mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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1007 lines
46 KiB
Python
1007 lines
46 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import configparser
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import time
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from pathlib import Path
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from typing import List, Union
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import numpy as np
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import torch
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from safetensors import safe_open
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from ..._utils import (numpy_to_torch, pad_vocab_size, str_dtype_to_torch,
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torch_to_numpy)
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from ...layers import MoeConfig
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from ...logger import logger
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from ...mapping import Mapping
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from ...quantization import QuantMode
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from ..modeling_utils import PretrainedConfig
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from .utils import (iterate_shard_files, load_state_dict,
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retrieved_layer_index_from_name)
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def gen_suffix(rank, use_smooth_quant, quant_per_channel):
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suffix = f"{rank}.bin"
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if use_smooth_quant:
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sq_prefix = "int8."
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if quant_per_channel:
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sq_prefix += "col."
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suffix = sq_prefix + suffix
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return suffix
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def extract_layer_idx(name):
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ss = name.split('.')
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for s in ss:
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if s.isdigit():
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return s
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return None
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def split(v: Union[np.ndarray, torch.Tensor],
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tp_size: int,
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tp_rank: int,
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dim=0):
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if tp_size == 1:
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return v
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assert len(v.shape) > 1 or dim == 0
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if isinstance(v, np.ndarray):
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return np.ascontiguousarray(
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np.split(v, tp_size, axis=dim)[tp_rank].copy())
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else:
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assert v.shape[dim] % tp_size == 0, \
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'Unable to split: shape={v.shape} (dim={dim}) tp_size={tp_size}.'
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split_size = v.shape[dim] // tp_size
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return v.split(split_size, dim=dim)[tp_rank].clone().detach()
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def dup_kv_weight(v, num_head, tp_size):
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assert tp_size % num_head == 0
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reps = tp_size // num_head
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head_size = v.shape[0] // num_head
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v = v.reshape(num_head, head_size,
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-1)[:, None, :, :].expand(num_head, reps, head_size,
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v.shape[1])
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return v.reshape(num_head * reps * head_size, -1).clone().detach()
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def parse_bin_config(ini_file):
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gpt_config = configparser.ConfigParser()
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gpt_config.read(ini_file)
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n_embd = gpt_config.getint('llama', 'hidden_size')
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n_head = gpt_config.getint('llama', 'num_attention_heads')
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n_layer = gpt_config.getint('llama', 'num_hidden_layers')
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n_positions = gpt_config.getint('llama', 'max_position_embeddings')
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vocab_size = gpt_config.getint('llama', 'vocab_size')
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hidden_act = gpt_config.get('llama', 'hidden_act')
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inter_size = gpt_config.getint('llama', 'intermediate_size', fallback=None)
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n_kv_head = gpt_config.getint('llama', 'num_key_value_heads', fallback=None)
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if inter_size is None:
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inter_size = 4 * n_embd
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return n_embd, n_head, n_layer, n_positions, vocab_size, hidden_act, inter_size, n_kv_head
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class QkvWeightHelper:
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""" A helper utility for loading QKV weights from sharded files. """
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def __init__(self, config: PretrainedConfig):
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.num_kv_heads = config.num_key_value_heads
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self.tp_size = config.mapping.tp_size
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self.tp_rank = config.mapping.tp_rank
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self.is_mha = self.num_heads == self.num_kv_heads
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self._qkv_weights = {}
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@staticmethod
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def is_qkv_weight(name):
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for k in ['q_proj', 'k_proj', 'v_proj']:
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if 'self_attn' in name and k in name:
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return True
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return False
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def add_weight(self, i: int, name: str, weight: torch.Tensor):
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if 'q_proj' in name:
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tag = 'q'
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elif 'k_proj' in name:
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tag = 'k'
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elif 'v_proj' in name:
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tag = 'v'
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else:
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raise ValueError(f'Got an unexpected parameter of name {name}')
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if i not in self._qkv_weights:
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self._qkv_weights[i] = {}
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self._qkv_weights[i][tag] = weight
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def is_qkv_prepared(self, layer_idx):
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if layer_idx not in self._qkv_weights:
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return False
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weights = self._qkv_weights[layer_idx]
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return 'q' in weights and 'k' in weights and 'v' in weights
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def split_qkv_weights(self, layer_idx):
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if not self.is_qkv_prepared(layer_idx):
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return None
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weights = self._qkv_weights.pop(layer_idx) # to prevent memory leak.
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q, k, v = (torch.tensor(weights[t]) for t in ['q', 'k', 'v'])
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if not self.is_mha:
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head_size = self.hidden_size // self.num_heads
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if self.num_kv_heads < self.tp_size:
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# duplicate the KV heads up to tensor_parallel
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k = dup_kv_weight(k, self.num_kv_heads, self.tp_size)
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v = dup_kv_weight(v, self.num_kv_heads, self.tp_size)
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assert k.shape[0] % (self.tp_size * head_size) == 0
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assert v.shape[0] % (self.tp_size * head_size) == 0
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wq = split(q, self.tp_size, self.tp_rank)
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wk = split(k, self.tp_size, self.tp_rank)
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wv = split(v, self.tp_size, self.tp_rank)
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fused_qkv = torch.cat((wq, wk, wv), dim=0)
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else:
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qkv = torch.cat([q, k, v], dim=0)
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qkv = qkv.reshape(3, q.shape[0], q.shape[1])
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fused_qkv = split(qkv, self.tp_size, self.tp_rank, dim=1)
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fused_qkv = fused_qkv.reshape(3 * (q.shape[0] // self.tp_size),
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q.shape[1])
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return fused_qkv
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def load_from_hf_checkpoint(model_dir, mapping=Mapping(), config=None):
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'''Weights-only quantization is the only supported quantization recipe here.'''
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logger.info('Loading weights from HF LLaMA...')
