""" This script has functions and utilties for model export. Basically, we have a bunch of versions of the model, and we want to export them to .bin files to be read from and inferenced in C. Among the "input" versions of PyTorch files/models: - Official Llama 2 weights released by Meta - Huggingface weights available on the hub - llama2.c (this repo) trained models Among the "output" versions of .bin files: - v0: Legacy files of the original llama2.c repo (will eventually be DEPRECATED) - v1-vN: Improved .bin files with a proper header, cache alignment, etc. This script aspires to provide all of these conversions. """ import os import gzip import shutil import struct import argparse import json from pathlib import Path import numpy as np import torch from torch import nn from model import ModelArgs, Transformer # ----------------------------------------------------------------------------- # common utilities def serialize_fp32(file, tensor): """ writes one fp32 tensor to file that is open in wb mode """ d = tensor.detach().cpu().view(-1).to(torch.float32).numpy() b = struct.pack(f'{len(d)}f', *d) file.write(b) def serialize_int8(file, tensor): """ writes one int8 tensor to file that is open in wb mode """ d = tensor.detach().cpu().view(-1).numpy().astype(np.int8) b = struct.pack(f'{len(d)}b', *d) file.write(b) def quantize_q80(w, group_size): """ takes a tensor and returns the Q8_0 quantized version i.e. symmetric quantization into int8, range [-127,127] """ assert w.numel() % group_size == 0 ori_shape = w.shape w = w.float() # convert to float32 w = w.reshape(-1, group_size) # find the max in each group wmax = torch.abs(w).max(dim=1).values # calculate the scaling factor such that float = quant * scale scale = wmax / 127.0 # scale into range [-127, 127] quant = w / scale[:,None] # round to nearest integer int8val = torch.round(quant).to(torch.int8) # dequantize by rescaling fp32val = (int8val.float() * scale[:,None]).view(-1) fp32valr = fp32val.reshape(-1, group_size) # calculate the max error in each group err = torch.abs(fp32valr - w).max(dim=1).values # find the max error across all groups maxerr = err.max().item() return int8val, scale, maxerr # ----------------------------------------------------------------------------- # legacy def legacy_export(model, filepath): """ Original export of llama2.c bin files, i.e. version v0 """ out_file = open(filepath, 'wb') # first write out the header hidden_dim = model.layers[0].feed_forward.w1.weight.shape[0] p = model.params shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight) # legacy format uses negative/positive vocab size as a shared classifier flag if not shared_classifier: p.vocab_size = -p.vocab_size n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads, n_kv_heads, p.vocab_size, p.max_seq_len) out_file.write(header) # next write out the embedding weights serialize_fp32(out_file, model.tok_embeddings.weight) # now all the layers # attention weights for layer in model.layers: serialize_fp32(out_file, layer.attention_norm.weight) for layer in model.layers: serialize_fp32(out_file, layer.attention.wq.weight) for layer in model.layers: serialize_fp32(out_file, layer.attention.wk.weight) for layer in model.layers: serialize_fp32(out_file, layer.attention.wv.weight) for layer in model.layers: serialize_fp32(out_file, layer.attention.wo.weight) # ffn weights for layer in model.layers: serialize_fp32(out_file, layer.ffn_norm.weight) for layer in model.layers: serialize_fp32(out_file, layer.feed_forward.w1.weight) for layer in model.layers: serialize_fp32(out_file, layer.feed_forward.w2.weight) for layer in model.layers: serialize_fp32(out_file, layer.feed_forward.w3.weight) # final rmsnorm serialize_fp32(out_file, model.norm.weight) # freqs_cis serialize_fp32(out_file, model.freqs_cos[:p.max_seq_len]) serialize_fp32(out_file, model.freqs_sin[:p.max_seq_len]) # final classifier weights if not shared_classifier: serialize_fp32(out_file, model.output.weight) # write to binary file out_file.close() print(f"wrote {filepath}") # ----------------------------------------------------------------------------- # new version def version1_export(model, filepath): """ Export the model weights in full float32 .bin file to be read from C. This is same as legacy_export, but with a proper