import json import os import re import shutil from argparse import ArgumentParser from glob import glob from tqdm import tqdm, trange import torch from safetensors.torch import safe_open, save_file FP4_TABLE = torch.tensor( [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0, 0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0], dtype=torch.float32 ) def cast_e2m1fn_to_e4m3fn(x: torch.Tensor, scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: """ Casts a tensor from e2m1fn to e4m3fn losslessly. """ assert x.dtype == torch.int8 assert x.ndim == 2 out_dim, in_dim = x.size() in_dim *= 2 fp8_block_size = 32 fp4_block_size = 32 assert in_dim % fp8_block_size == 0 and out_dim % fp8_block_size == 0 assert scale.size(0) == out_dim and scale.size(1) == in_dim // fp4_block_size x = x.view(torch.uint8) low = x & 0x0F high = (x >> 4) & 0x0F x = torch.stack([FP4_TABLE[low.long()], FP4_TABLE[high.long()]], dim=-1).flatten(2) # max_fp4 (6.0) * MAX_OFFSET must fit in e4m3fn (max 448) # 6.0 * 2^6 = 384 < 448; 6.0 * 2^7 = 768 > 448; so MAX_OFFSET_BITS = 6 MAX_OFFSET_BITS = 6 bOut = out_dim // fp8_block_size bIn = in_dim // fp8_block_size # bOut, bIn, fp8_block_size, fp8_block_size x = x.view(bOut, fp8_block_size, bIn, fp8_block_size).transpose(1, 2) # bOut, bIn, fp8_block_size * (fp8_block_size // fp4_block_size) scale = scale.float().view(bOut, fp8_block_size, bIn, -1).transpose(1, 2).flatten(2) ## bOut, bIn, 1 scale_max_offset_bits = scale.amax(dim=-1, keepdim=True) / (2**MAX_OFFSET_BITS) # bOut, bIn, fp8_block_size * (fp8_block_size // fp4_block_size) offset = scale / scale_max_offset_bits # bOut, bIn, fp8_block_size, fp8_block_size offset = offset.unflatten(-1, (fp8_block_size, -1)).repeat_interleave(fp4_block_size, dim=-1) x = (x * offset).transpose(1, 2).reshape(out_dim, in_dim) return x.to(torch.float8_e4m3fn), scale_max_offset_bits.squeeze(-1).to(torch.float8_e8m0fnu) mapping = { "embed": ("embed", 0), "wq_b": ("wq_b", 0), "wo_a": ("wo_a", 0), "wo_b": ("wo_b", 1), "head": ("head", 0), "attn_sink": ("attn_sink", 0), "weights_proj": ("weights_proj", 0), } def infer_num_experts(names) -> tuple[int, int]: """Number of routed experts in the backbone and in the MTP layers, from the weight names.""" counts = [0, 0] for name in names: name = name.removeprefix("model.") match = re.search(r"(?:mlp|ffn)\.experts\.(\d+)\.", name) if match: is_mtp = name.startswith("mtp.") counts[is_mtp] = max(counts[is_mtp], int(match.group(1)) + 1) assert counts[0], "no routed experts found in the checkpoint" return counts[0], counts[1] or counts[0] def main(hf_ckpt_path, save_path, mp, expert_dtype, tokenizer_path=None): """Shard an exported HuggingFace checkpoint into `mp` files for this inference stack.""" torch.set_num_threads(8) state_dicts = [{} for _ in range(mp)] os.makedirs(save_path, exist_ok=True) index_path = os.path.join(hf_ckpt_path, "model.safetensors.index.json") expected_names = set(json.load(open(index_path))["weight_map"]) if os.path.exists(index_path) else None seen_names = set() all_names = expected_names if all_names is None: all_names = set() for file_path in glob(os.path.join(hf_ckpt_path, "*.safetensors")): with safe_open(file_path, framework="pt", device="cpu") as f: all_names.update(f.keys()) n_experts, mtp_n_experts = infer_num_experts(all_names) assert n_experts % mp == 0 and mtp_n_experts % mp == 0, (n_experts, mtp_n_experts, mp) print(f"{n_experts=} {mtp_n_experts=}") for file_path in tqdm(glob(os.path.join(hf_ckpt_path, "*.safetensors"))): with safe_open(file_path, framework="pt", device="cpu") as f: for source_name in f.keys(): seen_names.add(source_name) name = source_name if name.startswith("model."): name = name[len("model.") :] param: torch.Tensor = f.get_tensor(source_name) # an MTP layer ties its token embedding and output head to the backbone's if name.startswith("mtp.") and name.split(".", 2)[-1] in ("embed.weight", "head.weight"): continue name = name.replace("self_attn", "attn") if not name.startswith("vision."): name = name.replace("mlp", "ffn") name = name.replace("weight_scale_inv", "scale") name = name.replace("e_score_correction_bias", "bias") if any( x in name for x in ["hc", "attn_sink", "tie2eid", "tid2eid", "ape", "image_"] ): # without .weight key = name.split(".")