3. LOADING TORCH MODEL (DETAILS)

In this chapter, we'll walk through the process of reading a tensor stored in the "consolidated.00.pth" file, step by step.

A Quick Reminder:
Pickle uses a simple stack-based virtual machine that records the instructions used to reconstruct the object. The file is just a list of serialized opcodes, the first one being expected to be the protocol version and the last one a stop opcode. When the stop opcode is met, the current object on the stack is popped.
See: Wikipedia | Diving into the Python Pickle format

Pickle format consists of pairs of opcode and data bytes. Each pair starts with one byte that stands for an "opcode" and then continues with data bytes varying by each type of opcode.

3.1. Loading PROTO

PROTO stands for Pickle Protocol version number. We start to reading with it.

Read key byte: 0x80, corresponding opcode: PROTO, function: load_proto
- Read one byte: 0x02, identifies Pickle Protocol v2.

pr.stack: {}
pr.metastack: {}
memo: {}

3.2. Preparation to read a Tensor

The file continues with data of the first weights tensor. Because of the Pickle format is generic, the file contains some instructions to construct some data structures.

Read key byte: 0x7D, char: '}', corresponding opcode: EMPTY_DICT, function: load_empty_dictionary
- Call NewPickleDict[interface{}]() to create a new empty pickle.PickleDict[interface {}] object
- Push it into the pr.stack

pr.stack: {
    PickleDict{}
}
pr.metastack: {}
memo: {}

Read key byte: 0x71, char: 'q', corresponding opcode: BINPUT, function: load_binput
- Read one byte: 0x00 as memo map key
- Take last element of pr.stack and assigns it as 0th item of pr.memo

pr.stack: {
    PickleDict{}
}
pr.metastack: {}
memo: {
    0: PickleDict{}
}

Read key byte: 0x28, char: '(', corresponding opcode: MARK, function: load_mark
- Append the pr.stack array into metastack array (array of arrays),
- Create new pr.stack array.

pr.stack: {}
pr.metastack: {
    PickleDict{}
}
memo: {
    0: PickleDict{}
}

3.3. Reading Name of the Tensor

The file continues with the name of the first tensor.

Read key byte: 0x58, char: 'X', corresponding opcode: BINUNICODE, function: load_binunicode
- Read 4 bytes: [0x15, 0x00, 0x00, 0x00], convert it to int32 as little-endian: 21 (decimal). Identifies length of unicode string.
- Read 21 bytes, convert it to string identifies the name of upcoming tensor: "tok_embeddings.weight".
- Push it into the pr.stack

pr.stack: {
    "tok_embeddings.weight"
}
pr.metastack: {
    {
        PickleDict{}
    }
}
memo: {
    0: PickleDict{}
}

Read key byte: 0x71, char: 'q', corresponding opcode: BINPUT, function: load_binput
- Read one byte: 0x01 as memo map key
- Take last element of pr.stack and assigns it as 1st item of pr.memo

pr.stack: {
    "tok_embeddings.weight"
}
pr.metastack: {
    {
        PickleDict{}
    }
}
memo: {
    0: PickleDict{}
    1: "tok_embeddings.weight"
}

3.4. Obtaining a TorchStorage Object

The file continues with the torch.rebuild_tensor_v2(...) function. Then, we will read some instructions that construct torch.TorchStorage object. This object will be one of arguments of for torch.rebuild_tensor_v2(...) function.

Read key byte: 0x63, char: 'c', corresponding opcode: GLOBAL, function: load_global
- Read one line string (until '\n' byte): "torch._utils", identifies Python module name
- Read one line string (until '\n' byte): "_rebuild_tensor_v2", identifies Python class/function name
- Call pr.findClass to get corresponding Go object to the module and name: torch.rebuild_tensor_v2 function.
- Push it into the pr.stack

pr.stack: {
    "tok_embeddings.weight",
    torch.rebuild_tensor_v2(...)
}
pr.metastack: {
    {
        PickleDict{}
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight"
}

Read key byte: 0x71, char: 'q', corresponding opcode: BINPUT, function: load_binput
- Read one byte: 0x02 as memo map key
- Take last element of pr.stack and assigns it as 2nd item of pr.memo

pr.stack: {
    "tok_embeddings.weight",
    torch.rebuild_tensor_v2(...)
}
pr.metastack: {
    {
        PickleDict{}
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...)
}

