chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
+575
@@ -0,0 +1,575 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from paddle.framework import core
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from paddle.utils import unique_name
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from ..common import OP_ROLE_KEY, OpRole, is_optimizer_op, is_update_op
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__all__ = []
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class PlaceType:
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# sync with memcpy op, maybe not a good design
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CPU = 0
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CUDA = 1
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CUDA_PINNED = 2
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XPU = 3 # unsupported for now
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@staticmethod
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def default_device():
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if core.is_compiled_with_cuda():
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return PlaceType.CUDA
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return PlaceType.CPU
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@staticmethod
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def default_pinned():
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if core.is_compiled_with_cuda():
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return PlaceType.CUDA_PINNED
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return PlaceType.CPU
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class OffloadHelper:
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cpu_place_type = 0
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cuda_place_type = PlaceType.default_device()
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cuda_pinned_place_type = PlaceType.default_pinned()
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def __init__(self, mp_ring_id=None, dp_ring_id=None):
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self.mp_ring_id = mp_ring_id
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self.dp_ring_id = dp_ring_id
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def _insert_cast_op(self, block, idx, src_name, dst_name):
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src_var = block.var(src_name)
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if not block.has_var(dst_name):
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block.create_var(
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name=dst_name,
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shape=src_var.shape,
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dtype=core.VarDesc.VarType.FP16,
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persistable=True,
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)
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dst_var = block.var(dst_name)
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assert dst_var.dtype == paddle.float16
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block._insert_op_without_sync(
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idx,
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type='cast',
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inputs={'X': src_var},
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outputs={'Out': dst_var},
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attrs={
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'in_dtype': src_var.dtype,
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'out_dtype': dst_var.dtype,
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OP_ROLE_KEY: OpRole.Optimize,
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},
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)
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def _insert_broadcast_op(self, block, idx, param_name):
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rings = []
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if self.dp_ring_id is not None:
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rings.append(self.dp_ring_id)
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# need sync non distributed param in mp group
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if self.mp_ring_id is not None:
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param = block.var(param_name)
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if not hasattr(param, 'is_distributed') or not param.is_distributed:
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rings.append(self.mp_ring_id)
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# the insert op order is: mp, dp
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for ring in rings:
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block._insert_op_without_sync(
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idx,
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type="broadcast",
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inputs={'x': param_name},
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outputs={'out': param_name},
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attrs={
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'ring_id': ring,
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'root': 0,
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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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def _insert_memcpy_op(self, block, idx, src_name, dst_name, dst_place_type):
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src_var = block.var(src_name)
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dst_var = block.var(dst_name)
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block._insert_op_without_sync(
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idx,
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type='memcpy',
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inputs={'X': src_var},
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outputs={'Out': dst_var},
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attrs={
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'dst_place_type': dst_place_type,
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OP_ROLE_KEY: OpRole.Optimize,
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},
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)
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def _insert_fetch_op(self, block, idx, src_name, dst_name):
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self._insert_memcpy_op(
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block, idx, src_name, dst_name, OffloadHelper.cuda_place_type
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)
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def _insert_offload_op(self, block, idx, src_name, dst_name):
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self._insert_memcpy_op(
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block, idx, src_name, dst_name, OffloadHelper.cuda_pinned_place_type
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)
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def _get_offload_var_name(self, name):
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return unique_name.generate(name + '@offload')
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def _create_offload_var(self, var_name, offload_var_name, blocks):
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for block in blocks:
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var = block.var(var_name)
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var.persistable = False
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offload_var = block.create_var(
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name=offload_var_name,
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shape=var.shape,
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dtype=var.dtype,
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persistable=True,
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)
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def offload_fp32param(self, block, startup_block, offload=True):
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"""
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(p_fp16) = cast(p)
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(p_fp16_recompute) = cast(p)
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(pout,) = adam(p)
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===========================>
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rename(p_fp16_recompute, p_fp16)
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(p,) = prefetch(p@offload)
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(pout,) = adam(p)
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(p_fp16) = cast(p)
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(p@offload) = memcpy(p)
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"""
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param_to_idx = {}
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param_to_fp16 = {}
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# recompute_var which need rename to fp16_param
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fp16_param_to_recompute = {}
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recompute_to_fp16 = {}
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def remove_param(input_name):
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param_to_idx.pop(input_name)
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if input_name in param_to_fp16:
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fp16_param = param_to_fp16.pop(input_name)
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if fp16_param in fp16_param_to_recompute:
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recompute = fp16_param_to_recompute.pop(fp16_param)
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recompute_to_fp16.pop(recompute)
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# step1: record param
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for idx, op in reversed(list(enumerate(block.ops))):
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if is_update_op(op):
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param = op.desc.input("Param")[0]
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param_to_idx[param] = idx
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# step2: remove param which can't offload and
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# record param->fp16param, fp16param->recompute_var
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for idx, op in enumerate(block.ops):
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if is_optimizer_op(op):
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break
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# TODO (Yuang Liu): tmp solution for fuse_grad_merge + optimize_cast
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if not offload and op.type == 'coalesce_tensor':
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continue
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for input_name in op.desc.input_arg_names():
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if input_name not in param_to_idx:
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continue
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# param which will be used by fp32 op
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if op.type != 'cast':
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remove_param(input_name)
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continue
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# param is only used by cast op,
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# which to cast fp32_param to fp16_param
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output_name = op.output_arg_names[0]
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if 'cast_fp16' not in output_name:
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remove_param(input_name)
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continue
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if 'subprog' not in output_name:
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assert output_name == input_name + '.cast_fp16'
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assert input_name not in param_to_fp16, (
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"There must be only one cast op from fp32 param to fp16 param."