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tik = time.time()
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weights = {}
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dtype = config.dtype
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if isinstance(dtype, str):
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dtype = str_dtype_to_torch(dtype)
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moe_config = MoeConfig(config.moe_num_experts, config.moe_top_k,
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config.moe_tp_mode, config.moe_normalization_mode)
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assert not moe_config.has_moe(), "MoE does not support sharded load"
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model_dir = Path(model_dir)
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from transformers import AutoConfig
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hf_config = AutoConfig.from_pretrained(model_dir)
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quant_mode = config.quant_mode
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if quant_mode.is_int8_weight_only():
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plugin_weight_only_quant_type = torch.int8
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elif quant_mode.is_int4_weight_only():
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plugin_weight_only_quant_type = torch.quint4x2
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use_weight_only = quant_mode.is_weight_only()
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layers_range = mapping.pp_layers(config.num_hidden_layers)
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qkv_weight_helper = QkvWeightHelper(config)
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for model_file in iterate_shard_files(model_dir,
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rank=mapping.tp_rank,
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progress_bar=False):
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logger.debug(f'Loading file {str(model_file)}...')
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model_params = load_state_dict(model_file, dtype=dtype)
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for name, param in model_params.items():
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logger.debug(f'Converting weight {name}...')
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layer_idx = retrieved_layer_index_from_name(name)
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if layer_idx is None:
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layer = None
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else:
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if layer_idx not in layers_range:
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continue
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tllm_prex = f'transformer.layers.{layer_idx}.'
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if 'model.embed_tokens.weight' in name:
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if hf_config.tie_word_embeddings:
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# lm_head.weight has the same weights as embedding
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if mapping.is_last_pp_rank():
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if config.vocab_size % mapping.tp_size != 0:
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# padding
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vocab_size_padded = pad_vocab_size(
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config.vocab_size, mapping.tp_size)
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pad_width = vocab_size_padded - config.vocab_size
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param = torch.from_numpy(
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np.pad(param.detach().cpu().numpy(),
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((0, pad_width), (0, 0)),
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'constant',
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constant_values=0))
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weights['lm_head.weight'] = split(
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param, mapping.tp_size, mapping.tp_rank)
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if config.use_parallel_embedding:
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param = split(param, mapping.tp_size, mapping.tp_rank,
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config.embedding_sharding_dim)
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if mapping.is_first_pp_rank():
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weights['transformer.vocab_embedding.weight'] = param
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elif 'model.norm.weight' in name:
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if mapping.is_last_pp_rank():
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weights['transformer.ln_f.weight'] = param
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elif 'lm_head.weight' in name:
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if mapping.is_last_pp_rank():
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if config.vocab_size % mapping.tp_size != 0:
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# padding
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vocab_size_padded = pad_vocab_size(
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config.vocab_size, mapping.tp_size)
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pad_width = vocab_size_padded - config.vocab_size
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param = torch.from_numpy(
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np.pad(param.detach().cpu().numpy(),
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((0, pad_width), (0, 0)),
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'constant',
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constant_values=0))
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weights['lm_head.weight'] = split(param, mapping.tp_size,
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mapping.tp_rank)
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elif 'input_layernorm.weight' in name:
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weights[tllm_prex + 'input_layernorm.weight'] = param
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elif 'post_attention_layernorm.weight' in name:
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weights[tllm_prex + 'post_layernorm.weight'] = param
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elif qkv_weight_helper.is_qkv_weight(name):
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qkv_weight_helper.add_weight(layer_idx, name, param)
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if not qkv_weight_helper.is_qkv_prepared(layer_idx):
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continue
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split_v = qkv_weight_helper.split_qkv_weights(layer_idx)
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if use_weight_only:
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param = split_v.transpose()
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processed_torch_weights, torch_weight_scales = \
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torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
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param, plugin_weight_only_quant_type)
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weights[tllm_prex +
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'attention.qkv.weight'] = processed_torch_weights
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weights[
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tllm_prex +
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'attention.qkv.per_channel_scale'] = torch_weight_scales
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else:
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weights[tllm_prex + 'attention.qkv.weight'] = split_v
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elif 'self_attn.o_proj.weight' in name:
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split_v = split(param, mapping.tp_size, mapping.tp_rank, dim=1)
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if use_weight_only:
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processed_torch_weights, torch_weight_scales = \
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torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
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split_v.transpose(), plugin_weight_only_quant_type)
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weights[tllm_prex +
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'attention.dense.weight'] = processed_torch_weights
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weights[
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tllm_prex +
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'attention.dense.per_channel_scale'] = torch_weight_scales
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else:
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weights[tllm_prex + 'attention.dense.weight'] = split_v
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elif 'mlp.up_proj.weight' in name:
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split_v = split(param, mapping.tp_size, mapping.tp_rank, dim=0)
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if use_weight_only:
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processed_torch_weights, torch_weight_scales = \
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torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
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split_v.transpose(), plugin_weight_only_quant_type)
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weights[tllm_prex +
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'mlp.gate.weight'] = processed_torch_weights
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weights[tllm_prex +
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'mlp.gate.per_channel_scale'] = torch_weight_scales
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else:
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weights[tllm_prex + 'mlp.gate.weight'] = split_v
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elif 'mlp.down_proj.weight' in name:
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split_v = split(param, mapping.tp_size, mapping.tp_rank, dim=1)
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if use_weight_only:
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processed_torch_weights, torch_weight_scales = \
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torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
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split_v.transpose(), plugin_weight_only_quant_type)
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weights[tllm_prex +
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'mlp.proj.weight'] = processed_torch_weights
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weights[tllm_prex +
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'mlp.proj.per_channel_scale'] = torch_weight_scales
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else:
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weights[tllm_prex + 'mlp.proj.weight'] = split_v
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elif 'mlp.gate_proj.weight' in name:
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split_v = split(param, mapping.tp_size, mapping.tp_rank, dim=0)
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if use_weight_only:
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processed_torch_weights, torch_weight_scales = \
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torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
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split_v.transpose(), plugin_weight_only_quant_type)
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layer.mlp.fc.weight.value = processed_torch_weights
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layer.mlp.fc.per_channel_scale.value = torch_weight_scales
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weights[tllm_prex +
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'mlp.fc.weight'] = processed_torch_weights
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weights[tllm_prex +
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'mlp.fc.per_channel_scale'] = torch_weight_scales
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else:
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weights[tllm_prex + 'mlp.fc.weight'] = split_v
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del model_params
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tok = time.time()
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t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
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logger.info(f'Weights loaded. Total time: {t}')
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return weights
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def load_from_hf_llama(tensorrt_llm_llama: 'LLaMAForCausalLM',
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hf_llama,
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mapping=Mapping(),
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dtype='float32',
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use_gemm_woq_plugin=True):
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logger.info('Loading weights from HF LLaMA...')
