423 lines
21 KiB
Zig
423 lines
21 KiB
Zig
const asynk = @import("async");
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const std = @import("std");
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const zml = @import("../zml.zig");
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const HostBuffer = @import("../hostbuffer.zig").HostBuffer;
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const toVoidSlice = @import("utils.zig").toVoidSlice;
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const eval = @import("torch/eval.zig");
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const utils = @import("torch/utils.zig");
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const value = @import("torch/value.zig");
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const Decoder = @import("torch/parser.zig").Decoder;
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const PersId = value.PersId;
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const PickleMemo = eval.PickleMemo;
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const PickleStack = eval.PickleStack;
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const Sequence = value.Sequence;
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const Value = value.Value;
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const ValueType = value.ValueType;
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const StringBuilder = std.ArrayListUnmanaged(u8);
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const Allocator = std.mem.Allocator;
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const log = std.log.scoped(.zml_io);
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/// Opens and loads a BufferStore from the torch file at the given path.
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pub fn open(allocator: Allocator, path: []const u8) !zml.aio.BufferStore {
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const file = asynk.File.open(path, .{}) catch |err| {
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log.err("Failed to open {s}: {}", .{ path, err });
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return err;
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};
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errdefer file.close() catch unreachable;
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var res: zml.aio.BufferStore = .{
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.arena = std.heap.ArenaAllocator.init(allocator),
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};
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const arena = res.arena.allocator();
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var tmp: PickleData = .{
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.data = try Decoder.init(arena, file),
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.memo = undefined,
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.stack = undefined,
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};
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tmp.stack, tmp.memo = try eval.evaluate(arena, tmp.data.ops, true);
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res.files = try arena.dupe(zml.aio.MemoryMappedFile, &.{tmp.data.buffer_file});
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try tmp.parseModel(arena, &res);
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return res;
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}
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/// Convert from a torch.<type>Storage to a `zml.DataType`.
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/// TODO: make this future proof, storage type are going to get replaced with torch.UntypedStorage
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/// See https://pytorch.org/docs/stable/storage.html
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fn storageToDtype(storage_type: []const u8) !zml.DataType {
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const torch_type = storage_type[0 .. storage_type.len - "Storage".len];
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const map = std.StaticStringMap(zml.DataType).initComptime(.{
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.{ "Double", .f64 },
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.{ "Float", .f32 },
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.{ "Half", .f16 },
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.{ "Long", .i64 },
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.{ "Int", .i32 },
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.{ "Short", .i16 },
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.{ "Char", .i8 },
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.{ "Byte", .u8 },
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.{ "Bool", .bool },
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.{ "BFloat16", .bf16 },
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.{ "ComplexDouble", .c128 },
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.{ "ComplexFloat", .c64 },
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// QUInt8Storage
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// QInt8Storage
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// QInt32Storage
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// QUInt4x2Storage
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// QUInt2x4Storage
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});
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return map.get(torch_type) orelse {
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log.err("Unsupported torch storage type: {s}", .{storage_type});
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return error.UnsupportedDataType;
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};
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}
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pub const PickleData = struct {
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stack: PickleStack,
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memo: PickleMemo,
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data: Decoder,
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fn basicTypeCheck(v: Value, ns: []const u8, name: []const u8) bool {
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return switch (v) {
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.global => |object| switch (object.member) {
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.raw => |raw| {
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if (std.mem.eql(u8, ns, raw.global.module) and std.mem.eql(u8, name, raw.global.class) and object.args[0] == .seq) {
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return true;
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} else return false;
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},
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else => false,
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},
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else => false,
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};
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}
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fn isTensor(v: Value) bool {
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if (basicTypeCheck(v, "torch._utils", "_rebuild_tensor_v2")) {
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const args = v.global.args[0].seq.values;
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if (args.len >= 5 and
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args[0] == .pers_id and
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args[1] == .int64 and
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args[2] == .seq and args[2].seq.type == .tuple and
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args[3] == .seq and args[3].seq.type == .tuple)
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{
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return true;
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} else @panic("Unexpected value in call to torch._utils._rebuild_tensor_v2");
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}
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return false;
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}
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fn dimsFromValues(values: []Value) [zml.Tensor.MAX_RANK]i64 {
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std.debug.assert(values.len <= zml.Tensor.MAX_RANK);
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var result: [zml.Tensor.MAX_RANK]i64 = undefined;
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for (values, result[0..values.len]) |val, *elem| {
