[autoparallel] refactor and add rotorc. (#1789)

* [autoparallel] refactor and add rotorc.

* [autoparallel] refactor and add rotorc.

colossalai/auto_parallel/checkpoint/build_c_ext.py

@@ -0,0 +1,16 @@
+import os
+
+from setuptools import Extension, setup
+
+this_dir = os.path.dirname(os.path.abspath(__file__))
+ext_modules = [Extension(
+    'rotorc',
+    sources=[os.path.join(this_dir, 'ckpt_solver_rotor.c')],
+)]
+
+setup(
+    name='rotor c extension',
+    version='0.1',
+    description='rotor c extension for faster dp computing',
+    ext_modules=ext_modules,
+)

colossalai/auto_parallel/checkpoint/ckpt_solver_rotor.c

@@ -0,0 +1,197 @@
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
+long* PySequenceToLongArray(PyObject* pylist) {
+  if (!(pylist && PySequence_Check(pylist))) return NULL;
+  Py_ssize_t len = PySequence_Size(pylist);
+  long* result = (long*)calloc(len + 1, sizeof(long));
+  for (Py_ssize_t i = 0; i < len; ++i) {
+    PyObject* item = PySequence_GetItem(pylist, i);
+    result[i] = PyLong_AsLong(item);
+    Py_DECREF(item);
+  }
+  result[len] = 0;
+  return result;
+}
+
+double* PySequenceToDoubleArray(PyObject* pylist) {
+  if (!(pylist && PySequence_Check(pylist))) return NULL;
+  Py_ssize_t len = PySequence_Size(pylist);
+  double* result = (double*)calloc(len + 1, sizeof(double));
+  for (Py_ssize_t i = 0; i < len; ++i) {
+    PyObject* item = PySequence_GetItem(pylist, i);
+    result[i] = PyFloat_AsDouble(item);
+    Py_DECREF(item);
+  }
+  result[len] = 0;
+  return result;
+}
+
+long* getLongArray(PyObject* container, const char* attributeName) {
+  PyObject* sequence = PyObject_GetAttrString(container, attributeName);
+  long* result = PySequenceToLongArray(sequence);
+  Py_DECREF(sequence);
+  return result;
+}
+
+double* getDoubleArray(PyObject* container, const char* attributeName) {
+  PyObject* sequence = PyObject_GetAttrString(container, attributeName);
+  double* result = PySequenceToDoubleArray(sequence);
+  Py_DECREF(sequence);
+  return result;
+}
+
+static PyObject* computeTable(PyObject* self, PyObject* args) {
+  PyObject* chainParam;
+  int mmax;
+
+  if (!PyArg_ParseTuple(args, "Oi", &chainParam, &mmax)) return NULL;
+
+  double* ftime = getDoubleArray(chainParam, "ftime");
+  if (!ftime) return NULL;
+
+  double* btime = getDoubleArray(chainParam, "btime");
+  if (!btime) return NULL;
+
+  long* x = getLongArray(chainParam, "x");
+  if (!x) return NULL;
+
+  long* xbar = getLongArray(chainParam, "xbar");
+  if (!xbar) return NULL;
+
+  long* ftmp = getLongArray(chainParam, "btmp");
+  if (!ftmp) return NULL;
+
+  long* btmp = getLongArray(chainParam, "btmp");
+  if (!btmp) return NULL;
+
+  long chainLength = PyObject_Length(chainParam);
+  if (!chainLength) return NULL;
+
+#define COST_TABLE(m, i, l)                               \
+  costTable[(m) * (chainLength + 1) * (chainLength + 1) + \
+            (i) * (chainLength + 1) + (l)]
+  double* costTable = (double*)calloc(
+      (mmax + 1) * (chainLength + 1) * (chainLength + 1), sizeof(double));
+
+#define BACK_PTR(m, i, l)                               \
+  backPtr[(m) * (chainLength + 1) * (chainLength + 1) + \
+          (i) * (chainLength + 1) + (l)]
+  long* backPtr = (long*)calloc(
+      (mmax + 1) * (chainLength + 1) * (chainLength + 1), sizeof(long));
+
+  for (long m = 0; m <= mmax; ++m)
+    for (long i = 0; i <= chainLength; ++i)
+      if ((m >= x[i + 1] + xbar[i + 1] + btmp[i]) &&
+          (m >= x[i + 1] + xbar[i + 1] + ftmp[i]))
+        COST_TABLE(m, i, i) = ftime[i] + btime[i];
+      else
+        COST_TABLE(m, i, i) = INFINITY;
+
+  for (long m = 0; m <= mmax; ++m)
+    for (long d = 1; d <= chainLength; ++d) {
+      for (long i = 0; i <= chainLength - d; ++i) {
+        long idx = i + d;