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tik = time.time()
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quant_mode = getattr(tensorrt_llm_llama, 'quant_mode', QuantMode(0))
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if quant_mode.is_int8_weight_only():
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plugin_weight_only_quant_type = torch.int8
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elif quant_mode.is_int4_weight_only():
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plugin_weight_only_quant_type = torch.quint4x2
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use_weight_only = quant_mode.is_weight_only()
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num_kv_heads = tensorrt_llm_llama.config.num_key_value_heads
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mha_mode = (num_kv_heads == tensorrt_llm_llama.config.num_attention_heads)
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model_params = dict(hf_llama.named_parameters())
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# concatenate, duplicate and reshape q, k, v -> qkv
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for l in range(hf_llama.config.num_hidden_layers):
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prefix = f'model.layers.{l}.self_attn.'
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q_weight = model_params[prefix + 'q_proj.weight']
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k_weight = model_params[prefix + 'k_proj.weight']
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v_weight = model_params[prefix + 'v_proj.weight']
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if not mha_mode:
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head_size = tensorrt_llm_llama.config.hidden_size // tensorrt_llm_llama.config.num_attention_heads
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if num_kv_heads < mapping.tp_size:
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# duplicate the KV heads up to tensor_parallel
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k_weight = dup_kv_weight(k_weight, num_kv_heads,
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mapping.tp_size)
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v_weight = dup_kv_weight(v_weight, num_kv_heads,
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mapping.tp_size)
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assert (k_weight.shape[0] % (mapping.tp_size * head_size)) == 0
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assert (v_weight.shape[0] % (mapping.tp_size * head_size)) == 0
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qkv_weight = [q_weight, k_weight, v_weight]
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else:
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qkv_weight = torch.cat([q_weight, k_weight, v_weight], dim=0)
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model_params[prefix + 'qkv_proj.weight'] = qkv_weight
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moe_config = MoeConfig(tensorrt_llm_llama.config.moe_num_experts,
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tensorrt_llm_llama.config.moe_top_k,
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tensorrt_llm_llama.config.moe_tp_mode,
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tensorrt_llm_llama.config.moe_normalization_mode)
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# concatenate MoE gated activations & stack experts
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for l in range(hf_llama.config.num_hidden_layers):
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if not moe_config.has_moe():
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continue
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rank_experts = list(range(moe_config.num_experts))
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if moe_config.tp_mode == moe_config.ParallelismMode.EXPERT_PARALLEL:
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rank_experts = mapping.ep_experts(moe_config.num_experts)
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for suffix in ["w1", "w2", "w3"]:
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model_params[f'model.layers.{l}.block_sparse_moe.experts.{suffix}.weight'] = \
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torch.stack(list(model_params[f'model.layers.{l}.block_sparse_moe.experts.{expert}.{suffix}.weight']
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for expert in rank_experts))
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w3 = model_params[
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f'model.layers.{l}.block_sparse_moe.experts.w3.weight']
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w2 = model_params[
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f'model.layers.{l}.block_sparse_moe.experts.w2.weight']
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w1 = model_params[
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f'model.layers.{l}.block_sparse_moe.experts.w1.weight']
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if moe_config.tp_mode == moe_config.ParallelismMode.TENSOR_PARALLEL:
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w3 = split(w3, mapping.tp_size, mapping.tp_rank, dim=1)
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w2 = split(w2, mapping.tp_size, mapping.tp_rank, dim=2)
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w1 = split(w1, mapping.tp_size, mapping.tp_rank, dim=1)
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# concat w3 and w1 for gated expert
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model_params[f'model.layers.{l}.block_sparse_moe.experts.w3w1.weight'] = \
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torch.concat([w3, w1], dim=-2)
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model_params[
