(state, edge, new_src = orig_node) + self.delete_copy(state, arr_node, orig_node) + self.traverse_n_replace(state, orig_node, arr_name, orig_name) + else: + tasklet = None + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet) and(node.label.startswith('copy_temp') or node.label.startswith('move')): + if state.out_edges(node)[0].data.data == arr_name: + tasklet = node + break + entry = state.entry_node(tasklet) + while True: + t = state.entry_node(entry) + if t: + entry = t + else: + break + orig_node = state.in_edges(entry)[0].src + orig_name = orig_node.data + if orig_node in state.source_nodes() and not out_edges: + # orig_node is input node + sdfg.remove_node(state) + state_0(sdfg).replace(arr_name, orig_name) + else: + #rmv_edge = state.out_edges(orig_node)[0] + for edge in out_edges: + dace.transformation.helpers.redirect_edge(state, edge, new_src = orig_node) + self.traverse_n_replace(state, orig_node, arr_name, orig_name) + self.delete_copy(state, arr_node, orig_node) + + sdfg.remove_data(arr_name) + + def delete_copy(self, state, node, stop_node): + if isinstance(node, dace.nodes.AccessNode) and node == stop_node: + return + in_edge = state.in_edges(node) + state.remove_node(node) + for edge in in_edge: + src = edge.src + #state.remove_edge(edge) + self.delete_copy(state, src, stop_node) + + def traverse_n_replace(self, state, node, old_name, new_name, start=True, backwards=False): + if not start: + if not isinstance(node, dace.nodes.MapEntry) and not isinstance(node, dace.nodes.MapExit): + return + + next_edges = None + if backwards: + next_edges = state.in_edges(node) + else: + next_edges = state.out_edges(node) + + for edge in next_edges: + if edge.data.data == old_name: + edge.data.data = new_name + if backwards: + self.traverse_n_replace(state, edge.src, old_name, new_name, False, True) + else: + self.traverse_n_replace(state, edge.dst, old_name, new_name, False) + +def state_0(sdfg): + for state in sdfg.nodes(): + if state.label == 'state_0': + return state \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/removeArrayDims.py b/dace/transformation/semantic_preserving/removeArrayDims.py new file mode 100644 index 0000000000..b6c6df3bf9 --- /dev/null +++ b/dace/transformation/semantic_preserving/removeArrayDims.py @@ -0,0 +1,376 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the removeArrayDims transformation. + +""" + +import dace + +class removeArrayDims(): + """ + This class implements the removeArrayDims transformation. It searches for arrays in the SDFG and removes a dimension. The transformation is only applied if the array is extendable. + This class is supposed to inverse the addArrayDims transformation. + Similarly, there are two modes for this transformation: + Mode 0: read to write access, read is target + Mode 1: write to read access, write is target + """ + + def __init__(self): + self.__name = 'removeArrayDims' + self.checked = False + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for arrays in the SDFG and returns a list of tuples containing the array name and the mode of the transformation. + Mode 0: read to write access, read is target + Mode 1: write to read access, write is target + + Args: + sdfg (dace.SDFG): The SDFG to search for arrays. + + Returns: + list: A list of tuples, where each tuple contains the array name and the mode of the transformation + """ + result = [] + for name, data in sdfg.arrays.items(): + if not data.transient: + continue + discovered_rtw, discovered_wtr = self.deleteable_dim_candidates(sdfg, name) + if discovered_rtw is not None: + map_dim = discovered_rtw.map.range.size()[0] + result.append((name, map_dim, 1)) + if discovered_wtr is not None: + map_dim = discovered_wtr.map.range.size()[0] + result.append((name, map_dim, 0)) + self.checked = True + return result + + def deleteable_dim_candidates(self, sdfg, array_name): + write_tasklets = self.find_write_tasklet(sdfg, array_name) + read_tasklets = self.find_read_tasklet(sdfg, array_name) + + discovered_rtw = None + discovered_wtr = None + + for w_state, w_node in write_tasklets: + if not w_state.label == 'state_0': + continue + map_scopes = self.get_entry_maps(w_state, w_node) + subset = w_state.out_edges(w_node)[0].data.subset + for write_loc, widx in reversed(list(enumerate(subset))): + idx = str(widx[0]) + if len(idx) != 2: + continue + candidate_write_scope = [entry for entry in map_scopes if entry.map.params[0] == idx][0] + candidate_dim = candidate_write_scope.map.range.size()[0] + candidate_rejected = False + read_scope_indices = [] + for r_state, r_node in read_tasklets: + if array_name == r_state.out_edges(r_node)[0].data.data: + continue + candidate_read_scope = None + index_dim_map = {entry.map.params[0]: entry.map.range.size()[0] for entry in self.get_entry_maps(r_state, r_node)} + for edge in r_state.in_edges(r_node): + if not edge.data.data == array_name: + continue + r_subset = edge.data.subset + for read_loc, ridx in reversed(list(enumerate(r_subset))): + read_idx = str(ridx[0]) + if not index_dim_map[read_idx] == candidate_dim: + continue + #if not idx in read_idx: + # continue + if len(read_idx) != 2: + candidate_rejected = True + break + if read_loc != write_loc: + candidate_rejected = True + break + cur_read_scope = [entry for entry in self.get_entry_maps(r_state, r_node) if entry.map.params[0] == read_idx][0] + if candidate_read_scope is None: + candidate_read_scope = cur_read_scope + read_scope_indices.append(candidate_read_scope.map.params[0]) + elif candidate_read_scope != cur_read_scope: + candidate_rejected = True + break + read_dim = cur_read_scope.map.range.size()[0] + if read_dim != candidate_dim: + candidate_rejected = True + break + if candidate_read_scope is None: + candidate_rejected = True + if candidate_rejected: + continue + + if discovered_rtw is None: + rtw_rejected = False + for edge in w_state.in_edges(w_node): + if edge.data.data == array_name: + continue + edge_subset = [str(idx[0]) for idx in list(edge.data.subset)] if edge.data.subset is not None else [] + if candidate_write_scope.map.params[0] in edge_subset: + rtw_rejected = True + if not rtw_rejected: + discovered_rtw = candidate_write_scope + + if discovered_wtr is None: + wtr_rejected = False + for (rstate, rop), read_map_idx in zip(read_tasklets, read_scope_indices): + r_map_scopes = self.get_entry_maps(rstate, rop) + read_scope_idx = [entry.map.params[0] for entry in r_map_scopes if entry.map.params[0] == read_map_idx][0] + out_subset = [str(idx[0]) for idx in rstate.out_edges(rop)[0].data.subset] + if read_scope_idx in out_subset: + wtr_rejected = True + + common_read_reduce = None + for rstate, rop in read_tasklets: + out_edge = rstate.out_edges(rop)[0] + if out_edge.data.wcr: + if common_read_reduce is None: + common_read_reduce = rop.label + elif common_read_reduce != rop.label: + wtr_rejected = True + + if not (w_node.label == 'move' or w_node.label == 'copy_temp'): + if common_read_reduce != w_node.label: + wtr_rejected = True + if not wtr_rejected: + discovered_wtr = candidate_write_scope + + return discovered_rtw, discovered_wtr + + def get_entry_maps(self, state, node): + dims = [] + entry = state.entry_node(node) + if not entry: + return dims + + while True: + dims.append(entry) + entry = state.entry_node(entry) + if not entry: + break + return dims + + def apply(self, sdfg, name, dim, mode): + """ + This method applies the removeArrayDims transformation to the given SDFG. It removes the given dimension from the array. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + name (str): The array to remove the dimension from. + dim (int): The dimension to remove. + mode (int): The mode of the transformation. 0: read to write access, read is target. 1: write to read access, write is target. + """ + if not self.checked and not (name, dim, mode) in self.find(sdfg): + return + self.checked = False + + self.adjust_data_array(sdfg, name, dim) + self.remove_read_dim(sdfg, name, dim) + self.remove_write_dim(sdfg, name, dim) + + w_tasklet = self.find_write_tasklet(sdfg, name) + if mode == 0: + r_tasklet = self.find_read_tasklet(sdfg, name) + reducation_type = r_tasklet[0][0].out_edges(r_tasklet[0][1])[0].data.wcr + for state, node in w_tasklet: + if state.label == 'state_0': + self.add_reduction(state, node, reducation_type) + + for state, node in r_tasklet: + target_entry = None + entries = self.get_entry_maps(state, node) + for