+        long mmin = x[idx + 1] + x[i + 1] + ftmp[i];
+        if (idx > i + 1) {
+          long maxCostFWD = 0;
+          for (long j = i + 1; j < idx; j++) {
+            maxCostFWD = fmaxl(maxCostFWD, x[j] + x[j + 1] + ftmp[j]);
+          }
+          mmin = fmaxl(mmin, x[idx + 1] + maxCostFWD);
+        }
+        if ((m >= mmin)) {
+          long bestLeaf = -1;
+          double sumFw = 0;
+          double bestLeafCost = INFINITY;
+          for (long j = i + 1; j <= idx; ++j) {
+            sumFw += ftime[j - 1];
+            if (m >= x[j]) {
+              double cost = sumFw + COST_TABLE(m - x[j], j, idx) +
+                            COST_TABLE(m, i, j - 1);
+              if (cost < bestLeafCost) {
+                bestLeafCost = cost;
+                bestLeaf = j;
+              }
+            }
+          }
+          double chainCost = INFINITY;
+          if (m >= xbar[i + 1])
+            chainCost =
+                COST_TABLE(m, i, i) + COST_TABLE(m - xbar[i + 1], i + 1, idx);
+          if (bestLeafCost <= chainCost) {
+            COST_TABLE(m, i, idx) = bestLeafCost;
+            BACK_PTR(m, i, idx) = bestLeaf;
+          } else {
+            COST_TABLE(m, i, idx) = chainCost;
+            BACK_PTR(m, i, idx) = -1;
+          }
+        } else
+          COST_TABLE(m, i, idx) = INFINITY;
+      }
+    }
+
+  free(ftime);
+  free(btime);
+  free(x);
+  free(xbar);
+  free(ftmp);
+  free(btmp);
+
+  PyObject* pyCostTable = PyList_New(mmax + 1);
+  PyObject* pyBackPtr = PyList_New(mmax + 1);
+
+  // Convert the result into Python world
+  for (long m = 0; m <= mmax; ++m) {
+    PyObject* pyCostTable_m = PyList_New(chainLength + 1);
+    PyList_SET_ITEM(pyCostTable, m, pyCostTable_m);
+    PyObject* pyBackPtr_m = PyList_New(chainLength + 1);
+    PyList_SET_ITEM(pyBackPtr, m, pyBackPtr_m);
+    for (long i = 0; i <= chainLength; ++i) {
+      PyObject* pyCostTable_m_i = PyDict_New();
+      PyList_SET_ITEM(pyCostTable_m, i, pyCostTable_m_i);
+      PyObject* pyBackPtr_m_i = PyDict_New();
+      PyList_SET_ITEM(pyBackPtr_m, i, pyBackPtr_m_i);
+      for (long l = i; l <= chainLength; ++l) {
+        PyObject* pyVar_l = PyLong_FromLong(l);
+        PyObject* pyCostTable_m_i_l = PyFloat_FromDouble(COST_TABLE(m, i, l));
+        PyDict_SetItem(pyCostTable_m_i, pyVar_l, pyCostTable_m_i_l);
+        Py_DECREF(pyCostTable_m_i_l);
+        PyObject* pyBackPtr_m_i_l;
+        if (BACK_PTR(m, i, l) < 0)
+          pyBackPtr_m_i_l = Py_BuildValue("(O)", Py_True);
+        else
+          pyBackPtr_m_i_l = Py_BuildValue("(Ol)", Py_False, BACK_PTR(m, i, l));
+        PyDict_SetItem(pyBackPtr_m_i, pyVar_l, pyBackPtr_m_i_l);
+        Py_DECREF(pyBackPtr_m_i_l);
+        Py_DECREF(pyVar_l);
+      }
+    }
+  }
+
+  free(costTable);
+  free(backPtr);
+
+  PyObject* result = PyTuple_Pack(2, pyCostTable, pyBackPtr);
+  Py_DECREF(pyCostTable);
+  Py_DECREF(pyBackPtr);
+  return result;
+}
+
+static PyMethodDef rotorMethods[] = {
+    {"compute_table", computeTable, METH_VARARGS,
+     "Compute the optimal table with the rotor algorithm."},
+    {NULL, NULL, 0, NULL} /* Sentinel */
+};
+
+static struct PyModuleDef rotorModule = {
+    PyModuleDef_HEAD_INIT, "rotorc", /* name of module */
+    "A simple implementation of dynamic programming algorithm rotor with C in "
+    "https://hal.inria.fr/hal-02352969. Some code are adapted from "
+    "https://gitlab.inria.fr/hiepacs/rotor.", /* module documentation, may be
+                                                 NULL */
+    -1, /* size of per-interpreter state of the module,
+                   or -1 if the module keeps state in global variables. */
+    rotorMethods};
+
+PyMODINIT_FUNC PyInit_rotorc(void) { return PyModule_Create(&rotorModule); }