Read key byte: 0x28, char: '(', corresponding opcode: MARK, function: load_mark
- Append the pr.stack array into metastack array (array of arrays),
- Create new pr.stack array.

pr.stack: {}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...)
}

Read key byte: 0x28, char: '(', corresponding opcode: MARK, function: load_mark
- Append the pr.stack array into metastack array (array of arrays),
- Create new pr.stack array.

pr.stack: {}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    },
    {}
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...)
}

Read key byte: 0x58, char: 'X', corresponding opcode: BINUNICODE, function: load_binunicode
- Read 4 bytes: [0x07, 0x00, 0x00, 0x00], convert it to int32 as little-endian: 7 (decimal). Identifies length of unicode string.
- Read 7 bytes, convert it to string identifies the name of upcoming tensor: "storage".
- Push it into the pr.stack

pr.stack: {
    "storage"
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    },
    {}
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...)
}

..... Some steps were taken

Current state:

pr.stack: {
    "storage",
    StorageKind{ml.DT_BF16},
    "0",
    "cpu",
    525336576
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    },
    {}
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu"
}

Read key byte: 0x74, char: 't', corresponding opcode: TUPLE, function: load_tuple
- Call pop_mark function to build tuple from topmost stack items
  - Backup current pr.stack
  - Pop last item from pr.metastack, result is an array
  - Assign the result to pr.stack
  - Return backed up stack array
- Push the returned stack array into the pr.stack as an array

pr.stack: {
    {
        "storage",
        StorageKind{ml.DT_BF16},
        "0",
        "cpu",
        525336576
    }
}
pr.metastack: {
    {
        PickleDict{}
    }
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu"
}

Read key byte: 0x71, char: 'q', corresponding opcode: BINPUT, function: load_binput
- Read one byte: 0x07 as memo map key
- Take last element of pr.stack and assigns it as 7th item of pr.memo

pr.stack: {
    {
        "storage",
        StorageKind{ml.DT_BF16},
        "0",
        "cpu",
        525336576
    }
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        }
}

Read key byte: 0x51, char: 'Q', corresponding opcode: BINPERSID, function: load_binpersid
- Pop last item from pr.stack, result is an array: {"storage", StorageKind{ml.DT_BF16}, "0", "cpu", 525336576}. Identifies "pid" argument for pr.persistentLoad(...) function
- pr.persistentLoad(...) function calls pr.PersistentLoadFn(...) custom function with "pid" array argument
- TorchModelReader.persistentLoad(...) function is called with "pid" array argument
  - This function parses the pid array {"storage", StorageKind{ml.DT_BF16}, "0", "cpu", 525336576}
    - pid[0] = "storage", it must be
    - pid[1] = StorageKind{ml.DT_BF16}, data type kind of defined storage
    - pid[2] = "0", filenameStem, filename is defined as: "consolidated/data/0"
    - pid[3] = "cpu", identifies the tensor device, we don't use this data
    - pid[4] = 525336576, identifies element count of the tensor contained by "consolidated/data/0" file
  - Find "consolidated/data/0" file entry in the ZIP file, get its storage offset, 34304 (starting location of the tensor bytes)
  - Create a TorchStorage object with given data type and storage offset
  - Calculate byte locations (starting location, end location) with given storage offfset and given element count
  - Do memory-mapping between TorchStorage.rawData and bytes in calculated locations. Now we have a memory-mapped to the file []byte array for bytes of the current tensor
  - Return the TorchStorage object
- Push the TorchStorage object into the pr.stack

pr.stack: {
    torch.TorchStorage {
        filename: "consolidated/data/0",
        kind: torch.StorageKind{dataType: ml.DT_BF16},
        storageOffset: 34304
    }
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        }
}

3.5. Continuing File Reading to Gather torch.rebuild_tensor_v2(...) Function Arguments

Now, we have torch.TorchStorage object. The file continues with other arguments of the torch.rebuild_tensor_v2(...) function. We will read some instructions that construct other arguments. We will call this function with these gathered arguments further.