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)
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param_to_fp16[input_name] = output_name
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else:
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# fp16-->recompute_var
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assert input_name in param_to_fp16, (
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"param must first be cast to fp16"
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)
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fp16_param = param_to_fp16[input_name]
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fp16_param_to_recompute[fp16_param] = output_name
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recompute_to_fp16[output_name] = fp16_param
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param_name_to_offload_name = {}
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# step3: main_block add offload, cast op
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# change recompute to fp16, remove cast(param) to fp16
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for idx, op in reversed(list(enumerate(block.ops))):
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if is_update_op(op):
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param = op.desc.input("Param")[0]
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if param not in param_to_idx:
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continue
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# step3.1: create offload_var
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offload_var_name = self._get_offload_var_name(param)
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param_name_to_offload_name[param] = offload_var_name
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if offload:
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self._create_offload_var(
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param, offload_var_name, [block, startup_block]
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)
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# step3.2: insert cast op and offload op
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self._insert_offload_op(
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block, idx + 1, param, offload_var_name
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)
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assert param in param_to_fp16
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fp16_param_name = param_to_fp16[param]
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fp16_param_var = block.var(fp16_param_name)
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fp16_param_var.persistable = True
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self._insert_cast_op(
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block, idx + 1, param, param_to_fp16[param]
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)
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if offload:
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# step3.3: insert fetch op
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self._insert_fetch_op(block, idx, offload_var_name, param)
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continue
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# step3.4: remove cast op
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if op.type == 'cast':
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input_name = op.desc.input_arg_names()[0]
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if input_name in param_to_idx:
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block._remove_op(idx, sync=False)
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continue
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# step3.5: change recompute_param to fp16_param
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for input_name in op.desc.input_arg_names():
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if input_name in recompute_to_fp16:
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op._rename_input(input_name, recompute_to_fp16[input_name])
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for output_name in op.desc.output_arg_names():
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if output_name in recompute_to_fp16:
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op._rename_output(
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output_name, recompute_to_fp16[output_name]
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)
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# step4: remove recompute_param
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for name in recompute_to_fp16.keys():
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block._remove_var(name, sync=False)
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# step5: startup_block add offload
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visited_vars = set()
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# FIXME(wangxi): should insert in idx, need move comm init to the head.
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insert_idx = len(startup_block.ops)
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for idx, op in reversed(list(enumerate(startup_block.ops))):
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for out_name in op.output_arg_names:
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if out_name in visited_vars:
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continue
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if out_name in param_name_to_offload_name:
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var_name = out_name
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if offload:
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offload_var_name = param_name_to_offload_name[var_name]
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self._insert_offload_op(
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startup_block,
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insert_idx,
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var_name,
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offload_var_name,
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)
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self._insert_cast_op(
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startup_block,
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insert_idx,
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var_name,
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param_to_fp16[var_name],
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)
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# NOTE(wangxi): cast and offload should insert after broadcast param.