colossalai/auto_parallel/checkpoint/ckpt_solver_rotor.py

@@ -1,5 +1,5 @@
 from copy import deepcopy
-from typing import Dict, List, Tuple
+from typing import Any, Dict, List, Tuple
 
 from torch import Tensor
 from torch.fx import Graph, Node
@@ -15,9 +15,9 @@ from colossalai.fx.profiler import (
 from colossalai.logging import get_dist_logger
 
 from .ckpt_solver_base import CheckpointSolverBase
-from .operation import Backward, Chain, ForwardCheck, ForwardEnable, ForwardNograd, Function, Loss, Sequence
+from .operation import Backward, Chain, ForwardCheck, ForwardEnable, ForwardNograd, Loss, Sequence
 
-__all__ = ['CheckpointSolverBase']
+__all__ = ['CheckpointSolverRotor']
 
 
 class CheckpointSolverRotor(CheckpointSolverBase):
@@ -59,11 +59,12 @@ class CheckpointSolverRotor(CheckpointSolverBase):
         self.back_ptr = None
         self.sequence = None
 
-    def solve(self, force_python: bool = False) -> Graph:
+    def solve(self, force_python: bool = False, verbose: bool = False) -> Graph:
         """Solve the checkpointing problem using rotor algorithm.
 
         Args:
             force_python (bool, optional): Use Python version of solver, else use C version. Defaults to False.
+            verbose (bool, optional): Print verbose information. Defaults to False.
 
         Returns:
             graph (Graph): The optimized graph, should be a copy of the original graph.
@@ -76,14 +77,22 @@ class CheckpointSolverRotor(CheckpointSolverBase):
         else:
             self.cost_table, self.back_ptr = self._compute_table_c(chain, self.memory_slots)
 
+        if verbose:
+            self.print_chain()
+
         # backtrack
         try:
-            self.sequence = self._backtrack(chain, 0, chain.length, self.memory_slots, self.cost_table, self.back_ptr)
+            self.sequence = self._backtrack(chain, 0, len(chain), self.memory_slots - chain.x[0], self.cost_table,
+                                            self.back_ptr)
             self._annotate_from_sequence(self.sequence, self.node_list)
-        except RuntimeError as e:
+        except ValueError as e:
             # using logger to annonce that the solver is failed
             logger = get_dist_logger()
             logger.warning(f'Checkpoint solver failed: {e}')
+            raise ValueError
+
+        if verbose:
+            self.print_sequence()
 
         return deepcopy(self.graph)
 
@@ -100,42 +109,42 @@ class CheckpointSolverRotor(CheckpointSolverBase):
     @classmethod
     def _construct_chain(cls, graph: Graph, node_list: List[List[Node]]) -> Chain:
         input_tensors = cls._extract_input(graph)
-        fwd_time, bwd_time, ftmp, btmp = list(), list(), list(), list()
+        ftime, btime, ftmp, btmp = list(), list(), list(), list()
         xbar, x = [activation_size(input_tensors)], [activation_size(input_tensors)]
 