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f'model.layers.{l}.block_sparse_moe.experts.w2.weight'] = w2
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torch_dtype = str_dtype_to_torch(dtype)
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layers_range = mapping.pp_layers(hf_llama.config.num_hidden_layers)
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vocab_size = hf_llama.config.vocab_size
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weights = {}
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for k, v in model_params.items():
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t_dtype = torch_dtype if "block_sparse_moe.gate" not in k else torch.float32
|
|
if isinstance(v, list):
|
|
v = [torch_to_numpy(vv.to(t_dtype).detach().cpu()) for vv in v]
|
|
else:
|
|
v = torch_to_numpy(v.to(t_dtype).detach().cpu())
|
|
if 'model.embed_tokens.weight' in k:
|
|
if hf_llama.config.tie_word_embeddings:
|
|
# lm_head.weight has the same weights as embedding
|
|
if mapping.is_last_pp_rank():
|
|
if vocab_size % mapping.tp_size != 0:
|
|
# padding
|
|
vocab_size_padded = pad_vocab_size(
|
|
vocab_size, mapping.tp_size)
|
|
pad_width = vocab_size_padded - vocab_size
|
|
v = torch.from_numpy(
|
|
np.pad(v.detach().cpu().numpy(),
|
|
((0, pad_width), (0, 0)),
|
|
'constant',
|
|
constant_values=0))
|
|
weights['lm_head.weight'] = split(v, mapping.tp_size,
|
|
mapping.tp_rank)
|
|
|
|
if tensorrt_llm_llama.config.use_parallel_embedding:
|
|
v = split(v, mapping.tp_size, mapping.tp_rank,
|
|
tensorrt_llm_llama.config.embedding_sharding_dim)
|
|
if mapping.is_first_pp_rank():
|
|
weights['transformer.vocab_embedding.weight'] = v
|
|
elif 'model.norm.weight' in k:
|
|
if mapping.is_last_pp_rank():
|
|
weights['transformer.ln_f.weight'] = v
|
|
|
|
elif 'lm_head.weight' in k:
|
|
if mapping.is_last_pp_rank():
|
|
if vocab_size % mapping.tp_size != 0:
|
|
# padding
|
|
vocab_size_padded = tensorrt_llm_llama.lm_head.out_features * mapping.tp_size
|
|
pad_width = vocab_size_padded - vocab_size
|
|
v = np.pad(v, ((0, pad_width), (0, 0)),
|
|
'constant',
|
|
constant_values=0)
|
|
|
|
weights['lm_head.weight'] = split(v, mapping.tp_size,
|
|
mapping.tp_rank)
|
|
else:
|
|
layer_idx = extract_layer_idx(k)
|
|
if layer_idx is None or int(layer_idx) not in layers_range:
|
|
continue
|
|
idx = int(layer_idx) - layers_range[0]
|
|
if 'input_layernorm.weight' in k:
|
|
weights['transformer.layers.{}.input_layernorm.weight'.format(
|
|
idx)] = v
|
|
elif 'post_attention_layernorm.weight' in k:
|
|
weights['transformer.layers.{}.post_layernorm.weight'.format(
|
|
idx)] = v
|
|
|
|
elif 'self_attn.qkv_proj.weight' in k:
|
|
if not mha_mode:
|
|
assert isinstance(v, list) and len(v) == 3
|
|
wq = split(v[0], mapping.tp_size, mapping.tp_rank)
|
|
wk = split(v[1], mapping.tp_size, mapping.tp_rank)
|
|
wv = split(v[2], mapping.tp_size, mapping.tp_rank)
|
|
split_v = np.concatenate((wq, wk, wv))
|
|
else:
|
|
q_emb = v.shape[0] // 3
|
|
model_emb = v.shape[1]
|
|
v = v.reshape(3, q_emb, model_emb)
|
|
split_v = split(v, mapping.tp_size, mapping.tp_rank, dim=1)
|
|
split_v = split_v.reshape(3 * (q_emb // mapping.tp_size),
|
|
model_emb)
|
|
if use_weight_only:
|
|
v = np.ascontiguousarray(split_v.transpose())
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
numpy_to_torch(v), plugin_weight_only_quant_type)
|
|
if not use_gemm_woq_plugin:
|
|
weights['transformer.layers.{}.attention.qkv.weight'.
|
|
format(idx)] = v
|
|
else:
|
|
weights['transformer.layers.{}.attention.qkv.weight'.
|
|
format(idx)] = processed_torch_weights
|
|
|
|
weights[
|
|
'transformer.layers.{}.attention.qkv.per_channel_scale'.
|
|
format(idx)] = torch_weight_scales
|
|
else:
|
|
weights['transformer.layers.{}.attention.qkv.weight'.format(
|
|
idx)] = split_v
|
|
|
|
elif 'self_attn.o_proj.weight' in k:
|
|
# dst = tensorrt_llm_llama.layers[idx].attention.dense.weight
|
|
split_v = split(v, mapping.tp_size, mapping.tp_rank, dim=1)
|
|
if use_weight_only:
|
|
v = np.ascontiguousarray(split_v.transpose())
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
numpy_to_torch(v), plugin_weight_only_quant_type)
|
|
if not use_gemm_woq_plugin:
|
|
weights['transformer.layers.{}.attention.dense.weight'.
|
|
format(idx)] = v
|
|
else:
|
|
weights['transformer.layers.{}.attention.dense.weight'.
|
|
format(idx)] = processed_torch_weights
|
|
|
|
weights[
|
|
'transformer.layers.{}.attention.dense.per_channel_scale'
|
|
.format(idx)] = torch_weight_scales
|
|
|
|
else:
|
|
weights['transformer.layers.{}.attention.dense.weight'.
|
|
format(idx)] = split_v
|
|
|
|
elif 'mlp.up_proj.weight' in k:
|
|
split_v = split(v, mapping.tp_size, mapping.tp_rank, dim=0)
|
|
if use_weight_only:
|
|
v = np.ascontiguousarray(split_v.transpose())
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
numpy_to_torch(v), plugin_weight_only_quant_type)
|
|
|
|
if not use_gemm_woq_plugin:
|
|
weights['transformer.layers.{}.mlp.gate.weight'.format(
|
|
idx)] = v
|
|
else:
|
|
weights['transformer.layers.{}.mlp.gate.weight'.format(
|
|
idx)] = processed_torch_weights
|
|
|
|
weights['transformer.layers.{}.mlp.gate.per_channel_scale'.
|
|
format(idx)] = torch_weight_scales
|
|
else:
|
|
weights['transformer.layers.{}.mlp.gate.weight'.format(
|
|
idx)] = split_v
|
|
|
|
elif 'mlp.down_proj.weight' in k:
|
|
# dst = tensorrt_llm_llama.layers[idx].mlp.proj.weight
|
|
split_v = split(v, mapping.tp_size, mapping.tp_rank, dim=1)
|
|
if use_weight_only:
|
|
v = np.ascontiguousarray(split_v.transpose())
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
numpy_to_torch(v), plugin_weight_only_quant_type)
|
|
if not use_gemm_woq_plugin:
|
|
weights['transformer.layers.{}.mlp.proj.weight'.format(
|
|
idx)] = v
|
|
else:
|
|
weights['transformer.layers.{}.mlp.proj.weight'.format(
|
|
idx)] = processed_torch_weights
|
|
|
|
weights['transformer.layers.{}.mlp.proj.per_channel_scale'.