header. """ version = 1 out_file = open(filepath, 'wb') # first write out the header. the header will be 256 bytes # 1) write magic, which will be uint32 of "ak42" in ASCII out_file.write(struct.pack('I', 0x616b3432)) # 2) write version, which will be int out_file.write(struct.pack('i', version)) # 3) write the params, which will be 7 ints p = model.params hidden_dim = model.layers[0].feed_forward.w1.weight.shape[0] n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads, n_kv_heads, p.vocab_size, p.max_seq_len) out_file.write(header) # 4) write some other flags shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight) out_file.write(struct.pack('B', int(shared_classifier))) pad = 256 - out_file.tell() # pad rest with zeros; tell returns current pos assert pad >= 0 out_file.write(b'\0' * pad) # now let's write out all the params weights = [ *[layer.attention_norm.weight for layer in model.layers], *[layer.ffn_norm.weight for layer in model.layers], model.norm.weight, model.tok_embeddings.weight, *[layer.attention.wq.weight for layer in model.layers], *[layer.attention.wk.weight for layer in model.layers], *[layer.attention.wv.weight for layer in model.layers], *[layer.attention.wo.weight for layer in model.layers], *[layer.feed_forward.w1.weight for layer in model.layers], *[layer.feed_forward.w2.weight for layer in model.layers], *[layer.feed_forward.w3.weight for layer in model.layers], ] if not shared_classifier: weights.append(model.output.weight) for w in weights: serialize_fp32(out_file, w) # write to binary file out_file.close() print(f"wrote {filepath}") def version2_export(model, filepath, group_size=64): """ Export the model weights in Q8_0 into .bin file to be read from C. That is: - quantize all weights to symmetric int8, in range [-127, 127] - all other tensors (the rmsnorm params) are kept and exported in fp32 - quantization is done in groups of group_size to reduce the effects of any outliers """ version = 2 # let's first do some validation for this export type while model.params.dim % group_size != 0: group_size //= 2 print(f"BACKOFF: reducing group size to {group_size} to fit hidden_dim") weights = [ model.tok_embeddings.weight, *[layer.attention.wq.weight for layer in model.layers], *[layer.attention.wk.weight for layer in model.layers], *[layer.attention.wv.weight for layer in model.layers], *[layer.attention.wo.weight for layer in model.layers], *[layer.feed_forward.w1.weight for layer in model.layers], *[layer.feed_forward.w2.weight for layer in model.layers], *[layer.feed_forward.w3.weight for layer in model.layers], ] shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight) if not shared_classifier: weights.append(model.output.weight) for w in weights: assert w.numel() % group_size == 0, f"weight {i} has numel {w.numel()}, not a multiple of group_size {group_size}" # write out_file = open(filepath, 'wb') # first write out the header. the header will be 256 bytes # 1) write magic, which will be uint32 of "ak42" in ASCII out_file.write(struct.pack('I', 0x616b3432)) # 2) write version, which will be int out_file.write(struct.pack('i', version)) # 3) write the params, which will be 7 ints p = model.params hidden_dim = model.layers[0].feed_forward.w1.weight.shape[0] n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads, n_kv_heads, p.vocab_size, p.max_seq_len) out_file.write(header) # 4) write some other flags out_file.write(struct.pack('B', int(shared_classifier))) out_file.write(struct.pack('i', group_size)) # group size used for quantization pad = 256 - out_file.tell() # pad rest with zeros; tell returns current pos assert pad >= 0 out_file.write(b'\0' * pad) # now that the header is done, let's write out the model # first let's write out all the params that we are keeping in fp32: the norms for layer in model.layers: # attention norms serialize_fp32(out_file, layer.attention_norm.weight) for layer in model.layers: # MLP norms serialize_fp32(out_file, layer.ffn_norm.weight) serialize_fp32(out_file, model.norm.weight) # final pre-classifier norm # now let's write out all the params that we are quantizing to Q8_0 # note we skip classifier weights, which are shared with the embedding ew = [] for i, w in enumerate(weights): # quantize this weight q, s, err = quantize_q80(w, group_size) # save the