-        for idx, node in enumerate(node_list):
+        for node in node_list:
             node_info = cls._extract_node_info(node)
-            fwd_time.append(node_info[0])
-            bwd_time.append(node_info[1])
+            ftime.append(node_info[0])
+            btime.append(node_info[1])
             x.append(node_info[2])
             xbar.append(node_info[3])
             ftmp.append(node_info[4])
             btmp.append(node_info[5])
 
         # currently we view loss backward temp as zero
-        bwd_time.append(0)
+        btime.append(0)
         btmp.append(0)
 
-        return Chain(fwd_time, bwd_time, x, xbar, ftmp, btmp)
+        return Chain(ftime, btime, x, xbar, ftmp, btmp)
 
     @classmethod
     def _extract_node_info(cls, node: List[Node]) -> Tuple[int, ...]:
         """Extract node info from a list of nodes"""
         xbar = 0
-        fwd_time = 0
-        bwd_time = 0
+        ftime = 0
+        btime = 0
         for n in node:
             assert isinstance(n, Node), f'{n} is not a Node'
             xbar += calculate_fwd_tmp(n) + calculate_fwd_out(n)
             # minimum flop count is required
-            fwd_time += max(calculate_fwd_time(n), 1.0)
-            bwd_time += max(calculate_bwd_time(n), 1.0)
+            ftime += max(calculate_fwd_time(n), 1.0)
+            btime += max(calculate_bwd_time(n), 1.0)
 
         x = calculate_fwd_out(node[-1])
         xbar = max(x, xbar)
         ftmp = cls._extract_ftmp(node)
         btmp = cls._extract_btmp(node)
-        return fwd_time, bwd_time, x, xbar, ftmp, btmp
+        return ftime, btime, x, xbar, ftmp, btmp
 
     @staticmethod
     def _extract_input(graph: Graph) -> Tuple[Tensor, ...]:
@@ -180,17 +189,17 @@ class CheckpointSolverRotor(CheckpointSolverBase):
         return btmp
 
     @staticmethod
-    def _compute_table(chain: Chain, mem_slots: int) -> Tuple:
+    def _compute_table(chain: Chain, mmax: int) -> Tuple:
         """Compute the table using dynamic programming. Returns the cost table and the backtracking pointer.
 
         Args:
             chain (Chain): A basic linearized structure for solving the dynamic programming problem.
-            mem_slots (int): Number of slots for discretizing memory budget.
+            mmax (int): Maximum number of memory slots.
 
         Returns:
-            cost_table (List[List[Dict[int, Tuple]]]): cost_table[m][lmin][lmax] with lmin = 0...chain.length
-                                     and lmax = lmin...chain.length (lmax is not included) and m = 0...mmax
-            back_ptr (List[List[Dict[int, Tuple]]]): back_ptr[m][lmin][lmax] is (True,) if the optimal choice
+            cost_table (List): cost_table[m][lhs][rhs] with lhs = 0...chain.length
+                                     and rhs = lhs...chain.length (lhs is not included) and m = 0...mmax
+            back_ptr (List): back_ptr[m][lhs][rhs] is (True,) if the optimal choice
                                      is a chain checkpoint (False, j) if the optimal choice is a leaf checkpoint
                                      of length j
         """
@@ -203,13 +212,13 @@ class CheckpointSolverRotor(CheckpointSolverBase):
         btmp = chain.btmp + [0]
 
         # Build table
-        cost_table = [[{} for _ in range(chain.length + 1)] for _ in range(mem_slots + 1)]
-        back_ptr = [[{} for _ in range(chain.length + 1)] for _ in range(mem_slots + 1)]
+        cost_table = [[{} for _ in range(len(chain) + 1)] for _ in range(mmax + 1)]
+        back_ptr = [[{} for _ in range(len(chain) + 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(mem_slots + 1):
-            for i in range(chain.length + 1):
+        for m in range(mmax + 1):
+            for i in range(len(chain) + 1):
                 limit = max(x[i + 1] + xbar[i + 1] + ftmp[i], x[i + 1] + xbar[i + 1] + btmp[i])
                 if m >= limit:    # Equation (1)
                     cost_table[m][i][i] = ftime[i] + btime[i]
@@ -217,9 +226,9 @@ class CheckpointSolverRotor(CheckpointSolverBase):
                     cost_table[m][i][i] = float("inf")
 