|
|
format(idx)] = torch_weight_scales
|
|
else:
|
|
weights['transformer.layers.{}.mlp.proj.weight'.format(
|
|
idx)] = split_v
|
|
elif 'mlp.gate_proj.weight' in k:
|
|
# dst = tensorrt_llm_llama.layers[idx].mlp.fc.weight
|
|
split_v = split(v, mapping.tp_size, mapping.tp_rank, dim=0)
|
|
if use_weight_only:
|
|
v = np.ascontiguousarray(split_v.transpose())
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
numpy_to_torch(v), plugin_weight_only_quant_type)
|
|
|
|
if not use_gemm_woq_plugin:
|
|
weights['transformer.layers.{}.mlp.fc.weight'.format(
|
|
idx)] = v
|
|
else:
|
|
weights['transformer.layers.{}.mlp.fc.weight'.format(
|
|
idx)] = processed_torch_weights
|
|
|
|
weights['transformer.layers.{}.mlp.fc.per_channel_scale'.
|
|
format(idx)] = torch_weight_scales
|
|
else:
|
|
# dst.value = np.ascontiguousarray(split_v)
|
|
weights['transformer.layers.{}.mlp.fc.weight'.format(
|
|
idx)] = split_v
|
|
elif 'experts.w2.weight' in k:
|
|
# Note: no need for splitting, it's already been done above
|
|
split_v = v
|
|
if use_weight_only:
|
|
v = np.ascontiguousarray(
|
|
np.transpose(split_v, axes=(0, 2, 1)))
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
numpy_to_torch(v), plugin_weight_only_quant_type)
|
|
weights['transformer.layers.{}.mlp.experts_weight_2'.format(
|
|
idx)] = processed_torch_weights
|
|
weights['transformer.layers.{}.mlp.experts_scale_2'.format(
|
|
idx)] = torch_weight_scales
|
|
|
|
else:
|
|
weights['transformer.layers.{}.mlp.experts_weight_2'.format(
|
|
idx)] = v
|
|
elif 'experts.w3w1.weight' in k:
|
|
# Note: no need for splitting, it's already been done above
|
|
split_v = v
|
|
if use_weight_only:
|
|
v = np.ascontiguousarray(
|
|
np.transpose(split_v, axes=(0, 2, 1)))
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
numpy_to_torch(v), plugin_weight_only_quant_type)
|
|
weights['transformer.layers.{}.mlp.experts_weight_1'.format(
|
|
idx)] = processed_torch_weights
|
|
weights['transformer.layers.{}.mlp.experts_scale_1'.format(
|
|
idx)] = torch_weight_scales
|
|
|
|
else:
|
|
weights['transformer.layers.{}.mlp.experts_weight_1'.format(
|
|
idx)] = v
|
|
|
|
elif 'block_sparse_moe.gate' in k:
|
|
v = split(v, mapping.tp_size, mapping.tp_rank, dim=-1)
|
|
weights['transformer.layers.{}.mlp.router.weight'.format(
|
|
idx)] = v
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'Weights loaded. Total time: {t}')
|
|
return weights
|
|
|
|
|
|
def load_from_gptq_llama(config: PretrainedConfig, quant_ckpt_path):
|
|
logger.info('Loading weights from groupwise GPTQ LLaMA safetensors...')
|
|
weights = {}
|
|
tik = time.time()
|
|
|
|
num_hidden_layers = config.num_hidden_layers
|
|
vocab_size = config.vocab_size
|
|
dtype = config.dtype
|
|
mapping = config.mapping
|
|
|
|
gptq_llama = safe_open(quant_ckpt_path, framework="pt", device=0)
|
|
gptq_prefix = "model."
|
|
gptq_suffix_list = [".qweight", ".qzeros", ".scales"]
|
|
gptq_key_list = [
|
|
"embed_tokens.weight", # vocab_embedding
|
|
"lm_head.weight", # lm_head
|
|
"norm.weight", # ln_f
|
|
"self_attn.", # attention.qkv
|
|
"_proj", # qkv suffix
|
|
"self_attn.o_proj", # attention.dense
|
|
"mlp.up_proj", # mlp.gate
|
|
"mlp.down_proj", # mlp.proj
|
|
"mlp.gate_proj", # mlp.fc
|
|
"input_layernorm.weight", # input_layernorm
|
|
"post_attention_layernorm.weight", # post_layernorm
|
|
]
|
|
split_sym = "."