Read key byte: 0x4b, char: 'K', corresponding opcode: BININT1, function: load_binint1
- Push 1-byte unsigned int
- Read one byte: 0x00
- Push it into the pr.stack

pr.stack: {
    torch.TorchStorage {
        filename: "consolidated/data/0",
        kind: torch.StorageKind{dataType: ml.DT_BF16},
        storageOffset: 34304
    }
    0
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        }
}

Read key byte: 0x4d, char: 'M', corresponding opcode: BININT2, function: load_binint2
- Push 2-byte unsigned int
- Read 2 bytes: [0x00, 0x7D], convert it to uint16 as little-endian: 128256 (decimal).
- Push it into the pr.stack

pr.stack: {
    torch.TorchStorage {
        filename: "consolidated/data/0",
        kind: torch.StorageKind{dataType: ml.DT_BF16},
        storageOffset: 34304
    },
    0,
    128256
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        }
}

Read key byte: 0x4d, char: 'M', corresponding opcode: BININT2, function: load_binint2
- Push 2-byte unsigned int
- Read 2 bytes: [0x00, 0x10], convert it to uint16 as little-endian: 4096 (decimal).
- Push it into the pr.stack

pr.stack: {
    torch.TorchStorage {
        filename: "consolidated/data/0",
        kind: torch.StorageKind{dataType: ml.DT_BF16},
        storageOffset: 34304
    },
    0,
    128256,
    4096,
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        }
}

..... Some steps were taken

Current state:

pr.stack: {
    torch.TorchStorage {
        filename: "consolidated/data/0",
        kind: torch.StorageKind{dataType: ml.DT_BF16},
        storageOffset: 34304
    },
    0,
    {128256, 4096},
    {4096, 1},
    false,
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        }
}

Read key byte: 0x63, char: 'c', corresponding opcode: GLOBAL, function: load_global
- Read one line string (until '\n' byte): "collections", identifies Python module name
- Read one line string (until '\n' byte): "OrderedDict", identifies Python class/function name
- Call pr.findClass to get corresponding Go object to the module and name: pickle.NewPickleDict[interface {}]() function.
- Push it into the pr.stack

pr.stack: {
    torch.TorchStorage {
        filename: "consolidated/data/0",
        kind: torch.StorageKind{dataType: ml.DT_BF16},
        storageOffset: 34304
    },
    0,
    {128256, 4096},
    {4096, 1},
    false,
    pickle.NewPickleDict[interface {}]()
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        }
}

..... Some steps were taken

Current state:

pr.stack: {
    torch.TorchStorage {
        filename: "consolidated/data/0",
        kind: torch.StorageKind{dataType: ml.DT_BF16},
        storageOffset: 34304
    },
    0,
    {128256, 4096},
    {4096, 1},
    false,
    pickle.NewPickleDict[interface {}](),
    {}
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        },
    8: {128256, 4096},
    9: {4096, 1},
    10: pickle.NewPickleDict[interface {}]()
}

Read key byte: 0x52, char: 'R', corresponding opcode: REDUCE, function: load_reduce
- Apply callable to argtuple, both on stack
- Pop last item from pr.stack, result is an empty array, identifies rawArgsArr array will be passed to upcoming function
- Take the last item in the pr.stack (this time we don't remove with pop)
- Taken item is our function: pickle.NewPickleDict[interface {}]()
- Convert rawArgsArr items to expected data types of reflected function object (an empty array)
- Call the pickle.NewPickleDict[interface {}]() with passing converted arguments, then replace the last item in the pr.stack with the function's result: an empty pickle.PickleDict[interface {}]{}

pr.stack: {
    torch.TorchStorage {
        filename: "consolidated/data/0",
        kind: torch.StorageKind{dataType: ml.DT_BF16},
        storageOffset: 34304
    },
    0,
    {128256, 4096},
    {4096, 1},
    false,
    PickleDict{}
}
pr.metastack: {
    {
        PickleDict{}
    },
    {
        "tok_embeddings.weight",
        torch.rebuild_tensor_v2(...)
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        },
    8: {128256, 4096},
    9: {4096, 1},
    10: pickle.NewPickleDict[interface {}]()
}