i, tmp_e in enumerate(entries): + if tmp_e.map.range.size()[0] == dim: + target_entry = tmp_e + break + exit = state.exit_node(target_entry) + self.fix_memlet_volume(state, node, name, target_entry, exit, dim) + self.remove_map(state, node, target_entry, exit) + self.delete_reduction(state, node, name) + else: + for state, node in w_tasklet: + target_entry = None + entries = self.get_entry_maps(state, node) + for i, tmp_e in enumerate(entries): + if tmp_e.map.range.size()[0] == dim: + target_entry = tmp_e + break + exit = state.exit_node(target_entry) + self.fix_memlet_volume(state, node, name, target_entry, exit, dim) + + self.remove_map(state, node, target_entry, exit) + + def add_reduction(self, state, node, wcr, name): + out_edges = state.out_edges(node) + if isinstance(node, dace.nodes.AccessNode) and node.data == name: + return + for edge in out_edges: + edge.data.wcr = wcr + self.add_reduction(state, edge.dst, wcr) + + def delete_reduction(self, state, node, name): + out_edges = state.out_edges(node) + if isinstance(node, dace.nodes.AccessNode): + return + for edge in out_edges: + edge.data.wcr = None + self.delete_reduction(state, edge.dst, name) + + def remove_map(self, state, node, entry, exit): + for out_edge in state.out_edges(entry): + for in_edge in state.in_edges(entry): + if in_edge.data.data == out_edge.data.data: + dace.transformation.helpers.redirect_edge(state, out_edge, new_src = in_edge.src) + + for in_edge in state.in_edges(exit): + for out_edge in state.out_edges(exit): + if in_edge.data.data == out_edge.data.data: + dace.transformation.helpers.redirect_edge(state, in_edge, new_dst = out_edge.dst) + + state.remove_node(exit) + state.remove_node(entry) + + def fix_memlet_volume(self, state, node, name, entry, exit, dim): + + def traverse(node, del_map, backwards=False, traversed=None, passed=False): + if traversed is None: + traversed = set() + next_edges = state.in_edges(node) if backwards else state.out_edges(node) + for edge in next_edges: + if edge.data.data == name and passed: + volume = edge.data.volume / dim + edge.data.volume = volume + elif not edge.data.data == name: + continue + next_node = edge.src if backwards else edge.dst + if not next_node in traversed and not (isinstance(next_node, dace.nodes.AccessNode) and next_node.data == name): + traversed.add(next_node) + t = passed + if next_node == del_map: + t = True + traverse(next_node, del_map, backwards, traversed, t) + traverse(node, entry, backwards=True, traversed=None, passed=False) + traverse(node, exit, backwards=False, traversed=None, passed=False) + + + def remove_read_dim(self, sdfg, target, dim): + r_tasklet = self.find_read_tasklet(sdfg, target) + for state, node in r_tasklet: + entries = self.get_entry_maps(state, node) + idx = [entry.map.params[0] for entry in entries if entry.map.range.size()[0] == dim][0] + self.traverse_and_replace(state, node, target, dim, idx, backwards=True) + + def remove_write_dim(self, sdfg, target, dim): + w_tasklet = self.find_write_tasklet(sdfg, target) + for state, node in w_tasklet: + entries = self.get_entry_maps(state, node) + idx = [entry.map.params[0] for entry in entries if entry.map.range.size()[0] == dim][0] + + self.traverse_and_replace(state, node, target, dim, idx) + + def traverse_and_replace(self, state, node, name, dim, idx, backwards=False, traversed=None): + if traversed is None: + traversed = set() + + if isinstance(node, dace.nodes.AccessNode) and node.data == name: + return + + next_edges = None + if backwards: + next_edges = state.in_edges(node) + else: + next_edges = state.out_edges(node) + for edge in next_edges: + if edge.data.data == name: + subset = list(edge.data.subset.ranges) + new_subset = [] + replaced = False + for sub_idx in reversed(subset): + if (str(sub_idx[0]) == str(idx)) and not replaced: + replaced = True + continue + new_subset.append(sub_idx) + if not replaced: + new_subset = [] + for sub_idx in reversed(subset): + if sub_idx[1]+1 == dim and not replaced: + replaced = True + continue + new_subset.append(sub_idx) + new_subset = dace.subsets.Range(reversed(new_subset)) + edge.data.subset = new_subset + + if not node in traversed: + traversed.add(node) + if