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switch (val) {
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.int64 => |int| elem.* = int,
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else => @panic("Bad value for shape item"),
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}
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}
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return result;
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}
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pub fn parseModel(self: *PickleData, allocator: Allocator, store: *zml.aio.BufferStore) !void {
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for (self.stack.stack) |item| {
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var prefix_buf: [1024]u8 = undefined;
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try self.parseValue(allocator, store, StringBuilder.initBuffer(&prefix_buf), item);
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}
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}
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fn tensorOffset(self: *PickleData, seekable_stream: anytype, sfile: []const u8) !u64 {
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if (self.data.file_map.get(sfile)) |entry| {
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const local_header = blk: {
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try seekable_stream.seekTo(entry.file_offset);
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break :blk try seekable_stream.context.reader().readStructEndian(std.zip.LocalFileHeader, .little);
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};
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if (!std.mem.eql(u8, &local_header.signature, &std.zip.local_file_header_sig))
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return error.ZipBadFileOffset;
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if (local_header.version_needed_to_extract != entry.version_needed_to_extract)
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return error.ZipMismatchVersionNeeded;
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if (local_header.last_modification_time != entry.last_modification_time)
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return error.ZipMismatchModTime;
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if (local_header.last_modification_date != entry.last_modification_date)
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return error.ZipMismatchModDate;
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if (@as(u16, @bitCast(local_header.flags)) != entry.flags)
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return error.ZipMismatchFlags;
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if (local_header.crc32 != 0 and local_header.crc32 != entry.crc32)
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return error.ZipMismatchCrc32;
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if (local_header.compressed_size != 0 and
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local_header.compressed_size != entry.compressed_size)
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return error.ZipMismatchCompLen;
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if (local_header.uncompressed_size != 0 and
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local_header.uncompressed_size != entry.uncompressed_size)
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return error.ZipMismatchUncompLen;
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if (local_header.filename_len != entry.filename_len)
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return error.ZipMismatchFilenameLen;
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return (try seekable_stream.context.getPos()) +
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@as(u64, local_header.filename_len) +
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@as(u64, local_header.extra_len);
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}
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std.log.err("Could not find file ending in `{s}` in archive", .{sfile});
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return error.TensorNotFound;
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}
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fn parseTorchGlobal(self: *PickleData, allocator: Allocator, store: *zml.aio.BufferStore, prefix: StringBuilder, v: Value) !bool {
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return switch (v) {
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.global => |object| {
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if (isTensor(v)) {
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const args = object.args[0].seq.values;
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const pidval: *PersId, var offs: u64, const raw_shape: Sequence, const raw_strides: Sequence = .{ args[0].pers_id, @intCast(args[1].int64), args[2].seq, args[3].seq };
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const rank = raw_shape.values.len;
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const shape = dimsFromValues(raw_shape.values);
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var strides = dimsFromValues(raw_strides.values);
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const storage_type, const sfile = switch (pidval.ref) {
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.seq => |seq| blk: {
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const sargs = seq.values;
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if (seq.type == .tuple and
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sargs.len >= 5 and
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sargs[0] == .string and std.mem.eql(u8, sargs[0].string, "storage") and
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sargs[1] == .raw and sargs[1].raw == .global and
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sargs[2] == .string and
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sargs[3] == .string)
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{
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const op = sargs[1].raw.global;
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const sfile = sargs[2].string;
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// const sdev = sargs[3].string;
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if (std.mem.eql(u8, "torch", op.module) and std.mem.endsWith(u8, op.class, "Storage")) {
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break :blk .{ op.class, sfile };
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} else @panic("Unexpected storage type part of persistant ID");
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} else @panic("Unexpected value for persistant ID");
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},
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else => @panic("Unexpected value for persistant ID"),
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};
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const data_type = try storageToDtype(storage_type);
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for (strides[0..rank]) |*s| s.* *= data_type.sizeOf();
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var sfile_buf = std.ArrayList(u8).init(allocator);
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defer sfile_buf.deinit();
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try sfile_buf.writer().print("{s}data/{s}", .{ self.data.zip_prefix, sfile });
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// find offsets for tensor zip file
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const absolute_offset = blk: {
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if (self.data.tar_file) |t| {
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break :blk try self.tensorOffset(t.seekableStream(), sfile_buf.items);
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} else {
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break :blk try self.tensorOffset(self.data.buffer_file.file.seekableStream(), sfile_buf.items);
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}
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};
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offs = offs * data_type.sizeOf();
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const key = try allocator.dupe(u8, prefix.items);
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const entry = try store.buffers.getOrPut(allocator, key);
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if (entry.found_existing) {
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log.warn("Duplicate key: {s}", .{prefix.items});