         # Compute everything
-        for m in range(mem_slots + 1):
-            for d in range(1, chain.length + 1):
-                for i in range(chain.length + 1 - d):
+        for m in range(mmax + 1):
+            for d in range(1, len(chain) + 1):
+                for i in range(len(chain) + 1 - d):
                     idx = i + d
                     mmin = x[idx + 1] + x[i + 1] + ftmp[i]
                     if idx > i + 1:
@@ -248,20 +257,46 @@ class CheckpointSolverRotor(CheckpointSolverBase):
         return cost_table, back_ptr
 
     @staticmethod
-    def _compute_table_c(chain: Chain, mem_slots: int) -> Tuple:
-        raise NotImplementedError("C implementation not available yet")
+    def _compute_table_c(chain: Chain, mmax: int) -> Tuple:
+        try:
+            from .rotorc import compute_table
 
-    def _backtrack(self, chain: Chain, lmin: int, lmax: int, mem_budget: int, cost_table: List[List[Dict[int, Tuple]]],
-                   back_ptr: List[List[Dict[int, int]]]) -> List[int]:
+        # build module if module not found
+        except ModuleNotFoundError:
+            import os
+            import subprocess
+            import sys
+            logger = get_dist_logger()
+            logger.info("rotorc hasn't been built! Building library...", ranks=[0])
+            this_dir = os.path.dirname(os.path.abspath(__file__))
+            result = subprocess.Popen(
+                [
+                    f"{sys.executable}", f"{os.path.join(this_dir, 'build_c_ext.py')}", "build_ext",
+                    f"--build-lib={this_dir}"
+                ],
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+            )
+            if result.wait() == 0:
+                logger.info("rotorc has been built!", ranks=[0])
+                from .rotorc import compute_table
+            else:
+                logger.warning("rotorc built failed! Using python version!", ranks=[0])
+                return CheckpointSolverRotor._compute_table(chain, mmax)
+        return compute_table(chain, mmax)
+
+    @staticmethod
+    def _backtrack(chain: Chain, lhs: int, rhs: int, budget: int, cost_table: List[Any],
+                   back_ptr: List[Any]) -> "Sequence":
         """Backtrack the cost table and retrieve the optimal checkpointing strategy.
 
         Args:
             chain (Chain): A basic linearized structure for solving the dynamic programming problem.
-            lmin (int): The left index of the interval to backtrack.
-            lmax (int): The right index of the interval to backtrack.
-            mem_budget (int): The memory budget for processing this interval.
-            cost_table (List[List[Dict[int, Tuple]]]): See _compute_table() for definitions
-            back_ptr (List[List[Dict[int, Tuple]]]): See _compute_table() for definitions
+            lhs (int): The left index of the interval to backtrack.
+            rhs (int): The right index of the interval to backtrack.
+            budget (int): The memory budget for processing this interval.
+            cost_table (List[Any]): See `._compute_table()` for definitions
+            back_ptr (List[Any]): See `._compute_table()` for definitions
 
         Raises:
             ValueError: Can not process the chain.
@@ -269,36 +304,45 @@ class CheckpointSolverRotor(CheckpointSolverBase):
         Returns:
             sequence (Sequence): The sequence of executing nodes with checkpoints.
         """
-        if mem_budget <= 0:
-            raise ValueError(f"Can not process a chain with negative memory {mem_budget}")
-        elif cost_table[mem_budget][lmin][lmax] == float("inf"):
-            raise ValueError(f"Can not process this chain from index {lmin} to {lmax} with memory {mem_budget}")
-
-        sequence = Sequence(Function("Persistent", lmax - lmin, mem_budget))
-        if lmin == lmax:
-            if lmin == chain.length:
-                sequence.insert(Loss())
+        if budget <= 0:
+            raise ValueError(f"Can not process a chain with negative memory {budget}")
+        elif cost_table[budget][lhs][rhs] == float("inf"):
+            raise ValueError(f"Can not process this chain from index {lhs} to {rhs} with memory {budget}")
+
+        sequence = Sequence()
+        if rhs == lhs:
+            if lhs == len(chain):
+                sequence += [Loss()]
             else:
-                sequence.insert(ForwardEnable(lmin))
-                sequence.insert(Backward(lmin))
+                sequence += [ForwardEnable(lhs), Backward(lhs)]
             return sequence
 
