From b2352755bb55b95f9cdd615c4c74203e2d3a55df Mon Sep 17 00:00:00 2001 From: sevhuber Date: Sun, 18 Aug 2024 22:34:04 +0200 Subject: [PATCH 1/4] added semantic-preserving Transformation --- .../semantic_preserving/optimize_sdfg.py | 2570 ++++ .../test_kernel/test_gemm.sdfg | 3603 +++++ .../test_kernel/test_gemm_const.sdfg | 3554 +++++ .../test_kernel/test_softmax.sdfg | 11595 ++++++++++++++++ 4 files changed, 21322 insertions(+) create mode 100644 dace/transformation/semantic_preserving/optimize_sdfg.py create mode 100644 dace/transformation/semantic_preserving/test_kernel/test_gemm.sdfg create mode 100644 dace/transformation/semantic_preserving/test_kernel/test_gemm_const.sdfg create mode 100644 dace/transformation/semantic_preserving/test_kernel/test_softmax.sdfg diff --git a/dace/transformation/semantic_preserving/optimize_sdfg.py b/dace/transformation/semantic_preserving/optimize_sdfg.py new file mode 100644 index 0000000000..902099f222 --- /dev/null +++ b/dace/transformation/semantic_preserving/optimize_sdfg.py @@ -0,0 +1,2570 @@ +import sys +import copy +from dace.transformation.dataflow.buffer_tiling import BufferTiling +from dace.transformation.dataflow.map_unroll import MapUnroll +from dace.transformation.dataflow.tiling import MapTiling +from dace.transformation.dataflow.map_fission import MapFission +from dace import registry, sdfg as sd, memlet as mm, subsets, data as dt +from dace.symbolic import pystr_to_symbolic +from copy import deepcopy as dcpy +from dace.sdfg.propagation import propagate_memlets_state +from dace.transformation import transformation +import dace +import numpy as np +import argparse +import math +import sympy +parser = argparse.ArgumentParser(description='Test kernels.') +parser.add_argument('--validate', action='store_true', help='see if the generated code is correct') +parser.add_argument('--print', action='store_true', help='show printed code') +parser.add_argument('--opt', type=str, help='optimization to apply', default='') +parser.add_argument('--kernel', type=str, help='kernel to apply the optimization to', default='gemm') +parser.add_argument('--sym', action='store_true', help='Symbolic or Constant Loop Sizes', default=True) +args = parser.parse_args() + + + +class SwapScope(): + + def find_swappable_maps(self, sdfg): + swappable_maps = [] + for state in sdfg.nodes(): + for edges in state.edges(): + if isinstance(edges.src, dace.nodes.MapEntry) and isinstance(edges.dst, dace.nodes.MapEntry): + src = edges.src.map + dst = edges.dst.map + for i, param in enumerate(src.params): + if param in dst.params: + continue + swappable_maps.append((edges.src, edges.dst)) + return 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, maps): + map_entry1, map_entry2 = maps + assert isinstance(map_entry1, dace.sdfg.nodes.MapEntry) + assert isinstance(map_entry2, dace.sdfg.nodes.MapEntry) + if not (map_entry1, map_entry2) in self.find_swappable_maps(sdfg): + return + exit1, exit2 = self.find_exit_maps(sdfg, map_entry1, map_entry2) + assert exit1 is not None + assert exit2 is not None + + for state in sdfg.nodes(): + for edge in state.edges(): + if edge.dst == map_entry1: + dace.transformation.helpers.redirect_edge(state, edge, new_dst = map_entry2) + elif edge.src == map_entry2: + dace.transformation.helpers.redirect_edge(state, edge, new_src = map_entry1) + elif edge.src == map_entry1 and edge.dst == map_entry2: + dace.transformation.helpers.redirect_edge(state, edge, new_src = map_entry2, new_dst = map_entry1) + elif edge.dst == exit2: + dace.transformation.helpers.redirect_edge(state, edge, new_dst = exit1) + elif edge.src == exit1: + dace.transformation.helpers.redirect_edge(state, edge, new_src = exit2) + elif edge.src == exit2 and edge.dst == exit1: + dace.transformation.helpers.redirect_edge(state, edge, new_src = exit1, new_dst = exit2) + +class SwapMapScope(): + + def find_swappable_maps(self, sdfg): + 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 state.entry_node(entry1) == state.entry_node(entry2): + continue + if not state1 == state2: + continue + if next: + break + next = True + output_1 = {edge.data.data for edge in state.out_edges(exit1)} + input_2 = {edge.data.data for edge in state.in_edges(entry2)} + if input_2 & output_1: + continue + result.append((entry1, entry2)) + + 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): + if not (entry1, exit2) in self.find_swappable_ops(sdfg): + return + state = find_state(sdfg, entry2) + exit2 = state.exit_node(entry2) + dst_node = [edge.dst for edge in state(sdfg).out_edges(exit2)][0] + state.add_nedge(dst_node, entry1, mm.Memlet()) + +class SwapOpertions(): + def find_swappable_ops(self, sdfg): + result = [] + tasklets = self.find_tasklets(sdfg) + for i in range(len(tasklets)): + tasklet1, state1 = tasklets[i] + next = False + for j in range(i+1, len(tasklets)): + tasklet2, state2 = tasklets[j] + if not state1 == state2: + continue + if not state1.entry_node(tasklet1) == state2.entry_node(tasklet2): + continue + if next: + break + next = True + 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 + result.append((tasklet1, tasklet2)) + return result + + 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): + if not (op1, op2) in self.find_swappable_ops(sdfg): + return + state = find_state(sdfg, op2) + out_node = [edge.dst for edge in state.out_edges(op2)][0] + state.add_nedge(out_node, op1, mm.Memlet()) + +class LoopTile(): + + def find_tileable_maps(self, sdfg): + tileable_maps = [] + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapEntry): + map = node.map + dim = map.range.size()[0] + if isinstance(dim, sympy.core.numbers.Integer) and int(dim) > 1: + dim = int(dim) + divisors = [i for i in range(2, math.floor(math.sqrt(dim))) if dim % i == 0] + tileable_maps.extend([(node, tile_size) for tile_size in divisors]) + if isinstance(dim, sympy.core.symbol.Symbol): + tileable_maps.extend([(node, tile_size) for tile_size in [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]]) + return tileable_maps + + def apply(self, sdfg, map_entry, tile_size): + if not (map_entry, tile_size) in self.find_tileable_maps(sdfg): + return + assert isinstance(map_entry, dace.sdfg.nodes.MapEntry) + assert len(map_entry.map.params) == 1 + tile_dict = {map_entry.map.params[0]: 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) + +class JoinMapScopes(): + + def find_joinable_mapscope(self, sdfg): + pass + def apply(self): + pass + +class SplitMapScopes(): + + def traverse_scope(self, state, entry): + map = entry.map + ops = [] + def traverse(node, sec=False): + if node in ops: + return + if isinstance(node, dace.nodes.MapExit) and node.map == map: + return + if isinstance(node, dace.nodes.Tasklet): + if sec: + ops.append(node) + else: + sec = True + for edge in state.edges(): + if edge.src == node: + traverse(edge.dst, sec) + traverse(entry) + + return ops + + def exit_node(self, state, entry): + def traverse(node, exit=False): + if isinstance(node, dace.nodes.MapExit) and node.map == entry.map: + exit = True + if isinstance(node, dace.nodes.AccessNode) and exit: + return node + for edge in state.edges(): + if edge.src == node: + return traverse(edge.dst, exit) + + return traverse(entry) + + + def find_splitable_scopes(self, sdfg): + splitable_scopes = [] + entry_set = set() + for state in sdfg.nodes(): + for edge in state.edges(): + if isinstance(edge.src, dace.nodes.MapEntry) and isinstance(edge.dst, dace.nodes.Tasklet): + map = edge.src + if not map in entry_set: + entry_set.add(map) + scope_ops = self.traverse_scope(state, map) + if scope_ops: + splitable_scopes.extend(scope_ops) + #splitable_scopes.append(self.exit_node(state, map)) + return splitable_scopes + + #unused + def find_exit_maps(self, sdfg, map): + map = map.map + exit = None + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.MapExit): + if node.map == map: + exit = node + return exit + + def find_input_nodes(self, sdfg, node): + input_nodes = [] + for state in sdfg.nodes(): + for edge in state.edges(): + if edge.dst == node: + input_nodes.append(edge.src) + return input_nodes + + def find_ingoing_edges(self, sdfg, node): + ingoing_edges = [] + for state in sdfg.nodes(): + for edge in state.edges(): + if edge.dst == node: + ingoing_edges.append(edge) + return ingoing_edges + + def find_outgoing_edges(self, sdfg, node): + outgoing_edges = [] + for state in sdfg.nodes(): + for edge in state.edges(): + if edge.src == node: + outgoing_edges.append(edge) + return outgoing_edges + + def find_edges(state, entry, target): + edges = [] + def traverse(node, entry, target, passed=False): + if node == target: + passed = True + for edge in state.edges(): + if edge.src == node: + if edge.dst == target: + edges.append(edge) + return + traverse(edge.dst, entry, target) + for edge in state.edges(): + if edge.src == entry: + traverse(edge.dst, entry, target) + return edges + + def copy_map_properties(self, map): + new_map = dace.nodes.Map(map.label, map.params, map.range) + new_map.unroll = map.unroll + new_map.schedule = map.schedule + return new_map + + def traverse_upwards(self, state, start_node, stop_map, visited = None, maps = None): + if visited is None: + visited = set() + if maps is None: + maps = set() + + # Stop condition if the start_node is a MapEntry with the specified name + if isinstance(start_node, dace.nodes.MapExit) and not start_node.map.label == stop_map: + return + + # Add the current node to the visited set + visited.add(id(start_node)) + if isinstance(start_node, dace.nodes.MapEntry): + exit_map = state.exit_node(start_node) + maps.add((start_node, exit_map)) + + # Traverse upwards from the current node + for edge in state.in_edges(start_node): + src_node = edge.src + if id(src_node) not in visited: + self.traverse_upwards(state, src_node, stop_map, visited, maps) + + + def traverse_downwards(self, state, start_node, stop_map,visited = None, maps = None, start=True): + if visited is None: + visited = set() + if maps is None: + maps = set() + print(f'start node {start_node}') + # Stop condition if the start_node is a MapEntry with the specified name + if isinstance(start_node, dace.nodes.MapEntry) and not start_node.map.label == stop_map: + return + + # Add the current node to the visited set + visited.add(id(start_node)) + + # fuse exit and entry nodes if they are connected by a single access node + if isinstance(start_node, dace.nodes.AccessNode) and not start: + in_nodes = [edge.src for edge in state.in_edges(start_node)] + out_nodes = [edge.dst for edge in state.out_edges(start_node)] + in_map = None + out_map = None + for node in in_nodes: + if isinstance(node, dace.nodes.MapExit): + in_map = node + break + for node in out_nodes: + if isinstance(node, dace.nodes.MapEntry): + out_map = node + break + if in_map and out_map: + self.checkMap(state, in_map, out_map) + transform = dace.transformation.dataflow.map_fusion.MapFusion() + transform.first_map_exit = in_map + transform.second_map_entry = out_map + if transform.can_be_applied(state, 0, sdfg): + print(f'fusion on {start_node}') + transform.apply(state, sdfg) + sdfg.view() + self.traverse_downwards(state, in_map, stop_map, visited, maps, True) + return + + # Traverse downwars from the current node + out_edges = state.out_edges(start_node) + for edge in out_edges: + dst_node = edge.dst + #if id(dst_node) not in visited: + self.traverse_downwards(state, dst_node, stop_map, visited, maps, False) + + + def find_fusion_map(self, state, node, end_nodes, target_nodes, failed, visited = None): + if node in end_nodes: + return None + + if visited is None: + visited = set() + + visited.add(id(node)) + + if isinstance(node, dace.nodes.AccessNode) and not node in target_nodes and not node in failed: + in_nodes = [edge.src for edge in state.in_edges(node)] + out_nodes = [edge.dst for edge in state.out_edges(node)] + in_map = None + out_map = None + for in_node in in_nodes: + if isinstance(in_node, dace.nodes.MapExit): + in_map = in_node + break + for out_node in out_nodes: + if isinstance(out_node, dace.nodes.MapEntry): + out_map = out_node + break + if in_map and out_map: + if self.checkMap(state, in_map, out_map): + return in_map, node, out_map + + for edge in state.out_edges(node): + if id(edge.dst) not in visited: + return self.find_fusion_map(state, edge.dst, end_nodes, target_nodes, visited) + + def checkMap(self, state, map1, map2): + assert map1.map.params == map2.map.params + assert map1.map.range == map2.map.range + if map1.label == map2.label: + return True + return False + + def apply4(self, sdfg, target): + if not target in self.find_splitable_scopes(sdfg): + return + assert(isinstance(target, dace.sdfg.nodes.Tasklet)) + + state = find_state(sdfg, target) + + in_edge = state.in_edges(target) + in_nodes = [edge.src for edge in in_edge] + + current_node = target + innermost_map, exit_map = self.find_scope_map(state, target) + + print(f'innermost scope {innermost_map}') + print(f'in nodes {in_nodes}') + + map_label = innermost_map.map.label + '_fission' + start_node = state.in_edges(innermost_map)[0].src + end_nodes = [edge.dst for edge in state.out_edges(exit_map)] + + transform = dace.transformation.dataflow.map_fission.MapFission() + transform.map_entry = innermost_map + assert transform.can_be_applied(state, 0, sdfg) + transform.apply(state, sdfg) + sdfg.view() + + fusion_entry, node, fusion_exit = None,None,None + transform = dace.transformation.dataflow.map_fusion.MapFusion() + failed = [] + while True: + fusion_exit, node, fusion_entry = self.find_fusion_map(state, start_node, end_nodes, in_nodes, failed) + if not fusion_entry: + break + transform.first_map_exit = fusion_exit + transform.second_map_entry = fusion_entry + fusion_entry = None + print(f'fusion on {node} ->', end=' ') + try: + transform.apply(state, sdfg) + print(f'success') + #sdfg.view() + except: + print(f'failed') + failed.append(node) + + + + """ + v1, m1, v2, m2 = set(), set(), set(), set() + for node in in_nodes: + self.traverse_upwards(state, node, map_label, v1, m1) + self.traverse_downwards(state, node, map_label, v2, m2) + """ + #print(f'm1 {m1}') + #print(f'm2 {m2}') + """ + for entry, exit in m2: + entry.map.label = entry.map.label + '_2' + exit.map.label = exit.map.label + '_2' + + + transform = dace.transformation.dataflow.map_fusion.MapFusion() + m2 = list(m2) + transform.first_map_exit = m2[0][1] + transform.second_map_entry = m2[1][0] + transform.apply(state, sdfg) + + transform = dace.transformation.dataflow.map_fusion.MapFusion() + break_i = False + for entry, exit in m2: + out_edges = state.out_edges(exit) + out_nodes = [edge.dst for edge in out_edges] + for entry2, exit2 in m2: + in_edges = state.in_edges(entry2) + in_nodes = [edge.src for edge in in_edges] + for node in out_nodes: + if node in in_nodes: + transform.first_map_exit = exit + transform.second_map_entry = entry2 + #transform.array = node + #if transform.can_be_applied(state,0, sdfg): + try: + transform.apply(state, sdfg) + except: + print(f'failed on {node}') + + m2.remove((entry, exit)) + m2.remove((entry2, exit2)) + m2.add((entry, exit2)) + + """ + + + def apply(self, sdfg, target): + if not target in self.find_splitable_scopes(sdfg): + return + assert isinstance(target, dace.sdfg.nodes.Tasklet) + + state = find_state(sdfg, target) + + current_node = target + innermost_scope = None + while current_node is not None: + # Get the scope parent of the current node + scope_parent = state.scope_dict()[current_node] + + # If there is no scope parent, break the loop + if scope_parent is None: + break + + # Update the innermost scope and the current node for the next iteration + innermost_scope = scope_parent + current_node = scope_parent + + entry = innermost_scope + exit = state.exit_node(entry) + + input_nodes = [edge.src for edge in state.in_edges(target)] + tasklet_edges = state.in_edges(target) + input_edges = [edge for node in input_nodes for edge in state.in_edges(node)] + print("input nodes", input_nodes) + print("tasklet edges", tasklet_edges) + print("input edges", input_edges) + + new_map = self.copy_map_properties(entry.map) + new_map_entry = dace.nodes.MapEntry(new_map) + new_map_exit = dace.nodes.MapExit(new_map) + + output_edges = [] + for node in input_nodes: + output_edges.extend(state.out_edges(node)) + + inbetween_edges = list(set(output_edges) | set(tasklet_edges)) + + + # redirect edges that have nothin to do with the split + tmp = [] + for edge in state.edges(): + if edge.src == entry: + if (not self.passes_through(state, edge, input_nodes)): + tmp.append((edge.data.data, edge)) + + special_edges = [] + for edge in state.edges(): + if edge.dst == entry: + for data, snd_edge in tmp: + if edge.data.data == data: + tmp.remove((data, snd_edge)) + special_edges.append((edge, snd_edge)) + + print(f'inbetween edges {inbetween_edges}') + print(f'special edges {special_edges}') + + # redirect edges + for edge in input_edges: + dace.transformation.helpers.reconnect_edge_through_map(state, edge, new_map_exit, False) + for edge in inbetween_edges: + dace.transformation.helpers.reconnect_edge_through_map(state, edge, new_map_entry, True) + for edge, snd_edge in special_edges: + new_edge1 = sdfg.add_edge(edge.src, new_map_entry, edge) + sdfg.remove_edge(edge) + new_edge2 = sdfg.add_edge(new_map_entry, snd_edge.dst, snd_edge) + sdfg.remove_edge(snd_edge) + #new_edge = dace.transformation.helpers.redirect_edge(state, edge, new_src = edge.src, new_dst = snd_edge.dst) + #dace.transformation.helpers.reconnect_edge_through_map(state, new_edge, new_map_entry, True) + + def find_scope_map(self, state, target): + current_node = target + innermost_scope = None + while current_node is not None: + # Get the scope parent of the current node + scope_parent = state.scope_dict()[current_node] + + # If there is no scope parent, break the loop + if scope_parent is None: + break + + # Update the innermost scope and the current node for the next iteration + innermost_scope = scope_parent + current_node = scope_parent + if innermost_scope: + return innermost_scope, state.exit_node(innermost_scope) + return None, None + + def apply2(self, sdfg, target): + if not target in self.find_splitable_scopes(sdfg): + return + state = find_state(sdfg, target) + entry, exit = self.find_scope_map(state, target) + assert entry and exit + + subgraph = state.scope_subgraph(entry, include_entry=False, include_exit=False) + modified_arrays = set() + outer_map = entry + mapsize = outer_map.range.size() + + components = [(target, target)] + for _,cout in components: + sources = subgraph.source_nodes() + sinks = subgraph.sink_nodes() + + external_edges_entry = list(state.out_edges(entry)) + external_edges_exit = list(state.in_edges(exit)) + + edge_to_outer = {} + for edge in external_edges_entry: + path = state.memlet_path(edge) + eindex = path.index(edge) + edge_to_outer[edge] = path[eindex-1] + for edge in external_edges_exit: + path = state.memlet_path(edge) + eindex = path.index(edge) + edge_to_outer[edge] = path[eindex+1] + + arrays = MapFission._border_arrays(sdfg, state, subgraph) + scalars = {} + for _,cout in components: + for e in subgraph.out_edges(cout): + if isinstance(e.dst, dace.nodes.CodeNode): + if not e.data.data in scalars: + scalars[e.data.data] = [e] + else: + scalars[e.data.data].append(e) + + for scalar, edges in scalars.items(): + desc = sdfg.arrays[scalar] + del sdfg.arrays[scalar] + name, newdesc = sdfg.add_transient(scalar, + mapsize, + desc.dtype, + desc.storage, + lifetime=desc.lifetime, + debuginfo=desc.debuginfo, + allow_conflicts=desc.allow_conflicts, + find_new_name=True) + for edge in edges: + anode = state.add_access(name) + sbs = subsets.Range.from_string(','.join(outer_map.params)) + # Offset memlet by map range begin (to fit the transient) + sbs.offset([r[0] for r in outer_map.range], True) + state.add_edge(edge.src, edge.src_conn, anode, None, + mm.Memlet.simple(name, sbs, num_accesses=outer_map.range.num_elements())) + state.add_edge(anode, None, edge.dst, edge.dst_conn, + mm.Memlet.simple(name, sbs, num_accesses=outer_map.range.num_elements())) + state.remove_edge(edge) + + new_map_entries = [] + for component_in, component_out in components: + me, mx = state.add_map(outer_map.label + '_fission', [(p, '0:1') for p in outer_map.params], + outer_map.schedule, + unroll=outer_map.unroll, + debuginfo=outer_map.debuginfo) + + for conn in entry.in_connectors: + if not conn.startswith('IN_'): + me.add_in_connector(conn) + + me.map.range = dcpy(outer_map.range) + new_map_entries.append(me) + + conn_idx = 0 + for e in state.in_edges(component_in): + if e.data.data: + in_conn = f"IN_{conn_idx}" + out_conn = f"OUT_{conn_idx}" + conn_idx += 1 + me.add_in_connector(in_conn) + me.add_out_connector(out_conn) + else: + in_conn = None + out_conn = None + state.add_edge(me, out_conn, e.dst, e.dst_conn, dcpy(e.data)) + # Reconnect inner edges at source directly to external nodes + if e in external_edges_entry: + state.add_edge(edge_to_outer[e].src, edge_to_outer[e].src_conn, me, in_conn, + dcpy(edge_to_outer[e].data)) + else: + state.add_edge(e.src, e.src_conn, me, in_conn, dcpy(e.data)) + state.remove_edge(e) + # Empty memlet edge in nested SDFGs + if state.in_degree(component_in) == 0: + state.add_edge(me, None, component_in, None, mm.Memlet()) + + conn_idx = 0 + for e in state.out_edges(component_out): + if e.data.data: + in_conn = f"IN_{conn_idx}" + out_conn = f"OUT_{conn_idx}" + conn_idx += 1 + mx.add_in_connector(in_conn) + mx.add_out_connector(out_conn) + else: + in_conn = None + out_conn = None + state.add_edge(e.src, e.src_conn, mx, in_conn, dcpy(e.data)) + # Reconnect inner edges at sink directly to external nodes + if e in external_edges_exit: + state.add_edge(mx, out_conn, edge_to_outer[e].dst, edge_to_outer[e].dst_conn, + dcpy(edge_to_outer[e].data)) + else: + state.add_edge(mx, out_conn, e.dst, e.dst_conn, dcpy(e.data)) + state.remove_edge(e) + # Empty memlet edge in nested SDFGs + if state.out_degree(component_out) == 0: + state.add_edge(component_out, None, mx, None, mm.Memlet()) + + for node in sources: + if isinstance(node, dace.nodes.AccessNode): + for edge in state.in_edges(node): + outer_edge = edge_to_outer[edge] + memlet = dcpy(edge.data) + memlet.subset = subsets.Range(outer_map.range.ranges + memlet.subset.ranges) + state.add_edge(outer_edge.src, outer_edge.src_conn, edge.dst, edge.dst_conn, memlet) + for node in sinks: + if isinstance(node, dace.nodes.AccessNode): + for edge in state.out_edges(node): + outer_edge = edge_to_outer[edge] + state.add_edge(edge.src, edge.src_conn, outer_edge.dst, outer_edge.dst_conn, + dcpy(outer_edge.data)) + # Augment arrays by prepending map dimensions + for array in arrays: + if array in modified_arrays: + continue + desc = sdfg.arrays[array] + if isinstance(desc, dt.Scalar): # Scalar needs to be augmented to an array + desc = dt.Array(desc.dtype, desc.shape, desc.transient, desc.allow_conflicts, desc.storage, + desc.location, desc.strides, desc.offset, False, desc.lifetime, 0, desc.debuginfo, + desc.total_size, desc.start_offset) + sdfg.arrays[array] = desc + for sz in reversed(mapsize): + desc.strides = [desc.total_size] + list(desc.strides) + desc.total_size = desc.total_size * sz + + desc.shape = mapsize + list(desc.shape) + desc.offset = [0] * len(mapsize) + list(desc.offset) + modified_arrays.add(array) + + # Fill scope connectors so that memlets can be tracked below + state.fill_scope_connectors() + + for node in subgraph.nodes(): + if isinstance(node, dace.nodes.AccessNode) and node.data in arrays: + for edge in state.all_edges(node): + for e in state.memlet_tree(edge): + # Prepend map dimensions to memlet + # NOTE: Do this only for the subset corresponding to `node.data`. If the edge is copying + # to/from another AccessNode, the other data may not need extra dimensions. For example, see + # `test.transformations.mapfission_test.MapFissionTest.test_array_copy_outside_scope`. + if e.data.data == node.data: + if e.data.subset: + e.data.subset = subsets.Range([(pystr_to_symbolic(d) - r[0], + pystr_to_symbolic(d) - r[0], 1) + for d, r in zip(outer_map.params, outer_map.range)] + + e.data.subset.ranges) + else: + if e.data.other_subset: + e.data.other_subset = subsets.Range( + [(pystr_to_symbolic(d) - r[0], pystr_to_symbolic(d) - r[0], 1) + for d, r in zip(outer_map.params, outer_map.range)] + + e.data.other_subset.ranges) + + state.remove_nodes_from([entry, exit]) + propagate_memlets_state(sdfg,state) + + def passes_through(self, state, cur_edge, nodes): + if cur_edge.dst in nodes: + return True + else: + for edge in state.edges(): + if edge.src == cur_edge.dst: + if self.passes_through(state, edge, nodes): + return True + return False + + def apply3(self,sdfg,target): + if not target in self.find_splitable_scopes(sdfg): + return + state = find_state(sdfg, target) + + entry, exit = self.find_scope_map(state, target) + new_map = self.copy_map_properties(entry.map) + new_map_entry = dace.nodes.MapEntry(new_map) + new_map_exit = dace.nodes.MapExit(new_map) + + subgraph = state.scope_subgraph(entry, include_entry=True, include_exit=True) + map_in_node = [(edge.src,edge) for node in subgraph.source_nodes() for edge in state.in_edges(node)] + input_nodes = [edge.src for edge in state.in_edges(target)] + scope1_exit_edge = [edge for node in input_nodes for edge in state.in_edges(node)] + #scope1_entry_edge = [edge for node in subgraph.source_nodes() for edge in state.in_edges(node)] + scope2_entry_edge = [edge for node in input_nodes for edge in state.out_edges(node)] + #scope2_exit_edge = [edge for node in subgraph.sink_nodes() for edge in state.out_edges(node)] + + for edge in scope1_exit_edge: + dace.transformation.helpers.reconnect_edge_through_map(state, edge, new_map_exit, False) + for edge in scope2_entry_edge: + dace.transformation.helpers.reconnect_edge_through_map(state, edge, new_map_entry, True) + subgraph1 = state.scope_subgraph(entry, include_entry=True, include_exit=True) + subgraph2 = state.scope_subgraph(new_map_entry, include_entry=False, include_exit=True) + print(f'subgraph1 {subgraph1.nodes()}') + + print(subgraph1.edges()) + for edge in subgraph1.edges(): + + if not self.passes_through(subgraph1, edge, [new_map_exit]): + print(f'edge {edge}, {edge.data.data}') + if isinstance(edge.src, dace.nodes.MapEntry): + red_edge = None + for node,edge2 in map_in_node: + print(f'\t{edge2}, {edge2.data.data}') + if edge2.data.data == edge.data.data: + red_edge = edge2 + break + if red_edge: + dace.transformation.helpers.redirect_edge(state, red_edge, new_dst=new_map_entry, new_dst_conn=f'IN_{edge.data.data}') + dace.transformation.helpers.redirect_edge(state, edge, new_src = new_map_entry, new_src_conn=f'OUT_{edge.data.data}') + else: + if isinstance(edge.src,dace.nodes.AccessNode): + pass + + + propagate_memlets_state(sdfg,state) + +class UnrollScope(): + max_unroll_factor = 10 + def find_unrollable_maps(self, sdfg): + """ + Find maps that can be unrolled + Only last level maps with constant size can be unrolled + """ + unrollable_maps = [] + for state in sdfg.nodes(): + for edge in state.edges(): + if isinstance(edge.src, dace.nodes.MapEntry) and isinstance(edge.dst, dace.nodes.Tasklet): + dim = edge.src.map.range.size()[0] + if isinstance(dim, sympy.core.numbers.Integer) and int(dim) > 1: + divisors = [i for i in range(2, max(self.max_unroll_factor, dim)) if dim % i == 0] + unrollable_maps.extend([(edge.src, div) for div in divisors]) + return unrollable_maps + + def apply(self, sdfg, map_entry, unroll_factor): + if not (map_entry, unroll_factor) in self.find_unrollable_maps(sdfg): + return + assert isinstance(map_entry, dace.sdfg.nodes.MapEntry) + #dace.transformation.dataflow.hbm_transform.HbmTransform.unroll_map(sdfg, state, map_entry, unroll_factor) + #map_entry.map.unroll = True + para = map_entry.map.params[0] + 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 edge in state.edges(): + if isinstance(edge.dst, dace.nodes.MapEntry) and edge.dst.map.params[0] == para: + new_map = edge.dst.map + new_map.unroll = True + new_map.schedule = dace.ScheduleType.Unrolled + break + +class addArrayCopies(): + def find_copyable_arrays(self, sdfg): + copyable_arrays = set() + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.AccessNode): + copyable_arrays.add(node.data) + + return sorted(list(copyable_arrays)) + + def apply(self, sdfg, arr_name): + if not arr_name in self.find_copyable_arrays(sdfg): + return + + state = state_0(sdfg) + data = sdfg.arrays[arr_name] + input_nodes = state.source_nodes() + output_nodes = state.sink_nodes() + adj_state = state + 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 + + new_name = '' + i = 0 + while True: + new_name = f'{arr_name}_copy_{i}' + if not new_name in sdfg.arrays: + break + i += 1 + """ + transient = True + if arr_name in [node.data for node in output_nodes]: + transient = False + o_data = sdfg.arrays[arr_name] + o_data.transient = True + """ + sdfg.add_array(name=new_name, shape=data.shape, dtype=data.dtype, storage=data.storage, transient=True, lifetime=data.lifetime) + + tasklet = adj_state.add_tasklet("copy_temp", {"in_1"}, {"out"}, "out = in_1") + + + 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 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) + +class removeArrayCopies(): + + def find_deleteable_arrays(self, sdfg): + 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 + if in_node.subset == out_node.subset: + deleteable_arrays.add(out_node.data) + + return sorted(list(deleteable_arrays)) + + def apply(self, sdfg, arr_name): + if not arr_name in self.find_deleteable_arrays(sdfg): + return + 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) + +class addArrayDims(): + """ + complex, keep for later + """ + 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_createable_dim(self, sdfg): + 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) + + return result + + def apply(self, sdfg, op, mode): + if not (op, mode) in self.find_createable_dim(sdfg): + return + + + # 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) + +class removeArrayDims(): + + def find_deleteable_dims(self, sdfg): + 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)) + 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 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): + if not (name, dim, mode) in self.find_deleteable_dims(sdfg): + return + + 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 + +class reuseArray(): + """ + change semantics and fzse two arrays completley + """ + def find_reusable_arrays(self, sdfg): + 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) + + 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 isinstance(data1, dace.data.Array): + if data1.shape == data2.shape and data1.dtype == data2.dtype: + 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)) + + + + #outputs = sdfg.sink_nodes() + 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] + reusable_arrays = [] + + for arr1, arr2 in same_data: + + works = True + if not arr2 in input_names: + for node in inputs: + check = self.traverser_check(state, arr1, arr2, node, False) + if not check: + works = False + break + else: + works = False + + if not arr1 in input_names: + if not works: + works = True + for node in inputs: + check = self.traverser_check(state, arr2, arr1, node, False) + if not check: + works = False + break + + if works: + 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 + + return sorted((reusable_arrays)) + + 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): + if not (array1, array2) in self.find_reusable_arrays(sdfg): + return + + + for state in sdfg.nodes(): + state.replace(array1, array2) + + data1 = sdfg.arrays[array1] + if not data1.transient: + data2 = sdfg.arrays[array2] + data2.transient = False + + #sdfg.remove_data(array1) + # keep array1 for now, information not lost + + """ + data1 = sdfg.arrays[array1] + print(f'old {data1} and {sdfg.arrays[array2]}') + sdfg.arrays[array2] = data1 + + does not work: Invalid SDFG: Duplicate data descriptor object detected: "". Please copy objects rather than using multiple references to the same one + """ + +class reuseArrayLocations(): + + 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_reusable_dimensions(self, sdfg): + 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 + read_ops = self.get_read_ops(state, node) + write_ops = self.get_write_ops(state, node) + shape = data_node.shape + tmp = set() + + 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} + + 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])) + + return sorted(list(reusable_dims), key=lambda x: x[0].data) + + + def apply(self, sdfg, node, dim): + if not (node,dim) in self.find_reusable_dimensions(sdfg): + return + + data_node = sdfg.arrays[node.data] + shape = data_node.shape + strides = list(data_node.strides) + print(f'old strides: {data_node.strides}') + new_dim = [] + for i,dims in enumerate(shape): + if dims == dim: + strides[i] = 0 + data_node.strides = tuple(strides) + print(f'new strides: {data_node.strides}') + +class StackToHeap(): + def apply(self, data): + data.storage = dace.StorageType.CPU_Heap + +class HeapToStack(): + def