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allocator.free(key);
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}
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const out_shape = zml.Shape.init(shape[0..rank], data_type);
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entry.value_ptr.* = HostBuffer.fromStridedSlice(
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out_shape,
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self.data.buffer_file.mappedSlice((if (self.data.tar_file) |t| t.start else 0) + absolute_offset + offs, out_shape.byteSize()),
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strides[0..rank],
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);
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return true;
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} else if (basicTypeCheck(v, "torch", "Size")) {
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const size = object.args[0].seq.values[0].seq.values;
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const key = try allocator.dupe(u8, prefix.items);
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const entry = try store._metadata.getOrPut(allocator, key);
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if (entry.found_existing) {
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log.warn("Duplicate key: {s}", .{prefix.items});
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allocator.free(key);
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}
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const d = try allocator.alloc(i64, size.len);
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for (d, 0..) |*di, i| di.* = size[i].int64;
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entry.value_ptr.* = .{ .array = .{ .item_type = .int64, .data = std.mem.sliceAsBytes(d) } };
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return true;
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} else if (basicTypeCheck(v, "fractions", "Fraction")) {
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const fraction_str = object.args[0].seq.values[0].string;
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if (std.mem.indexOfScalar(u8, fraction_str, '/')) |split_idx| {
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{
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var new_prefix = prefix;
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new_prefix.appendSliceAssumeCapacity(".numerator");
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try store._metadata.put(allocator, try allocator.dupe(u8, new_prefix.items), .{ .int64 = try std.fmt.parseInt(i64, fraction_str[0..split_idx], 10) });
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}
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{
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var new_prefix = prefix;
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new_prefix.appendSliceAssumeCapacity(".denominator");
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try store._metadata.put(allocator, try allocator.dupe(u8, new_prefix.items), .{ .int64 = try std.fmt.parseInt(i64, fraction_str[split_idx + 1 ..], 10) });
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}
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return true;
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}
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}
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return false;
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},
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else => false,
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};
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}
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pub fn parseValue(self: *PickleData, allocator: Allocator, store: *zml.aio.BufferStore, prefix: StringBuilder, v: Value) !void {
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switch (v) {
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.app, .object, .global => |object| {
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if (!(try self.parseTorchGlobal(allocator, store, prefix, v))) {
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try self.parseValue(allocator, store, prefix, object.member);
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for (object.args) |item| {
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// if possible, coerce to `kv_tuple` (only if key val doesn't match root of prefix)
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if (item == .seq and item.seq.type == .tuple and item.seq.values.len == 2 and item.seq.values[0] == .string) {
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try self.parseValue(allocator, store, prefix, .{ .seq = .{ .type = .kv_tuple, .values = item.seq.values } });
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} else try self.parseValue(allocator, store, prefix, item);
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}
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}
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},
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.build => |build| {
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// `build` contains info about python struct being constructed
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switch (build.member) {
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.object => |obj| switch (obj.member) {
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.raw => |raw| switch (raw) {
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.global => |global| {
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// in this case, we can capture the name of the python type
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// which can be used for codegen (e.g. `torch.nn.modules.conv.Conv2d`)
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var new_prefix = prefix;
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if (prefix.items.len > 0) {
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new_prefix.appendAssumeCapacity('.');
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}
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new_prefix.appendSliceAssumeCapacity("_gen_type_helper");
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const key = try allocator.dupe(u8, new_prefix.items);
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const d = try store._metadata.getOrPut(allocator, key);
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if (d.found_existing) {
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log.err("Duplicate key: {s}", .{new_prefix.items});
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allocator.free(key);
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} else {
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const val = try std.mem.join(allocator, ".", &.{ global.module, global.class });
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d.value_ptr.* = .{ .string = val };
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}
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},
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else => try self.parseValue(allocator, store, prefix, build.member), // parse normally
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},
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else => try self.parseValue(allocator, store, prefix, build.member), // parse normally
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},
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else => try self.parseValue(allocator, store, prefix, build.member), // parse normally
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}
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try self.parseValue(allocator, store, prefix, build.args);
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},
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.pers_id => |pers_id| try self.parseValue(allocator, store, prefix, pers_id.ref),
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.seq => |seq| {
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switch (seq.type) {
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.list, .tuple, .set, .frozen_set => {
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if (seq.values.len == 0) return;
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var valid_slice = true;
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switch (seq.values[0]) {
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inline .int64, .float64, .boolval => |val0, tag| {
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const ItemType = switch (tag) {
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.int64 => i64,