int8 weights to file serialize_int8(out_file, q) # save the tensor in int8 serialize_fp32(out_file, s) # save scale factors # logging ew.append((err, w.shape)) print(f"{i+1}/{len(weights)} quantized {tuple(w.shape)} to Q8_0 with max error {err}") # print the highest error across all weights, should be very small, e.g. O(~0.001) ew.sort(reverse=True) print(f"max quantization group error across all weights: {ew[0][0]}") # write to binary file out_file.close() print(f"wrote {filepath}") def hf_export(llama_model, filepath, group_size=64, dtype=torch.float32): """ Generate the pytorch_model.bin state_dict and config.json for HuggingFace """ try: from transformers.models.llama.configuration_llama import LlamaConfig except ImportError: print("Error: transformers package is required to load huggingface models") print("Please run `pip install transformers` to install it") return None # Generate LlamaModel state_dict hf_state_dict = {} # Sometimes we have repeated key values for the heads dim = llama_model.params.dim num_key_value_heads = llama_model.params.n_kv_heads n_rep = llama_model.params.n_heads // num_key_value_heads key_value_dim = dim // n_rep # HuggingFace needs the weights permuted. # See: https://github.com/huggingface/transformers/blob/b132c1703eb1c8bd9dfa4ad6a9be2bfd6ef819e9/src/transformers/models/llama/convert_llama_weights_to_hf.py#L122 def permute_original(w, n_heads=llama_model.params.n_heads, dim1=dim, dim2=dim): return w.view(dim1, dim2).reshape(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2) # Transfer weights from llama model to the HF state dictionary format hf_state_dict['model.embed_tokens.weight'] = llama_model.tok_embeddings.weight.clone().to(dtype) hf_state_dict['model.norm.weight'] = llama_model.norm.weight.clone().to(dtype) # Add each layer's weights to the HF state dictionary for i, layer in enumerate(llama_model.layers): layer_id = layer.layer_id hf_state_dict[f'model.layers.{i}.input_layernorm.weight'] = llama_model.layers[layer_id].attention_norm.weight.clone().to(dtype) hf_state_dict[f'model.layers.{i}.self_attn.q_proj.weight'] = permute_original(llama_model.layers[layer_id].attention.wq.weight.clone()).to(dtype) hf_state_dict[f'model.layers.{i}.self_attn.k_proj.weight'] = permute_original(llama_model.layers[layer_id].attention.wk.weight.clone(), num_key_value_heads, key_value_dim, dim).to(dtype) hf_state_dict[f'model.layers.{i}.self_attn.v_proj.weight'] = llama_model.layers[layer_id].attention.wv.weight.clone().to(dtype) hf_state_dict[f'model.layers.{i}.self_attn.o_proj.weight'] = llama_model.layers[layer_id].attention.wo.weight.clone().to(dtype) hf_state_dict[f'model.layers.{i}.post_attention_layernorm.weight'] = llama_model.layers[layer_id].ffn_norm.weight.clone().to(dtype) hf_state_dict[f'model.layers.{i}.mlp.gate_proj.weight'] = llama_model.layers[layer_id].feed_forward.w1.weight.clone().to(dtype) hf_state_dict[f'model.layers.{i}.mlp.down_proj.weight'] = llama_model.layers[layer_id].feed_forward.w2.weight.clone().to(dtype) hf_state_dict[f'model.layers.{i}.mlp.up_proj.weight'] = llama_model.layers[layer_id].feed_forward.w3.weight.clone().to(dtype) # llama2.c usually uses tied weights -> reference the embed_tokens.weights instead hf_state_dict['lm_head.weight'] = hf_state_dict['model.embed_tokens.weight'] # We check that the embeddings are tied, else use manual output weights _embeddings_are_tied: bool = torch.equal(llama_model.tok_embeddings.weight, llama_model.output.weight) if not _embeddings_are_tied: hf_state_dict['lm_head.weight'] = llama_model.output.weight.clone().to(dtype) # Generate LlamaConfig (seen in transformers.models.llama.configuration_llama) # Extract necessary attributes from llama.c model vocab_size = llama_model.params.vocab_size hidden_size = llama_model.params.dim intermediate_size = llama_model.layers[0].feed_forward.w1.weight.shape[0] num_hidden_layers = llama_model.params.n_layers num_attention_heads = llama_model.params.n_heads num_key_value_heads = llama_model.params.n_kv_heads max_position_embeddings = llama_model.params.max_seq_len rms_norm_eps = llama_model.params.norm_eps # TODO check values for: # pretraining_tp, initializer_range, use_cache, # rope_theta, and rope_scaling. config = LlamaConfig( vocab_size=vocab_size, hidden_size=hidden_size, intermediate_size=intermediate_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, num_key_value_heads=num_key_value_heads, max_position_embeddings=max_position_embeddings, rms_norm_eps=rms_norm_eps, tie_word_embeddings=_embeddings_are_tied, # Manual architectures=["LlamaForCausalLM"], hidden_act="silu", ) # Save files in directory filepath # First make the directory if it doesn't exist os.makedirs(filepath, exist_ok=True) # Save the state dictionary in .bin format, and config as .json torch.save(hf_state_dict, os.path.join(filepath, "pytorch_model.bin")) config.save_pretrained(filepath) # ----------------------------------------------------------------------------- # Load / import functions def load_checkpoint(checkpoint): # load the provided model checkpoint checkpoint_dict = torch.load(checkpoint, map_location='cpu') gptconf = ModelArgs(**checkpoint_dict['model_args']) model = Transformer(gptconf) state_dict = checkpoint_dict['model'] unwanted_prefix = '_orig_mod.' for k,v in list(state_dict.items()): if k.startswith(unwanted_prefix): state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) model.load_state_dict(state_dict, strict=False) model.eval() return model def load_meta_model(model_path): params_path = os.path.join(model_path, 'params.json') with open(params_path) as f: params = json.load(f) print(params) model_paths = sorted(list(Path(model_path).glob('consolidated.*.pth'))) models = [torch.load(p, map_location='cpu') for p in model_paths] def concat_weights(models): state_dict = {} for name in list(models[0]): tensors = [model[name] for model in models] if len(tensors) == 1 or len(tensors[0].shape) == 1: state_dict[name] = tensors[0] continue is_axis_1 = ( name.startswith('tok_embeddings.') or name.endswith('.attention.wo.weight') or name.endswith('.feed_forward.w2.weight') ) axis = 1 if is_axis_1 else 0 state_dict[name] = torch.cat(tensors, dim=axis) for model in models: del model[name] return state_dict state_dict = concat_weights(models) del models # set ModelArgs config = ModelArgs() config.dim = params["dim"] config.n_layers = params["n_layers"] config.n_heads = params["n_heads"] config.n_kv_heads = params.get('n_kv_heads') or params['n_heads'] config.multiple_of = params["multiple_of"] config.norm_eps = params["norm_eps"] config.vocab_size = state_dict['tok_embeddings.weight'].shape[0] config.max_seq_len = 2048 # create a new Transformer object and set weights model = Transformer(config) model.tok_embeddings.weight = nn.Parameter(state_dict['tok_embeddings.weight']) model.norm.weight = nn.Parameter(state_dict['norm.weight']) for layer in model.layers: i = layer.layer_id layer.attention_norm.weight = nn.Parameter(state_dict[f'layers.{i}.attention_norm.weight']) layer.attention.wq.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wq.weight']) layer.attention.wk.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wk.weight']) layer.attention.wv.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wv.weight']) layer.attention.wo.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wo.weight']) layer.ffn_norm.weight = nn.Parameter(state_dict[f'layers.{i}.ffn_norm.weight']) layer.feed_forward.w1.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w1.weight']) layer.feed_forward.w2.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w2.weight']) layer.feed_forward.w3.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w3.weight']) # final classifier model.output.weight = nn.Parameter(state_dict['output.weight']) model.eval() return model def load_hf_model(model_path): try: from transformers import AutoModelForCausalLM except ImportError: print("Error: transformers package is required to load huggingface models") print("Please run `pip install transformers` to install it") return None # load HF model hf_model = AutoModelForCausalLM.from_pretrained(model_path) hf_dict = hf_model.state_dict() # convert LlamaConfig to ModelArgs config = ModelArgs() config.dim = hf_model.config.hidden_size config.n_layers = hf_model.config.num_hidden_layers config.n_heads = hf_model.config.num_attention_heads config.n_kv_heads = hf_model.config.num_attention_heads config.vocab_size = hf_model.config.vocab_size config.hidden_dim = hf_model.config.intermediate_size config.norm_eps = hf_model.config.rms_norm_eps config.max_seq_len = hf_model.config.max_position_embeddings # create a new Transformer object and set weights model = Transformer(config) model.tok_embeddings.weight = nn.Parameter(hf_dict['model.embed_tokens.weight']) model.norm.weight = nn.Parameter(hf_dict['model.norm.weight']) # huggingface permutes WQ and WK, this function reverses it def permute_reverse(w, n_heads=config.n_heads, dim1=config.dim, dim2=config.dim): return w.view(n_heads, 2, dim1 // n_heads // 2, dim2).transpose(1, 2).reshape(dim1, dim2) for layer in model.layers: i = layer.layer_id layer.attention_norm.weight = nn.Parameter(hf_dict[f'model.layers.