import math import re from collections import OrderedDict import torch import torch.nn.functional as F from transformers import GPT2Config, GPTBigCodeConfig, PretrainedConfig def remap_state_dict_hf_bigcode(state_dict, config: PretrainedConfig): """ Map the state_dict of a Huggingface BigCode model to be flash_attn compatible. """ # Word embedding and position embedding def key_mapping_pos_emb(key): return re.sub(r"^transformer.wpe.", "transformer.embeddings.position_embeddings.", key) state_dict = OrderedDict((key_mapping_pos_emb(k), v) for k, v in state_dict.items()) word_embeddings = state_dict.pop("transformer.wte.weight") # It's possible that vocab_size is padded to be a multiple of 8, for example. pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1) vocab_size = math.ceil(config.vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple state_dict["transformer.embeddings.word_embeddings.weight"] = F.pad( word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0]) ) state_dict["lm_head.weight"] = state_dict["transformer.embeddings.word_embeddings.weight"] # LayerNorm def key_mapping_ln(key): key = re.sub(r"^transformer.ln_f.(weight|bias)", r"transformer.ln_f.\1", key) key = re.sub( r"^transformer.h.(\d+).ln_(1|2).(weight|bias)", r"transformer.layers.\1.norm\2.\3", key, ) return key state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items()) def key_mapping_mlp(key): key = re.sub( r"^transformer.h.(\d+).mlp.c_fc.weight", r"transformer.layers.\1.mlp.fc1.weight", key, ) key = re.sub( r"^transformer.h.(\d+).mlp.c_proj.weight", r"transformer.layers.\1.mlp.fc2.weight", key, ) key = re.sub( r"^transformer.h.(\d+).mlp.c_fc.bias", r"transformer.layers.\1.mlp.fc1.bias", key, ) key = re.sub( r"^transformer.h.(\d+).mlp.c_proj.bias", r"transformer.layers.\1.mlp.fc2.bias", key, ) return key state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items()) # TODO: add support for multi-head attention assert config.multi_query, "Only multi-query attention is supported" # Attention for d in range(config.num_hidden_layers): embed_dim = config.n_embd head_dim = embed_dim // config.n_head c_attn_weight = state_dict.pop(f"transformer.h.{d}.attn.c_attn.weight") # with multi-query attention, the weights have shape (embed_dim, embed_dim + head_dim + head_dim) # see https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py#L112 # see also https://github.com/ggerganov/ggml/blob/dd1d575956e54c5bdc07632f25506b3b1884dbd2/examples/starcoder/convert-hf-to-ggml.py#L183 # ((n_head + 2) * head_dim, embed_dim) -> (3 * n_heads * head_dim, hidden_dim) q, k, v = torch.split(c_attn_weight, [embed_dim, head_dim, head_dim], dim=0) # duplicate k, v along the first axis (head_dim, hidden_dim) -> (n_heads * head_dim, hidden_dim) k = torch.tile(k, (config.n_head, 1)) v = torch.tile(v, (config.n_head, 1)) state_dict[f"transformer.layers.{d}.mixer.Wqkv.weight"] = torch.cat((q, k, v), dim=0) # same deal with the bias c_attn_bias = state_dict.pop(f"transformer.h.{d}.attn.c_attn.bias") # ((n_head + 2) * head_dim, embed_dim) -> (3 * n_heads * head_dim, hidden_dim) q, k, v = torch.split(c_attn_bias, [embed_dim, head_dim, head_dim], dim=0) # duplicate k, v along the first axis (head_dim, hidden_dim) -> (n_heads * head_dim, hidden_dim) k = torch.tile(k, (config.n_head,)) v = torch.tile(v, (config.n_head,)) state_dict[f"transformer.layers.{d}.mixer.Wqkv.bias"] = torch.cat((q, k, v), dim=0) def key_mapping_attn(key): key = re.sub( r"^transformer.h.(\d+).attn.c_proj.weight", r"transformer.layers.\1.mixer.out_proj.weight", key, ) key = re.sub( r"^transformer.h.(\d+).attn.c_proj.bias", r"transformer.layers.