key_pass = key[..., self.rotary_ndims :]# 计算旋转嵌入的令牌偏移量(在解码时)seq_len = key.shape[-2] offset =0ifhas_layer_past: offset = layer_past[0].shape[-2] seq_len += offset cos, sin = self.rotary_emb(value, seq_len=seq_len) query, key = apply_rotary_pos_emb(query...
x2 = x[..., x.shape[-1] //2:]returntorch.cat((-x2, x1), dim=-1)# 从transformers.models.llama.modeling_llama.apply_rotary_pos_emb复制并修改# TODO @Arthur 在静态缓存后不再从LLama复制defapply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):"""将Rotary位置嵌入...
这里比Q的头数少,是为了减少内存占用和计算量 self.num_key_value_groups = self.num_heads // self.num_key_value_heads # 4,因为最终KV的维度还是得和Q对上,所以需要算一下KV需要重复几次 self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.is_c...
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() sin = emb.sin() # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention cos = cos *...
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - Fix bug in apply_rotary_pos_emb_flashatt: in Qwen2-5-VL (#36065) · huggingface/transformers@014047e
A device mismatch occurred in the apply_rotary_pos_emb function of the Qwen2 model. Specifically, the cos and sin tensors (used for rotary positional embeddings) were on the CPU, while the q, k, and position_ids tensors were on the GPU. This mismatch led to a runtime error during tr...
]# 返回将输入张量的后一半维度内容反向排列、加上前一半维度内容的张量拼接结果returntorch.cat((-x2, x1), dim=-1)# 从transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb复制而来defapply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):"""Applies Rotary Position ...
x2 = x[..., x.shape[-1] //2:]returntorch.cat((-x2, x1), dim=-1)# 从transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb复制defapply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):"""Applies Rotary Position Embedding to the query and key tensors...
(config.vocab_size,config.hidden_size,self.padding_idx)self.layers=nn.ModuleList([LlamaDecoderLayer(config)for_inrange(config.num_hidden_layers)])self.norm=LlamaRMSNorm(config.hidden_size,eps=config.rms_norm_eps)self.gradient_checkpointing=False# Initialize weights and apply final processingself....
You can also do Transformer-XL recurrence, by simply passing in a max_mem_len in the TransformerWrapper class, and then making sure your Decoder has rel_pos_bias (or rotary_pos_emb) set to True. Then, you can retrieve the memories at each step with the return_mems keyword and pass it...