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# the insert op order is: {mp, dp}broadcast, cast, offload
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self._insert_broadcast_op(
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startup_block, insert_idx, var_name
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)
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visited_vars.add(out_name)
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block._sync_with_cpp()
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startup_block._sync_with_cpp()
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def cast_fp32param_in_optimize(self, block, startup_block):
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"""
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(p_fp16) = cast(p)
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(p_fp16_recompute) = cast(p)
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(pout,) = adam(p)
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===========================>
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rename(p_fp16_recompute, p_fp16)
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(pout,) = adam(p)
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(p_fp16) = cast(p)
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"""
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self.offload_fp32param(block, startup_block, offload=False)
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def offload(self, block, startup_block):
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"""
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(m1, m2) = prefetch(m1@offload, m2@offload)
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(m1out, m2out, pout) = adam(m1, m2, p)
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(m1@offload, m2@offload) = memcpy(m1, m2)
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"""
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vars_name_to_offload_name = {}
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# main_block add offload
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for idx, op in reversed(list(enumerate(block.ops))):
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if not is_optimizer_op(op):
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break
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vars_name = []
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if op.type == "adam" or op.type == "adamw":
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# {Moment1Out = [''], Moment2Out = [''], ParamOut = ['']} =
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# adam(inputs={Moment1 = [''], Moment2 = [''], Param = ['']})
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vars_name.append(op.desc.input("Moment1")[0])
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vars_name.append(op.desc.input("Moment2")[0])
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elif op.type == 'momentum':
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pass
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elif op.type == 'lars':
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pass
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elif op.type == 'lamb':
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pass
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# step1: create and init offload_var
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for var_name in vars_name:
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assert var_name not in vars_name_to_offload_name
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offload_var_name = self._get_offload_var_name(var_name)
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vars_name_to_offload_name[var_name] = offload_var_name
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self._create_offload_var(
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var_name, offload_var_name, [block, startup_block]
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)
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# step2: insert offload op
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for var_name in vars_name:
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offload_var_name = vars_name_to_offload_name[var_name]
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self._insert_offload_op(
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block, idx + 1, var_name, offload_var_name
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)
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# step3: insert fetch op
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for var_name in vars_name:
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offload_var_name = vars_name_to_offload_name[var_name]
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self._insert_fetch_op(block, idx, offload_var_name, var_name)
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# startup_block add offload
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visited_vars = set()
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for idx, op in reversed(list(enumerate(startup_block.ops))):
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for out_name in op.output_arg_names:
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if out_name in visited_vars:
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continue
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if out_name in vars_name_to_offload_name:
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var_name = out_name
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offload_var_name = vars_name_to_offload_name[var_name]
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# insert offload op after var is generated
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self._insert_offload_op(
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startup_block, idx + 1, var_name, offload_var_name
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)
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visited_vars.add(out_name)
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block._sync_with_cpp()
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startup_block._sync_with_cpp()
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def opt_sharding_cast_fp32param(
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self, block, startup_block, params, offload=False
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):
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"""
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(p_fp16) = cast(p)
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(p_fp16_recompute) = cast(p)
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(pout,) = adam(p)
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===========================>
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rename(p_fp16_recompute, p_fp16)
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(pout,) = adam(p)
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(p_fp16) = cast(p)
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broadcast(p_fp16)
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"""
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global_params = set()
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local_params = set()
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param_to_fp16 = {}
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# recompute_var which need rename to fp16_param
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fp16_param_to_recompute = {}
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recompute_to_fp16 = {}
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def remove_param(input_name):
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global_params.remove(input_name)
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if input_name in local_params: # noqa: FURB132
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local_params.remove(input_name)
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if input_name in param_to_fp16:
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fp16_param = param_to_fp16.pop(input_name)
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if fp16_param in fp16_param_to_recompute:
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recompute = fp16_param_to_recompute.pop(fp16_param)
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recompute_to_fp16.pop(recompute)
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# step1: record param
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global_params = set(params)
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for idx, op in reversed(list(enumerate(block.ops))):
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if is_update_op(op):
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param = op.desc.input("Param")[0]
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local_params.add(param)
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# step2: remove param which can't offload and
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# record param->fp16param, fp16param->recompute_var
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for idx, op in enumerate(block.ops):
|
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if is_optimizer_op(op):
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break
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# TODO (Yuang Liu): tmp solution for fuse_grad_merge + optimize_cast
|
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if op.type == 'coalesce_tensor':
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continue
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for input_name in op.desc.input_arg_names():
|
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if input_name not in global_params:
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continue
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# param which will be used by fp32 op
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if op.type != 'cast':
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remove_param(input_name)
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continue
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# param is only used by cast op,
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# which to cast fp32_param to fp16_param
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output_name = op.output_arg_names[0]
|
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if 'cast_fp16' not in output_name:
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remove_param(input_name)
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continue
|
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|
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if 'subprog' not in output_name:
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assert output_name == input_name + '.cast_fp16'
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assert input_name not in param_to_fp16, (
|
||||
"There must be only one cast op from fp32 param to fp16 param."