apply(self, data): + data.storage = dace.StorageType.Register + +class changeMemoryLayout(): + def find_changeable_arrays(self, sdfg): + 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) + + return (list(changeable_arrays)) + + def apply(self, sdfg, node, dim_order): + if not node in self.find_changeable_arrays(sdfg): + return + 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 + + print(node.data) + print(shape) + print(dim_order) + + 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 + print(f'old strides: {data.strides}, new strides: {new_strides}') + data.strides = tuple(new_strides) + return + + """ + state = state_0(sdfg) + #in_edges = state.in_edges(node) + #out_edges = state.out_edges(node) + orig_shape = data.shape + assert len(orig_shape) == len(dim_order) + new_shape = [orig_shape[dim_order.index(i)] for i in range(len(orig_shape))] + print(f'orig shape: {orig_shape}, new shape: {new_shape}') + #edge = in_edges[0] + + entry = state.entry_node(node) + if entry: + dim_to_idx = {} + while True: + idx = entry.map.params[0] + dim = entry.map.range.size()[0] + dim_to_idx[dim] = idx + entry = state.entry_node(entry) + if not entry: + break + + else: + up_map = {} + down_map = {} + self.get_loop_indices(state, node.data, node, up_map, downwards=False) + self.get_loop_indices(state, node.data, node, down_map, downwards=True) + print(up_map) + print(down_map) + + self.replace_index(state, node.data, node, new_shape, up_map, downwards=False) + self.replace_index(state, node.data, node, new_shape, down_map, downwards=True) + + + def get_loop_indices(self, state, data, node, dim_map, visited = None, downwards=False): + if visited is None: + visited = [] + + if downwards and isinstance(node, dace.nodes.MapEntry) or not downwards and isinstance(node, dace.nodes.MapExit): + idx = node.map.params[0] + dim = node.map.range.size()[0] + dim_map[dim] = idx + + visited.append(node) + + adj_edges = state.out_edges(node) if downwards else state.in_edges(node) + for edge in adj_edges: + if not edge.data.data == data: + continue + target = edge.dst if downwards else edge.src + if not target in visited: + self.get_loop_indices(state, data, target, dim_map, visited, downwards) + + return + + def replace_index(self, state, data, node, dim_order, dim_map, visited = None, downwards=False): + if visited is None: + visited = [] + + adj_edges = state.out_edges(node) if downwards else state.in_edges(node) + for edge in adj_edges: + if not edge.data.data == data: + continue + else: + print(edge.data.data) + print(edge.data.subset) + new_subset = edge.data.subset + for i, subs in enumerate(edge.data.subset): + if isinstance(subs[1], dace.symbolic.symbol): + new_dim = dim_order[i] + new_idx = dim_map[new_dim] + print(f'{str(subs[1])} => {new_dim},{new_idx}') + if subs[1] == new_idx: + continue + for j,tmp in enumerate(edge.data.subset): + if list(tmp[1].free_symbols)[0] == new_dim or str(tmp[1]) == new_idx: + if (new_subset[j] == subs): + break + a = tmp + new_subset[j] = subs + new_subset[i] = a + break + else: + dim = list(subs[1].free_symbols)[0] + new_dim = dim_order[i] + if dim == new_dim: + continue + new_idx = dim_map[new_dim] + print(f'{str(dim)} => {new_dim},{new_idx}') + for j,tmp in enumerate(edge.data.subset): + if list(tmp[1].free_symbols)[0] == new_dim or str(tmp[1]) == new_idx: + if (new_subset[j] == subs): + break + a = tmp + new_subset[j] = subs + new_subset[i] = a + break + + edge.data.subset = new_subset + print(edge.data.subset) + print() + + target = edge.dst if downwards else edge.src + if not target in visited: + visited.append(target) + self.replace_index(state, data, target, dim_order, dim_map, visited, downwards) + + return + """ + +def get_source_node(state, node, data, visited = []): + if isinstance(node, dace.nodes.AccessNode): + if node.data == data: + return node + visited.append(node) + + for edge in state.in_edges(node): + if not edge.src in visited: + get_source_node(state, edge.src, data) + + return None + +def get_sink_node(state, node, data, visited = []): + if isinstance(node, dace.nodes.AccessNode): + if node.data == data: + return node + visited.append(node) + + for edge in state.out_edges(node): + if not edge.dst in visited: + get_sink_node(state, edge.dst, data) + + return None + +def find_state(sdfg, node): + for sdfg_state in sdfg.nodes(): + if node in sdfg_state.nodes(): + return sdfg_state + +def find_state_by_name(sdfg, name): + for state in sdfg.nodes(): + for node in state.nodes(): + if isinstance(node, dace.nodes.AccessNode): + if node.data == name: + return state + +def start_nodes(state): + incoming_edges = {node: 0 for node in state.nodes()} + for edge in state.edges(): + incoming_edges[edge.dst] += 1 + starting_nodes = [node for node, count in incoming_edges.items() if count == 0] + return starting_nodes + +def traverse_sdfg(state): + pass + +def order_sdfg(state): + incoming_edges = {node: 0 for node in state.nodes()} + for edge in state.edges(): + incoming_edges[edge.dst] += 1 + starting_nodes = [node for node, count in incoming_edges.items() if count == 0] + + order = [] + visited = set() + + # Stack to manage nested structures + stack = [order] + + def traverse(node): + if node in visited: + return + visited.add(node) + + # Check if node is a MapEntry, if so, create a new sublist + if isinstance(node, dace.nodes.MapEntry): + current_list = [] + stack[-1].append(node) + stack[-1].append(current_list) + stack.append(current_list) + else: + stack[-1].append(node) + + for edge in state.edges(): + if edge.src == node: + traverse(edge.dst) + + # If node is a MapExit, pop the last list from the stack + if isinstance(node, dace.nodes.MapExit): + stack.pop() + + for node in starting_nodes: + traverse(node) + + return order + +def state_0(sdfg): + for state in sdfg.nodes(): + if state.label == 'state_0': + return state + +def optimize_sdfg(sdfg, optimization): + # Apply transformations + pass + +if __name__ == "__main__": + print("==== Program start ====") + + source = '' + sdfg_name = '' + if args.kernel == "gemm": + postfix = "" + if args.sym: + source = "test_kernel/test_gemm.sdfg" + #source = ".dacecache/affine_grid_2d/program.sdfg" + else: + source = "test_kernel/test_gemm_const.sdfg" + postfix = "_const" + sdfg_name = "gemm_" + args.opt + postfix + elif args.kernel == "softmax": + postfix = "" + if args.sym: + source = "test_kernel/test_softmax.sdfg" + else: + source = "test_kernel/test_softmax_const.sdfg" + postfix = "_const" + sdfg_name = "softmax_" + args.opt + postfix + else: + sys.exit("Invalid kernel") + + sdfg = dace.SDFG.from_file(source) + sdfg.name = sdfg_name + + if args.opt == "tile": + transformation = LoopTile() + locations = transformation.find_tileable_maps(sdfg) + if locations: + loc = locations[0] + print("tile location", loc) + transformation.apply(sdfg, loc[0], loc[1]) + print("applied loop tile") + + if args.opt == "swap": + transformation = SwapScope() + locations = transformation.find_swappable_maps(sdfg) + if locations: + loc = locations[0] + print("swap location", loc) + transformation.apply(sdfg, loc) + print("applied swap scope") + + if args.opt == "unroll": + transformation = UnrollScope() + locations = transformation.find_unrollable_maps(sdfg) + if locations: + loc = locations[0] + print("unroll location", loc) + transformation.apply(sdfg, loc[0], loc[1]) + print("applied unroll scope") + + if args.opt == "split": + transformation = SplitMapScopes() + locations = transformation.find_splitable_scopes(sdfg) + if locations: + loc = locations[0] + print("split location", loc) + transformation.apply4(sdfg, loc) + print("applied split scope") + + if args.opt == "reuseArray": + transformation = reuseArray() + locations = transformation.find_reusable_arrays(sdfg) + if locations: + print("reuse array locations", locations) + loc = locations[1] + print("reuse array location", loc) + transformation.apply(sdfg, loc[0], loc[1]) + print("applied reuse array") + + if args.opt == "addCopies": + transformation = addArrayCopies() + locations = transformation.find_copyable_arrays(sdfg) + if locations: + loc = "dst" + print("add copies location", loc) + transformation.apply(sdfg, loc) + print("applied add copies") + + if args.opt == "removeCopies": + transformation = addArrayCopies() + loc = "dst" + transformation.apply(sdfg, loc) + transformation = removeArrayCopies() + locations = transformation.find_deleteable_arrays(sdfg) + print(f'applicable locations {locations}') + if locations: + loc = loc + "_copy_0" + print("remove copies location", loc) + transformation.apply(sdfg, loc) + print("applied remove copies") + + if args.opt == "untile": + transformation = LoopTile() + locations = transformation.find_tiled_maps(sdfg) + if locations: + loc = locations[0] + transformation.apply(sdfg, loc) + transformation = LoopUntile() + locations = transformation.find_untileable_maps(sdfg) + if locations: + loc = locations[0] + print("untile location", loc) + transformation.apply(sdfg, loc) + print("applied untile scope") + + if args.opt == "reuseLoc": + transformation = reuseArrayLocations() + locations = transformation.find_reusable_dimensions(sdfg) + print(f'reusable dimensions {locations}') + if locations: + loc = locations[0] + print("reuse location", loc) + transformation.apply(sdfg, loc[0], loc[1]) + print("applied reuse location") + + if args.opt == "changeLayout": + transformation = changeMemoryLayout() + locations = transformation.find_changeable_arrays(sdfg) + print(f'changeable arrays {locations}') + if locations: + loc = locations[0] + print("change layout location", loc) + transformation.apply(sdfg, loc, [1,0]) + print("applied change layout") + + if args.opt == "addDimension": + transformation = addArrayDims() + locations = transformation.find_createable_dim(sdfg) + print(f'createable dimensions {locations}') + if locations: + loc = locations[0] + print("add dimension location", loc) + transformation.apply(sdfg, loc[0], loc[1]) + print("applied add dimension") + + if args.opt == "removeDimension": + transformation = addArrayDims() + locations = transformation.find_createable_dim(sdfg) + if locations: + loc = locations[0] + transformation.apply(sdfg, loc[0], loc[1]) + transformation = removeArrayDims() + locations = transformation.find_deleteable_dims(sdfg) + print(f'deleteable dimensions {locations}') + if locations: + loc = locations[0] + print("remove dimension location", loc) + print(f'{loc[0]} {loc[1]} {loc[2]}') + transformation.apply(sdfg, loc[0], loc[1], loc[2]) + print("applied remove dimension") + + if args.opt == "swapScopes": + transformation = SwapMapScope() + locations = transformation.find_swappable_maps(sdfg) + if locations: + loc = locations[0] + print("swap operation location", loc) + transformation.apply(sdfg, loc[0], loc[1]) + print("applied swap operation") + + if args.opt == "swapOp": + transformation = SwapOpertions() + locations = transformation.find_swappable_ops(sdfg) + print(f'swappable operations {locations}') + if locations: + loc = locations[0] + 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a/dace/transformation/semantic_preserving/optimize_sdfg.py +++ b/dace/transformation/semantic_preserving/optimize_sdfg.py @@ -1,43 +1,105 @@ +""" +***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. + +Transformation Classes: + SwapLoopMap + SwapMapScope + SwapOperations + TileMapScope + JoinMapScopes + SplitMapScopes + UnrollMapScope + addArrayCopies + removeArrayCopies + addArrayDims + removeArrayDims + reuseArray + reuseArrayLocation + StackToHeap + HeapToStack + changeMemoryLayout + +Every class contains a find and apply method. The find method searches for possible transformations in the given SDFG and the apply method applies the transformation to the SDFG. +""" + +###* Imports *### + import sys import copy -from dace.transformation.dataflow.buffer_tiling import BufferTiling -from dace.transformation.dataflow.map_unroll import MapUnroll -from dace.transformation.dataflow.tiling import MapTiling -from dace.transformation.dataflow.map_fission import MapFission -from dace import registry, sdfg as sd, memlet as mm, subsets, data as dt -from dace.symbolic import pystr_to_symbolic -from copy import deepcopy as dcpy -from dace.sdfg.propagation import propagate_memlets_state -from dace.transformation import transformation +#from dace.transformation import transformation +#from dace.transformation.helpers import redirect_edge +#import pdb +import traceback +import random import dace import numpy as np -import argparse import math import sympy -parser = argparse.ArgumentParser(description='Test kernels.') -parser.add_argument('--validate', action='store_true', help='see if the generated code is correct') -parser.add_argument('--print', action='store_true', help='show printed code') -parser.add_argument('--opt', type=str, help='optimization to apply', default='') -parser.add_argument('--kernel', type=str, help='kernel to apply the optimization to', default='gemm') -parser.add_argument('--sym', action='store_true', help='Symbolic or Constant Loop Sizes', default=True) -args = parser.parse_args() +import argparse +###* Transformation Classes *### +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 -class SwapScope(): + @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. - def find_swappable_maps(self, sdfg): - 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.append((edges.src, edges.dst)) - return swappable_maps + 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 @@ -53,34 +115,70 @@ def find_exit_maps(self, sdfg, map1, map2): exit2 = node return exit1, exit2 - def apply(self, sdfg, maps): - map_entry1, map_entry2 = maps + 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 map to swap. + map_entry2 (dace.MapEntry): The second map to swap. + """ assert isinstance(map_entry1, dace.sdfg.nodes.MapEntry) assert isinstance(map_entry2, dace.sdfg.nodes.MapEntry) - if not (map_entry1, map_entry2) in self.find_swappable_maps(sdfg): + 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 - for state in sdfg.nodes(): - for edge in state.edges(): - if edge.dst == map_entry1: - dace.transformation.helpers.redirect_edge(state, edge, new_dst = map_entry2) - elif edge.src == map_entry2: - dace.transformation.helpers.redirect_edge(state, edge, new_src = map_entry1) - elif edge.src == map_entry1 and edge.dst == map_entry2: - dace.transformation.helpers.redirect_edge(state, edge, new_src = map_entry2, new_dst = map_entry1) - elif edge.dst == exit2: - dace.transformation.helpers.redirect_edge(state, edge, new_dst = exit1) - elif edge.src == exit1: - dace.transformation.helpers.redirect_edge(state, edge, new_src = exit2) - elif edge.src == exit2 and edge.dst == exit1: - dace.transformation.helpers.redirect_edge(state, edge, new_src = exit1, new_dst = exit2) + 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) class SwapMapScope(): - - def find_swappable_maps(self, sdfg): + """ + 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)): @@ -88,19 +186,19 @@ def find_swappable_maps(self, sdfg): next = False for j in range(i+1, len(map_scopes)): entry2, exit2, state2 = map_scopes[j] - if not state.entry_node(entry1) == state.entry_node(entry2): + 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 state.out_edges(exit1)} - input_2 = {edge.data.data for edge in state.in_edges(entry2)} + 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): @@ -114,15 +212,50 @@ def find_map_scopes(self, sdfg): return map_scopes def apply(self, sdfg, entry1, entry2): - if not (entry1, exit2) in self.find_swappable_ops(sdfg): + """ + 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(sdfg).out_edges(exit2)][0] - state.add_nedge(dst_node, entry1, mm.Memlet()) + state.add_nedge(dst_node, entry1, dace.memlet.Memlet()) class SwapOpertions(): - def find_swappable_ops(self, sdfg): + """ + 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 are consecutive + - The output of the first tasklet is not used in the input of 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 i in range(len(tasklets)): @@ -142,6 +275,7 @@ def find_swappable_ops(self, sdfg): if input_2 & output_1: continue result.append((tasklet1, tasklet2)) + self.checked = True return result def find_tasklets(self, sdfg): @@ -153,731 +287,949 @@ def find_tasklets(self, sdfg): return result def apply(self, sdfg, op1, op2): - if not (op1, op2) in self.find_swappable_ops(sdfg): + """ + 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. + """ + if not self.checked and not (op1, op2) in self.find(sdfg): return + self.checked = False state = find_state(sdfg, op2) out_node = [edge.dst for edge in state.out_edges(op2)][0] - state.add_nedge(out_node, op1, mm.Memlet()) + state.add_nedge(out_node, op1, dace.memlet.Memlet()) + +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): + self.