{i}.input_layernorm.weight']) layer.attention.wq.weight = nn.Parameter(permute_reverse(hf_dict[f'model.layers.{i}.self_attn.q_proj.weight'])) layer.attention.wk.weight = nn.Parameter(permute_reverse(hf_dict[f'model.layers.{i}.self_attn.k_proj.weight'])) layer.attention.wv.weight = nn.Parameter(hf_dict[f'model.layers.{i}.self_attn.v_proj.weight']) layer.attention.wo.weight = nn.Parameter(hf_dict[f'model.layers.{i}.self_attn.o_proj.weight']) layer.ffn_norm.weight = nn.Parameter(hf_dict[f'model.layers.{i}.post_attention_layernorm.weight']) layer.feed_forward.w1.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.gate_proj.weight']) layer.feed_forward.w2.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.down_proj.weight']) layer.feed_forward.w3.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.up_proj.weight']) # final classifier model.output.weight = nn.Parameter(hf_dict['lm_head.weight']) model.eval() return model # ----------------------------------------------------------------------------- # API entrypoint def model_export(model, filepath, version, dtype=torch.float32): """ Versions docs: v-1:huggingface export, i.e. intended for use outside of this repo, in HF v0: legacy llama2.c float format, DEPRECATED v1: float32 export v2: int8 quantized Q8_0 export, similar to llama.cpp, in groups # TODO: add dtype export support for other versions (?) """ if version == 0: legacy_export(model, filepath) elif version == 1: version1_export(model, filepath) elif version == 2: version2_export(model, filepath) elif version == -1: hf_export(model, filepath, dtype) else: raise ValueError(f"unknown version {version}") def torchscript_export(model, filepath, zero_params=False, gzip_output=False): """ (This was submitted via a PR earlier. Leaving it here, but "orphaned" for now) Saves the model as a TorchScript. The resulting file can be loaded in C++ code and then used for training or inference with: #include torch::jit::Module module = torch::jit::load("model.pt") Note that the serialized model includes the initial parameters and with the default ModelArgs the file is 59M and gzips down to 55M. If you want to serialize/distribute the model parameters separately you can zero out the parameters before saving it and it will gzip down to 780K. """ # If requested zero params before saving the model. This is useful in # conjunction with gzip_output. if zero_params: for p in model.parameters(): p.detach().zero_() torch.jit.save(torch.jit.script(model), filepath) if gzip_output: with open(filepath, "rb") as f_in: with gzip.open(f"{filepath}.gz", "wb") as f_out: shutil.copyfileobj(f_in, f_out) os.unlink(filepath) # ----------------------------------------------------------------------------- # CLI entrypoint if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("filepath", type=str, help="the output filepath") parser.add_argument("--version", default=0, type=int, help="the version to export with") parser.add_argument("--dtype", type=str, help="dtype of the model (fp16, fp32)", default="fp32") group = parser.add_mutually_exclusive_group(required=True) group.add_argument("--checkpoint", type=str, help="model checkpoint, .pt file") group.add_argument("--meta-llama", type=str, help="meta llama model path") group.add_argument("--hf", type=str, help="huggingface model path") args = parser.parse_args() dtype = {"fp16": torch.float16, "fp32": torch.float32}[args.dtype] if args.checkpoint: model = load_checkpoint(args.checkpoint) elif args.meta_llama: model = load_meta_model(args.meta_llama) elif args.hf: model = load_hf_model(args.hf) if model is None: parser.error("Can't load input model!") # export model_export(model, args.filepath, args.version, args.dtype)