[fx] Add activation checkpoint solver rotor

colossalai/fx/__init__.py

@@ -1 +1,2 @@
 from .tracer import ColoTracer
+from .graph_module import ColoGraphModule

colossalai/fx/passes/algorithms/__init__.py

@@ -1 +1,3 @@
 from .ckpt_solver_chen import chen_greedy
+from .linearize import linearize
+from .ckpt_solver_rotor import solver_rotor

colossalai/fx/passes/algorithms/ckpt_solver_rotor.py

@@ -0,0 +1,198 @@
+from typing import List, Set, Tuple, Dict
+import torch
+from torch.fx import GraphModule, Node
+import math
+from .linearize import linearize
+from .utils import *
+from colossalai.fx.profiler import profile_function, profile_module
+from colossalai.fx.passes.meta_info_prop import MetaInfoProp
+
+
+# this is the python compute table code from rotor
+# https://gitlab.inria.fr/hiepacs/rotor
+# paper link: https://hal.inria.fr/hal-02352969
+def _compute_table(chain: Chain, mmax) -> Tuple:
+    """Returns the optimal table: a tuple containing: 
+    Opt[m][lmin][lmax] with lmin = 0...chain.length
+         and lmax = lmin...chain.length (lmax is not included) and m = 0...mmax
+    what[m][lmin][lmax] is (True,) if the optimal choice is a chain checkpoint
+                           (False, j) if the optimal choice is a leaf checkpoint of length j
+    The computation uses dynamic programming"""
+
+    fw = chain.fweight + [0]    ## forward time
+    bw = chain.bweight    ## backward time, not used
+    cw = chain.cweight + [0]    ## size of x (and of y)
+    cbw = chain.cbweight + [0]    ## size of xbar
+    fwd_tmp = chain.fwd_tmp + [0]
+    bwd_tmp = chain.bwd_tmp + [0]
+
+    # Build table
+    opt = [[{} for _ in range(chain.length + 1)] for _ in range(mmax + 1)]
+    what = [[{} for _ in range(chain.length + 1)] for _ in range(mmax + 1)]
+    ## Last one is a dict because its indices go from i to l. Renumbering will wait for C implementation
+
+    # Initialize borders of the tables for lmax-lmin = 0
+    for m in range(mmax + 1):
+        for i in range(chain.length + 1):
+            #lmax-lmin = 0
+            limit = max(cw[i + 1] + cbw[i + 1] + fwd_tmp[i], cw[i] + cw[i + 1] + cbw[i + 1] + bwd_tmp[i])
+            if m >= limit:    ## Equation (1)
+                opt[m][i][i] = fw[i] + bw[i]
+            else:
+                opt[m][i][i] = float("inf")
+
+    # Compute everything
+    for m in range(mmax + 1):
+        for d in range(1, chain.length + 1):
+            for i in range(chain.length + 1 - d):
+                # for idx in range(i+1, chain.length + 1):
+                idx = i + d
+                mmin = cw[idx + 1] + cw[i + 1] + fwd_tmp[i]
+                if idx > i + 1:
+                    mmin = max(mmin, cw[idx + 1] + max(cw[j] + cw[j + 1] + fwd_tmp[j] for j in range(i + 1, idx)))
+                if m < mmin:
+                    opt[m][i][idx] = float("inf")
+                else:
+                    leaf_checkpoints = [(j, sum(fw[i:j]) + opt[m - cw[j]][j][idx] + opt[m][i][j - 1])
+                                        for j in range(i + 1, idx + 1)
+                                        if m >= cw[j]]
+                    if leaf_checkpoints:
+                        best_leaf = min(leaf_checkpoints, key=lambda t: t[1])
+                    else:
+                        best_leaf = None
+                    if m >= cbw[i + 1]:
+                        chain_checkpoint = opt[m][i][i] + opt[m - cbw[i + 1]][i + 1][idx]
+                    else:
+                        chain_checkpoint = float("inf")
+                    if best_leaf and best_leaf[1] <= chain_checkpoint:
+                        opt[m][i][idx] = best_leaf[1]
+                        what[m][i][idx] = (False, best_leaf[0])
+                    else:
+                        opt[m][i][idx] = chain_checkpoint
+                        what[m][i][idx] = (True,)
+    return (opt, what)
+
+
+def _rec(chain, lmin, lmax, cmem, opt_table):
+    """ chain : the class describing the AC graph
+        lmin : index of the first forward to execute
+        lmax : upper bound index of the last forward to execute (not included)
+        cmem : number of available memory slots
+        Return the optimal sequence of makespan Opt_hete[cmem][lmin][lmax-lmin]"""
+    if cmem <= 0:
+        raise ValueError("Can not process a chain with negative memory {cmem}".format(cmem=cmem))
+    opt, what = opt_table
+    sequence = Sequence(Function("Persistent", lmax - lmin, cmem))