|
|
|
|
packer = torch.ops.trtllm.pack_int8_tensor_to_packed_int4
|
|
preprocessor = torch.ops.trtllm.preprocess_weights_for_mixed_gemm
|
|
torch_dtype = str_dtype_to_torch(dtype)
|
|
|
|
def load(key, no_prefix=0):
|
|
if no_prefix:
|
|
return gptq_llama.get_tensor(key)
|
|
else:
|
|
return gptq_llama.get_tensor(gptq_prefix + key)
|
|
|
|
def torch_split(v, dim):
|
|
if v.shape[dim] % mapping.tp_size != 0:
|
|
logger.error(
|
|
"Current weight shape is invalid for mapping.tp_size=" +
|
|
str(mapping.tp_size))
|
|
assert False, "Invalid TP size"
|
|
return v.split(v.shape[dim] // mapping.tp_size,
|
|
dim=dim)[mapping.tp_rank]
|
|
|
|
def unpack_int32_into_int8(w_packed):
|
|
# Unpack inputs packed in int32/float32 into uint4 and store them in int8 format
|
|
w_packed_int4x2 = w_packed.contiguous().view(torch.uint8)
|
|
w_unpacked = torch.zeros(w_packed_int4x2.shape[0],
|
|
w_packed_int4x2.shape[1] * 2,
|
|
dtype=torch.int8)
|
|
w_unpacked[:, ::2] = w_packed_int4x2 % 16
|
|
w_unpacked[:, 1::2] = w_packed_int4x2 // 16
|
|
return w_unpacked.contiguous()
|
|
|
|
def process_and_assign_weight(v: List[torch.Tensor],
|
|
tllm_prex: str,
|
|
tp_dim: int = -1):
|
|
if tp_dim == -1:
|
|
qweight_int32, qzeros_int32, scales_fp16 = [
|
|
item.cpu() for item in v
|
|
]
|
|
else:
|
|
qweight_int32, qzeros_int32, scales_fp16 = [
|
|
torch_split(item, tp_dim).cpu() for item in v
|
|
]
|
|
|
|
USE_UINT4_INPUT = 1 # Set to true if checkpoint store UINT4 weights
|
|
USE_GPTQ_FOR_LLAMA = 1 # GPTQ-for-LLaMA added 1 to zeros
|
|
|
|
qweight_unpacked_int8 = unpack_int32_into_int8(
|
|
qweight_int32.T).T.contiguous() - 8
|
|
qweight_interleaved = preprocessor(packer(qweight_unpacked_int8),
|
|
torch.quint4x2).view(torch.float16)
|
|
# zeros = zeros * scales
|
|
qzeros_unpacked_int32 = unpack_int32_into_int8(qzeros_int32)
|
|
if not USE_UINT4_INPUT:
|
|
# Correcting UINT4 values back to INT4 order
|
|
mask_negative = qzeros_unpacked_int32[qzeros_unpacked_int32 < 0]
|
|
mask_positive = qzeros_unpacked_int32[qzeros_unpacked_int32 >= 0]
|
|
qzeros_unpacked_int32 = qzeros_unpacked_int32 + 16 * mask_negative - 16 * mask_positive
|
|
zeros_x_scales_fp16 = (-qzeros_unpacked_int32 + 8 * USE_UINT4_INPUT -
|
|
USE_GPTQ_FOR_LLAMA) * scales_fp16
|
|
zeros_x_scales_fp16 = zeros_x_scales_fp16.half()
|
|
|
|
results = {
|
|
f'{tllm_prex}.weight': qweight_interleaved,
|
|
f'{tllm_prex}.weights_scaling_factor': scales_fp16,
|
|
f'{tllm_prex}.zero': zeros_x_scales_fp16,
|
|
}
|
|
return results
|
|
|
|
# Load weights from GPTQ checkpoint into TRT-LLM module
|
|
# 1. vocab_embedding
|
|
v = load(gptq_key_list[0])
|
|
if mapping.is_first_pp_rank():
|
|
# tensorrt_llm_llama.vocab_embedding.weight.value = v.to(
|
|
# torch_dtype).cpu().numpy()
|
|
weights['transformer.vocab_embedding.weight'] = v.to(torch_dtype)
|
|
# 2. lm_head
|
|
v = load(gptq_key_list[1], "no_prefix")
|
|
if mapping.is_last_pp_rank():
|
|
# tensorrt_llm_llama.lm_head.weight.value = torch_split(
|
|
# v, 0).to(torch_dtype).cpu().numpy()
|
|
if vocab_size % mapping.tp_size != 0:
|
|
# padding
|
|
vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
|
|
pad_width = vocab_size_padded - vocab_size
|
|
v = torch.from_numpy(
|
|
np.pad(v.detach().cpu().numpy(), ((0, pad_width), (0, 0)),
|
|
'constant',
|
|
constant_values=0))
|
|
weights['lm_head.weight'] = torch_split(v, 0).to(torch_dtype)
|
|
|
|
# 3. ln_f
|
|
v = load(gptq_key_list[2])
|
|
if mapping.is_last_pp_rank():
|
|
# tensorrt_llm_llama.ln_f.weight.value = v.to(torch_dtype).cpu().numpy()
|
|
weights['transformer.ln_f.weight'] = v.to(torch_dtype)
|
|
# 4. Weights inside each layer
|
|
layers_range = mapping.pp_layers(num_hidden_layers)
|
|
for l in layers_range:
|
|
layer_idx = l - layers_range[0]
|
|
prefix = "layers" + split_sym + str(layer_idx) + split_sym
|
|
logger.info(f'Process weights in layer: {layer_idx}')
|
|
# layer = tensorrt_llm_llama.layers[layer_idx]
|
|
tllm_prex = f'transformer.layers.{l-layers_range[0]}'
|
|
# 4.1 attention.qkv
|
|
qkv_weight_list = []
|
|
for suf in gptq_suffix_list:
|
|
qkv_list = []
|
|
for comp in ["q", "k", "v"]:
|
|
comp_part = load(prefix + gptq_key_list[3] + comp +
|
|
gptq_key_list[4] + suf)
|
|
comp_part = torch_split(comp_part, 1)
|
|
qkv_list.append(comp_part)
|
|
qkv_weight_list.append(torch.cat(qkv_list, dim=1))
|
|
|
|
# process_and_assign_weight(layer.attention.qkv, qkv_weight_list)
|
|
weights.update(
|
|
process_and_assign_weight(qkv_weight_list,
|
|
f'{tllm_prex}.attention.qkv'))
|
|
# 4.2 attention.dense
|
|
v = [load(prefix + gptq_key_list[5] + suf) for suf in gptq_suffix_list]
|
|
