mirror of https://github.com/InternLM/InternLM
feat(seed): set global seed for every model initialization (#496)
* bind seed * bind seedpull/507/head v0.2.1dev20231121
parent
679ed3c8ca
commit
eba2b859fc
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@ -27,6 +27,7 @@ else:
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get_numa = True
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get_numa = True
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logger = get_logger(__file__)
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logger = get_logger(__file__)
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GLOBAL_SEED = 1024
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def get_default_parser():
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def get_default_parser():
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@ -543,6 +544,9 @@ def initialize_distributed_env(
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else:
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else:
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assert launcher in ["slurm", "torch"], "launcher only support slurm or torch"
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assert launcher in ["slurm", "torch"], "launcher only support slurm or torch"
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global GLOBAL_SEED
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GLOBAL_SEED = seed
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if args_check:
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if args_check:
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args_sanity_check()
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args_sanity_check()
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@ -2,6 +2,7 @@
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# -*- encoding: utf-8 -*-
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# -*- encoding: utf-8 -*-
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import math
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import math
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from functools import wraps
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from typing import Optional
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from typing import Optional
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import torch
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import torch
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@ -11,7 +12,9 @@ from torch import nn
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from internlm.core.context import IS_SEQUENCE_PARALLEL, IS_TENSOR_PARALLEL, ParallelMode
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from internlm.core.context import IS_SEQUENCE_PARALLEL, IS_TENSOR_PARALLEL, ParallelMode
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from internlm.core.context.parallel_context import global_context as gpc
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from internlm.core.context.parallel_context import global_context as gpc
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from internlm.core.context.random import _SEED_MANAGER
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from internlm.initialize.initialize_tensor import normal_, scaled_init_method_normal
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from internlm.initialize.initialize_tensor import normal_, scaled_init_method_normal
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from internlm.initialize.launch import GLOBAL_SEED
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from internlm.model.embedding import Embedding1D
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from internlm.model.embedding import Embedding1D
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from internlm.model.linear import (
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from internlm.model.linear import (
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FeedForward,
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FeedForward,
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@ -81,6 +84,7 @@ class PackedFlashBaseLayer1D(nn.Module):
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self.use_flash_attn = use_flash_attn
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self.use_flash_attn = use_flash_attn
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head_dim = hidden_size // num_attention_heads
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head_dim = hidden_size // num_attention_heads
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self.mixer = MHA(
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self.mixer = MHA(
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embed_dim=hidden_size,
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embed_dim=hidden_size,
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num_heads=num_attention_heads,
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num_heads=num_attention_heads,
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@ -410,6 +414,16 @@ class PackedFlashInternLm1D(nn.Module):
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return hidden_states
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return hidden_states
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def fix_seed(func):
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@wraps(func)
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def wrapper(*args, **kwargs):
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_SEED_MANAGER.reset()
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gpc.set_seed(GLOBAL_SEED)
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func(*args, **kwargs)
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return wrapper
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def _build_generic_model_1d(num_layers, num_chunks, device=torch.device("cuda"), **kwargs):
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def _build_generic_model_1d(num_layers, num_chunks, device=torch.device("cuda"), **kwargs):
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"""
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"""
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build generic model 1d
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build generic model 1d
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@ -429,6 +443,7 @@ def _build_generic_model_1d(num_layers, num_chunks, device=torch.device("cuda"),
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logger.info(f"The layer sharding is {all_parts}.")
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logger.info(f"The layer sharding is {all_parts}.")
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models = []
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models = []
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PackedFlashInternLm1D.__init__ = fix_seed(PackedFlashInternLm1D.__init__)
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for start, end in parts:
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for start, end in parts:
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kwargs["num_layers"] = end - start
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kwargs["num_layers"] = end - start
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