__name = 'TileMapScope' + self.checked = False + # if set, the tiled map is put as innermost map + self.put_last = True + # if set, only the innermost map is tiled + self.only_innermost = False + # maximum tile size + self.max_tile = 1024 + # for symbolic loop bounds + self.tile_sizes = [64] #None + + @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 -class LoopTile(): + Args: + sdfg (dace.SDFG): The SDFG to search for tileable map scopes. - def find_tileable_maps(self, sdfg): + 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 map = node.map dim = map.range.size()[0] if isinstance(dim, sympy.core.numbers.Integer) and int(dim) > 1: dim = int(dim) - divisors = [i for i in range(2, math.floor(math.sqrt(dim))) if dim % i == 0] - tileable_maps.extend([(node, tile_size) for tile_size in divisors]) - if isinstance(dim, sympy.core.symbol.Symbol): - tileable_maps.extend([(node, tile_size) for tile_size in [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]]) + 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): - if not (map_entry, tile_size) in self.find_tileable_maps(sdfg): + """ + 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 - tile_dict = {map_entry.map.params[0]: tile_size} + 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) + + 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 + opt = SwapLoopMap() + opt.apply(sdfg, prop_map, swap_map) 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 find_joinable_mapscope(self, sdfg): - pass - def apply(self): - pass + def __init__(self): + self.__name = 'JoinMapScopes' + self.checked = False + + @property + def name(self): + return self.__name -class SplitMapScopes(): + 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. - def traverse_scope(self, state, entry): - map = entry.map - ops = [] - def traverse(node, sec=False): - if node in ops: - return - if isinstance(node, dace.nodes.MapExit) and node.map == map: - return - if isinstance(node, dace.nodes.Tasklet): - if sec: - ops.append(node) - else: - sec = True - for edge in state.edges(): - if edge.src == node: - traverse(edge.dst, sec) - traverse(entry) + 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 - return ops - - def exit_node(self, state, entry): - def traverse(node, exit=False): - if isinstance(node, dace.nodes.MapExit) and node.map == entry.map: - exit = True - if isinstance(node, dace.nodes.AccessNode) and exit: - return node - for edge in state.edges(): - if edge.src == node: - return traverse(edge.dst, exit) - - return traverse(entry) + Args: + sdfg (dace.SDFG): The SDFG to search for joinable map scopes. - - def find_splitable_scopes(self, sdfg): - splitable_scopes = [] - entry_set = set() - for state in sdfg.nodes(): - for edge in state.edges(): - if isinstance(edge.src, dace.nodes.MapEntry) and isinstance(edge.dst, dace.nodes.Tasklet): - map = edge.src - if not map in entry_set: - entry_set.add(map) - scope_ops = self.traverse_scope(state, map) - if scope_ops: - splitable_scopes.extend(scope_ops) - #splitable_scopes.append(self.exit_node(state, map)) - return splitable_scopes - - #unused - def find_exit_maps(self, sdfg, map): - map = map.map - exit = None - for state in sdfg.nodes(): - for node in state.nodes(): - if isinstance(node, dace.nodes.MapExit): - if node.map == map: - exit = node - return exit - - def find_input_nodes(self, sdfg, node): - input_nodes = [] - for state in sdfg.nodes(): - for edge in state.edges(): - if edge.dst == node: - input_nodes.append(edge.src) - return input_nodes - - def find_ingoing_edges(self, sdfg, node): - ingoing_edges = [] + 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(): - for edge in state.edges(): - if edge.dst == node: - ingoing_edges.append(edge) - return ingoing_edges + 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 find_outgoing_edges(self, sdfg, node): - outgoing_edges = [] + 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: + dst_shape = in_edge.data.dst_subset.size() + if not dst_shape == entry1.map.range.size(): + reject = True + if not reject: + return True + return False + + # not used currently + def find_2(self, sdfg): + transform = dace.transformation.dataflow.map_fusion.MapFusion() + candidates = [] for state in sdfg.nodes(): - for edge in state.edges(): - if edge.src == node: - outgoing_edges.append(edge) - return outgoing_edges - - def find_edges(state, entry, target): - edges = [] - def traverse(node, entry, target, passed=False): - if node == target: - passed = True - for edge in state.edges(): - if edge.src == node: - if edge.dst == target: - edges.append(edge) - return - traverse(edge.dst, entry, target) - for edge in state.edges(): - if edge.src == entry: - traverse(edge.dst, entry, target) - return edges + 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. - def copy_map_properties(self, map): - new_map = dace.nodes.Map(map.label, map.params, map.range) - new_map.unroll = map.unroll - new_map.schedule = map.schedule - return new_map - - def traverse_upwards(self, state, start_node, stop_map, visited = None, maps = None): - if visited is None: - visited = set() - if maps is None: - maps = set() + 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) - # Stop condition if the start_node is a MapEntry with the specified name - if isinstance(start_node, dace.nodes.MapExit) and not start_node.map.label == stop_map: - return + 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 + +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. + """ - # Add the current node to the visited set - visited.add(id(start_node)) - if isinstance(start_node, dace.nodes.MapEntry): - exit_map = state.exit_node(start_node) - maps.add((start_node, exit_map)) + def __init__(self): + self.__name = 'SplitMapScopes' + self.checked = False - # Traverse upwards from the current node - for edge in state.in_edges(start_node): - src_node = edge.src - if id(src_node) not in visited: - self.traverse_upwards(state, src_node, stop_map, visited, maps) - + @property + def name(self): + return self.__name - def traverse_downwards(self, state, start_node, stop_map,visited = None, maps = None, start=True): + 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() - if maps is None: - maps = set() - print(f'start node {start_node}') - # Stop condition if the start_node is a MapEntry with the specified name - if isinstance(start_node, dace.nodes.MapEntry) and not start_node.map.label == stop_map: - return - # Add the current node to the visited set - visited.add(id(start_node)) + 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) - # fuse exit and entry nodes if they are connected by a single access node - if isinstance(start_node, dace.nodes.AccessNode) and not start: - in_nodes = [edge.src for edge in state.in_edges(start_node)] - out_nodes = [edge.dst for edge in state.out_edges(start_node)] - in_map = None - out_map = None - for node in in_nodes: - if isinstance(node, dace.nodes.MapExit): - in_map = node - break - for node in out_nodes: + 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): - out_map = node + 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 - if in_map and out_map: - self.checkMap(state, in_map, out_map) - transform = dace.transformation.dataflow.map_fusion.MapFusion() - transform.first_map_exit = in_map - transform.second_map_entry = out_map - if transform.can_be_applied(state, 0, sdfg): - print(f'fusion on {start_node}') - transform.apply(state, sdfg) - sdfg.view() - self.traverse_downwards(state, in_map, stop_map, visited, maps, True) - return - - # Traverse downwars from the current node - out_edges = state.out_edges(start_node) - for edge in out_edges: - dst_node = edge.dst - #if id(dst_node) not in visited: - self.traverse_downwards(state, dst_node, stop_map, visited, maps, False) - + 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. - def find_fusion_map(self, state, node, end_nodes, target_nodes, failed, visited = None): - if node in end_nodes: - return None - - if visited is None: - visited = set() + 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. - visited.add(id(node)) + Returns: + list: A list of tuples, where each tuple contains a the state and the tasklet to split. + """ - if isinstance(node, dace.nodes.AccessNode) and not node in target_nodes and not node in failed: - in_nodes = [edge.src for edge in state.in_edges(node)] - out_nodes = [edge.dst for edge in state.out_edges(node)] - in_map = None - out_map = None - for in_node in in_nodes: - if isinstance(in_node, dace.nodes.MapExit): - in_map = in_node - break - for out_node in out_nodes: - if isinstance(out_node, dace.nodes.MapEntry): - out_map = out_node - break - if in_map and out_map: - if self.checkMap(state, in_map, out_map): - return in_map, node, out_map - - for edge in state.out_edges(node): - if id(edge.dst) not in visited: - return self.find_fusion_map(state, edge.dst, end_nodes, target_nodes, visited) + 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)) - def checkMap(self, state, map1, map2): - assert map1.map.params == map2.map.params - assert map1.map.range == map2.map.range - if map1.label == map2.label: - return True - return False + 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. - def apply4(self, sdfg, target): - if not target in self.find_splitable_scopes(sdfg): + 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 - assert(isinstance(target, dace.sdfg.nodes.Tasklet)) - - state = find_state(sdfg, target) + self.checked = False - in_edge = state.in_edges(target) - in_nodes = [edge.src for edge in in_edge] + 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)] - current_node = target - innermost_map, exit_map = self.find_scope_map(state, target) + #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]}'}) - print(f'innermost scope {innermost_map}') - print(f'in nodes {in_nodes}') - - map_label = innermost_map.map.label + '_fission' - start_node = state.in_edges(innermost_map)[0].src - end_nodes = [edge.dst for edge in state.out_edges(exit_map)] - - transform = dace.transformation.dataflow.map_fission.MapFission() - transform.map_entry = innermost_map - assert transform.can_be_applied(state, 0, sdfg) - transform.apply(state, sdfg) - sdfg.view() + # 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 - fusion_entry, node, fusion_exit = None,None,None - transform = dace.transformation.dataflow.map_fusion.MapFusion() - failed = [] - while True: - fusion_exit, node, fusion_entry = self.find_fusion_map(state, start_node, end_nodes, in_nodes, failed) - if not fusion_entry: - break - transform.first_map_exit = fusion_exit - transform.second_map_entry = fusion_entry - fusion_entry = None - print(f'fusion on {node} ->', end=' ') + # 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: - transform.apply(state, sdfg) - print(f'success') - #sdfg.view() + if not edge1 in keep: + state.remove_edge(edge1) except: - print(f'failed') - failed.append(node) - - - - """ - v1, m1, v2, m2 = set(), set(), set(), set() - for node in in_nodes: - self.traverse_upwards(state, node, map_label, v1, m1) - self.traverse_downwards(state, node, map_label, v2, m2) - """ - #print(f'm1 {m1}') - #print(f'm2 {m2}') - """ - for entry, exit in m2: - entry.map.label = entry.map.label + '_2' - exit.map.label = exit.map.label + '_2' + 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)) - transform = dace.transformation.dataflow.map_fusion.MapFusion() - m2 = list(m2) - transform.first_map_exit = m2[0][1] - transform.second_map_entry = m2[1][0] - transform.apply(state, sdfg) + # 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 - transform = dace.transformation.dataflow.map_fusion.MapFusion() - break_i = False - for entry, exit in m2: - out_edges = state.out_edges(exit) - out_nodes = [edge.dst for edge in out_edges] - for entry2, exit2 in m2: - in_edges = state.in_edges(entry2) - in_nodes = [edge.src for edge in in_edges] - for node in out_nodes: - if node in in_nodes: - transform.first_map_exit = exit - transform.second_map_entry = entry2 - #transform.array = node - #if transform.can_be_applied(state,0, sdfg): - try: - transform.apply(state, sdfg) - except: - print(f'failed on {node}') - - m2.remove((entry, exit)) - m2.remove((entry2, exit2)) - m2.add((entry, exit2)) - - """ - + # 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) - def apply(self, sdfg, target): - if not target in self.find_splitable_scopes(sdfg): - return - assert isinstance(target, dace.sdfg.nodes.Tasklet) - - state = find_state(sdfg, target) - - current_node = target - innermost_scope = None - while current_node is not None: - # Get the scope parent of the current node - scope_parent = state.scope_dict()[current_node] - - # If there is no scope parent, break the loop - if scope_parent is None: - break - - # Update the innermost scope and the current node for the next iteration - innermost_scope = scope_parent - current_node = scope_parent + # 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) - entry = innermost_scope - exit = state.exit_node(entry) + # 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 - input_nodes = [edge.src for edge in state.in_edges(target)] - tasklet_edges = state.in_edges(target) - input_edges = [edge for node in input_nodes for edge in state.in_edges(node)] - print("input nodes", input_nodes) - print("tasklet edges", tasklet_edges) - print("input edges", input_edges) + def find_scope_nodes(self, state, node, scope_nodes, visited=None): + if visited is None: + visited = set() - new_map = self.copy_map_properties(entry.map) - new_map_entry = dace.nodes.MapEntry(new_map) - new_map_exit = dace.nodes.MapExit(new_map) + 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 - output_edges = [] - for node in input_nodes: - output_edges.extend(state.out_edges(node)) +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. - inbetween_edges = list(set(output_edges) | set(tasklet_edges)) + 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). + """ - # redirect edges that have nothin to do with the split - tmp = [] - for edge in state.edges(): - if edge.src == entry: - if (not self.passes_through(state, edge, input_nodes)): - tmp.append((edge.data.data, edge)) + def __init__(self, unroll_factor = 2): + self.__name = 'UnrollMapScope' + self.checked = False + self.max_unroll_factor = unroll_factor + self.explicit_factor = True + + @property + def name(self): + return self.