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.float64 => f64,
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.boolval => bool,
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else => unreachable,
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};
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var values: std.ArrayListUnmanaged(ItemType) = .{};
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try values.append(allocator, val0);
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for (seq.values[1..], 1..) |val, i| {
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if (std.meta.activeTag(val) != tag) valid_slice = false;
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if (valid_slice) {
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try values.append(allocator, @field(val, @tagName(tag)));
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} else {
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var new_prefix = prefix;
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if (prefix.items.len > 0) {
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new_prefix.appendAssumeCapacity('.');
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}
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new_prefix.items.len += std.fmt.formatIntBuf(new_prefix.unusedCapacitySlice(), i, 10, .lower, .{});
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try self.parseValue(allocator, store, new_prefix, val);
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}
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}
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if (valid_slice) {
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try store._metadata.put(
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allocator,
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try allocator.dupe(u8, prefix.items),
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.{ .array = .{ .item_type = std.meta.stringToEnum(zml.aio.Value.Slice.ItemType, @tagName(tag)).?, .data = std.mem.sliceAsBytes(try values.toOwnedSlice(allocator)) } },
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);
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} else {
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for (values.items, 0..) |val, i| {
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var new_prefix = prefix;
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if (prefix.items.len > 0) {
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new_prefix.appendAssumeCapacity('.');
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}
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new_prefix.items.len += std.fmt.formatIntBuf(new_prefix.unusedCapacitySlice(), i, 10, .lower, .{});
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try store._metadata.put(allocator, try allocator.dupe(u8, new_prefix.items), @unionInit(zml.aio.Value, @tagName(tag), val));
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}
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}
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},
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else => {
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for (seq.values, 0..) |item, i| {
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var new_prefix = prefix;
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if (v.isPrimitive()) {
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if (prefix.items.len > 0) {
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new_prefix.appendAssumeCapacity('.');
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}
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new_prefix.items.len += std.fmt.formatIntBuf(new_prefix.unusedCapacitySlice(), i, 10, .lower, .{});
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}
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try self.parseValue(allocator, store, new_prefix, item);
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}
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},
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}
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},
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.dict => for (seq.values) |item| {
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try self.parseValue(allocator, store, prefix, item);
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},
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.kv_tuple => {
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const key, const val = seq.values[0..2].*;
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switch (key) {
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.string => |s| {
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// Handle Pytorch specific fields
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if (std.mem.eql(u8, s, "_modules") or std.mem.eql(u8, s, "_parameters") or std.mem.eql(u8, s, "_buffers")) {
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try self.parseValue(allocator, store, prefix, val);
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} else {
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var new_prefix = prefix;
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if (prefix.items.len > 0) {
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new_prefix.appendAssumeCapacity('.');
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}
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new_prefix.appendSliceAssumeCapacity(s);
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try self.parseValue(allocator, store, new_prefix, val);
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}
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},
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.int64 => |int| {
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var new_prefix = prefix;
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if (prefix.items.len > 0) {
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new_prefix.appendAssumeCapacity('.');
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}
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new_prefix.items.len += std.fmt.formatIntBuf(new_prefix.unusedCapacitySlice(), int, 10, .lower, .{});
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try self.parseValue(allocator, store, new_prefix, val);
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},
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inline else => |_, tag| std.debug.panic("Unexpected key type: {s}", .{@tagName(tag)}),
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}
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},
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}
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},
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.bytes => |val| {
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const key = try allocator.dupe(u8, prefix.items);
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const d = try store._metadata.getOrPut(allocator, key);
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if (d.found_existing) {
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log.warn("Duplicate key: {s}", .{prefix.items});
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allocator.free(key);
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} else d.value_ptr.* = .{ .array = .{ .item_type = .uint8, .data = @constCast(val) } };
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},
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inline .float64, .int64, .boolval, .bigint, .string => |val, tag| {
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const key = try allocator.dupe(u8, prefix.items);
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const d = try store._metadata.getOrPut(allocator, key);
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if (d.found_existing) {
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log.warn("Duplicate key: {s}", .{prefix.items});
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allocator.free(key);
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} else d.value_ptr.* = @unionInit(zml.aio.Value, @tagName(tag), val);
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},
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else => {},
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}
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}
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};
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test {
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std.testing.refAllDecls(@This());
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}
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