+    if opt[cmem][lmin][lmax] == float("inf"):
+        raise ValueError("Can not process this chain from index {lmin} to {lmax} with memory {cmem}".format(lmin=lmin,
+                                                                                                            lmax=lmax,
+                                                                                                            cmem=cmem))
+    if lmin == lmax:
+        if lmin == chain.length:
+            sequence.insert(Loss())
+        else:
+            sequence.insert(ForwardEnable(lmin))
+            sequence.insert(Backward(lmin))
+        return sequence
+
+    if what[cmem][lmin][lmax][0]:
+        sequence.insert(ForwardEnable(lmin))
+        sequence.insert_sequence(_rec(chain, lmin + 1, lmax, cmem - chain.cbweigth[lmin + 1], opt_table))
+        sequence.insert(Backward(lmin))
+    else:
+        j = what[cmem][lmin][lmax][1]
+        sequence.insert(ForwardCheck(lmin))
+        for k in range(lmin + 1, j):
+            sequence.insert(ForwardNograd(k))
+        sequence.insert_sequence(_rec(chain, j, lmax, cmem - chain.cweigth[j], opt_table))
+        sequence.insert_sequence(_rec(chain, lmin, j - 1, cmem, opt_table))
+    return sequence
+
+
+def _discretize(mem_unit, values):
+    return [math.ceil(value / mem_unit) for value in values]
+
+
+def _construct_chain(node_dict: Dict[int, Node], data: torch.Tensor, mem_unit: int) -> Chain:
+
+    fwd_time = []
+    bwd_time = []
+    xbar_sizes = [data.numel() * data.element_size()]
+    x_sizes = [data.numel() * data.element_size()]
+
+    # currently we can't get the temp memory needed in fwd and bwd
+    tmp_fwd = [0] * len(node_dict)
+    tmp_bwd = [0] * (len(node_dict) + 1)
+
+    for key in node_dict.keys():
+        fwd_time.append(0)
+        bwd_time.append(0)
+        xbar_sizes.append(0)
+        x_sizes.append(node_dict[key][-1].meta['tensor_meta'].numel *
+                       torch.tensor([], dtype=node_dict[key][-1].meta['tensor_meta'].dtype).element_size())
+        for node in node_dict[key]:
+            fwd_time[-1] += node.__flops__
+
+            # currently we haven't patched the backward flops count
+            bwd_time[-1] += node.__flops__ * 2
+
+            xbar_sizes[-1] += node.__activation__
+
+        xbar_sizes[-1] = max(xbar_sizes[-1], x_sizes[-1])
+
+    bwd_time.append(0)
+
+    fwd_time = _discretize(mem_unit, fwd_time)
+    bwd_time = _discretize(mem_unit, bwd_time)
+    xbar_sizes = _discretize(mem_unit, xbar_sizes)
+    x_sizes = _discretize(mem_unit, x_sizes)
+    tmp_fwd = _discretize(mem_unit, tmp_fwd)
+    tmp_bwd = _discretize(mem_unit, tmp_bwd)
+
+    return Chain(fwd_time, bwd_time, x_sizes, xbar_sizes, tmp_fwd, tmp_bwd)
+
+
+def _annotate_from_sequence(sequence: Sequence, node_dict: Dict[int, Node]) -> GraphModule:
+    op_list = sequence.list_operations()
+    loss_op = [op for op in op_list if isinstance(op, Loss)][0]
+    op_list = op_list[:op_list.index(loss_op)]
+    ckpt_idx = 0
+    in_ckpt = False
+    ckpt_region = []
+    for idx, op in enumerate(op_list, 1):
+        if in_ckpt:
+            if isinstance(op, ForwardNograd):
+                ckpt_region.append(idx)
+
+            elif isinstance(op, ForwardEnable):
+                in_ckpt = False
+                for idx in ckpt_region:
+                    for node in node_dict[idx]:
+                        setattr(node, "activation_checkpoint", ckpt_idx)
+
+                ckpt_idx += 1
+                ckpt_region = []
+
+            elif isinstance(op, ForwardCheck):
+                for idx in ckpt_region:
+                    for node in node_dict[idx]:
+                        setattr(node, "activation_checkpoint", ckpt_idx)
+
+                ckpt_idx += 1
+                ckpt_region = [idx]
+
+        else:
+            if isinstance(op, ForwardCheck):
+                in_ckpt = True
+                ckpt_region.append(idx)
+
+
+def solver_rotor(gm: GraphModule, data: torch.Tensor, mem_limit: int, mem_slots: int = 500) -> GraphModule:
+    node_dict = linearize(gm)
+    mem_unit = mem_limit // mem_slots
+    MetaInfoProp(gm).run(data)
+    chain: Chain = _construct_chain(node_dict, data, mem_unit)
+    opt_table = _compute_table(chain, mem_slots)
+    sequence = _rec(chain, 0, chain.length, mem_slots - chain.cweight[0], opt_table)
+    _annotate_from_sequence(sequence, node_dict)
+    return gm

