refactor(mge/dtr): update dtr api

GitOrigin-RevId: dc366c65be

imperative/python/megengine/__init__.py

@@ -76,6 +76,7 @@ from .core._imperative_rt.core2 import full_sync as _full_sync
 from .core._imperative_rt.core2 import sync as _sync
 from .core._imperative_rt.utils import _set_fork_exec_path_for_timed_func
 from .device import *
+from .dtr import *
 from .logger import enable_debug_log, get_logger, set_log_file, set_log_level
 from .serialization import load, save
 from .tensor import Parameter, Tensor, tensor

imperative/python/megengine/dtr.py

@@ -0,0 +1,131 @@
+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import re
+from typing import Union
+
+from mprop import mproperty
+
+from .core._imperative_rt.core2 import set_option
+from .core._imperative_rt.utils import _set_defrag
+
+_eviction_threshold = 0
+_evictee_minimum_size = 1024 ** 2
+
+
+def str2bytes(text: str) -> int:
+    regex = re.compile(r"(\d+(?:\.\d+)?)\s*([kmg]?b)", re.IGNORECASE)
+    order = ["b", "kb", "mb", "gb"]
+    result = regex.findall(text)
+    if len(result) != 1:
+        raise ValueError(
+            "Formatting of `value` only supports bytes(B), kilobyte(KB), megabyte(MB) and gigabyte(GB) units"
+        )
+    return int(float(result[0][0]) * 1024 ** order.index(result[0][1].lower()))
+
+
+@mproperty
+def eviction_threshold(mod):
+    r"""
+    Returns the eviction threshold in bytes.
+
+    .. note::
+
+       When GPU memory usage exceeds this value, DTR will heuristically select
+       and evict resident tensors until the amount of used memory falls below
+       this threshold.
+
+    """
+    return mod._eviction_threshold
+
+
+@eviction_threshold.setter
+def eviction_threshold(mod, value: Union[int, str]):
+    r"""
+    Change the eviction threshold. If `value` is an int, it represents the
+    number of bytes. If `value` is a string, its formatting supports bytes(B),
+    kilobyte(KB), megabyte(MB) and gigabyte(GB) units.
+
+    Examples:
+
+    .. code-block::
+
+        import megengine as mge
+        mge.dtr.eviction_threshold = 2 * 1024 ** 3
+        mge.dtr.eviction_threshold = "2GB"
+        mge.dtr.eviction_threshold = "2048MB"
+
+    """
+    if isinstance(value, str):
+        mod._eviction_threshold = mod.str2bytes(value)
+    elif isinstance(value, int):
+        mod._eviction_threshold = value
+    else:
+        raise TypeError("`value` should be a str or an int")
+    set_option("dtr_eviction_threshold", mod._eviction_threshold)
+
+
+@mproperty
+def evictee_minimum_size(mod):
+    r"""
+    Returns the memory threshold of tensors in bytes.
+
+    .. note::
+
+       Only tensors whose size exceeds this threshold will be added to the
+       candidate set. A tensor that is not added to the candidate set will
+       never be evicted during its lifetime.
+
+    """
+    return mod._evictee_minimum_size
+
+
+@evictee_minimum_size.setter
+def evictee_minimum_size(mod, value: Union[int, str]):
+    r"""
+    Change the memory threshold of tensors. If `value` is an int, it represents
+    the number of bytes. If `value` is a string, its formatting supports bytes(B),
+    kilobyte(KB), megabyte(MB) and gigabyte(GB) units.
+
+    Examples:
+
+    .. code-block::
+
+        import megengine as mge
+        mge.dtr.evictee_minimum_size = 2 * 1024 ** 2
+        mge.dtr.evictee_minimum_size = "2MB"
+        mge.dtr.evictee_minimum_size = "2048KB"
+
+    """
+    if isinstance(value, str):
+        mod._evictee_minimum_size = mod.str2bytes(value)
+    elif isinstance(value, int):
+        mod._evictee_minimum_size = value
+    else:
+        raise TypeError("`value` should be a str or an int")
+    set_option("dtr_evictee_minimum_size", mod._evictee_minimum_size)
+
+
+def enable():
+    r"""
+    Enable to record computing path of tensors and to perform DTR policy.
+    """
+    _set_defrag(True)
+    set_option("enable_dtr_auto_drop", 1)
+    set_option("enable_drop", 1)
+    set_option("buffer_length", 0)
+    set_option("record_computing_path", 1)
+
+
+def disable():
+    r"""
+    Stop recording computing path of tensors and performing DTR policy.
+    """
+    set_option("enable_dtr_auto_drop", 0)
+    set_option("enable_drop", 0)
+    set_option("record_computing_path", 0)