backwards: + self.traverse_and_replace(state, edge.src, name, dim, idx, backwards, traversed) + else: + self.traverse_and_replace(state, edge.dst, name, dim, idx, backwards, traversed) + + def adjust_data_array(self, sdfg, target, dim): + data_array = sdfg.arrays[target] + data_array.total_size //= dim + shape = list(data_array.shape) + new_shape = [] + removed = False + pos = -1 + length = len(shape) + for i,s_dim in reversed(list(enumerate(shape))): + if s_dim == dim and not removed: + removed = True + pos = i + else: + new_shape.append(s_dim) + data_array.shape = tuple(new_shape) + strides = list(data_array.strides) + new_strides = [] + for j in range(len(strides)-1): + if j == pos: + continue + new_strides.append(strides[j]//dim) + data_array.strides = tuple(new_strides) + offset = list(data_array.offset) + new_offset = [] + for k in range(len(offset)-1): + if k == pos: + continue + new_offset.append(offset[k]) + data_array.offset = tuple(new_offset) + + def find_write_tasklet(self, sdfg, target): + result = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + out_edges = state.out_edges(node) + for edge in out_edges: + if edge.data.data == target: + result.append((state, node)) + return result + + def find_read_tasklet(self, sdfg, target): + result = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + in_edges = state.in_edges(node) + for edge in in_edges: + if edge.data.data == target: + result.append((state, node)) + break + return result \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/reuseArray.py b/dace/transformation/semantic_preserving/reuseArray.py new file mode 100644 index 0000000000..38cdfe489e --- /dev/null +++ b/dace/transformation/semantic_preserving/reuseArray.py @@ -0,0 +1,266 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the reuseArray transformation. + +""" + +import dace + +class reuseArray(): + """ + This class implements the reuseArray transformation. It searches for arrays in the SDFG that have the same shape and data type and are transient. The transformation is only applied if the read access is before the write access. + + Attributes: + remove_reused (bool): If True, the array that is reused is removed from the SDFG. Doesn't preserve the original information about the sdfg. + """ + def __init__(self): + self.__name = 'reuseArray' + self.checked = False + self.remove_reused = True + + @property + def name(self): + return self.__name + + def find(self, sdfg): + """ + This method searches for arrays in the SDFG that can be reused. It returns a list of tuples containing the array names that can be reused. + + This transformation is applicable to the following cases: + - Two arrays have the same shape and data type + - The arrays have no overlapping accesses / live ranges + - The array to be replaced has to be a transient array, i.e. now input or output to the kernel + + Args: + sdfg (dace.SDFG): The SDFG to search for arrays. + + Returns: + list: A list of tuples containing two array names that can be reused. + """ + same_data = set() + data = sdfg.arrays + all_nodes = set() + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.AccessNode): + all_nodes.add(node.data) + + state = None + for tmpstate in sdfg.nodes(): + if tmpstate.label == 'state_0': + state = tmpstate + inputs = state.source_nodes() + input_names = [node.data for node in inputs if isinstance(node, dace.nodes.AccessNode)] + + for arr_name1, data1 in data.items(): + for arr_name2, data2 in data.items(): + if arr_name1 == arr_name2: + continue + if not type(data1) == type(data2): + continue + if not data1.transient: #or not data2.transient: # later check is not necessaryily needed, but safer + continue + # if second array has a reduction (i.e. the output node is given also as input to tasklet), could overwritte initialisation value (0, min_val, max_val) + if (arr_name2 in input_names and data2.transient): + continue + + if isinstance(data1, dace.data.Array): + if not arr_name1 in all_nodes or not arr_name2 in all_nodes: + continue + if data1.shape == data2.shape and data1.dtype == data2.dtype and data1.strides == data2.strides: + same_data.add((arr_name1, arr_name2)) #if arr_name1 < arr_name2 else same_data.add((arr_name2, arr_name1)) + if