wehub-resource-sync
182 lines
7.6 KiB
Python
182 lines
7.6 KiB
Python
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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import importlib
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import torch
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from torch.nn.parameter import Parameter
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from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
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from deepspeed.utils.types import ActivationFuncType, NormType
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from ..policy import (TransformerPolicy, maybe_copy, maybe_copy_geglu, maybe_copy_qkv, maybe_get_lora,
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transformer_param_names)
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from .base import *
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from .features import HybridGatedMLPContainer, HybridSplitQKVContainer
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class DS_InternLMContainer(HybridGatedMLPContainer, HybridSplitQKVContainer, BaseTransformerContainer):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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# All model specific things should be defined here instead of the base class.
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def create_module(self, config=None):
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_config = config if config is not None else self.ds_model_config
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_config.rotate_half = True
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_config.rotate_every_two = False
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_config.rotary_dim = self.hidden_size // self.num_attention_heads
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self.module = DeepSpeedGPTInference(_config, mp_group=self.mp_group)
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return self.module
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def set_lora_params(self):
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"""
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Necessary to implement for `HybridEngineContainer`
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"""
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self.lora_params = [
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maybe_get_lora(p) for p in [
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self.policy.client_module.mlp.up_proj.weight, self.policy.client_module.mlp.gate_proj.weight,
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self.policy.client_module.mlp.down_proj.weight, self.policy.client_module.self_attn.q_proj.weight,
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self.policy.client_module.self_attn.k_proj.weight, self.policy.client_module.self_attn.v_proj.weight,
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self.policy.client_module.self_attn.o_proj.weight
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]
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]
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def get_lora_matched_pair(self):
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up_proj_lora, gate_proj_lora, down_proj_lora, q_lora, k_lora, v_lora, out_lora = self.get_lora_params()
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ret = [(up_proj_lora, self.inter_up_w), (gate_proj_lora, self.inter_gate_w), (down_proj_lora, self._4hh_w),
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(out_lora, self.dense_w), (q_lora, self.qw), (k_lora, self.kw), (v_lora, self.vw)]
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return ret
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def set_q_k_v(self):
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"""
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Necessary to implement for `HybridSplitQKVContainer`
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"""
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self.qw = self.policy.client_module.self_attn.q_proj.weight
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self.qb = self.policy.client_module.self_attn.q_proj.bias
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self.kw = self.policy.client_module.self_attn.k_proj.weight
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self.kb = self.policy.client_module.self_attn.k_proj.bias
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self.vw = self.policy.client_module.self_attn.v_proj.weight
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self.vb = self.policy.client_module.self_attn.v_proj.bias
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def set_mlp_gate(self):
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"""
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Necessary to implement for `HybridGatedMLPContainer`
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"""
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self.inter_up_w = self.policy.client_module.mlp.up_proj.weight
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self.inter_up_b = None
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self.inter_gate_w = self.policy.client_module.mlp.gate_proj.weight
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self.inter_gate_b = None
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def load_params(self, module, sd, weight_quantizer, mp_replace, prefix):
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param_names = (
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'self_attn.q_proj.weight', \
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'self_attn.k_proj.weight', \
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'self_attn.v_proj.weight', \
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'self_attn.o_proj.weight', \
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'mlp.up_proj.weight', \
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'mlp.gate_proj.weight', \
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'mlp.down_proj.weight', \
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'input_layernorm.weight', \
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'post_attention_layernorm.weight'
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'self_attn.q_proj.bias', \
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'self_attn.k_proj.bias', \
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'self_attn.v_proj.bias', \
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'self_attn.o_proj.bias', \
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)
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maybe_copy_qkv(module.attention,
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sd,
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weight_quantizer,
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mp_replace,
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'attn_qkvw', [prefix + param_names[0], prefix + param_names[1], prefix + param_names[2]],
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split_qkv=self.policy.split_qkv)
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maybe_copy_qkv(module.attention,
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sd,
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weight_quantizer,
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mp_replace,
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'attn_qkvb', [prefix + param_names[9], prefix + param_names[10], prefix + param_names[11]],
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split_qkv=self.policy.split_qkv)
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maybe_copy(module.attention, sd, weight_quantizer, mp_replace, transformer_param_names[2],
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prefix + param_names[3])
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maybe_copy(module.attention, sd, weight_quantizer, mp_replace, transformer_param_names[3],
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prefix + param_names[12])
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maybe_copy_geglu(module.mlp, sd, weight_quantizer, mp_replace, 'inter_w',
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[prefix + param_names[4], prefix + param_names[5]])
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maybe_copy(module.mlp, sd, weight_quantizer, mp_replace, 'output_w', prefix + param_names[6])
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maybe_copy(module.mlp, sd, weight_quantizer, mp_replace, transformer_param_names[8], prefix + param_names[7])
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maybe_copy(module, sd, weight_quantizer, mp_replace, transformer_param_names[10], prefix + param_names[8])
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class InternLMLayerPolicy(TransformerPolicy):
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_orig_layer_class = []
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_orig_layer_class_inited = False
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def __init__(self, client_module, inference=True):
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super().__init__(
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inference,
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mlp_act_func_type=ActivationFuncType.GATED_SILU,
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norm_type=NormType.RMSNorm,
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)
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self.client_module = client_module
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self._init_orig_layer_class_once()
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def _init_orig_layer_class_once(self):
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if InternLMLayerPolicy._orig_layer_class_inited:
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return
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for sub_pkg in ['', '.internlm-7b', '.internlm-chat-7b']:
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try:
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from transformers.utils import TRANSFORMERS_DYNAMIC_MODULE_NAME
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module = importlib.import_module(f"{TRANSFORMERS_DYNAMIC_MODULE_NAME}{sub_pkg}.modeling_internlm")
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if module.InternLMDecoderLayer not in InternLMLayerPolicy._orig_layer_class:
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InternLMLayerPolicy._orig_layer_class.append(module.InternLMDecoderLayer)
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except ImportError:
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continue
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InternLMLayerPolicy._orig_layer_class_inited = True
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def get_hidden_heads(self):
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return self.client_module.self_attn.q_proj.weight.shape[1], \
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self.client_module.self_attn.num_heads, \
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self.client_module.input_layernorm.variance_epsilon, \
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self.client_module.mlp.gate_proj.weight.shape[0]
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def attention(self, enable_training=False):
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qw = self.client_module.self_attn.q_proj.weight
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kw = self.client_module.self_attn.k_proj.weight
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vw = self.client_module.self_attn.v_proj.weight
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qb = self.client_module.self_attn.q_proj.bias
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kb = self.client_module.self_attn.k_proj.bias
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vb = self.client_module.self_attn.v_proj.bias
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qkvw = Parameter(torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training)
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qkvb = Parameter(torch.cat((qb, kb, vb), dim=0), requires_grad=enable_training)
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return qkvw, \
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qkvb, \
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self.client_module.self_attn.o_proj.weight, \
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self.client_module.self_attn.o_proj.bias
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def mlp(self, enable_training=False):
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mlp1_up = self.client_module.mlp.up_proj.weight
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mlp1_gate = self.client_module.mlp.gate_proj.weight
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mlp2 = self.client_module.mlp.down_proj.weight
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mlp1 = Parameter(torch.cat((mlp1_up, mlp1_gate), dim=0), requires_grad=enable_training)
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return mlp1, None, mlp2, None
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def layernorm(self):
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return self.client_module.post_attention_layernorm.weight, \
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None, \
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self.client_module.input_layernorm.weight, \
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None
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