# process_and_assign_weight(layer.attention.dense, v, 0)
|
|
weights.update(
|
|
process_and_assign_weight(v,
|
|
f'{tllm_prex}.attention.dense',
|
|
tp_dim=0))
|
|
# 4.3 mlp.gate
|
|
v = [load(prefix + gptq_key_list[6] + suf) for suf in gptq_suffix_list]
|
|
# process_and_assign_weight(layer.mlp.gate, v, 1)
|
|
weights.update(
|
|
process_and_assign_weight(v, f'{tllm_prex}.mlp.gate', tp_dim=1))
|
|
# 4.4 mlp.proj
|
|
v = [load(prefix + gptq_key_list[7] + suf) for suf in gptq_suffix_list]
|
|
# process_and_assign_weight(layer.mlp.proj, v, 0)
|
|
weights.update(
|
|
process_and_assign_weight(v, f'{tllm_prex}.mlp.proj', tp_dim=0))
|
|
# 4.5 mlp.fc
|
|
v = [load(prefix + gptq_key_list[8] + suf) for suf in gptq_suffix_list]
|
|
# process_and_assign_weight(layer.mlp.fc, v, 1)
|
|
weights.update(
|
|
process_and_assign_weight(v, f'{tllm_prex}.mlp.fc', tp_dim=1))
|
|
# 4.6 input_layernorm
|
|
v = load(prefix + gptq_key_list[9])
|
|
# layer.input_layernorm.weight.value = v.to(torch_dtype).cpu().numpy()
|
|
weights[f'{tllm_prex}.input_layernorm.weight'] = v.to(torch_dtype)
|
|
|
|
# 4.7 post_layernorm
|
|
v = load(prefix + gptq_key_list[10])
|
|
# layer.post_layernorm.weight.value = v.to(torch_dtype).cpu().numpy()
|
|
weights[f'{tllm_prex}.post_layernorm.weight'] = v.to(torch_dtype)
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
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|
logger.info(f'Weights loaded. Total time: {t}')
|
|
|
|
return weights
|
|
|
|
|
|
def load_from_meta_llama(meta_ckpt_dir, mapping, config):
|
|
torch_dtype = str_dtype_to_torch(config.dtype)
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|
weights = {}
|
|
|
|
def gather_ckpts(ckpts):
|
|
gathered = {}
|
|
for k in ckpts[0]:
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|
d = 0
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|
if any([n in k for n in ["wo", "w2", "tok"]]):
|
|
d = 1
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if "norm" in k or "rope" in k: # no TP
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|
gathered[k] = ckpts[0][k].clone()
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|
else:
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|
gathered[k] = torch.cat([pt[k] for pt in ckpts], dim=d).clone()
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|
return gathered
|
|
|
|
def split_ckpt(ckpt, ranks_per_ckpt, ckpt_rank):
|
|
split_ckpt = {}
|
|
for k, v in ckpt.items():
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|
d = 0
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|
if any(n in k for n in
|
|
["wo", "feed_forward.w2", "tok", "feed_forward.gate"]):
|
|
d = 1
|
|
if "norm" in k or "rope" in k: # no TP
|
|
split_ckpt[k] = v.clone()
|
|
elif config.num_key_value_heads < mapping.tp_size and any(
|
|
n in k for n in ["wk", "wv"]):
|
|
assert mapping.tp_size % config.num_key_value_heads == 0
|
|
# special case: we need to duplicate KV head
|
|
tmp = dup_kv_weight(v, config.num_key_value_heads,
|
|
mapping.tp_size)
|
|
split_ckpt[k] = torch.split(tmp,
|
|
tmp.shape[d] // ranks_per_ckpt,
|
|
dim=d)[ckpt_rank].clone()
|
|
else:
|
|
split_ckpt[k] = torch.split(v,
|
|
v.shape[d] // ranks_per_ckpt,
|
|
dim=d)[ckpt_rank].clone()
|
|
return split_ckpt
|
|
|
|
def get_current_weights(num_ckpts):
|
|
if num_ckpts > mapping.tp_size:
|
|
# combine ckpts
|
|
assert (num_ckpts % mapping.tp_size) == 0
|
|
nf = num_ckpts // mapping.tp_size
|
|
fs = nf * mapping.tp_rank
|
|
file_ids = list(range(fs, fs + nf))
|
|
ckpts = []
|
|
for f in file_ids:
|
|
ckpt = torch.load(Path(meta_ckpt_dir,
|
|
f"consolidated.{f:02d}.pth"),
|
|
map_location="cpu")
|
|
ckpts.append(ckpt)
|
|
return gather_ckpts(ckpts)
|
|
elif num_ckpts < mapping.tp_size:
|
|
# split ckpt
|
|
assert (mapping.tp_size % num_ckpts) == 0
|
|
ranks_per_ckpt = mapping.tp_size // num_ckpts
|
|
ckpt_fid = mapping.tp_rank // ranks_per_ckpt
|
|
ckpt_rank = mapping.tp_rank % ranks_per_ckpt
|
|
nH_per_ckpt = config.num_attention_heads // num_ckpts
|
|
assert (nH_per_ckpt % ranks_per_ckpt) == 0
|
|
ckpt = torch.load(Path(meta_ckpt_dir,
|
|
f"consolidated.{ckpt_fid:02d}.pth"),
|
|
map_location="cpu")
|
|
return split_ckpt(ckpt, ranks_per_ckpt, ckpt_rank)
|
|
|
|
# num_ckpts == tensor_parallel, 1:1 mapping from files to TP
|
|
return torch.load(Path(meta_ckpt_dir,
|
|
f"consolidated.{mapping.tp_rank:02d}.pth"),
|
|
map_location="cpu")
|
|
|
|
def permute(w, nH, d, dH):
|
|
# due to MQA's wk, nH*dH != d could be true
|
|
return w.view(nH, dH // 2, 2, d).transpose(1, 2).reshape(nH * dH, d)
|
|
|
|
def extract_layer_idx(name):
|
|
ss = name.split('.')
