mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-04-27 19:36:13 +00:00
fd6482ad8c
* [inference] support only TP (#4998) * support only tp * enable tp * add support for bloom (#5008) * [refactor] refactor gptq and smoothquant llama (#5012) * refactor gptq and smoothquant llama * fix import error * fix linear import torch-int * fix smoothquant llama import error * fix import accelerate error * fix bug * fix import smooth cuda * fix smoothcuda * [Inference Refactor] Merge chatglm2 with pp and tp (#5023) merge chatglm with pp and tp * [Refactor] remove useless inference code (#5022) * remove useless code * fix quant model * fix test import bug * mv original inference legacy * fix chatglm2 * [Refactor] refactor policy search and quant type controlling in inference (#5035) * [Refactor] refactor policy search and quant type controling in inference * [inference] update readme (#5051) * update readme * update readme * fix architecture * fix table * fix table * [inference] udpate example (#5053) * udpate example * fix run.sh * fix rebase bug * fix some errors * update readme * add some features * update interface * update readme * update benchmark * add requirements-infer --------- Co-authored-by: Bin Jia <45593998+FoolPlayer@users.noreply.github.com> Co-authored-by: Zhongkai Zhao <kanezz620@gmail.com>
68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
"""
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Utils for model inference
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"""
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import os
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import torch
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from colossalai.kernel.triton.copy_kv_cache_dest import copy_kv_cache_to_dest
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def copy_kv_to_mem_cache(layer_id, key_buffer, value_buffer, context_mem_index, mem_manager):
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"""
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This function copies the key and value cache to the memory cache
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Args:
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layer_id : id of current layer
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key_buffer : key cache
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value_buffer : value cache
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context_mem_index : index of memory cache in kv cache manager
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mem_manager : cache manager
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"""
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copy_kv_cache_to_dest(key_buffer, context_mem_index, mem_manager.key_buffer[layer_id])
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copy_kv_cache_to_dest(value_buffer, context_mem_index, mem_manager.value_buffer[layer_id])
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def init_to_get_rotary(self, base=10000, use_elem=False):
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"""
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This function initializes the rotary positional embedding, it is compatible for all models and is called in ShardFormer
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Args:
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self : Model that holds the rotary positional embedding
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base : calculation arg
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use_elem : activated when using chatglm-based models
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"""
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self.config.head_dim_ = self.config.hidden_size // self.config.num_attention_heads
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if not hasattr(self.config, "rope_scaling"):
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rope_scaling_factor = 1.0
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else:
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rope_scaling_factor = self.config.rope_scaling.factor if self.config.rope_scaling is not None else 1.0
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if hasattr(self.config, "max_sequence_length"):
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max_seq_len = self.config.max_sequence_length
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elif hasattr(self.config, "max_position_embeddings"):
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max_seq_len = self.config.max_position_embeddings * rope_scaling_factor
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else:
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max_seq_len = 2048 * rope_scaling_factor
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base = float(base)
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# NTK ref: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
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ntk_alpha = os.environ.get("INFER_NTK_ALPHA", None)
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if ntk_alpha is not None:
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ntk_alpha = float(ntk_alpha)
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assert ntk_alpha >= 1, "NTK alpha must be greater than or equal to 1"
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if ntk_alpha > 1:
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print(f"Note: NTK enabled, alpha set to {ntk_alpha}")
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max_seq_len *= ntk_alpha
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base = base * (ntk_alpha ** (self.head_dim_ / (self.head_dim_ - 2))) # Base change formula
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n_elem = self.config.head_dim_
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if use_elem:
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n_elem //= 2
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inv_freq = 1.0 / (base ** (torch.arange(0, n_elem, 2, device="cpu", dtype=torch.float32) / n_elem))
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t = torch.arange(max_seq_len + 1024 * 64, device="cpu", dtype=torch.float32) / rope_scaling_factor
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freqs = torch.outer(t, inv_freq)
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self._cos_cached = torch.cos(freqs).to(torch.float16).cuda()
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self._sin_cached = torch.sin(freqs).to(torch.float16).cuda()
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