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from .base import * |
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from .features import MetaTensorContainer, HybridSplitQKVContainer |
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from deepspeed.model_implementations.transformers.ds_opt import DeepSpeedOPTInference |
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import torch |
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from torch.nn.parameter import Parameter |
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from ..policy import TransformerPolicy |
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from ..policy import transformer_param_names |
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from ..policy import maybe_copy |
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from ..policy import maybe_copy_qkv |
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from ..policy import maybe_get_lora |
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from deepspeed.utils.types import ActivationFuncType |
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class DS_OPTContainer(MetaTensorContainer, HybridSplitQKVContainer, BaseTransformerContainer): |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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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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self.module = DeepSpeedOPTInference(_config, mp_group=self.mp_group) |
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self.module.config.scale_attention = self.scale_attention |
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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.fc1, |
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self.policy.client_module.fc2, |
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self.policy.client_module.self_attn.q_proj, |
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self.policy.client_module.self_attn.k_proj, |
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self.policy.client_module.self_attn.v_proj, |
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self.policy.client_module.self_attn.out_proj, |
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] |
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] |
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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 get_lora_matched_pair(self): |
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fc1_lora, fc2_lora, q_lora, k_lora, v_lora, out_lora = self.get_lora_params() |
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ret = [(fc1_lora, self._h4h_w), (fc2_lora, self._4hh_w), (out_lora, self.dense_w), (q_lora, self.qw), |
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(k_lora, self.kw), (v_lora, self.vw)] |
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return ret |
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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.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.out_proj.weight', \ |
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'self_attn.out_proj.bias', \ |
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'fc1.weight', \ |
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'fc1.bias', \ |
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'fc2.weight', \ |
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'fc2.bias', \ |
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'final_layer_norm.weight', \ |
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'final_layer_norm.bias', \ |
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'self_attn_layer_norm.weight', \ |
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'self_attn_layer_norm.bias' |
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) |
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for i in range(0, 6, 3): |
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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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transformer_param_names[i // 3], |
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[prefix + param_names[i], prefix + param_names[i + 1], prefix + param_names[i + 2]], |
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split_qkv=self.policy.split_qkv) |
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for i in range(6, 8): |
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maybe_copy(module.attention, sd, weight_quantizer, mp_replace, transformer_param_names[i - 4], |
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prefix + param_names[i]) |
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for i in range(8, 14): |
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maybe_copy(module.mlp, sd, weight_quantizer, mp_replace, transformer_param_names[i - 4], |
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prefix + param_names[i]) |
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for i in range(14, 16): |
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maybe_copy(module, sd, weight_quantizer, mp_replace, transformer_param_names[i - 4], |
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prefix + param_names[i]) |
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class HFOPTLayerPolicy(TransformerPolicy): |
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_orig_layer_class = None |
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def __init__(self, client_module, inference=True, use_load_prefix=True): |
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super().__init__(inference, linear_layer=True, pre_attn_norm=True, use_load_prefix=use_load_prefix) |
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self.client_module = client_module |
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try: |
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import transformers |
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HFOPTLayerPolicy._orig_layer_class = transformers.models.opt.modeling_opt.OPTDecoderLayer |
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except: |
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HFOPTLayerPolicy._orig_layer_class = None |
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if hasattr(TransformerPolicy, "hf_model_config") and hasattr(TransformerPolicy.hf_model_config, |
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"activation_function"): |
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if TransformerPolicy.hf_model_config.activation_function == "relu": |
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self.mlp_act_func_type = ActivationFuncType.ReLU |
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elif TransformerPolicy.hf_model_config.activation_function in ["gelu", "gelu_new"]: |
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self.mlp_act_func_type = ActivationFuncType.GELU |
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else: |
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raise ValueError("Unsupported activation function: {}".format( |
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TransformerPolicy.hf_model_config.activation_function)) |
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else: |
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self.mlp_act_func_type = ActivationFuncType.ReLU |
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def get_hidden_heads(self): |
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return self.client_module.self_attn.embed_dim, \ |
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self.client_module.self_attn.num_heads, \ |
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self.client_module.self_attn_layer_norm.eps, \ |
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DEFAULT_INTERMEDIATE_SIZE |
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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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qb = self.client_module.self_attn.q_proj.bias |
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kw = self.client_module.self_attn.k_proj.weight |
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kb = self.client_module.self_attn.k_proj.bias |
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vw = self.client_module.self_attn.v_proj.weight |
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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.out_proj.weight, \ |
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self.client_module.self_attn.out_proj.bias |
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def mlp(self, enable_training=False): |
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return self.client_module.fc1.weight, \ |
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self.client_module.fc1.bias, \ |
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self.client_module.fc2.weight, \ |
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self.client_module.fc2.bias |
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def layernorm(self): |
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return self.client_module.final_layer_norm.weight, \ |
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self.client_module.final_layer_norm.bias, \ |
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self.client_module.self_attn_layer_norm.weight, \ |
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self.client_module.self_attn_layer_norm.bias |
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