isinstance(data1, dace.data.Scalar): + if not arr_name1 in all_nodes or not arr_name2 in all_nodes: + continue + if data1.dtype == data2.dtype: + same_data.add((arr_name1, arr_name2)) #if arr_name1 < arr_name2 else same_data.add((arr_name2, arr_name1)) + + reusable_arrays = [] + + for arr1, arr2 in same_data: + last_read1 = self.get_last_read(state, arr1) + #last_read2 = self.get_last_read(state, arr2) + #first_write1 = self.get_first_write(state, arr1) + first_write2 = self.get_first_write(state, arr2) + is_reusable = True + + # last read arr1 <= first write arr2 + if last_read1 is None or first_write2 is None: + is_reusable = False + else: + r1w2 = self.trav_check(state, last_read1, [first_write2]) + if r1w2: + is_reusable = False + if is_reusable: + reusable_arrays.append((arr1, arr2)) + + #TODO: check if arr1 used as input and arr2 as output in same map scope, that their index are matching => no index mismatch + self.checked = True + return sorted((reusable_arrays)) + + def get_last_read(self, state, arr): + sink = state.sink_nodes() + reads = set() + visited_reads = set() + for node in sink: + res = self.traverse(node, state, arr, visited=visited_reads,reverse=True) + if res: + reads = reads | res + if len(reads) == 0: + return None + elif len(reads) == 1: + return list(reads)[0] + else: + new_reads = [] + for read in reads: + b = self.trav_check(state, read, reads-{read}) + if b: + new_reads.append(read) + return new_reads[0] if len(new_reads) == 1 else None + + def get_first_write(self, state, arr): + source = state.source_nodes() + visited = set() + writes = set() + for node in source: + res = self.traverse(node, state, arr, visited) + if res: + writes = writes | res + if len(writes) == 0: + return None + elif len(writes) == 1: + return list(writes)[0] + else: + new_writes = [] + for write in writes: + b = self.trav_check(state, write, writes-{write}, reverse=True) + if b: + new_writes.append(write) + + return new_writes[0] if len(new_writes) == 1 else None + + def read_tasklet(self, state, node, arr_name, res=None): + if res is None: + res = set() + out_edges = state.out_edges(node) + for edge in out_edges: + if not edge.data.data == arr_name: + continue + if isinstance(edge.dst, (dace.nodes.AccessNode, dace.nodes.MapExit, dace.nodes.MapEntry)): + self.read_tasklet(state, edge.dst, arr_name, res) + elif isinstance(edge.dst, dace.nodes.Tasklet): + res.add(edge.dst) + + def write_tasklet(self, state, node, arr_name, res=None): + if res is None: + res = set() + in_edges = state.in_edges(node) + for edge in in_edges: + if not edge.data.data == arr_name: + continue + if isinstance(edge.src, (dace.nodes.AccessNode, dace.nodes.MapEntry, dace.nodes.MapExit)): + self.write_tasklet(state, edge.src, arr_name, res) + elif isinstance(edge.src, dace.nodes.Tasklet): + res.add(edge.src) + + def trav_check(self, state, cur_node, check_set, visited = None, reverse = False): + if not visited: + visited = set() + if cur_node in check_set: + return False + visited.add(cur_node) + next_edge = state.out_edges(cur_node) if not reverse else state.in_edges(cur_node) + for edge in next_edge: + next_node = edge.dst if not reverse else edge.src + if not next_node in visited: + tmp = self.trav_check(state, next_node, check_set, visited, reverse=reverse) + if not tmp: + return False + + return True + + def traverse(self, cur_node, state, search_item, visited = None, reverse = False): + if not visited: + visited = set() + if isinstance(cur_node, dace.nodes.AccessNode): + if cur_node.data == search_item: + res = set() + if reverse: + self.read_tasklet(state, cur_node, search_item, res) + else: + self.write_tasklet(state, cur_node, search_item, res) + return res + + visited.add(cur_node) + next_edges = state.in_edges(cur_node) if reverse else state.out_edges(cur_node) + tmp_ret = set() + for edge in next_edges: + next_node = edge.src if reverse else edge.dst + if not next_node in visited: + ret = self.traverse(next_node, state, search_item, visited, reverse=reverse) + if ret: + tmp_ret = tmp_ret | ret + if tmp_ret: + return tmp_ret + return None + + def traverser_check(self, state, arr1, arr2, node, write, visited = None): + + if visited