__name - special_edges = [] - for edge in state.edges(): - if edge.dst == entry: - for data, snd_edge in tmp: - if edge.data.data == data: - tmp.remove((data, snd_edge)) - special_edges.append((edge, snd_edge)) - - print(f'inbetween edges {inbetween_edges}') - print(f'special edges {special_edges}') - - # redirect edges - for edge in input_edges: - dace.transformation.helpers.reconnect_edge_through_map(state, edge, new_map_exit, False) - for edge in inbetween_edges: - dace.transformation.helpers.reconnect_edge_through_map(state, edge, new_map_entry, True) - for edge, snd_edge in special_edges: - new_edge1 = sdfg.add_edge(edge.src, new_map_entry, edge) - sdfg.remove_edge(edge) - new_edge2 = sdfg.add_edge(new_map_entry, snd_edge.dst, snd_edge) - sdfg.remove_edge(snd_edge) - #new_edge = dace.transformation.helpers.redirect_edge(state, edge, new_src = edge.src, new_dst = snd_edge.dst) - #dace.transformation.helpers.reconnect_edge_through_map(state, new_edge, new_map_entry, True) - - def find_scope_map(self, state, target): - current_node = target - innermost_scope = None - while current_node is not None: - # Get the scope parent of the current node - scope_parent = state.scope_dict()[current_node] - - # If there is no scope parent, break the loop - if scope_parent is None: - break - - # Update the innermost scope and the current node for the next iteration - innermost_scope = scope_parent - current_node = scope_parent - if innermost_scope: - return innermost_scope, state.exit_node(innermost_scope) - return None, None - - def apply2(self, sdfg, target): - if not target in self.find_splitable_scopes(sdfg): - return - state = find_state(sdfg, target) - entry, exit = self.find_scope_map(state, target) - assert entry and exit - - subgraph = state.scope_subgraph(entry, include_entry=False, include_exit=False) - modified_arrays = set() - outer_map = entry - mapsize = outer_map.range.size() - - components = [(target, target)] - for _,cout in components: - sources = subgraph.source_nodes() - sinks = subgraph.sink_nodes() - - external_edges_entry = list(state.out_edges(entry)) - external_edges_exit = list(state.in_edges(exit)) - - edge_to_outer = {} - for edge in external_edges_entry: - path = state.memlet_path(edge) - eindex = path.index(edge) - edge_to_outer[edge] = path[eindex-1] - for edge in external_edges_exit: - path = state.memlet_path(edge) - eindex = path.index(edge) - edge_to_outer[edge] = path[eindex+1] - - arrays = MapFission._border_arrays(sdfg, state, subgraph) - scalars = {} - for _,cout in components: - for e in subgraph.out_edges(cout): - if isinstance(e.dst, dace.nodes.CodeNode): - if not e.data.data in scalars: - scalars[e.data.data] = [e] - else: - scalars[e.data.data].append(e) - - for scalar, edges in scalars.items(): - desc = sdfg.arrays[scalar] - del sdfg.arrays[scalar] - name, newdesc = sdfg.add_transient(scalar, - mapsize, - desc.dtype, - desc.storage, - lifetime=desc.lifetime, - debuginfo=desc.debuginfo, - allow_conflicts=desc.allow_conflicts, - find_new_name=True) - for edge in edges: - anode = state.add_access(name) - sbs = subsets.Range.from_string(','.join(outer_map.params)) - # Offset memlet by map range begin (to fit the transient) - sbs.offset([r[0] for r in outer_map.range], True) - state.add_edge(edge.src, edge.src_conn, anode, None, - mm.Memlet.simple(name, sbs, num_accesses=outer_map.range.num_elements())) - state.add_edge(anode, None, edge.dst, edge.dst_conn, - mm.Memlet.simple(name, sbs, num_accesses=outer_map.range.num_elements())) - state.remove_edge(edge) - - new_map_entries = [] - for component_in, component_out in components: - me, mx = state.add_map(outer_map.label + '_fission', [(p, '0:1') for p in outer_map.params], - outer_map.schedule, - unroll=outer_map.unroll, - debuginfo=outer_map.debuginfo) - - for conn in entry.in_connectors: - if not conn.startswith('IN_'): - me.add_in_connector(conn) - - me.map.range = dcpy(outer_map.range) - new_map_entries.append(me) - - conn_idx = 0 - for e in state.in_edges(component_in): - if e.data.data: - in_conn = f"IN_{conn_idx}" - out_conn = f"OUT_{conn_idx}" - conn_idx += 1 - me.add_in_connector(in_conn) - me.add_out_connector(out_conn) - else: - in_conn = None - out_conn = None - state.add_edge(me, out_conn, e.dst, e.dst_conn, dcpy(e.data)) - # Reconnect inner edges at source directly to external nodes - if e in external_edges_entry: - state.add_edge(edge_to_outer[e].src, edge_to_outer[e].src_conn, me, in_conn, - dcpy(edge_to_outer[e].data)) - else: - state.add_edge(e.src, e.src_conn, me, in_conn, dcpy(e.data)) - state.remove_edge(e) - # Empty memlet edge in nested SDFGs - if state.in_degree(component_in) == 0: - state.add_edge(me, None, component_in, None, mm.Memlet()) - - conn_idx = 0 - for e in state.out_edges(component_out): - if e.data.data: - in_conn = f"IN_{conn_idx}" - out_conn = f"OUT_{conn_idx}" - conn_idx += 1 - mx.add_in_connector(in_conn) - mx.add_out_connector(out_conn) - else: - in_conn = None - out_conn = None - state.add_edge(e.src, e.src_conn, mx, in_conn, dcpy(e.data)) - # Reconnect inner edges at sink directly to external nodes - if e in external_edges_exit: - state.add_edge(mx, out_conn, edge_to_outer[e].dst, edge_to_outer[e].dst_conn, - dcpy(edge_to_outer[e].data)) - else: - state.add_edge(mx, out_conn, e.dst, e.dst_conn, dcpy(e.data)) - state.remove_edge(e) - # Empty memlet edge in nested SDFGs - if state.out_degree(component_out) == 0: - state.add_edge(component_out, None, mx, None, mm.Memlet()) - - for node in sources: - if isinstance(node, dace.nodes.AccessNode): - for edge in state.in_edges(node): - outer_edge = edge_to_outer[edge] - memlet = dcpy(edge.data) - memlet.subset = subsets.Range(outer_map.range.ranges + memlet.subset.ranges) - state.add_edge(outer_edge.src, outer_edge.src_conn, edge.dst, edge.dst_conn, memlet) - for node in sinks: - if isinstance(node, dace.nodes.AccessNode): - for edge in state.out_edges(node): - outer_edge = edge_to_outer[edge] - state.add_edge(edge.src, edge.src_conn, outer_edge.dst, outer_edge.dst_conn, - dcpy(outer_edge.data)) - # Augment arrays by prepending map dimensions - for array in arrays: - if array in modified_arrays: + 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 - desc = sdfg.arrays[array] - if isinstance(desc, dt.Scalar): # Scalar needs to be augmented to an array - desc = dt.Array(desc.dtype, desc.shape, desc.transient, desc.allow_conflicts, desc.storage, - desc.location, desc.strides, desc.offset, False, desc.lifetime, 0, desc.debuginfo, - desc.total_size, desc.start_offset) - sdfg.arrays[array] = desc - for sz in reversed(mapsize): - desc.strides = [desc.total_size] + list(desc.strides) - desc.total_size = desc.total_size * sz - - desc.shape = mapsize + list(desc.shape) - desc.offset = [0] * len(mapsize) + list(desc.offset) - modified_arrays.add(array) - - # Fill scope connectors so that memlets can be tracked below - state.fill_scope_connectors() - - for node in subgraph.nodes(): - if isinstance(node, dace.nodes.AccessNode) and node.data in arrays: - for edge in state.all_edges(node): - for e in state.memlet_tree(edge): - # Prepend map dimensions to memlet - # NOTE: Do this only for the subset corresponding to `node.data`. If the edge is copying - # to/from another AccessNode, the other data may not need extra dimensions. For example, see - # `test.transformations.mapfission_test.MapFissionTest.test_array_copy_outside_scope`. - if e.data.data == node.data: - if e.data.subset: - e.data.subset = subsets.Range([(pystr_to_symbolic(d) - r[0], - pystr_to_symbolic(d) - r[0], 1) - for d, r in zip(outer_map.params, outer_map.range)] + - e.data.subset.ranges) - else: - if e.data.other_subset: - e.data.other_subset = subsets.Range( - [(pystr_to_symbolic(d) - r[0], pystr_to_symbolic(d) - r[0], 1) - for d, r in zip(outer_map.params, outer_map.range)] + - e.data.other_subset.ranges) - - state.remove_nodes_from([entry, exit]) - propagate_memlets_state(sdfg,state) - - def passes_through(self, state, cur_edge, nodes): - if cur_edge.dst in nodes: - return True - else: - for edge in state.edges(): - if edge.src == cur_edge.dst: - if self.passes_through(state, edge, nodes): - return True - return False - - def apply3(self,sdfg,target): - if not target in self.find_splitable_scopes(sdfg): - return - state = find_state(sdfg, target) - - entry, exit = self.find_scope_map(state, target) - new_map = self.copy_map_properties(entry.map) - new_map_entry = dace.nodes.MapEntry(new_map) - new_map_exit = dace.nodes.MapExit(new_map) - - subgraph = state.scope_subgraph(entry, include_entry=True, include_exit=True) - map_in_node = [(edge.src,edge) for node in subgraph.source_nodes() for edge in state.in_edges(node)] - input_nodes = [edge.src for edge in state.in_edges(target)] - scope1_exit_edge = [edge for node in input_nodes for edge in state.in_edges(node)] - #scope1_entry_edge = [edge for node in subgraph.source_nodes() for edge in state.in_edges(node)] - scope2_entry_edge = [edge for node in input_nodes for edge in state.out_edges(node)] - #scope2_exit_edge = [edge for node in subgraph.sink_nodes() for edge in state.out_edges(node)] - - for edge in scope1_exit_edge: - dace.transformation.helpers.reconnect_edge_through_map(state, edge, new_map_exit, False) - for edge in scope2_entry_edge: - dace.transformation.helpers.reconnect_edge_through_map(state, edge, new_map_entry, True) - subgraph1 = state.scope_subgraph(entry, include_entry=True, include_exit=True) - subgraph2 = state.scope_subgraph(new_map_entry, include_entry=False, include_exit=True) - print(f'subgraph1 {subgraph1.nodes()}') - - print(subgraph1.edges()) - for edge in subgraph1.edges(): - - if not self.passes_through(subgraph1, edge, [new_map_exit]): - print(f'edge {edge}, {edge.data.data}') - if isinstance(edge.src, dace.nodes.MapEntry): - red_edge = None - for node,edge2 in map_in_node: - print(f'\t{edge2}, {edge2.data.data}') - if edge2.data.data == edge.data.data: - red_edge = edge2 - break - if red_edge: - dace.transformation.helpers.redirect_edge(state, red_edge, new_dst=new_map_entry, new_dst_conn=f'IN_{edge.data.data}') - dace.transformation.helpers.redirect_edge(state, edge, new_src = new_map_entry, new_src_conn=f'OUT_{edge.data.data}') - else: - if isinstance(edge.src,dace.nodes.AccessNode): - pass + if not edge.dst in traversed: + if not self.isInnermost(state, edge.dst, traversed): + return False + return True - - propagate_memlets_state(sdfg,state) - -class UnrollScope(): - max_unroll_factor = 10 - def find_unrollable_maps(self, sdfg): + def find(self, sdfg): """ - Find maps that can be unrolled - Only last level maps with constant size can be unrolled + 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 edge in state.edges(): - if isinstance(edge.src, dace.nodes.MapEntry) and isinstance(edge.dst, dace.nodes.Tasklet): - dim = edge.src.map.range.size()[0] + 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 + dim = node.map.range.size()[0] if isinstance(dim, sympy.core.numbers.Integer) and int(dim) > 1: - divisors = [i for i in range(2, max(self.max_unroll_factor, dim)) if dim % i == 0] - unrollable_maps.extend([(edge.src, div) for div in divisors]) + 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): - if not (map_entry, unroll_factor) in self.find_unrollable_maps(sdfg): + """ + 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) - #dace.transformation.dataflow.hbm_transform.HbmTransform.unroll_map(sdfg, state, map_entry, unroll_factor) - #map_entry.map.unroll = True 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 edge in state.edges(): - if isinstance(edge.dst, dace.nodes.MapEntry) and edge.dst.map.params[0] == para: - new_map = edge.dst.map - new_map.unroll = True - new_map.schedule = dace.ScheduleType.Unrolled - break + 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 class addArrayCopies(): - def find_copyable_arrays(self, sdfg): + """ + 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(): for node in state.nodes(): if isinstance(node, dace.nodes.AccessNode): copyable_arrays.add(node.data) - + self.checked = True return sorted(list(copyable_arrays)) def apply(self, sdfg, arr_name): - if not arr_name in self.find_copyable_arrays(sdfg): + """ + 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() @@ -896,16 +1248,10 @@ def apply(self, sdfg, arr_name): if not new_name in sdfg.arrays: break i += 1 - """ - transient = True - if arr_name in [node.data for node in output_nodes]: - transient = False - o_data = sdfg.arrays[arr_name] - o_data.transient = True - """ + sdfg.add_array(name=new_name, shape=data.shape, dtype=data.dtype, storage=data.storage, transient=True, lifetime=data.lifetime) - tasklet = adj_state.add_tasklet("copy_temp", {"in_1"}, {"out"}, "out = in_1") + tasklet = adj_state.add_tasklet("move", {"in_1"}, {"out"}, "out = in_1") output_node = adj_state.add_access(new_name) @@ -999,8 +1345,33 @@ def traverse_n_replace(self, state, node, old_name, new_name, start=True, backwa self.traverse_n_replace(state, edge.dst, old_name, new_name, False) 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. - def find_deleteable_arrays(self, sdfg): + 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()] @@ -1011,14 +1382,26 @@ def find_deleteable_arrays(self, sdfg): if out_node.data in out: continue in_node = state.in_edges(node)[0].data - if in_node.subset == out_node.subset: + 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): - if not arr_name in self.find_deleteable_arrays(sdfg): + """ + 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 @@ -1101,8 +1484,42 @@ def traverse_n_replace(self, state, node, old_name, new_name, start=True, backwa class addArrayDims(): """ - complex, keep for later + 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) @@ -1148,9 +1565,27 @@ def single_map_scope(self,state, node): return True - def find_createable_dim(self, sdfg): - result = [] + 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(): @@ -1274,12 +1709,21 @@ def find_createable_dim(self, sdfg): result.append((node, 1)) candidates.remove(edge.data.data) + self.checked = True return result def apply(self, sdfg, op, mode): - if not (op, mode) in self.find_createable_dim(sdfg): + """ + 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: @@ -1537,8 +1981,34 @@ def get_read_memlet(self, state, node, target, result, traverse=None): self.get_read_memlet(state, edge.src, target, result, traverse) 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 - def find_deleteable_dims(self, sdfg): + 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: @@ -1550,6 +2020,7 @@ def find_deleteable_dims(self, sdfg): 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): @@ -1660,9 +2131,19 @@ def get_entry_maps(self, state, node): return dims def apply(self, sdfg, name, dim, mode): - if not (name, dim, mode) in self.find_deleteable_dims(sdfg): + """ + 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) @@ -1861,71 +2342,201 @@ def find_read_tasklet(self, sdfg, target): class reuseArray(): """ - change semantics and fzse two arrays completley + 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 find_reusable_arrays(self, 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: - same_data.add((arr_name1, arr_name2)) if arr_name1 < arr_name2 else same_data.add((arr_name2, arr_name1)) + 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)) - - - - #outputs = sdfg.sink_nodes() - 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] + 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: - - works = True - if not arr2 in input_names: - for node in inputs: - check = self.traverser_check(state, arr1, arr2, node, False) - if not check: - works = False - break - else: - works = False + 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 - if not arr1 in input_names: - if not works: - works = True - for node in inputs: - check = self.traverser_check(state, arr2, arr1, node, False) - if not check: - works = False - break - - if works: + # 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) - return sorted((reusable_arrays)) + 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): @@ -1955,10 +2566,19 @@ def traverser_check(self, state, arr1, arr2, node, write, visited = None): return True def apply(self, sdfg, array1, array2): - if not (array1, array2) in self.find_reusable_arrays(sdfg): + """ + 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) @@ -1966,19 +2586,29 @@ def apply(self, sdfg, array1, array2): if not data1.transient: data2 = sdfg.arrays[array2] data2.transient = False + + if self.remove_reused: + sdfg.remove_data(array1) - #sdfg.remove_data(array1) - # keep array1 for now, information not lost +class reuseArrayLocations(): + """ + This class implements the reuseArrayLocations transformation. It avoids materializing dimensions. - """ - data1 = sdfg.arrays[array1] - print(f'old {data1} and {sdfg.arrays[array2]}') - sdfg.arrays[array2] = data1 + 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. + """ - does not work: Invalid SDFG: Duplicate data descriptor object detected: "". Please copy objects rather than using multiple references to the same one - """ + 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 -class reuseArrayLocations(): + @property + def name(self): + return self.__name def get_read_ops(self, state, target): ops = set() @@ -2013,7 +2643,19 @@ def get_entry_maps(self, state, node): return dims - def find_reusable_dimensions(self, sdfg): + 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() @@ -2026,15 +2668,34 @@ def find_reusable_dimensions(self, sdfg): 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} - + 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)): @@ -2044,35 +2705,277 @@ def find_reusable_dimensions(self, sdfg): 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): - if not (node,dim) in self.find_reusable_dimensions(sdfg): + """ + 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) - print(f'old strides: {data_node.strides}') - new_dim = [] - for i,dims in enumerate(shape): + dim_pos = -1 + for i,dims in reversed(list(enumerate(shape))): if dims == dim: - strides[i] = 0 + dim_pos = i + if self.keep_sizes: + strides[i] = 0 + break data_node.strides = tuple(strides) - print(f'new strides: {data_node.