imperative/python/megengine/utils/dtr.py

@@ -1,44 +0,0 @@
-# -*- coding: utf-8 -*-
-# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
-#
-# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-
-from ..core._imperative_rt.core2 import set_option
-from ..core._imperative_rt.utils import _set_defrag
-
-
-class DTR:
-    r"""
-    DTR implements `Dynamic Tensor Rematerialization <https://arxiv.org/abs/2006.09616>`_ in MegEngine.
-
-    It is basically an online algorithm for checkpointing driven by certain eviction policies.
-
-    .. code-block::
-    
-        from megengine.utils.dtr import DTR
-
-        ds = DTR(memory_budget=5*1024**3)
-
-        # your training code
-
-    """
-
-    def __init__(self, memory_budget=0, tensor_lowerbound=1048576):
-        r"""
-        :param memory_budget: int. The threshold of memory usage. When memory
-        usage exceeds this value, auto evict will be triggered.
-        :param tensor_lowerbound: int. The minimum memory limit of the tensor
-        that can be evicted. Default: 1MB.
-        """
-        if memory_budget > 0:
-            set_option("enable_auto_drop", 1)
-            set_option("enable_drop", 1)
-            set_option("buffer_length", 0)
-            set_option("memory_budget", memory_budget)
-            set_option("tensor_lowerbound", tensor_lowerbound)
-            set_option("record_computing_path", 1)
-            _set_defrag(True)

imperative/python/requires.txt

@@ -6,3 +6,4 @@ tabulate
 tqdm
 redispy
 deprecated
+mprop

imperative/src/impl/interpreter/interpreter_impl.cpp

@@ -422,7 +422,7 @@ void ChannelImpl::do_drop(TensorInfo* ptr, bool user=false) {
 }
 