\1.mixer.out_proj.bias", key, ) return key state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items()) return state_dict def inv_remap_state_dict_hf_bigcode(state_dict, config: PretrainedConfig): """ Map the state_dict of a flash_attn model to be Huggingface BigCode compatible. This function is meant to be the inverse of remap_state_dict_hf_bigcode. """ # Word embedding and position embeddings def inv_key_mapping_pos_emb(key): return re.sub(r"^transformer.embeddings.position_embeddings.", "transformer.wpe.", key) state_dict = OrderedDict((inv_key_mapping_pos_emb(k), v) for k, v in state_dict.items()) word_embeddings = state_dict.pop("transformer.embeddings.word_embeddings.weight") word_embeddings = word_embeddings[:, : config.vocab_size] state_dict["transformer.wte.weight"] = word_embeddings state_dict["lm_head.weight"] = word_embeddings # LayerNorm def inv_key_mapping_ln(key): key = re.sub(r"^transformer.ln_f.(weight|bias)", r"transformer.ln_f.\1", key) key = re.sub( r"^transformer.layers.(\d+).norm(1|2).(weight|bias)", r"transformer.h.\1.ln_\2.\3", key, ) return key state_dict = OrderedDict((inv_key_mapping_ln(k), v) for k, v in state_dict.items()) # MLPs def inv_key_mapping_mlp(key): key = re.sub( r"^transformer.layers.(\d+).mlp.fc1.weight", r"transformer.h.\1.mlp.c_fc.weight", key, ) key = re.sub( r"^transformer.layers.(\d+).mlp.fc2.weight", r"transformer.h.\1.mlp.c_proj.weight", key, ) key = re.sub( r"^transformer.layers.(\d+).mlp.fc1.bias", r"transformer.h.\1.mlp.c_fc.bias", key, ) key = re.sub( r"^transformer.layers.(\d+).mlp.fc2.bias", r"transformer.h.\1.mlp.c_proj.bias", key, ) return key state_dict = OrderedDict((inv_key_mapping_mlp(k), v) for k, v in state_dict.items()) # Attention for d in range(config.num_hidden_layers): embed_dim = config.n_embd head_dim = embed_dim // config.n_head Wqkv_weight = state_dict.pop(f"transformer.layers.{d}.mixer.Wqkv.weight") q, k, v = torch.split( Wqkv_weight, [embed_dim, head_dim * config.n_head, head_dim * config.n_head], dim=0 ) c_attn_weight = torch.cat((q, k[:head_dim], v[:head_dim]), dim=0) state_dict[f"transformer.h.{d}.attn.c_attn.weight"] = c_attn_weight # Same deal with the bias Wqkv_bias = state_dict.pop(f"transformer.layers.{d}.mixer.Wqkv.bias") q, k, v = torch.split( Wqkv_bias, [embed_dim, head_dim * config.n_head, head_dim * config.n_head], dim=0 ) c_attn_bias = torch.cat((q, k[:head_dim], v[:head_dim]), dim=0) state_dict[f"transformer.h.{d}.attn.c_attn.bias"] = c_attn_bias def inv_key_mapping_attn(key): key = re.sub( r"^transformer.layers.(\d+).mixer.out_proj.weight", r"transformer.h.\1.attn.c_proj.weight", key, ) key = re.sub( r"^transformer.layers.(\d+).mixer.out_proj.bias", r"transformer.h.\1.attn.c_proj.bias", key, ) return key state_dict = OrderedDict((inv_key_mapping_attn(k), v) for k, v in state_dict.items()) return state_dict def bigcode_config_to_gpt2_config(bigcode_config: GPTBigCodeConfig) -> GPT2Config: return GPT2Config( activation_function=bigcode_config.activation_function, attn_pdrop=bigcode_config.attn_pdrop, bos_token_id=bigcode_config.bos_token_id, embd_pdrop=bigcode_config.embd_pdrop, eos_token_id=bigcode_config.eos_token_id, initializer_range=bigcode_config.initializer_range, layer_norm_epsilon=bigcode_config.layer_norm_epsilon, max_batch_size=bigcode_config.max_batch_size, max_sequence_length=bigcode_config.max_sequence_length, model_type=bigcode_config.model_type, multi_query=bigcode_config.multi_query, n_embd=bigcode_config.n_embd, n_head=bigcode_config.n_head, n_inner=bigcode_config.n_inner, n_layer=bigcode_config.n_layer, n_positions=bigcode_config.n_positions, resid_pdrop=bigcode_config.resid_pdrop, scale_attn_weights=bigcode_config.scale_attn_weights, summary_activation=bigcode_config.summary_activation, summary_first_dropout=bigcode_config.summary_first_dropout, summary_proj_to_labels=bigcode_config.summary_proj_to_labels, summary_type=bigcode_config.summary_type, summary_use_proj=bigcode_config.summary_use_proj, use_cache=bigcode_config.use_cache, vocab_size=bigcode_config.vocab_size, )