|
|
for s in ss:
|
|
if s.isdigit():
|
|
return s
|
|
return None
|
|
|
|
if not hasattr(load_from_meta_llama, "saved_embed"):
|
|
load_from_meta_llama.saved_embed = None
|
|
|
|
def gather_embedding(cur_embed, name: str, num_ckpts):
|
|
if mapping.tp_size == 1:
|
|
# even if num_ckpts > 1, get_current_weights will already have it gathered
|
|
return cur_embed
|
|
if load_from_meta_llama.saved_embed is None:
|
|
embeds = [None] * num_ckpts
|
|
for i in range(num_ckpts):
|
|
ckpt = torch.load(Path(meta_ckpt_dir,
|
|
f"consolidated.{i:02d}.pth"),
|
|
map_location="cpu")
|
|
embeds[i] = ckpt[name]
|
|
embed = torch.cat(embeds, dim=1).to(torch_dtype)
|
|
load_from_meta_llama.saved_embed = embed
|
|
|
|
return load_from_meta_llama.saved_embed
|
|
|
|
logger.info('Loading weights from Meta LLaMA checkpoints ...')
|
|
tik = time.time()
|
|
|
|
num_kv_heads = config.num_key_value_heads
|
|
mha_mode = (num_kv_heads == config.num_attention_heads)
|
|
|
|
ckpts = list(Path(meta_ckpt_dir).glob("consolidated.*.pth"))
|
|
num_ckpts = len(ckpts)
|
|
# llama/llama2 doesn't have MQA. So, simplifying loader logic by not worrying about it.
|
|
assert num_kv_heads > 1 or num_kv_heads >= num_ckpts, \
|
|
f"We don't know how the {num_kv_heads} KV heads are distributed among {num_ckpts} checkpoints."
|
|
|
|
head_size = config.hidden_size // config.num_attention_heads
|
|
ckpt = get_current_weights(num_ckpts)
|
|
layers_range = mapping.pp_layers(config.num_hidden_layers)
|
|
|
|
for l in layers_range:
|
|
prefix = f'layers.{l}.attention.'
|
|
q_weight = permute(ckpt[prefix + 'wq.weight'].clone(),
|
|
nH=(config.num_attention_heads // mapping.tp_size),
|
|
d=config.hidden_size,
|
|
dH=head_size)
|
|
if num_kv_heads < mapping.tp_size and num_ckpts >= mapping.tp_size:
|
|
assert mapping.tp_size % num_kv_heads == 0
|
|
assert False, "Not supported yet"
|
|
k_weight = permute(ckpt[prefix + 'wk.weight'].clone(),
|
|
nH=((num_kv_heads + mapping.tp_size - 1) //
|
|
mapping.tp_size),
|
|
d=config.hidden_size,
|
|
dH=head_size)
|
|
v_weight = ckpt[prefix + 'wv.weight'].clone()
|
|
|
|
qkv_weight = torch.cat([q_weight, k_weight, v_weight], dim=0)
|
|
ckpt[prefix + 'qkv.weight'] = qkv_weight
|
|
|
|
for k, v in ckpt.items():
|
|
dtype = torch_dtype if 'feed_forward.gate' not in k else torch.float32
|
|
|
|
v = v.to(dtype)
|
|
if "tok_embeddings" in k:
|
|
if not config.use_parallel_embedding:
|
|
v = gather_embedding(v, k, num_ckpts)
|
|
elif config.embedding_sharding_dim == 0:
|
|
# this needs a gather and then resplit along different dims
|
|
v = gather_embedding(v, k, num_ckpts)
|
|
v = split(v, mapping.tp_size, mapping.tp_rank, 0)
|
|
if mapping.is_first_pp_rank():
|
|
weights['transformer.vocab_embedding.weight'] = v
|
|
elif "output" in k:
|
|
if mapping.is_last_pp_rank():
|
|
if config.vocab_size % mapping.tp_size != 0:
|
|
# padding
|
|
vocab_size_padded = pad_vocab_size(config.vocab_size,
|
|
mapping.tp_size)
|
|
pad_width = vocab_size_padded - config.vocab_size
|
|
v = torch.from_numpy(
|
|
np.pad(v.detach().cpu().numpy(),
|
|
((0, pad_width), (0, 0)),
|
|
'constant',
|
|
constant_values=0))
|
|
weights['lm_head.weight'] = v
|
|
elif k == "norm.weight":
|
|
if mapping.is_last_pp_rank():
|
|
weights['transformer.ln_f.weight'] = v
|
|
else:
|
|
# layer specific weights
|
|
layer_idx = extract_layer_idx(k)
|
|
|
|
if layer_idx is None or int(layer_idx) not in layers_range:
|
|
continue
|
|
idx = int(layer_idx) - layers_range[0]
|
|
tllm_prex = f'transformer.layers.{idx}.'
|
|
|
|
if 'attention_norm.weight' in k:
|
|
weights[tllm_prex + 'input_layernorm.weight'] = v
|
|
elif 'ffn_norm.weight' in k:
|
|
weights[tllm_prex + 'post_layernorm.weight'] = v
|
|
elif 'feed_forward.w3.weight' in k:
|
|
weights[tllm_prex + 'mlp.gate.weight'] = v
|
|
elif 'feed_forward.w2.weight' in k:
|
|
weights[tllm_prex + 'mlp.proj.weight'] = v
|
|
elif 'feed_forward.w1.weight' in k:
|
|
weights[tllm_prex + 'mlp.fc.weight'] = v
|
|
elif 'attention.wo.weight' in k:
|
|
weights[tllm_prex + 'attention.dense.weight'] = v
|
|
elif 'attention.qkv.weight' in k:
|
|
weights[tllm_prex + 'attention.qkv.weight'] = v
|
|
elif 'feed_forward.gate' in k:
|
|
weights[tllm_prex + 'mlp.router.weight'] = v
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'Weights loaded. Total time: {t}')
|
|
return weights
|