weights.py

import os
from pathlib import Path
from typing import List, Dict, Optional, Tuple
from safetensors import safe_open, SafetensorError
import torch
from huggingface_hub import hf_hub_download
import json


class Weights:
    def __init__(
        self,
        filenames: List[Path],
        device,
        dtype,
        process_group,
        aliases: Optional[Dict[str, List[str]]] = None,
        prefix: Optional[str] = None
    ):
        routing = {}
        for filename in filenames:
            with safe_open(filename, framework="pytorch") as f:
                for k in f.keys():
                    if k in routing:
                        raise RuntimeError(
                            f"Key {k} was found in multiple files: {filename} and {routing[k]}"
                        )
                    routing[k] = filename
        if aliases is None:
            aliases = {}
        self.aliases = aliases
        self.routing = routing
        self.device = device
        self.dtype = dtype
        self.process_group = process_group
        self.prefix = prefix
        self._handles = {}

    def _get_handle(self, filename):
        if filename not in self._handles:
            f = safe_open(filename, framework="pytorch")
            self._handles[filename] = f

        return self._handles[filename]

    def get_filename(self, tensor_name: str):

        names = [tensor_name]
        if self.prefix is not None:
            prefixed = f"{self.prefix}.{tensor_name}"
            names.append(prefixed)
        for name in names:
            filename = self.routing.get(name, None)
            if filename is not None:
                return str(filename), name

            aliases = self.aliases.get(name, [])
            for alias in aliases:
                filename = self.routing.get(alias, None)
                if filename is not None:
                    return str(filename), alias
        raise RuntimeError(f"weight {tensor_name} does not exist")

    def _get_slice(self, tensor_name: str):
        filename, tensor_name = self.get_filename(tensor_name)
        f = self._get_handle(filename)
        slice_ = f.get_slice(tensor_name)
        return slice_

    def get_shape(self, tensor_name: str):
        return self._get_slice(tensor_name).get_shape()

    def get_tensor(self, tensor_name: str, to_device=True):
        filename, tensor_name = self.get_filename(tensor_name)
        f = self._get_handle(filename)
        tensor = f.get_tensor(tensor_name)
        # Special case for gptq which shouldn't convert
        # u4 which are disguised as int32
        if tensor.dtype not in [torch.int32, torch.int64]:
            tensor = tensor.to(dtype=self.dtype)
        if to_device:
            tensor = tensor.to(device=self.device)
        return tensor

    def get_partial_sharded(self, tensor_name: str, dim: int):
        filename, tensor_name = self.get_filename(tensor_name)
        f = self._get_handle(filename)
        slice_ = f.get_slice(tensor_name)
        world_size = self.process_group.size()
        rank = self.process_group.rank()

        size = slice_.get_shape()[dim]
        block_size = size // world_size
        start = rank * block_size
        stop = (rank + 1) * block_size

        if dim == 0:
            tensor = slice_[start:stop]
        elif dim == 1:
            tensor = slice_[:, start:stop]
        else:
            raise NotImplementedError("Let's make that generic when needed")
        # Special case for gptq which shouldn't convert
        # u4 which are disguised as int32
        if tensor.dtype != torch.int32:
            tensor = tensor.to(dtype=self.dtype)
        tensor = tensor.to(device=self.device)
        return tensor

    def get_sharded(self, tensor_name: str, dim: int):
        filename, tensor_name = self.get_filename(tensor_name)
        f = self._get_handle(filename)
        slice_ = f.get_slice(tensor_name)
        world_size = self.process_group.size()
        size = slice_.get_shape()[dim]
        assert (
            size % world_size == 0
        ), f"The choosen size {size} is not compatible with sharding on {world_size} shards"
        return self.get_partial_sharded(tensor_name, dim)

    def _get_qweight(self, name: str):
        slice_ = self._get_slice(name)
        total_size = slice_.get_shape()[1]
        assert total_size % 3 == 0, "Prepacked quantized qkv is not divisible by 3"
        single_size = total_size // 3
        world_size = self.process_group.size()
        rank = self.process_group.rank()

        assert (
            single_size % world_size == 0
        ), f"Prepacked quantized qkv cannot be sharded across {world_size} shards"
        block_size = single_size // world_size
        start = rank * block_size
        stop = (rank + 1) * block_size
        q = slice_[:, start:stop]
        k = slice_[:, start + single_size : stop + single_size]
        v = slice_[:, start + 2 * single_size : stop + 2 * single_size]
        weight = torch.cat([q, k, v], dim=1)
        weight = weight.to(device=self.device)
        return weight

    def get_weights_col_packed_qkv(self, prefix: str, quantize: str):
        """
        Highly specific when the underlying tensor is a simple cat of Q,K,V instead of being
        already alternating Q,K,V within the main tensor
        """
        slice_ = self._get_slice(f"{prefix}.weight")
        total_size = slice_.get_shape()[0]
        assert total_size % 3 == 0, "Prepacked qkv is not divisible by 3"
        single_size = total_size // 3
        world_size = self.process_group.size()
        rank = self.process_group.rank()

        assert (
            single_size % world_size == 0
        ), f"Prepacked qkv cannot be sharded across {world_size} shards"
        block_size = single_size // world_size
        start = rank * block_size
        stop = (rank + 1) * block_size
        q = slice_[start:stop]
        k = slice_[start + single_size : stop + single_size]
        v = slice_[start + 2 * single_size : stop + 2 * single_size]
        weight = torch.cat([q, k, v], dim=0)
        weight = weight.to(device=self.device)
        weight = weight.to(dtype=self.dtype)
        return weight

    def get_multi_weights_col(self, prefixes: List[str], quantize: str, dim: int):
        w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
        weight = torch.cat(w, dim=dim)
        return weight

    def get_tensor_shard(self, var, dim):
        world_size = self.process_group.size()
        rank = self.process_group.rank()
        block_size = var.size()[dim] // world_size
        start = rank * block_size
        stop = (rank + 1) * block_size
        if dim == 0:
            tensor = var[start:stop]
        elif dim == 1:
            tensor = var[:, start:stop]
        else:
            raise NotImplementedError("Let's make that generic when needed")
        tensor = tensor.to(dtype=self.dtype)
        tensor = tensor.to(device=self.device)
        return tensor

    def get_multi_weights_row(self, prefix: str, quantize: str):
        weight = self.get_sharded(f"{prefix}.weight", dim=1)
        return weight