[-1] else: key = name.split(".")[-2] if key in mapping: new_key, dim = mapping[key] else: new_key, dim = key, None name = name.replace(key, new_key) for i in range(mp): new_param = param if "experts" in name and "shared_experts" not in name: current_n_experts = mtp_n_experts if name.startswith("mtp.") else n_experts n_local_experts = current_n_experts // mp idx = int(name.split(".")[-3]) if idx < i * n_local_experts or idx >= (i + 1) * n_local_experts: continue elif ".engram.embed." in name: shard_size = (param.size(0) + mp - 1) // mp new_param = param[i * shard_size : (i + 1) * shard_size].contiguous() if new_param.size(0) < shard_size: pad_value = 1 if name.endswith(".scale") else 0 padding = param.new_full((shard_size - new_param.size(0), param.size(1)), pad_value) new_param = torch.cat([new_param, padding]) elif dim is not None: assert param.size(dim) % mp == 0, f"Dimension {dim} must be divisible by {mp}" shard_size = param.size(dim) // mp new_param = param.narrow(dim, i * shard_size, shard_size).contiguous() state_dicts[i][name] = new_param if expected_names is not None: assert seen_names == expected_names, ( f"checkpoint shards incomplete: {len(expected_names - seen_names)} tensors missing, " f"{len(seen_names - expected_names)} unexpected (source may be mid-upload)" ) for i in trange(mp): names = list(state_dicts[i].keys()) for name in names: if name.endswith("wo_a.weight"): weight = state_dicts[i][name] scale = state_dicts[i].pop(name.replace("weight", "scale")) assert weight.size(0) % scale.size(0) == 0 assert weight.size(1) % scale.size(1) == 0 out_block_size = weight.size(0) // scale.size(0) in_block_size = weight.size(1) // scale.size(1) assert (out_block_size, in_block_size) in ((32, 32), (128, 128)), ( name, weight.shape, scale.shape, ) weight = ( weight.unflatten(0, (-1, out_block_size)).unflatten(-1, (-1, in_block_size)).float() * scale[:, None, :, None].float() ) state_dicts[i][name] = weight.flatten(2, 3).flatten(0, 1).bfloat16() elif "experts" in name and state_dicts[i][name].dtype == torch.int8: if expert_dtype == "fp8": scale_name = name.replace("weight", "scale") weight = state_dicts[i].pop(name) scale = state_dicts[i].pop(scale_name) state_dicts[i][name], state_dicts[i][scale_name] = cast_e2m1fn_to_e4m3fn(weight, scale) else: state_dicts[i][name] = state_dicts[i][name].view(torch.float4_e2m1fn_x2) save_file(state_dicts[i], os.path.join(save_path, f"model{i}-mp{mp}.safetensors")) tokenizer_path = tokenizer_path or hf_ckpt_path for file in ["tokenizer.json", "tokenizer_config.json"]: old_file_path = os.path.join(tokenizer_path, file) new_file_path = os.path.join(save_path, file) if os.path.exists(old_file_path): shutil.copyfile(old_file_path, new_file_path) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--hf-ckpt-path", type=str, required=True) parser.add_argument("--save-path", type=str, required=True) parser.add_argument("--model-parallel", type=int, required=True) parser.add_argument("--expert-dtype", type=str, choices=["fp8", "fp4"], default=None) parser.add_argument( "--tokenizer-path", type=str, default=None, help="Optional tokenizer directory when the HF checkpoint does not contain tokenizer files", ) args = parser.parse_args() main(args.hf_ckpt_path, args.save_path, args.model_parallel, args.expert_dtype, args.tokenizer_path)