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, dim_pos): + #source_nodes = state.source_nodes() + #visited = set() + #self.traverse_subsets(state, state.entry_node(node), node.data, dim_pos, visited, scope_only=True) + for other_states in sdfg.nodes(): + visited = set() + sources = other_states.source_nodes() + for source in sources: + self.traverse_subsets(other_states, source, node.data, dim_pos, visited, scope_only=False) + + def traverse_subsets(self, state, node, data, dim_pos, visited = None, scope_only = False): + if visited is None: + visited = set() + visited.add(node) + if scope_only and isinstance(node, dace.nodes.MapExit): + return + out_edges = state.out_edges(node) + for edge in out_edges: + if edge.data.data == data: + #print(f'{data} {dim_pos} {edge.data.subset} and {type(edge.data.subset)}') + self.update_subset(state, edge, dim_pos) + if not edge.dst in visited: + self.traverse_subsets(state, edge.dst, data, dim_pos, visited, scope_only) + + def update_subset(self, state, edge, dim_pos): + subset = list(edge.data.subset.ranges) + new_subset = [] + for i,sub in enumerate(subset): + if i == dim_pos: + continue + new_subset.append(sub) + if not new_subset: + new_subset.append((0,0,1)) + edge.data.subset = dace.subsets.Range(new_subset) + #print(f'new subset {edge.data.subset} and {type(edge.data.subset)}\n') class StackToHeap(): - def apply(self, data): + """ + 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(data) + self.checked = True + return result + + def apply(self, sdfg, data): + """ + This method applies the StackToHeap transformation to the given SDFG. It changes the storage type of the array to the heap. + """ + if not self.checked and not data in self.find(sdfg): + return + self.checked = False + data.storage = dace.StorageType.CPU_Heap class HeapToStack(): - def apply(self, data): + """ + 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 = None #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 data arrays that are not stored on the stack. + """ + 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(data) + self.checked = True + return result + + def apply(self, sdfg, data): + if not self.checked and not data in self.find(sdfg): + return + self.checked = False data.storage = dace.StorageType.Register class changeMemoryLayout(): - def find_changeable_arrays(self, sdfg): + """ + 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() @@ -2080,12 +2983,22 @@ def find_changeable_arrays(self, sdfg): 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): - if not node in self.find_changeable_arrays(sdfg): + """ + 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. + """ + if not self.checked and not node in self.find(sdfg): return + self.checked = False + data = sdfg.arrays[node.data] shape = data.shape @@ -2094,10 +3007,6 @@ def apply(self, sdfg, node, dim_order): assert max(dim_order) == len(shape) - 1 assert min(dim_order) == 0 - print(node.data) - print(shape) - print(dim_order) - m = dict(zip(shape, dim_order)) new_strides = list() for dim in shape: @@ -2108,119 +3017,11 @@ def apply(self, sdfg, node, dim_order): new_strides.append(stride) # set new stride - print(f'old strides: {data.strides}, new strides: {new_strides}') data.strides = tuple(new_strides) return - - """ - state = state_0(sdfg) - #in_edges = state.in_edges(node) - #out_edges = state.out_edges(node) - orig_shape = data.shape - assert len(orig_shape) == len(dim_order) - new_shape = [orig_shape[dim_order.index(i)] for i in range(len(orig_shape))] - print(f'orig shape: {orig_shape}, new shape: {new_shape}') - #edge = in_edges[0] - - entry = state.entry_node(node) - if entry: - dim_to_idx = {} - while True: - idx = entry.map.params[0] - dim = entry.map.range.size()[0] - dim_to_idx[dim] = idx - entry = state.entry_node(entry) - if not entry: - break - - else: - up_map = {} - down_map = {} - self.get_loop_indices(state, node.data, node, up_map, downwards=False) - self.get_loop_indices(state, node.data, node, down_map, downwards=True) - print(up_map) - print(down_map) - - self.replace_index(state, node.data, node, new_shape, up_map, downwards=False) - self.replace_index(state, node.data, node, new_shape, down_map, downwards=True) - - - def get_loop_indices(self, state, data, node, dim_map, visited = None, downwards=False): - if visited is None: - visited = [] - - if downwards and isinstance(node, dace.nodes.MapEntry) or not downwards and isinstance(node, dace.nodes.MapExit): - idx = node.map.params[0] - dim = node.map.range.size()[0] - dim_map[dim] = idx - - visited.append(node) - - adj_edges = state.out_edges(node) if downwards else state.in_edges(node) - for edge in adj_edges: - if not edge.data.data == data: - continue - target = edge.dst if downwards else edge.src - if not target in visited: - self.get_loop_indices(state, data, target, dim_map, visited, downwards) - - return - - def replace_index(self, state, data, node, dim_order, dim_map, visited = None, downwards=False): - if visited is None: - visited = [] - - adj_edges = state.out_edges(node) if downwards else state.in_edges(node) - for edge in adj_edges: - if not edge.data.data == data: - continue - else: - print(edge.data.data) - print(edge.data.subset) - new_subset = edge.data.subset - for i, subs in enumerate(edge.data.subset): - if isinstance(subs[1], dace.symbolic.symbol): - new_dim = dim_order[i] - new_idx = dim_map[new_dim] - print(f'{str(subs[1])} => {new_dim},{new_idx}') - if subs[1] == new_idx: - continue - for j,tmp in enumerate(edge.data.subset): - if list(tmp[1].free_symbols)[0] == new_dim or str(tmp[1]) == new_idx: - if (new_subset[j] == subs): - break - a = tmp - new_subset[j] = subs - new_subset[i] = a - break - else: - dim = list(subs[1].free_symbols)[0] - new_dim = dim_order[i] - if dim == new_dim: - continue - new_idx = dim_map[new_dim] - print(f'{str(dim)} => {new_dim},{new_idx}') - for j,tmp in enumerate(edge.data.subset): - if list(tmp[1].free_symbols)[0] == new_dim or str(tmp[1]) == new_idx: - if (new_subset[j] == subs): - break - a = tmp - new_subset[j] = subs - new_subset[i] = a - break - - edge.data.subset = new_subset - print(edge.data.subset) - print() - - target = edge.dst if downwards else edge.src - if not target in visited: - visited.append(target) - self.replace_index(state, data, target, dim_order, dim_map, visited, downwards) - - return - """ +###* Helper functions *### +# NOT USED def get_source_node(state, node, data, visited = []): if isinstance(node, dace.nodes.AccessNode): if node.data == data: @@ -2233,6 +3034,7 @@ def get_source_node(state, node, data, visited = []): return None +# NOT USED def get_sink_node(state, node, data, visited = []): if isinstance(node, dace.nodes.AccessNode): if node.data == data: @@ -2256,7 +3058,8 @@ def find_state_by_name(sdfg, name): if isinstance(node, dace.nodes.AccessNode): if node.data == name: return state - + +# NOT USED def start_nodes(state): incoming_edges = {node: 0 for node in state.nodes()} for edge in state.edges(): @@ -2264,9 +3067,6 @@ def start_nodes(state): starting_nodes = [node for node, count in incoming_edges.items() if count == 0] return starting_nodes -def traverse_sdfg(state): - pass - def order_sdfg(state): incoming_edges = {node: 0 for node in state.nodes()} for edge in state.edges(): @@ -2310,19 +3110,126 @@ def state_0(sdfg): for state in sdfg.nodes(): if state.label == 'state_0': return state - + +###* Optimizing SDFG *### + def optimize_sdfg(sdfg, optimization): - # Apply transformations - pass + """ + This method applies the given optimization to the SDFG. + + Args: + sdfg (dace.SDFG): The SDFG to apply the optimization to. + optimization: The optimization to apply. + """ + # Apply + transformation = optimization() + locations = transformation.find() + argument = random.choice(locations) + if isinstance(argument,(tuple,list)): + transformation.apply(sdfg, *argument) + else: + transformation.apply(sdfg, argument) -if __name__ == "__main__": +###* Kernel Testing *### + +def validate(sdfg): + """ + This method tests the SDFG by comparing the output of the SDFG with the expected output. + Only works for the gemm and softmax kernel (args.kernel). + """ + size = 32 + + inputs = set() + for state in sdfg.nodes(): + source = state.source_nodes() + for src in source: + if isinstance(src, dace.nodes.AccessNode): + if not 'copy' in src.data: + inputs.add(src) + + input_names = [node.data for node in inputs if not node.desc(sdfg).transient] + outputs = [node for node in state_0(sdfg).sink_nodes() if isinstance(node, dace.nodes.AccessNode)] + output_names = [node.data for node in outputs] + data = sdfg.arrays + + loop_index = {name: size for name, data in data.items() if isinstance(data, dace.data.Scalar) and not name in [n.data for n in state_0(sdfg).nodes() if isinstance(n, dace.nodes.AccessNode)]} + input_data = {} + for output_name in output_names: + if output_name in input_names: + continue + data_container = data[output_name] + if isinstance(data_container, dace.data.Scalar): + input_data[output_name] = np.zeros(1) + else: + shape = data_container.shape + np_shape = [] + for dim in shape: + d = str(dim) + if d in loop_index: + np_shape.append(loop_index[d]) + input_data[output_name] = np.zeros(shape=np_shape) + + for input_name in input_names: + data_container = data[input_name] + if isinstance(data_container, dace.data.Scalar): + input_data[input_name] = np.random.rand() + else: + shape = data_container.shape + np_shape = [] + for dim in shape: + d = str(dim) + if d in loop_index: + np_shape.append(loop_index[d]) + input_data[input_name] = np.random.rand(*np_shape) + + + expected = np.array([]) + output = np.array([]) + if args.kernel == "gemm": + expected = input_data['alpha'] * input_data['A'] @ input_data['B'] + input_data['beta']*input_data['C'] + output = input_data['D'] + elif args.kernel == "softmax": + argsrc = np.copy(input_data['src']) + argsrc -= np.max(argsrc, axis=1, keepdims=True) + np.exp(argsrc, out=argsrc) + argsrc /= np.sum(argsrc, axis=1, keepdims=True) + expected = argsrc + output = input_data['dst'] + + input_data.update(loop_index) + try: + sdfg(**input_data) + except Exception as e: + print(e) + + diff = np.linalg.norm(output - expected) + print("Difference:", diff) + if diff >= 1e-4: + print("Validation failed") + else: + print("Validation successful") + print("==== Program end ====") + + +def main(args): + """ + This method is the main method of the optimize_sdfg.py script. It applies the given optimization to the SDFG and validates the output. + Currently only the gemm and softmax kernel are supported to test in this script. Refer to mk_kernel_tests.py for other kernels. + + Call the script with the following arguments: + - kernel: The kernel to test (gemm, softmax) (--kernel gemm) + - opt: The optimization to apply (tile, swap, unroll, split, join, reuseArray, addCopies, removeCopies, reuseLoc) (--opt tile) + - const: If True, the kernel with constant values is used (gemm_const, softmax_const) (--const) + - validate: If True, the output of the SDFG is validated (--validate) + """ + print("==== Program start ====") source = '' sdfg_name = '' if args.kernel == "gemm": postfix = "" - if args.sym: + if not args.const: source = "test_kernel/test_gemm.sdfg" #source = ".dacecache/affine_grid_2d/program.sdfg" else: @@ -2331,7 +3238,7 @@ def optimize_sdfg(sdfg, optimization): sdfg_name = "gemm_" + args.opt + postfix elif args.kernel == "softmax": postfix = "" - if args.sym: + if not args.const: source = "test_kernel/test_softmax.sdfg" else: source = "test_kernel/test_softmax_const.sdfg" @@ -2344,26 +3251,28 @@ def optimize_sdfg(sdfg, optimization): sdfg.name = sdfg_name if args.opt == "tile": - transformation = LoopTile() - locations = transformation.find_tileable_maps(sdfg) + transformation = TileMapScope() + locations = transformation.find(sdfg) if locations: + print("tile locations", locations) loc = locations[0] print("tile location", loc) transformation.apply(sdfg, loc[0], loc[1]) print("applied loop tile") if args.opt == "swap": - transformation = SwapScope() - locations = transformation.find_swappable_maps(sdfg) + transformation = SwapLoopMap() + locations = transformation.find(sdfg) if locations: loc = locations[0] print("swap location", loc) - transformation.apply(sdfg, loc) + transformation.apply(sdfg, loc[0], loc[1]) print("applied swap scope") if args.opt == "unroll": - transformation = UnrollScope() - locations = transformation.find_unrollable_maps(sdfg) + print("unroll") + transformation = UnrollMapScope() + locations = transformation.find(sdfg) if locations: loc = locations[0] print("unroll location", loc) @@ -2372,26 +3281,62 @@ def optimize_sdfg(sdfg, optimization): if args.opt == "split": transformation = SplitMapScopes() - locations = transformation.find_splitable_scopes(sdfg) + locations = transformation.find(sdfg) + print(f'splitable locations {locations}') if locations: - loc = locations[0] - print("split location", loc) - transformation.apply4(sdfg, loc) + #loc = locations[0] + loc = None + for t in locations: + if t[1].label == 'mul': + if isinstance(t[0].out_edges(t[1])[0].dst, dace.nodes.AccessNode): + if t[0].out_edges(t[1])[0].dst.data == 'inv_sum_val_div_u':#'f32_copyfrom_i32': + loc = t + break + + print("split location", loc[1]) + transformation.apply(sdfg, loc[0], loc[1]) print("applied split scope") + + if args.opt == "join": + """ + # first do a split + transformation = SplitMapScopes() + locations = transformation.find(sdfg) + print(f'splitable locations {locations}') + if locations: + loc = None + for t in locations: + if t[1].label == 'fnmsub': + if isinstance(t[0].out_edges(t[1])[0].dst, dace.nodes.AccessNode): + if t[0].out_edges(t[1])[0].dst.data == 'inv_sum_val_inv_th_2':#'f32_copyfrom_i32': + loc = t + break + transformation.apply(sdfg, loc[0], loc[1]) + sdfg.view() + """ + #join + transformation = JoinMapScopes() + locations = transformation.find(sdfg) + print(f'joinable locations {locations}') + if locations: + loc = locations[-1] + print("join location", loc[0], loc[1], loc[2]) + transformation.apply(sdfg, loc[0], loc[1], loc[2]) + print("applied join") if args.opt == "reuseArray": transformation = reuseArray() - locations = transformation.find_reusable_arrays(sdfg) + locations = transformation.find(sdfg) if locations: print("reuse array locations", locations) - loc = locations[1] + loc = locations[0] print("reuse array location", loc) transformation.apply(sdfg, loc[0], loc[1]) print("applied reuse array") if args.opt == "addCopies": transformation = addArrayCopies() - locations = transformation.find_copyable_arrays(sdfg) + locations = transformation.find(sdfg) if locations: loc = "dst" print("add copies location", loc) @@ -2403,31 +3348,17 @@ def optimize_sdfg(sdfg, optimization): loc = "dst" transformation.apply(sdfg, loc) transformation = removeArrayCopies() - locations = transformation.find_deleteable_arrays(sdfg) + locations = transformation.find(sdfg) print(f'applicable locations {locations}') if locations: loc = loc + "_copy_0" print("remove copies location", loc) transformation.apply(sdfg, loc) print("applied remove copies") - - if args.opt == "untile": - transformation = LoopTile() - locations = transformation.find_tiled_maps(sdfg) - if locations: - loc = locations[0] - transformation.apply(sdfg, loc) - transformation = LoopUntile() - locations = transformation.find_untileable_maps(sdfg) - if locations: - loc = locations[0] - print("untile location", loc) - transformation.apply(sdfg, loc) - print("applied untile scope") if args.opt == "reuseLoc": transformation = reuseArrayLocations() - locations = transformation.find_reusable_dimensions(sdfg) + locations = transformation.find(sdfg) print(f'reusable dimensions {locations}') if locations: loc = locations[0] @@ -2437,7 +3368,7 @@ def optimize_sdfg(sdfg, optimization): if args.opt == "changeLayout": transformation = changeMemoryLayout() - locations = transformation.find_changeable_arrays(sdfg) + locations = transformation.find(sdfg) print(f'changeable arrays {locations}') if locations: loc = locations[0] @@ -2447,7 +3378,7 @@ def optimize_sdfg(sdfg, optimization): if args.opt == "addDimension": transformation = addArrayDims() - locations = transformation.find_createable_dim(sdfg) + locations = transformation.find(sdfg) print(f'createable dimensions {locations}') if locations: loc = locations[0] @@ -2457,12 +3388,12 @@ def optimize_sdfg(sdfg, optimization): if args.opt == "removeDimension": transformation = addArrayDims() - locations = transformation.find_createable_dim(sdfg) + locations = transformation.find(sdfg) if locations: loc = locations[0] transformation.apply(sdfg, loc[0], loc[1]) transformation = removeArrayDims() - locations = transformation.find_deleteable_dims(sdfg) + locations = transformation.find(sdfg) print(f'deleteable dimensions {locations}') if locations: loc = locations[0] @@ -2473,7 +3404,7 @@ def optimize_sdfg(sdfg, optimization): if args.opt == "swapScopes": transformation = SwapMapScope() - locations = transformation.find_swappable_maps(sdfg) + locations = transformation.find(sdfg) if locations: loc = locations[0] print("swap operation location", loc) @@ -2482,89 +3413,145 @@ def optimize_sdfg(sdfg, optimization): if args.opt == "swapOp": transformation = SwapOpertions() - locations = transformation.find_swappable_ops(sdfg) + locations = transformation.find(sdfg) print(f'swappable operations {locations}') if locations: loc = locations[0] print("swap operation location", loc) transformation.apply(sdfg, loc[0], loc[1]) print("applied swap operation") - + + if args.opt == "test_split_join": + transformation1 = SplitMapScopes() + locations = transformation1.find(sdfg) + for loc in locations: + copy_sdfg = copy.deepcopy(sdfg) + locations1 = transformation1.find(copy_sdfg) + loc1 = None + for locs in locations1: + if not locs[1].label == loc[1].label: + continue + out_edge1 = [edge.data.data for edge in locs[0].out_edges(locs[1])] + out_edge0 = [edge.data.data for edge in loc[0].out_edges(loc[1])] + if not out_edge1 == out_edge0: + continue + else: + loc1 = locs + break + if loc1: + try: + transformation1.apply(copy_sdfg, loc1[0], loc1[1]) + print(f"applied split scope on {loc1[1]} ({loc1[0].out_edges(loc1[1])[0].data.data})") + transformation2 = JoinMapScopes() + locations2 = transformation2.find(copy_sdfg) + if locations2: + loc2 = locations2[-1] + try: + transformation2.apply(copy_sdfg, loc2[0], loc2[1], loc2[2]) + print(f"applied join scope on {loc2[1]} and {loc2[2]}") + try: + copy_sdfg.validate() + print(f'==> validated {loc1[1]}\n') + except Exception as e: + print(e) + sdfg.view() + print(f'==> invalid {loc1[1]}\n') + except Exception as e: + print(f'invalid join {loc2[1]}, abort\n') + + except Exception as e: + print(f'invalid split {loc1[1]}, abort\n') + + if args.opt == "test_split_join_2": + trans_split = SplitMapScopes() + trans_join = JoinMapScopes() + while True: + split_locs = trans_split.find(sdfg) + if not split_locs: + break + loc1 = random.choice(split_locs) + try: + trans_split.apply(sdfg, loc1[0], loc1[1]) + print(f'applied split {loc1[1]} ({loc1[0].out_edges(loc1[1])[0].data.data})') + join_locs = trans_join.find(sdfg) + if not join_locs: + break + loc = random.choice(join_locs) + try: + trans_join.apply(sdfg, loc[0], loc[1], loc[2]) + print(f'applied join {loc[1]} and {loc[2]}') + try: + sdfg.validate() + print(f'==> validated {loc[1]} and {loc[2]}\n') + except Exception as e: + traceback.print_exc() + print(f'==> invalid {loc[1]} and {loc[2]}\n') + except Exception as e: + traceback.print_exc() + print(f'failed to join {loc[1]}\n') + break + except Exception as e: + traceback.print_exc() + print(f'failed to split {loc1[1]}\n') + break + + if args.opt == "test_split_join_3": + trans_join = JoinMapScopes() + trans_split = SplitMapScopes() + violation = False + its = 10 + while True: + split_locs = trans_split.find(sdfg) + if not split_locs or its == 0: + break + its -= 1 + loc1 = random.choice(split_locs) + try: + trans_split.apply(sdfg, loc1[0], loc1[1]) + print(f'applied split {loc1[1]} ({loc1[0].out_edges(loc1[1])[0].data.data})') + try: + sdfg.validate() + except Exception as e: + violation = True + print(f'==> invalid {loc1[1]}\n') + break + except Exception as e: + violation = True + print(f'failed to split {loc1[1]} ({loc1[0].out_edges(loc1[1])[0].data.data})\n') + break + its = 10 + while not violation: + join_locs = trans_join.find(sdfg) + if not join_locs or its == 0: + break + its -= 1 + loc = random.choice(join_locs) + try: + trans_join.apply(sdfg, loc[0], loc[1], loc[2]) + print(f'applied join {loc[1]} and {loc[2]}') + try: + sdfg.validate() + except Exception as e: + print(f'==> invalid {loc[1]} and {loc[2]}\n') + break + except Exception as e: + print(f'failed to join {loc[1]}\n') + break + sdfg.view() csdfg = sdfg.compile() - - if args.validate: - size = 32 - - inputs = set() - for state in sdfg.nodes(): - source = state.source_nodes() - for src in source: - if isinstance(src, dace.nodes.AccessNode): - if not 'copy' in src.data: - inputs.add(src) - - input_names = [node.data for node in inputs if not node.desc(sdfg).transient] - outputs = state_0(sdfg).sink_nodes() - output_names = [node.data for node in outputs] - data = sdfg.arrays + validate(sdfg) - loop_index = {name: size for name, data in data.items() if isinstance(data, dace.data.Scalar) and not name in [n.data for n in state_0(sdfg).nodes() if isinstance(n, dace.nodes.AccessNode)]} - #print(f'inputs {input_names}, outputs {output_names}, loop index {loop_index}') - input_data = {} - for output_name in output_names: - if output_name in input_names: - continue - data_container = data[output_name] - if isinstance(data_container, dace.data.Scalar): - input_data[output_name] = np.zeros(1) - else: - shape = data_container.shape - np_shape = [] - for dim in shape: - d = str(dim) - if d in loop_index: - np_shape.append(loop_index[d]) - input_data[output_name] = np.zeros(shape=np_shape) - - for input_name in input_names: - data_container = data[input_name] - if isinstance(data_container, dace.data.Scalar): - input_data[input_name] = np.random.rand() - else: - shape = data_container.shape - np_shape = [] - for dim in shape: - d = str(dim) - if d in loop_index: - np_shape.append(loop_index[d]) - input_data[input_name] = np.random.rand(*np_shape) - - - expected = np.array([]) - output = np.array([]) - if args.kernel == "gemm": - expected = input_data['alpha'] * input_data['A'] @ input_data['B'] + input_data['beta']*input_data['C'] - output = input_data['D'] - elif args.kernel == "softmax": - argsrc = np.copy(input_data['src']) - argsrc -= np.max(argsrc, axis=1, keepdims=True) - np.exp(argsrc, out=argsrc) - argsrc /= np.sum(argsrc, axis=1, keepdims=True) - expected = argsrc - output = input_data['dst'] - - input_data.update(loop_index) - try: - csdfg(**input_data) - except Exception as e: - print(e) +def parse_args(): + parser = argparse.ArgumentParser(description='Optimize SDFGs.') + parser.add_argument('--validate', action='store_true', help='see if the generated code is correct') + parser.add_argument('--print', action='store_true', help='show printed code') + parser.add_argument('--opt', type=str, help='optimization to apply', default='') + parser.add_argument('--kernel', type=str, help='kernel to apply the optimization to', default='gemm') + parser.add_argument('--const', action='store_true', help='Symbolic or Constant Loop Sizes', default=False) + return parser.parse_args() - diff = np.linalg.norm(output - expected) - print("Difference:", diff) - if diff >= 1e-4: - print("Validation failed") - else: - print("Validation successful") - print("==== Program end ====") +if __name__ == "__main__": + args = parse_args() + main(args) \ No newline at end of file From 177634bda747052f8ef162dc9d1b6c206fe2fdba Mon Sep 17 00:00:00 2001 From: sevhuber Date: Mon, 14 Oct 2024 14:57:58 +0200 Subject: [PATCH 3/4] semantic preserving transformations --- .../semantic_preserving/HeapToStack.py | 130 + .../semantic_preserving/JoinMapScopes.py | 360 + .../semantic_preserving/SplitMapScopes.py | 387 + .../semantic_preserving/StackToHeap.py | 59 + .../semantic_preserving/SwapLoopMap.py | 125 + .../semantic_preserving/SwapMapScope.py | 100 + .../semantic_preserving/SwapOperations.py | 113 + .../semantic_preserving/TileMapScope.py | 157 + .../semantic_preserving/UnrollMapScope.py | 129 + .../semantic_preserving/addArrayCopies.py | 196 + .../semantic_preserving/addArrayDims.py | 518 + .../semantic_preserving/changeMemoryLayout.py | 85 + .../semantic_preserving/optimize_sdfg.py | 3557 ----- .../semantic_preserving/removeArrayCopies.py | 159 + .../semantic_preserving/removeArrayDims.py | 376 + .../semantic_preserving/reuseArray.py | 266 + .../reuseArrayLocations.py | 238 + .../test_kernel/test_gemm.sdfg | 3603 ----- .../test_kernel/test_gemm_const.sdfg | 3554 ----- .../test_kernel/test_softmax.sdfg | 11595 ---------------- 20 files changed, 3398 insertions(+), 22309 deletions(-) create mode 100644 dace/transformation/semantic_preserving/HeapToStack.py create mode 100644 dace/transformation/semantic_preserving/JoinMapScopes.py create mode 100644 dace/transformation/semantic_preserving/SplitMapScopes.py create mode 100644 dace/transformation/semantic_preserving/StackToHeap.py create mode 100644 dace/transformation/semantic_preserving/SwapLoopMap.py create mode 100644 dace/transformation/semantic_preserving/SwapMapScope.py create mode 100644 dace/transformation/semantic_preserving/SwapOperations.py create mode 100644 dace/transformation/semantic_preserving/TileMapScope.py create mode 100644 dace/transformation/semantic_preserving/UnrollMapScope.py create mode 100644 dace/transformation/semantic_preserving/addArrayCopies.py create mode 100644 dace/transformation/semantic_preserving/addArrayDims.py create mode 100644 dace/transformation/semantic_preserving/changeMemoryLayout.py delete mode 100644 dace/transformation/semantic_preserving/optimize_sdfg.py create mode 100644 dace/transformation/semantic_preserving/removeArrayCopies.py create mode 100644 dace/transformation/semantic_preserving/removeArrayDims.py create mode 100644 dace/transformation/semantic_preserving/reuseArray.py create mode 100644 dace/transformation/semantic_preserving/reuseArrayLocations.py delete mode 100644 dace/transformation/semantic_preserving/test_kernel/test_gemm.sdfg delete mode 100644 dace/transformation/semantic_preserving/test_kernel/test_gemm_const.sdfg delete mode 100644 dace/transformation/semantic_preserving/test_kernel/test_softmax.sdfg 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/optimize_sdfg.py b/dace/transformation/semantic_preserving/optimize_sdfg.py deleted file mode 100644 index 7acb11207d..0000000000 --- a/dace/transformation/semantic_preserving/optimize_sdfg.py +++ /dev/null @@ -1,3557 +0,0 @@ -""" -***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. - -Transformation Classes: - SwapLoopMap - SwapMapScope - SwapOperations - TileMapScope - JoinMapScopes - SplitMapScopes - UnrollMapScope - addArrayCopies - removeArrayCopies - addArrayDims - removeArrayDims - reuseArray - reuseArrayLocation - StackToHeap - HeapToStack - changeMemoryLayout - -Every class contains a find and apply method. The find method searches for possible transformations in the given SDFG and the apply method applies the transformation to the SDFG. -""" - -###* Imports *### - -import sys -import copy -#from dace.transformation import transformation -#from dace.transformation.helpers import redirect_edge -#import pdb -import traceback -import random -import dace -import numpy as np -import math -import sympy -import argparse - -###* Transformation Classes *### - -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 map to swap. - map_entry2 (dace.MapEntry): The second 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) - -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(sdfg).out_edges(exit2)][0] - state.add_nedge(dst_node, entry1, dace.memlet.Memlet()) - -class SwapOpertions(): - """ - 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 are consecutive - - The output of the first tasklet is not used in the input of 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 i in range(len(tasklets)): - tasklet1, state1 = tasklets[i] - next = False - for j in range(i+1, len(tasklets)): - tasklet2, state2 = tasklets[j] - if not state1 == state2: - continue - if not state1.entry_node(tasklet1) == state2.entry_node(tasklet2): - continue - if next: - break - next = True - 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 - result.append((tasklet1, tasklet2)) - self.checked = True - return result - - 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. - """ - if not self.checked and not (op1, op2) in self.find(sdfg): - return - self.checked = False - state = find_state(sdfg, op2) - out_node = [edge.dst for edge in state.out_edges(op2)][0] - state.add_nedge(out_node, op1, dace.memlet.Memlet()) - -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): - self.__name = 'TileMapScope' - self.checked = False - # if set, the tiled map is put as innermost map - self.put_last = True - # if set, only the innermost map is tiled - self.only_innermost = False - # maximum tile size - self.max_tile = 1024 - # for symbolic loop bounds - self.tile_sizes = [64] #None - - @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 - 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) - - 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 - opt = SwapLoopMap() - opt.apply(sdfg, prop_map, swap_map) - -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: - dst_shape = in_edge.data.dst_subset.size() - if not dst_shape == entry1.map.range.size(): - reject = True - if not reject: - return True - return False - - # 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 - -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 - -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 = 2): - self.__name = 'UnrollMapScope' - self.checked = False - self.max_unroll_factor = unroll_factor - self.explicit_factor = True - - @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 - 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 - -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(): - for node in state.nodes(): - if isinstance(node, dace.nodes.AccessNode): - 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() - output_nodes = state.sink_nodes() - adj_state = state - 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 - - new_name = '' - i = 0 - while True: - new_name = f'{arr_name}_copy_{i}' - if not new_name in sdfg.arrays: - break - i += 1 - - sdfg.add_array(name=new_name, shape=data.shape, dtype=data.dtype, storage=data.storage, transient=True, lifetime=data.lifetime) - - tasklet = adj_state.add_tasklet("move", {"in_1"}, {"out"}, "out = in_1") - - - 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 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) - -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) - -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) - -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 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 - -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: - 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) - -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, dim_pos): - #source_nodes = state.source_nodes() - #visited = set() - #self.traverse_subsets(state, state.entry_node(node), node.data, dim_pos, visited, scope_only=True) - for other_states in sdfg.nodes(): - visited = set() - sources = other_states.source_nodes() - for source in sources: - self.traverse_subsets(other_states, source, node.data, dim_pos, visited, scope_only=False) - - def traverse_subsets(self, state, node, data, dim_pos, visited = None, scope_only = False): - if visited is None: - visited = set() - visited.add(node) - if scope_only and isinstance(node, dace.nodes.MapExit): - return - out_edges = state.out_edges(node) - for edge in out_edges: - if edge.data.data == data: - #print(f'{data} {dim_pos} {edge.data.subset} and {type(edge.data.subset)}') - self.update_subset(state, edge, dim_pos) - if not edge.dst in visited: - self.traverse_subsets(state, edge.dst, data, dim_pos, visited, scope_only) - - def update_subset(self, state, edge, dim_pos): - subset = list(edge.data.subset.ranges) - new_subset = [] - for i,sub in enumerate(subset): - if i == dim_pos: - continue - new_subset.append(sub) - if not new_subset: - new_subset.append((0,0,1)) - edge.data.subset = dace.subsets.Range(new_subset) - #print(f'new subset {edge.data.subset} and {type(edge.data.subset)}\n') - -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(data) - self.checked = True - return result - - def apply(self, sdfg, data): - """ - This method applies the StackToHeap transformation to the given SDFG. It changes the storage type of the array to the heap. - """ - if not self.checked and not data in self.find(sdfg): - return - self.checked = False - - data.storage = dace.StorageType.CPU_Heap - -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 = None #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 data arrays that are not stored on the stack. - """ - 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(data) - self.checked = True - return result - - def apply(self, sdfg, data): - if not self.checked and not data in self.find(sdfg): - return - self.checked = False - data.storage = dace.StorageType.Register - -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. - """ - 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 - -###* Helper functions *### -# NOT USED -def get_source_node(state, node, data, visited = []): - if isinstance(node, dace.nodes.AccessNode): - if node.data == data: - return node - visited.append(node) - - for edge in state.in_edges(node): - if not edge.src in visited: - get_source_node(state, edge.src, data) - - return None - -# NOT USED -def get_sink_node(state, node, data, visited = []): - if isinstance(node, dace.nodes.AccessNode): - if node.data == data: - return node - visited.append(node) - - for edge in state.out_edges(node): - if not edge.dst in visited: - get_sink_node(state, edge.dst, data) - - return None - -def find_state(sdfg, node): - for sdfg_state in sdfg.nodes(): - if node in sdfg_state.nodes(): - return sdfg_state - -def find_state_by_name(sdfg, name): - for state in sdfg.nodes(): - for node in state.nodes(): - if isinstance(node, dace.nodes.AccessNode): - if node.data == name: - return state - -# NOT USED -def start_nodes(state): - incoming_edges = {node: 0 for node in state.nodes()} - for edge in state.edges(): - incoming_edges[edge.dst] += 1 - starting_nodes = [node for node, count in incoming_edges.items() if count == 0] - return starting_nodes - -def order_sdfg(state): - incoming_edges = {node: 0 for node in state.nodes()} - for edge in state.edges(): - incoming_edges[edge.dst] += 1 - starting_nodes = [node for node, count in incoming_edges.items() if count == 0] - - order = [] - visited = set() - - # Stack to manage nested structures - stack = [order] - - def traverse(node): - if node in visited: - return - visited.add(node) - - # Check if node is a MapEntry, if so, create a new sublist - if isinstance(node, dace.nodes.MapEntry): - current_list = [] - stack[-1].append(node) - stack[-1].append(current_list) - stack.append(current_list) - else: - stack[-1].append(node) - - for edge in state.edges(): - if edge.src == node: - traverse(edge.dst) - - # If node is a MapExit, pop the last list from the stack - if isinstance(node, dace.nodes.MapExit): - stack.pop() - - for node in starting_nodes: - traverse(node) - - return order - -def state_0(sdfg): - for state in sdfg.nodes(): - if state.label == 'state_0': - return state - -###* Optimizing SDFG *### - -def optimize_sdfg(sdfg, optimization): - """ - This method applies the given optimization to the SDFG. - - Args: - sdfg (dace.SDFG): The SDFG to apply the optimization to. - optimization: The optimization to apply. - """ - # Apply - transformation = optimization() - locations = transformation.find() - argument = random.choice(locations) - if isinstance(argument,(tuple,list)): - transformation.apply(sdfg, *argument) - else: - transformation.apply(sdfg, argument) - -###* Kernel Testing *### - -def validate(sdfg): - """ - This method tests the SDFG by comparing the output of the SDFG with the expected output. - Only works for the gemm and softmax kernel (args.kernel). - """ - size = 32 - - inputs = set() - for state in sdfg.nodes(): - source = state.source_nodes() - for src in source: - if isinstance(src, dace.nodes.AccessNode): - if not 'copy' in src.data: - inputs.add(src) - - input_names = [node.data for node in inputs if not node.desc(sdfg).transient] - outputs = [node for node in state_0(sdfg).sink_nodes() if isinstance(node, dace.nodes.AccessNode)] - output_names = [node.data for node in outputs] - data = sdfg.arrays - - loop_index = {name: size for name, data in data.items() if isinstance(data, dace.data.Scalar) and not name in [n.data for n in state_0(sdfg).nodes() if isinstance(n, dace.nodes.AccessNode)]} - input_data = {} - for output_name in output_names: - if output_name in input_names: - continue - data_container = data[output_name] - if isinstance(data_container, dace.data.Scalar): - input_data[output_name] = np.zeros(1) - else: - shape = data_container.shape - np_shape = [] - for dim in shape: - d = str(dim) - if d in loop_index: - np_shape.append(loop_index[d]) - input_data[output_name] = np.zeros(shape=np_shape) - - for input_name in input_names: - data_container = data[input_name] - if isinstance(data_container, dace.data.Scalar): - input_data[input_name] = np.random.rand() - else: - shape = data_container.shape - np_shape = [] - for dim in shape: - d = str(dim) - if d in loop_index: - np_shape.append(loop_index[d]) - input_data[input_name] = np.random.rand(*np_shape) - - - expected = np.array([]) - output = np.array([]) - if args.kernel == "gemm": - expected = input_data['alpha'] * input_data['A'] @ input_data['B'] + input_data['beta']*input_data['C'] - output = input_data['D'] - elif args.kernel == "softmax": - argsrc = np.copy(input_data['src']) - argsrc -= np.max(argsrc, axis=1, keepdims=True) - np.exp(argsrc, out=argsrc) - argsrc /= np.sum(argsrc, axis=1, keepdims=True) - expected = argsrc - output = input_data['dst'] - - input_data.update(loop_index) - try: - sdfg(**input_data) - except Exception as e: - print(e) - - diff = np.linalg.norm(output - expected) - print("Difference:", diff) - if diff >= 1e-4: - print("Validation failed") - else: - print("Validation successful") - print("==== Program end ====") - - -def main(args): - """ - This method is the main method of the optimize_sdfg.py script. It applies the given optimization to the SDFG and validates the output. - Currently only the gemm and softmax kernel are supported to test in this script. Refer to mk_kernel_tests.py for other kernels. - - Call the script with the following arguments: - - kernel: The kernel to test (gemm, softmax) (--kernel gemm) - - opt: The optimization to apply (tile, swap, unroll, split, join, reuseArray, addCopies, removeCopies, reuseLoc) (--opt tile) - - const: If True, the kernel with constant values is used (gemm_const, softmax_const) (--const) - - validate: If True, the output of the SDFG is validated (--validate) - """ - - print("==== Program start ====") - - source = '' - sdfg_name = '' - if args.kernel == "gemm": - postfix = "" - if not args.const: - source = "test_kernel/test_gemm.sdfg" - #source = ".dacecache/affine_grid_2d/program.sdfg" - else: - source = "test_kernel/test_gemm_const.sdfg" - postfix = "_const" - sdfg_name = "gemm_" + args.opt + postfix - elif args.kernel == "softmax": - postfix = "" - if not args.const: - source = "test_kernel/test_softmax.sdfg" - else: - source = "test_kernel/test_softmax_const.sdfg" - postfix = "_const" - sdfg_name = "softmax_" + args.opt + postfix - else: - sys.exit("Invalid kernel") - - sdfg = dace.SDFG.from_file(source) - sdfg.name = sdfg_name - - if args.opt == "tile": - transformation = TileMapScope() - locations = transformation.find(sdfg) - if locations: - print("tile locations", locations) - loc = locations[0] - print("tile location", loc) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied loop tile") - - if args.opt == "swap": - transformation = SwapLoopMap() - locations = transformation.find(sdfg) - if locations: - loc = locations[0] - print("swap location", loc) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied swap scope") - - if args.opt == "unroll": - print("unroll") - transformation = UnrollMapScope() - locations = transformation.find(sdfg) - if locations: - loc = locations[0] - print("unroll location", loc) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied unroll scope") - - if args.opt == "split": - transformation = SplitMapScopes() - locations = transformation.find(sdfg) - print(f'splitable locations {locations}') - if locations: - #loc = locations[0] - loc = None - for t in locations: - if t[1].label == 'mul': - if isinstance(t[0].out_edges(t[1])[0].dst, dace.nodes.AccessNode): - if t[0].out_edges(t[1])[0].dst.data == 'inv_sum_val_div_u':#'f32_copyfrom_i32': - loc = t - break - - print("split location", loc[1]) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied split scope") - - if args.opt == "join": - """ - # first do a split - transformation = SplitMapScopes() - locations = transformation.find(sdfg) - print(f'splitable locations {locations}') - if locations: - loc = None - for t in locations: - if t[1].label == 'fnmsub': - if isinstance(t[0].out_edges(t[1])[0].dst, dace.nodes.AccessNode): - if t[0].out_edges(t[1])[0].dst.data == 'inv_sum_val_inv_th_2':#'f32_copyfrom_i32': - loc = t - break - transformation.apply(sdfg, loc[0], loc[1]) - sdfg.view() - """ - #join - transformation = JoinMapScopes() - locations = transformation.find(sdfg) - print(f'joinable locations {locations}') - if locations: - loc = locations[-1] - print("join location", loc[0], loc[1], loc[2]) - transformation.apply(sdfg, loc[0], loc[1], loc[2]) - print("applied join") - - if args.opt == "reuseArray": - transformation = reuseArray() - locations = transformation.find(sdfg) - if locations: - print("reuse array locations", locations) - loc = locations[0] - print("reuse array location", loc) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied reuse array") - - if args.opt == "addCopies": - transformation = addArrayCopies() - locations = transformation.find(sdfg) - if locations: - loc = "dst" - print("add copies location", loc) - transformation.apply(sdfg, loc) - print("applied add copies") - - if args.opt == "removeCopies": - transformation = addArrayCopies() - loc = "dst" - transformation.apply(sdfg, loc) - transformation = removeArrayCopies() - locations = transformation.find(sdfg) - print(f'applicable locations {locations}') - if locations: - loc = loc + "_copy_0" - print("remove copies location", loc) - transformation.apply(sdfg, loc) - print("applied remove copies") - - if args.opt == "reuseLoc": - transformation = reuseArrayLocations() - locations = transformation.find(sdfg) - print(f'reusable dimensions {locations}') - if locations: - loc = locations[0] - print("reuse location", loc) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied reuse location") - - if args.opt == "changeLayout": - transformation = changeMemoryLayout() - locations = transformation.find(sdfg) - print(f'changeable arrays {locations}') - if locations: - loc = locations[0] - print("change layout location", loc) - transformation.apply(sdfg, loc, [1,0]) - print("applied change layout") - - if args.opt == "addDimension": - transformation = addArrayDims() - locations = transformation.find(sdfg) - print(f'createable dimensions {locations}') - if locations: - loc = locations[0] - print("add dimension location", loc) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied add dimension") - - if args.opt == "removeDimension": - transformation = addArrayDims() - locations = transformation.find(sdfg) - if locations: - loc = locations[0] - transformation.apply(sdfg, loc[0], loc[1]) - transformation = removeArrayDims() - locations = transformation.find(sdfg) - print(f'deleteable dimensions {locations}') - if locations: - loc = locations[0] - print("remove dimension location", loc) - print(f'{loc[0]} {loc[1]} {loc[2]}') - transformation.apply(sdfg, loc[0], loc[1], loc[2]) - print("applied remove dimension") - - if args.opt == "swapScopes": - transformation = SwapMapScope() - locations = transformation.find(sdfg) - if locations: - loc = locations[0] - print("swap operation location", loc) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied swap operation") - - if args.opt == "swapOp": - transformation = SwapOpertions() - locations = transformation.find(sdfg) - print(f'swappable operations {locations}') - if locations: - loc = locations[0] - print("swap operation location", loc) - transformation.apply(sdfg, loc[0], loc[1]) - print("applied swap operation") - - if args.opt == "test_split_join": - transformation1 = SplitMapScopes() - locations = transformation1.find(sdfg) - for loc in locations: - copy_sdfg = copy.deepcopy(sdfg) - locations1 = transformation1.find(copy_sdfg) - loc1 = None - for locs in locations1: - if not locs[1].label == loc[1].label: - continue - out_edge1 = [edge.data.data for edge in locs[0].out_edges(locs[1])] - out_edge0 = [edge.data.data for edge in loc[0].out_edges(loc[1])] - if not out_edge1 == out_edge0: - continue - else: - loc1 = locs - break - if loc1: - try: - transformation1.apply(copy_sdfg, loc1[0], loc1[1]) - print(f"applied split scope on {loc1[1]} ({loc1[0].out_edges(loc1[1])[0].data.data})") - transformation2 = JoinMapScopes() - locations2 = transformation2.find(copy_sdfg) - if locations2: - loc2 = locations2[-1] - try: - transformation2.apply(copy_sdfg, loc2[0], loc2[1], loc2[2]) - print(f"applied join scope on {loc2[1]} and {loc2[2]}") - try: - copy_sdfg.validate() - print(f'==> validated {loc1[1]}\n') - except Exception as e: - print(e) - sdfg.view() - print(f'==> invalid {loc1[1]}\n') - except Exception as e: - print(f'invalid join {loc2[1]}, abort\n') - - except Exception as e: - print(f'invalid split {loc1[1]}, abort\n') - - if args.opt == "test_split_join_2": - trans_split = SplitMapScopes() - trans_join = JoinMapScopes() - while True: - split_locs = trans_split.find(sdfg) - if not split_locs: - break - loc1 = random.choice(split_locs) - try: - trans_split.apply(sdfg, loc1[0], loc1[1]) - print(f'applied split {loc1[1]} ({loc1[0].out_edges(loc1[1])[0].data.data})') - join_locs = trans_join.find(sdfg) - if not join_locs: - break - loc = random.choice(join_locs) - try: - trans_join.apply(sdfg, loc[0], loc[1], loc[2]) - print(f'applied join {loc[1]} and {loc[2]}') - try: - sdfg.validate() - print(f'==> validated {loc[1]} and {loc[2]}\n') - except Exception as e: - traceback.print_exc() - print(f'==> invalid {loc[1]} and {loc[2]}\n') - except Exception as e: - traceback.print_exc() - print(f'failed to join {loc[1]}\n') - break - except Exception as e: - traceback.print_exc() - print(f'failed to split {loc1[1]}\n') - break - - if args.opt == "test_split_join_3": - trans_join = JoinMapScopes() - trans_split = SplitMapScopes() - violation = False - its = 10 - while True: - split_locs = trans_split.find(sdfg) - if not split_locs or its == 0: - break - its -= 1 - loc1 = random.choice(split_locs) - try: - trans_split.apply(sdfg, loc1[0], loc1[1]) - print(f'applied split {loc1[1]} ({loc1[0].out_edges(loc1[1])[0].data.data})') - try: - sdfg.validate() - except Exception as e: - violation = True - print(f'==> invalid {loc1[1]}\n') - break - except Exception as e: - violation = True - print(f'failed to split {loc1[1]} ({loc1[0].out_edges(loc1[1])[0].data.data})\n') - break - its = 10 - while not violation: - join_locs = trans_join.find(sdfg) - if not join_locs or its == 0: - break - its -= 1 - loc = random.choice(join_locs) - try: - trans_join.apply(sdfg, loc[0], loc[1], loc[2]) - print(f'applied join {loc[1]} and {loc[2]}') - try: - sdfg.validate() - except Exception as e: - print(f'==> invalid {loc[1]} and {loc[2]}\n') - break - except Exception as e: - print(f'failed to join {loc[1]}\n') - break - - sdfg.view() - csdfg = sdfg.compile() - if args.validate: - validate(sdfg) - -def parse_args(): - parser = argparse.ArgumentParser(description='Optimize SDFGs.') - parser.add_argument('--validate', action='store_true', help='see if the generated code is correct') - parser.add_argument('--print', action='store_true', help='show printed code') - parser.add_argument('--opt', type=str, help='optimization to apply', default='') - parser.add_argument('--kernel', type=str, help='kernel to apply the optimization to', default='gemm') - parser.add_argument('--const', action='store_true', help='Symbolic or Constant Loop Sizes', default=False) - return parser.parse_args() - -if __name__ == "__main__": - args = parse_args() - main(args) \ 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, dim_pos): + #source_nodes = state.source_nodes() + #visited = set() + #self.traverse_subsets(state, state.entry_node(node), node.data, dim_pos, visited, scope_only=True) + for other_states in sdfg.nodes(): + visited = set() + sources = other_states.source_nodes() + for source in sources: + self.traverse_subsets(other_states, source, node.data, dim_pos, visited, scope_only=False) + + def traverse_subsets(self, state, node, data, dim_pos, visited = None, scope_only = False): + if visited is None: + visited = set() + visited.add(node) + if scope_only and isinstance(node, dace.nodes.MapExit): + return + out_edges = state.out_edges(node) + for edge in out_edges: + if edge.data.data == data: + #print(f'{data} {dim_pos} {edge.data.subset} and {type(edge.data.subset)}') + self.update_subset(state, edge, dim_pos) + if not edge.dst in visited: + self.traverse_subsets(state, edge.dst, data, dim_pos, visited, scope_only) + + def update_subset(self, state, edge, dim_pos): + subset = 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