is None: + visited = set() + visited.add(node) + + # last read arr1 < first write arr2 + if isinstance(node, dace.nodes.Tasklet): + in_edges = state.in_edges(node) + out_edges = state.out_edges(node) + for edge in in_edges: + if edge.data.data == arr1: + if write: + return False + for edge in out_edges: + if edge.data.data == arr2: + write = True + + out_edges = state.out_edges(node) + for edge in out_edges: + if not edge.dst in visited: + ret = self.traverser_check(state, arr1, arr2, edge.dst, write, visited) + if not ret: + return False + + return True + + def apply(self, sdfg, array1, array2): + """ + This method applies the reuseArray transformation to the given SDFG. It replaces all accesses to the first array with accesses to the second array. + If the remove_reused attribute is set to True, the first array is removed from the SDFG. + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + array1 (str): The array to replace. + array2 (str): The array to replace with. + """ + if not self.checked and not (array1, array2) in self.find(sdfg): + return + self.checked = False + + for state in sdfg.nodes(): + state.replace(array1, array2) + + data1 = sdfg.arrays[array1] + if not data1.transient: + data2 = sdfg.arrays[array2] + data2.transient = False + + if self.remove_reused: + sdfg.remove_data(array1) \ No newline at end of file diff --git a/dace/transformation/semantic_preserving/reuseArrayLocations.py b/dace/transformation/semantic_preserving/reuseArrayLocations.py new file mode 100644 index 0000000000..8c5e2eb854 --- /dev/null +++ b/dace/transformation/semantic_preserving/reuseArrayLocations.py @@ -0,0 +1,238 @@ +""" +***ETH Zurich SPCL - Bachelors Thesis: BSc thesis: Deep learning transformations with semantic-preservation in DaCe by Severin Huber*** + +This module contains several classes that implement various transformations and optimizations for SDFGs (Stateful DataFlow Graphs) using the DaCe (Data-Centric Parallel Programming) framework. + +This is part of the bachelor thesis of Severin Huber at SPCL. The topic is semantic-preserving transformations. + +This optimization transformations are implemented not for general sdfgs, but for those generated +from Andrei Ivanovs microkernel IR with a converter implemented as a part of this thesis. +General usage of these transformations is not guaranteed. + +This class implements the reuseArrayLocations transformation. + +""" + +import dace + +class reuseArrayLocations(): + """ + This class implements the reuseArrayLocations transformation. It avoids materializing dimensions. + + Attributes: + race_cond_check (bool): If True, all the map involved in the scope of the array to be reused are set to sequential schedule to avoid race condtions. + keep_sizes (bool): If True, the sizes of the array are kept. If False, the sizes are adjusted to the new array size. + """ + + def __init__(self): + self.__name = 'reuseArrayLocations' + self.checked = False + # reusing a dimension of an array can lead to incorrect results when using OMP for parallelization (race conditions) + self.race_cond_check = True + self.keep_sizes = True + + @property + def name(self): + return self.__name + + def get_read_ops(self, state, target): + ops = set() + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + in_edges = state.in_edges(node) + for edge in in_edges: + if edge.data.data == target.data: + ops.add(node) + continue + return ops + + def get_write_ops(self, state, target): + ops = set() + for node in state.nodes(): + if isinstance(node, dace.nodes.Tasklet): + out_edges = state.out_edges(node) + for edge in out_edges: + if edge.data.data == target.data: + ops.add(node) + return ops + + def get_entry_maps(self, state, node): + dims = [] + entry = state.entry_node(node) + + while True: + dims.append(entry) + entry = state.entry_node(entry) + if not entry: + break + + return dims + + def find(self, sdfg): + """ + This method searches for arrays in the SDFG that can be reused. It returns a list of tuples containing the array names and the dimensions that can be reused. + + This transformation is applicable to the following cases: + - all accesses to the dimension of the array for reuse refer to the same