colossalai/fx/passes/algorithms/linearize.py

@@ -0,0 +1,89 @@
+from typing import OrderedDict
+from torch.fx import GraphModule
+from collections import OrderedDict
+import pdb
+
+
+def linearize(gm: GraphModule) -> dict:
+    status_dict = {}
+    node_dict = OrderedDict()
+    node_idx = 0
+    for node in gm.graph.nodes:
+        last_dict_len = len(status_dict)
+        # remove node from users list in status_dict
+        for item in status_dict.values():
+            if node in item:
+                item.remove(node)
+
+        # pop node from status_dict if it is fully used
+        for key in list(status_dict):
+            if len(status_dict[key]) == 0:
+                status_dict.pop(key)
+
+        # first node in graph, it should be in n0-n1 type,
+        # where n0 contains only input op, i.e. placeholder
+        if last_dict_len == 0:
+            node_dict[node_idx] = [node]
+            status_dict[node.name] = list(node.users)
+            node_idx += 1
+            node_dict[node_idx] = []
+
+            continue
+
+        # boundary case
+        if len(status_dict) == 0:
+            # current node region end point = next node region start point
+            # i.e. n1-n2-n3-... type node, each node contains only one op
+            if last_dict_len == 1:
+                if len(node_dict[node_idx]) > 0:
+                    node_idx += 1
+                    node_dict[node_idx] = []
+                node_dict[node_idx].append(node)
+                status_dict[node.name] = list(node.users)
+
+                continue
+
+            # n1-n2-n3, if n1 has multiple ops, the last op in n1 will be
+            # the one who is able to clean all others in status_dict
+            # and as the last_dict_len > 1, there are multiple ops are used
+            # by this node, we view it as the end of one node and start a new node
+            else:
+
+                node_dict[node_idx].append(node)
+                status_dict[node.name] = list(node.users)
+                node_idx += 1
+                node_dict[node_idx] = []
+
+                continue
+
+        else:
+            # currently I will use bigger node structure
+            # if the following region is activated, the node will be smaller
+            #################################################
+            # if last_dict_len == 1:
+            #     if len(node_dict[node_idx]) > 0:
+            #         node_idx += 1
+            #     node_dict[node_idx] = [node]
+            #     status_dict[node.name] = list(node.users)
+            #
+            #     continue
+            #################################################
+
+            # in-node case, as the current node can not clean status_dict
+            # we view it as in-node status, the node will be appended to the
+            # current node_idx
+            node_dict[node_idx].append(node)
+            status_dict[node.name] = list(node.users)
+
+            continue
+
+    # If the output node use multiple nodes, there might be an
+    # empty node after the output node
+    if len(node_dict[node_idx]) == 0:
+        node_dict.pop[node_idx]
+        node_idx -= 1
+
+    # pop the last two nodes
+    node_dict.pop(0)
+    node_dict.pop(node_idx)
+    return node_dict