-        if back_ptr[mem_budget][lmin][lmax][0]:
-            sequence.insert(ForwardEnable(lmin))
-            sequence.insert_sequence(
-                self._backtrack(chain, lmin + 1, lmax, mem_budget - chain.xbar[lmin + 1], cost_table, back_ptr))
-            sequence.insert(Backward(lmin))
+        if back_ptr[budget][lhs][rhs][0]:
+            sequence += [
+                ForwardEnable(lhs),
+                CheckpointSolverRotor._backtrack(chain, lhs + 1, rhs, budget - chain.xbar[lhs + 1], cost_table,
+                                                 back_ptr),
+                Backward(lhs),
+            ]
         else:
-            j = back_ptr[mem_budget][lmin][lmax][1]
-            sequence.insert(ForwardCheck(lmin))
-            for k in range(lmin + 1, j):
-                sequence.insert(ForwardNograd(k))
-            sequence.insert_sequence(self._backtrack(chain, j, lmax, mem_budget - chain.xbar[j], cost_table, back_ptr))
-            sequence.insert_sequence(self._backtrack(chain, lmin, j - 1, mem_budget, cost_table, back_ptr))
+            best_leaf = back_ptr[budget][lhs][rhs][1]
+            sequence += [ForwardCheck(lhs)]
+            sequence += [ForwardNograd(k) for k in range(lhs + 1, best_leaf)]
+            sequence += [
+                CheckpointSolverRotor._backtrack(chain, best_leaf, rhs, budget - chain.x[best_leaf], cost_table,
+                                                 back_ptr),
+                CheckpointSolverRotor._backtrack(chain, lhs, best_leaf - 1, budget, cost_table, back_ptr),
+            ]
         return sequence
 
     @staticmethod
     def _annotate_from_sequence(sequence: Sequence, node_list: List[List[Node]]):
+        """Annotate the nodes in the node_list with activation checkpoint from the sequence.
+
+        Args:
+            sequence (Sequence): The sequence of executing nodes with activation checkpoint annotations.
+            node_list (List[List[Node]]): The list of nodes to annotate.
+        """
         op_list = sequence.list_operations()
         loss_op = next(op for op in op_list if isinstance(op, Loss))
         fwd_list = op_list[:op_list.index(loss_op)]

colossalai/auto_parallel/checkpoint/operation.py

@@ -1,6 +1,6 @@
 import math
 from abc import ABC
-from typing import List
+from typing import Any, Iterable, List
 
 from torch.utils._pytree import tree_map
 
@@ -33,23 +33,25 @@ class Chain:
         self.xbar = xbar
         self.ftmp = ftmp
         self.btmp = btmp
-        self.length = len(ftime)
         if check_consistency and not self.check_lengths():
             raise AttributeError("In Chain, input lists do not have consistent lengths")
 
     def check_lengths(self):
-        return ((len(self.ftime) == self.length) and (len(self.btime) == self.length + 1)
-                and (len(self.x) == self.length + 1) and (len(self.ftmp) == self.length)
-                and (len(self.btmp) == self.length + 1) and (len(self.xbar) == self.length + 1))
+        return ((len(self.ftime) == len(self)) and (len(self.btime) == len(self) + 1) and (len(self.x) == len(self) + 1)
+                and (len(self.ftmp) == len(self)) and (len(self.btmp) == len(self) + 1)
+                and (len(self.xbar) == len(self) + 1))
 
     def __repr__(self):
         chain_list = []
-        for i in range(self.length):
+        for i in range(len(self)):
             chain_list.append((self.ftime[i], self.btime[i], self.x[i], self.xbar[i], self.ftmp[i], self.btmp[i]))
-        i = self.length
+        i = len(self)
         chain_list.append((None, self.btime[i], self.x[i], self.xbar[i], None, self.btmp[i]))
         return chain_list.__repr__()
 