|
||||
)
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param_to_fp16[input_name] = output_name
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else:
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# fp16-->recompute_var
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||||
assert input_name in param_to_fp16, (
|
||||
"param must first be cast to fp16"
|
||||
)
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||||
fp16_param = param_to_fp16[input_name]
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||||
fp16_param_to_recompute[fp16_param] = output_name
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||||
recompute_to_fp16[output_name] = fp16_param
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||||
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param_name_to_offload_name = {}
|
||||
# step3: main_block add offload, cast op
|
||||
# change recompute to fp16, remove cast(param) to fp16
|
||||
for idx, op in reversed(list(enumerate(block.ops))):
|
||||
if is_update_op(op):
|
||||
param = op.desc.input("Param")[0]
|
||||
if param not in global_params:
|
||||
continue
|
||||
# step3.1: create offload_var
|
||||
offload_var_name = self._get_offload_var_name(param)
|
||||
param_name_to_offload_name[param] = offload_var_name
|
||||
if offload:
|
||||
self._create_offload_var(
|
||||
param, offload_var_name, [block, startup_block]
|
||||
)
|
||||
|
||||
# step3.2: insert cast op and offload op
|
||||
self._insert_offload_op(
|
||||
block, idx + 1, param, offload_var_name
|
||||
)
|
||||
|
||||
assert param in param_to_fp16
|
||||
fp16_param_name = param_to_fp16[param]
|
||||
fp16_param_var = block.var(fp16_param_name)
|
||||
fp16_param_var.persistable = True
|
||||
self._insert_cast_op(
|
||||
block, idx + 1, param, param_to_fp16[param]
|
||||
)
|
||||
|
||||
if offload:
|
||||
# step3.3: insert fetch op
|
||||
self._insert_fetch_op(block, idx, offload_var_name, param)
|
||||
|
||||
continue
|
||||
|
||||
# step3.4: remove cast op
|
||||
if op.type == 'cast':
|
||||
input_name = op.desc.input_arg_names()[0]
|
||||
if input_name in global_params:
|
||||
block._remove_op(idx, sync=False)
|
||||
continue
|
||||
|
||||
# step3.5: change recompute_param to fp16_param
|
||||
for input_name in op.desc.input_arg_names():
|
||||
if input_name in recompute_to_fp16:
|
||||
op._rename_input(input_name, recompute_to_fp16[input_name])
|
||||
for output_name in op.desc.output_arg_names():
|
||||
if output_name in recompute_to_fp16:
|
||||
op._rename_output(
|
||||
output_name, recompute_to_fp16[output_name]
|
||||
)
|
||||
|
||||
# step4: remove recompute_param
|
||||
for name in recompute_to_fp16.keys():
|
||||
block._remove_var(name, sync=False)
|
||||
|
||||
# step5: remove fp32 param which not need
|
||||
for idx, op in enumerate(block.ops):
|
||||
if op.type not in ['coalesce_tensor', 'c_broadcast', 'broadcast']:
|
||||
continue
|
||||
for input_name in op.desc.input_arg_names():
|
||||
if input_name in param_to_fp16:
|
||||
op._rename_input(input_name, param_to_fp16[input_name])
|
||||
for output_name in op.desc.output_arg_names():
|
||||
if output_name in param_to_fp16:
|
||||
op._rename_output(output_name, param_to_fp16[output_name])
|
||||
|
||||
for param in global_params:
|
||||
assert param in param_to_fp16
|
||||
fp16_param_name = param_to_fp16[param]
|
||||
fp16_param_var = block.var(fp16_param_name)
|
||||
fp16_param_var.persistable = True
|
||||
|
||||
if param not in local_params:
|
||||
block._remove_var(param, sync=False)
|
||||
|
||||
# step6: startup_block add offload
|
||||
visited_vars = set()
|
||||
insert_idx = len(startup_block.ops)
|
||||
for idx, op in reversed(list(enumerate(startup_block.ops))):
|
||||
for out_name in op.output_arg_names:
|
||||
if out_name in visited_vars:
|
||||
continue
|
||||
|
||||
if out_name in param_to_fp16:
|
||||
var_name = out_name
|
||||
if offload:
|
||||
self._insert_offload_op(
|
||||
startup_block,
|
||||
idx + 1,
|
||||
var_name,
|
||||
param_name_to_offload_name[var_name],
|
||||
)
|
||||
|
||||
self._insert_cast_op(
|
||||
startup_block,
|
||||
insert_idx,
|
||||
var_name,
|
||||
param_to_fp16[var_name],
|
||||
)
|
||||
|
||||
# NOTE(wangxi): cast and offload should insert after broadcast param.
|
||||
# the insert op order is: {mp, dp}broadcast, cast, offload
|
||||
self._insert_broadcast_op(
|
||||
startup_block, insert_idx, var_name
|
||||
)
|
||||
|
||||
if var_name not in local_params:
|
||||
param = startup_block.var(out_name)
|
||||
param.persistable = False
|
||||
|
||||
visited_vars.add(out_name)
|
||||
|
||||
block._sync_with_cpp()
|
||||
startup_block._sync_with_cpp()
|
||||
Reference in New Issue
Block a user