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from .base import * |
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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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class DS_CLIPContainer(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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class HFCLIPLayerPolicy(TransformerPolicy): |
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def __init__(self, client_module, inference=False): |
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super().__init__(inference, pre_attn_norm=True, scale_attention=True) |
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self.client_module = client_module |
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self.cuda_graph_supported = True |
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if HFCLIPLayerPolicy._orig_layer_class is None: |
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try: |
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import transformers |
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HFCLIPLayerPolicy._orig_layer_class = transformers.models.clip.modeling_clip.CLIPEncoderLayer |
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except: |
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HFCLIPLayerPolicy._orig_layer_class = None |
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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.layer_norm1.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.mlp.fc1.weight, \ |
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self.client_module.mlp.fc1.bias, \ |
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self.client_module.mlp.fc2.weight, \ |
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self.client_module.mlp.fc2.bias |
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def layernorm(self): |
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return self.client_module.layer_norm2.weight, \ |
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self.client_module.layer_norm2.bias, \ |
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self.client_module.layer_norm1.weight, \ |
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self.client_module.layer_norm1.bias |
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