+    def __len__(self):
+        return len(self.ftime)
+
     def discretize_all(self, unit: int):
         """Discretize the chain into a list of chains according to unit size."""
         discretizer = lambda val: math.ceil(val / unit)
@@ -163,79 +165,20 @@ class DiscardMemory(MemoryAccess):
     name = "DM"
 
 
-class Function:
-
-    def __init__(self, name, *args):
-        self.name = name
-        self.args = args
-        self.str_args = ','.join(str(v) for v in self.args)
-
-    def __repr__(self):
-        return "{n}({args})".format(n=self.name, args=self.str_args)
-
-
-class Sequence:
+class Sequence(list):
 
-    def __init__(self, function):
-        self.sequence = []    #List of Operation and Sequence
-        self.function = function    #Description the function (name and parameters)
+    def __init__(self):
+        super().__init__()
 
     def __repr__(self):
         return repr(self.list_operations())
 
     def list_operations(self):
         op_list = []
-        for x in self.sequence:
+        for x in self:
             if isinstance(x, Operation):
                 op_list.append(x)
             else:
                 assert isinstance(x, Sequence)
                 op_list += x.list_operations()
         return op_list
-
-    def insert(self, operation):
-        self.sequence.append(operation)
-
-    def remove(self, operation_index):
-        del self.sequence[operation_index]
-
-    def insert_sequence(self, sequence):
-        self.sequence.append(sequence)
-
-    def shift(self, value):
-        for x in self.sequence:
-            x.shift(value)
-        return self
-
-    def remove_useless_write(self):
-        if self.sequence:
-            if isinstance(self.sequence[0], WriteMemory):
-                self.remove(0)
-        return self
-
-    def get_makespan(self, chain):
-        return sum(op.cost(chain) for op in self.list_operations())
-
-    def without_suffix(self):
-        ops = self.list_operations()
-        end_of_first_phase = [i for i in range(len(ops)) if type(ops[i]) is Loss][0]
-        try:
-            last_idx = max(i for i in range(end_of_first_phase) if not type(ops[i]) is ForwardEnable)
-        except ValueError:
-            last_idx = -1
-        if last_idx == end_of_first_phase - 1:
-            return (self, None)
-        chain_length = ops[end_of_first_phase -
-                           1].index    ## Some assumption here about the sequence (finishes with Forward_L
-        start_of_fwd_enable_chain = ops[last_idx + 1].index    ## And starts with B_L), but should be fine in practice
-        result = Sequence(Function("Strip", self.function.name, *self.function.args, start_of_fwd_enable_chain))
-        for i in range(last_idx + 1):
-            result.insert(ops[i])
-        result.insert(Loss())
-        for i in range(chain_length, start_of_fwd_enable_chain - 1, -1):
-            position = end_of_first_phase + 1 + (chain_length - i)
-            assert type(ops[position]) is Backward
-            assert ops[position].index == i
-        for i in range(end_of_first_phase + 1 + 1 + chain_length - start_of_fwd_enable_chain, len(ops)):
-            result.insert(ops[i])
-        return (result, start_of_fwd_enable_chain)

colossalai/fx/profiler/profiler.py

@@ -328,6 +328,8 @@ def profile_function(target: 'Target', device: str = 'meta') -> Callable:
             out, meta = _profile_concrete(func, *args, **kwargs)
         if inplace:
             kwargs['inplace'] = True
+            meta.bwd_mem_tmp = 0
+            meta.bwd_mem_out = 0
         do_not_cache = False
 
         meta.bwd_mem_out -= param_size
@@ -394,6 +396,8 @@ def profile_module(module: torch.nn.Module, device: str = 'meta') -> Callable:
             out, meta = _profile_concrete(func, *args, **kwargs)
         if inplace:
             module.inplace = True
+            meta.bwd_mem_tmp = 0
+            meta.bwd_mem_out = 0
         do_not_cache = False
 
         # grad for param will not be counted