 void ChannelImpl::free(TensorInfo* ptr) {
-    if (m_worker_state.options.enable_auto_drop) {
+    if (m_worker_state.options.enable_dtr_auto_drop) {
         // Evicting a tensor, rather than freeing it, can avoid pinning
         // potentially exploding amounts of memory and allow us to save
         // more memory.
@@ -459,7 +459,7 @@ void ChannelImpl::real_free(TensorInfo* ptr) {
     if (m_channel_state.profiler->is_profiling()) {
         m_channel_state.profiler->record_host<TensorEraseEvent>(ptr->id);
     }
-    if (ptr->size_exceeds_thd(m_worker_state.options.tensor_lowerbound)) {
+    if (ptr->size_exceeds_thd(m_worker_state.options.dtr_evictee_minimum_size)) {
         m_dtr.erase_candidate(ptr);
     }
     detach_users(ptr);
@@ -487,7 +487,7 @@ void ChannelImpl::produce_tensor(TensorInfo* dest, TensorPtr ptr, bool notice=tr
     dest->memory = ptr->blob()->size();
     dest->ptr = std::move(ptr);
     dest->evict_type = EvictType::NONE;
-    if (notice && dest->size_exceeds_thd(m_worker_state.options.tensor_lowerbound)) {
+    if (notice && dest->size_exceeds_thd(m_worker_state.options.dtr_evictee_minimum_size)) {
         m_dtr.insert_candidate(dest);
     }
     if (notice && m_waitee == dest) {
@@ -519,7 +519,7 @@ void ChannelImpl::recompute(TensorInfo::ComputePath* path) {
         inputs.push_back(i->ptr);
         m_dtr.update_used_time(i);
     }
-    if (m_worker_state.options.enable_auto_drop && m_worker_state.options.memory_budget > 0) {
+    if (m_worker_state.options.enable_dtr_auto_drop && m_worker_state.options.dtr_eviction_threshold > 0) {
         auto_evict();
     }
     auto outputs = OpDef::apply_on_physical_tensor(*path->op, inputs);
@@ -531,7 +531,7 @@ void ChannelImpl::recompute(TensorInfo::ComputePath* path) {
             o->recompute_times ++;
             if (!o->ptr) {
                 produce_tensor(o, std::move(outputs[i]), false);
-                if (m_worker_state.options.enable_auto_drop) {
+                if (m_worker_state.options.enable_dtr_auto_drop) {
                     m_dtr.update_dsu_after_recompute(o);
                 }
             }
@@ -544,7 +544,7 @@ void ChannelImpl::auto_evict() {
         return;
     }
     size_t current_memory = m_dtr.comp_node.get_used_memory();
-    while (current_memory > m_worker_state.options.memory_budget) {
+    while (current_memory > m_worker_state.options.dtr_eviction_threshold) {
         auto best = m_dtr.find_best_tensor();
         if (!best) {
             if (!m_dtr.warn_printed) {
@@ -642,7 +642,7 @@ void ChannelImpl::process_one_task(IdentifiedCommand& icmd) {
                 uint64_t apply_id = ++m_last_id;
                 SmallVector<TensorPtr> tensor_inputs;
                 SmallVector<CompNode> devices;
-                if (m_worker_state.options.enable_auto_drop) {
+                if (m_worker_state.options.enable_dtr_auto_drop) {
                     m_dtr.pin(cmd.inputs);
                 }
                 for (auto i : cmd.inputs) {
@@ -696,7 +696,7 @@ void ChannelImpl::process_one_task(IdentifiedCommand& icmd) {
                         m_worker_state.profiler->record_device<DeviceOpExecuteEvent>(device, event_data);
                     }
                 }
-                if (m_worker_state.options.enable_auto_drop && m_worker_state.options.memory_budget > 0) {
+                if (m_worker_state.options.enable_dtr_auto_drop && m_worker_state.options.dtr_eviction_threshold > 0) {
                     auto_evict();
                 }
                 // Apply op
@@ -712,7 +712,7 @@ void ChannelImpl::process_one_task(IdentifiedCommand& icmd) {
                 }
                 // End profiling operator
                 double estimate_compute_time = 0;
-                if (m_worker_state.options.enable_auto_drop) {
+                if (m_worker_state.options.enable_dtr_auto_drop) {
                     for (auto i : cmd.inputs) {
                         estimate_compute_time += i->memory;
                     }
@@ -735,7 +735,7 @@ void ChannelImpl::process_one_task(IdentifiedCommand& icmd) {
                         continue;
                     }
                     produce_tensor(cmd.outputs[i], std::move(tensor_outputs[i]));
-                    if (m_worker_state.options.enable_auto_drop) {
+                    if (m_worker_state.options.enable_dtr_auto_drop) {
                         cmd.outputs[i]->dsu_ptr = std::make_shared<DsuNode>(estimate_compute_time);
                     }
                 }
@@ -774,7 +774,7 @@ void ChannelImpl::process_one_task(IdentifiedCommand& icmd) {
                         TensorInfo::ComputePath::make(cmd.op, cmd.inputs, cmd.outputs);
                         size_t detach_cnt = 0;
                         for (auto output : cmd.outputs) {
-                            if (!output->size_exceeds_thd(m_worker_state.options.tensor_lowerbound)) {
+                            if (!output->size_exceeds_thd(m_worker_state.options.dtr_evictee_minimum_size)) {
                                 output->detach_producer();
                                 detach_cnt ++;
                             }

imperative/src/impl/interpreter/option_manager.h

@@ -39,10 +39,10 @@ public:
         "set command buffer length.");
     DEF_OPTION(enable_host_compute,     "MEGENGINE_HOST_COMPUTE",           1,
         "enable host compute, thus computation may be done in host event if it's device is gpu.");
-    DEF_OPTION(enable_auto_drop,        "MEGENGINE_AUTO_DROP",              0, "");
-    DEF_OPTION(memory_budget,           "MEGENGINE_MEMORY_BUDGET",          0,
+    DEF_OPTION(enable_dtr_auto_drop,    "MEGENGINE_DTR_AUTO_DROP",          0, "");
+    DEF_OPTION(dtr_eviction_threshold,  "MEGENGINE_DTR_EVICTION_THRESHOLD", 0,
         "auto drop will start whenever gpu memory usage exceeds this value.");
-    DEF_OPTION(tensor_lowerbound,       "MEGENGINE_TENSOR_LOWERBOUND",      1048576,
+    DEF_OPTION(dtr_evictee_minimum_size, "MEGENGINE_DTR_EVICTEE_MINIMUM_SIZE", 1048576,
         "the minimum memory value of a tensor added to the candidate set");
     DEF_OPTION(record_computing_path,   "MEGENGINE_RECORD_COMPUTING_PATH",  0, "");