..... Some steps were taken

Current state:

pr.stack: {
    "tok_embeddings.weight",
    torch.rebuild_tensor_v2(...),
    {
        torch.TorchStorage {
            filename: "consolidated/data/0",
            kind: torch.StorageKind{dataType: ml.DT_BF16},
            storageOffset: 34304
        },
        0,
        {128256, 4096},
        {4096, 1},
        false,
        PickleDict{}
    }
}
pr.metastack: {
    {
        PickleDict{}
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        },
    8: {128256, 4096},
    9: {4096, 1},
    10: pickle.NewPickleDict[interface {}]()
}

3.6. Calling torch.rebuild_tensor_v2(...) Function and Obtaining First Tensor

Now, we have all required arguments to call torch.rebuild_tensor_v2(...) function in our pr.stack.

Read key byte: 0x52, char: 'R', corresponding opcode: REDUCE, function: load_reduce
- Apply callable to argtuple, both on stack
- Pop last item from pr.stack, result is an array array, identifies rawArgsArr array will be passed to upcoming function, result array: {torch.TorchStorage {...}, 0, {128256, 4096}, {4096, 1}, false, PickleDict{}}
- Take the last item in the pr.stack (this time we don't remove with pop)
- Taken item is our function: torch.rebuild_tensor_v2(...)
- Convert rawArgsArr items to expected data types of reflected function object
- Call the torch.rebuild_tensor_v2(...) with passing converted arguments, then replace the last item in the pr.stack with the function's result: a ml.Tensor object with Size=[128256,4096], Stride=[4096,1], DataType=ml.DT_BF16, RawData=(memory mapped []byte)

pr.stack: {
    "tok_embeddings.weight"
    ml.Tensor{Size=[128256,4096], ...}
}
pr.metastack: {
    {
        PickleDict{}
    }
}
memo: {
    0: PickleDict{},
    1: "tok_embeddings.weight",
    2: torch.rebuild_tensor_v2(...),
    3: "storage",
    4: StorageKind{ml.DT_BF16},
    5: "0",
    6: "cpu",
    7:  {
            "storage",
            StorageKind{ml.DT_BF16},
            "0",
            "cpu",
            525336576
        },
    8: {128256, 4096},
    9: {4096, 1},
    10: pickle.NewPickleDict[interface {}](),
    11: PickleDict{}
}

..... Now,

The first item of our pr.stack is "tok_embeddings.weight" (the name of the tensor)
The second item of our pr.stack is ml.Tensor{Size=[128256,4096], ...} (the tensor itself)
In the next steps, this flow will be recurred for other tensors.

3.7. Completion of Reading All Tensors

Now, we have all of 291 tensors with names in our pr.stack. A healthy Pickle file ends with a StopSignal instruction.

..... Some steps were taken

Current state:

pr.stack: {
    PickleDict {
        a PickleDict (map) of 291 tensor names and corresponding ml.Tensor objects
    }
}
pr.metastack: {}
memo: {an array of 2334 items}

Read key byte: 0x2e, char: '.', corresponding opcode: STOP, function: load_stop
- Every pickle ends with STOP
- Pop last item from pr.stack, result is a PickleDict (map) of tensor names and corresponding ml.Tensor objects
- Create a StopSignal signal object with Value of the result PickleDict (map) and return it
Finished.

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03-LOADING-TORCH-MODEL-DETAILS.md

03-LOADING-TORCH-MODEL-DETAILS.md

3. LOADING TORCH MODEL (DETAILS)

3.1. Loading PROTO

3.2. Preparation to read a Tensor

3.3. Reading Name of the Tensor

3.4. Obtaining a TorchStorage Object

3.5. Continuing File Reading to Gather torch.rebuild_tensor_v2(...) Function Arguments

3.6. Calling torch.rebuild_tensor_v2(...) Function and Obtaining First Tensor

3.7. Completion of Reading All Tensors

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3. LOADING TORCH MODEL (DETAILS)

3.1. Loading PROTO

3.2. Preparation to read a Tensor

3.3. Reading Name of the Tensor

3.4. Obtaining a TorchStorage Object

3.5. Continuing File Reading to Gather torch.rebuild_tensor_v2(...) Function Arguments

3.6. Calling torch.rebuild_tensor_v2(...) Function and Obtaining First Tensor

3.7. Completion of Reading All Tensors