chore: import upstream snapshot with attribution

python/paddle/distributed/auto_tuner/cost_model.py

@@ -0,0 +1,143 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def all_params(mp, pp, sharding, h, l, V):
+    # TODO: TBD - add some fixed structure models.
+    return 1
+
+
+def full_recompute_acts(mp, pp, s, b, h, l):
+    # TODO: TBD - add some fixed structure models.
+    return 1
+
+
+def all_acts(mp, pp, s, b, h, l, a):
+    # TODO: TBD - add some fixed structure models.
+    return 1
+
+
+def to_gb(p):
+    return p / (2**30)
+
+
+def get_mem(total_cards, parallel_cfg, l, h, a, V, s, gbs):
+    """Estimate the memory of model unset parallel strategy."""
+    sharding = parallel_cfg["sharding_degree"]
+    mp = parallel_cfg["mp_degree"]
+    b = parallel_cfg["micro_batch_size"]
+    pp = parallel_cfg["pp_degree"]
+    vpp = parallel_cfg["vpp_degree"]
+    use_recompute = parallel_cfg["use_recompute"]
+
+    sep = 1
+
+    lbs = int(gbs / sharding / s)
+    lbs = int(lbs / pp) * pp
+    assert s % sep == 0
+    s_sep = s // sep
+    assert a % (sep * mp) == 0, f'{a} vs {sep * mp}'
+
+    vpp_ratio = 1
+    if vpp > 1:
+        assert l % (pp * vpp) == 0
+        vpp_ratio = 1 + (pp - 1) / (pp * vpp)
+
+    params = to_gb(all_params(mp, pp, sharding, h, l, V))
+
+    acts = 0
+    assert l % pp == 0
+
+    if use_recompute:
+        acts = to_gb(full_recompute_acts(mp, pp, s_sep, b, h, l)) * vpp_ratio
+    else:
+        acts = to_gb(all_acts(mp, pp, s, b, h, l, a)) * vpp_ratio
+    assert acts > 0
+
+    peak_mem = params + acts
+    return peak_mem
+
+
+def divisor(num, reverse=False):
+    """Get the divisor of a given number."""
+    results = set()
+    i = 1
+    mid = num // 2 + 1
+    while i < mid:
+        if num % i == 0:
+            results.add(i)
+            results.add(num // i)
+        i += 1
+    results = list(results)
+    return sorted(results, reverse=reverse)
+
+
+def get_not_oom_cfgs(cfgs, tuner_cfg):
+    """Get not OOM parallel strategies."""
+    total_cards, l, h, a, V, s, gbs, per_card_memory = (
+        tuner_cfg["search_algo"]["estimated_num_gpus"],
+        tuner_cfg["model_cfg"]["num_layers"],
+        tuner_cfg["model_cfg"]["hidden_size"],
+        tuner_cfg["model_cfg"]["num_attention_heads"],
+        tuner_cfg["model_cfg"]["vocab_size"],
+        tuner_cfg["model_cfg"]["seq_length"],
+        tuner_cfg["model_cfg"]["global_batch_size"],
+        tuner_cfg.get("per_card_memory", 80),
+    )
+    pruned_cfgs = []
+    for cfg in cfgs:
+        mp = cfg["mp_degree"]
+        sharding = cfg["sharding_degree"]
+        mbs = cfg["micro_batch_size"]
+        pp = cfg["pp_degree"]
+        vpp = cfg["vpp_degree"]
+        dp = cfg["dp_degree"]
+        use_recompute = cfg["use_recompute"]
+
+        if mp * sharding * pp * dp != total_cards:
+            continue
+        if gbs % sharding != 0:
+            continue
+        if gbs // sharding % dp != 0:
+            continue
+        if gbs // sharding // dp % mbs != 0:
+            continue
+        if l % pp != 0:
+            continue
+        if l // pp % vpp != 0:
+            continue
+        if vpp != 1 and pp <= 2:
+            continue
+        if a % mp != 0 or V % mp != 0 or h % mp != 0:
+            continue
+
+        pruned_cfgs.append(cfg)
+    valid_cfgs = []
+    for cfg in pruned_cfgs:
+        mem = get_mem(total_cards, cfg, l, h, a, V, s, gbs)
+        # TODO: Uncomment when it is actually implemented.
+        # if (
+        #     mem < per_card_memory
+        #     and mem
+        #     > tuner_cfg.get(
+        #         "search_algo", {"name": "dp_estimation", "threshold": 0.7}
+        #     ).get("threshold", 0.7)
+        #     * per_card_memory
+        # ):
+        # cfg["memory_cost"] = mem
+        # valid_cfgs.append(cfg)
+        cfg["memory_cost"] = mem
+        valid_cfgs.append(cfg)
+    assert valid_cfgs
+    return valid_cfgs