scope s and only to this scope + + Args: + sdfg (dace.SDFG): The SDFG to search for arrays. + + Returns: + list: A list of tuples containing the array names and the dimensions that can be reused. + """ + reusable_dims = set() + for state in sdfg.nodes(): + #source, sink = state.source_nodes(), state.sink_nodes() + for node in state.nodes(): + if isinstance(node, dace.nodes.AccessNode): + data_node = sdfg.arrays[node.data] + if not data_node.transient: + continue + if data_node.shape is [1]: + continue + if not state.entry_node(node): + continue + + # get all read and write tasklets + node_entry = state.entry_node(node) + read_ops = self.get_read_ops(state, node) + write_ops = self.get_write_ops(state, node) + + # applic. when all accesses to the dimension of the array targeted for reuse refer to the same scope s and only to this scope + reusable = True + for tasklet in read_ops: + if not state.entry_node(tasklet) == node_entry: + reusable = False + break + if not reusable: + continue + for tasklet in write_ops: + if not state.entry_node(tasklet) == node_entry: + reusable = False + break + if not reusable: + continue + + # get the dimension that is not used anymore + shape = data_node.shape + tmp = set() + shape_and_stride = {shape[i]: data_node.strides[i] for i in range(len(shape))} + for op in write_ops: + maps = self.get_entry_maps(state, op) + tmp = {map_entry for map_entry in maps if map_entry.range.size()[0] in shape and not shape_and_stride[map_entry.range.size()[0]] == 0} + for op in read_ops: + entry_dims = self.get_entry_maps(state, op) + if set(tmp).issubset(set(entry_dims)): + for maps in tmp: + reusable_dims.add((node, maps.range.size()[0])) + else: + diff = set(tmp) - set(entry_dims) + for maps in diff: + reusable_dims.add((node, maps.range.size()[0])) + self.checked = True + return sorted(list(reusable_dims), key=lambda x: x[0].data) + + + def apply(self, sdfg, node, dim): + """ + This method applies the reuseArrayLocations transformation to the given SDFG. It avoids materializing the given dimension of the array by setting the stride to 0. + If keep_sizes is set to True, the sizes of the array are kept. If False, the sizes are adjusted to the new array size. + If race_cond_check is set to True, all the map involved in the scope of the array to be reused are set to sequential schedule + + Args: + sdfg (dace.SDFG): The SDFG to apply the transformation to. + node (dace.nodes.AccessNode): The array to apply the transformation to. + dim (Str): The dimension to remove. + """ + + if not self.checked and not (node,dim) in self.find(sdfg): + return + self.checked = False + + data_node = sdfg.arrays[node.data] + shape = data_node.shape + strides = list(data_node.strides) + dim_pos = -1 + for i,dims in reversed(list(enumerate(shape))): + if dims == dim: + dim_pos = i + if self.keep_sizes: + strides[i] = 0 + break + data_node.strides = tuple(strides) + + # applying this optimization and using OMP for parallel can lead to race condtions, this always sees that it keeps first shape + if self.race_cond_check: + entry_maps = self.get_entry_maps(find_state(sdfg, node), node) + for entry in entry_maps: + entry.schedule = dace.ScheduleType.Sequential + + state = find_state(sdfg, node) + # remove the dimension that is not used anymore (lose of information of total size before optimization) + if not self.keep_sizes: + new_shape = [] + new_offset = [] + offset = list(data_node.offset) + strides = list(data_node.strides) + new_strides = [] + for i,dims in reversed(list(enumerate(shape))): + if dims == dim: + continue + new_shape.append(dims) + new_offset.append(offset[i]) + new_strides.append(strides[i]) + if not new_shape: + new_shape.append(1) + new_offset.append(0) + new_strides.append(1) + data_node.shape = tuple(new_shape) + data_node.offset = tuple(new_offset) + data_node.strides = tuple(new_strides) + data_node.total_size = data_node.total_size // dim + #print(f'{data_node} and {data_node.shape} and {data_node.total_size} and {data_node.strides} ({dim})') + if not dim_pos == -1: + self.adjust_memlets(sdfg, state, node, dim_pos) + + + def adjust_memlets(self, sdfg, state, node, 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