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from .base import * |
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from .features.meta_tensor import MetaTensorContainer |
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from .features.split_qkv import HybridSplitQKVContainer |
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from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference |
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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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class DS_GPTJContainer(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 = DeepSpeedGPTInference(_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.mlp.fc_in, self.policy.client_module.mlp.fc_out, |
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self.policy.client_module.attn.q_proj, self.policy.client_module.attn.k_proj, |
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self.policy.client_module.attn.v_proj, self.policy.client_module.attn.out_proj |
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] |
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] |
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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 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.attn.q_proj.weight |
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self.qb = None |
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self.kw = self.policy.client_module.attn.k_proj.weight |
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self.kb = None |
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self.vw = self.policy.client_module.attn.v_proj.weight |
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self.vb = 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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'attn.q_proj.weight', \ |
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'attn.k_proj.weight', \ |
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'attn.v_proj.weight', \ |
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'attn.out_proj.weight', \ |
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'mlp.fc_in.weight', \ |
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'mlp.fc_in.bias', \ |
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'mlp.fc_out.weight', \ |
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'mlp.fc_out.bias', \ |
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'ln_1.weight', \ |
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'ln_1.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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for i in range(3, 4): |
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maybe_copy(module.attention, sd, weight_quantizer, mp_replace, transformer_param_names[i - 1], |
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prefix + param_names[i]) |
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for i in range(4, 8): |
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maybe_copy(module.mlp, sd, weight_quantizer, mp_replace, transformer_param_names[i], |
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prefix + param_names[i]) |
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for i in range(8, 10): |
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maybe_copy(module, sd, weight_quantizer, mp_replace, transformer_param_names[i + 2], |
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prefix + param_names[i]) |
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class HFGPTJLayerPolicy(TransformerPolicy): |
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_orig_layer_class = None |
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def __init__(self, client_module, inference=True): |
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super().__init__(inference, scale_attention=True) |
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self.client_module = client_module |
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try: |
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import transformers |
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HFGPTJLayerPolicy._orig_layer_class = transformers.models.gptj.modeling_gptj.GPTJBlock |
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except: |
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HFGPTJLayerPolicy._orig_layer_class = None |
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def get_hidden_heads(self): |
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return self.client_module.attn.embed_dim, \ |
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self.client_module.attn.num_attention_heads, \ |
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self.client_module.ln_1.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.attn.q_proj.weight |
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kw = self.client_module.attn.k_proj.weight |
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vw = self.client_module.attn.v_proj.weight |
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qkvw = Parameter(torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training) |
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return qkvw, \ |
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None, \ |
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self.client_module.attn.out_proj.weight, \ |
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None, |
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def mlp(self, enable_training=False): |
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return self.client_module.mlp.fc_in.weight, \ |
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self.client_module.mlp.fc_in.bias, \ |
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self.client_module.mlp.fc_out.weight, \ |
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self.client_module.mlp.fc_out.bias |
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def layernorm(self): |
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return None, \ |
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None, \ |
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self.client_module.ln_1.weight, \ |
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self.client_module.ln_1.bias |
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