mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
gguf-py : simplify support for quant types (#8838)
* gguf-py : use classes for quants * convert_hf : simplify internal quantization type selection * gguf-py : fix flake8 lint * gguf-py : fix BF16 numpy view type * gguf-py : remove LlamaFileTypeMap Too specific to 'llama.cpp', and would be a maintenance burden to keep up to date. * gguf-py : add generic quantize and dequantize functions The quant classes no longer need to be known, only the target or the source type, for 'quantize' and 'dequantize', respectively.
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@ -251,12 +251,7 @@ class Model:
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return [(self.map_tensor_name(name), data_torch)]
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def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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del name, new_name, bid, n_dims # unused
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return False
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def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
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del name, new_name, bid, n_dims # unused
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return False
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@ -285,55 +280,47 @@ class Model:
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for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
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data: np.ndarray # type hint
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n_dims = len(data.shape)
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data_dtype = data.dtype
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data_qtype: gguf.GGMLQuantizationType | None = None
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# when both are True, f32 should win
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extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims)
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extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims)
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data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
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# Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
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# Conditions should closely match those in llama_model_quantize_internal in llama.cpp
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extra_f32 = any(cond for cond in (
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extra_f32,
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n_dims == 1,
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new_name.endswith("_norm.weight"),
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))
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# Some tensor types are always in float32
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extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in (
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gguf.MODEL_TENSOR.FFN_GATE_INP,
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gguf.MODEL_TENSOR.POS_EMBD,
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gguf.MODEL_TENSOR.TOKEN_TYPES,
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))
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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extra_f16 = any(cond for cond in (
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extra_f16,
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(name.endswith(".weight") and n_dims >= 2),
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))
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if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
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if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
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data = gguf.quantize_bf16(data)
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assert data.dtype == np.uint16
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data_qtype = gguf.GGMLQuantizationType.BF16
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elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data):
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data = gguf.quantize_q8_0(data)
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assert data.dtype == np.uint8
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data_qtype = gguf.GGMLQuantizationType.Q8_0
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else: # default to float16 for quantized tensors
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if data_dtype != np.float16:
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data = data.astype(np.float16)
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data_qtype = gguf.GGMLQuantizationType.F16
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if data_qtype is None: # by default, convert to float32
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if data_dtype != np.float32:
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data = data.astype(np.float32)
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if n_dims <= 1 or new_name.endswith("_norm.weight"):
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data_qtype = gguf.GGMLQuantizationType.F32
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# Conditions should closely match those in llama_model_quantize_internal in llama.cpp
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# Some tensor types are always in float32
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if data_qtype is False and (
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any(
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self.match_model_tensor_name(new_name, key, bid)
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for key in (
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gguf.MODEL_TENSOR.FFN_GATE_INP,
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gguf.MODEL_TENSOR.POS_EMBD,
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gguf.MODEL_TENSOR.TOKEN_TYPES,
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)
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)
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or not name.endswith(".weight")
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):
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data_qtype = gguf.GGMLQuantizationType.F32
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# No override (data_qtype is False), or wants to be quantized (data_qtype is True)
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if isinstance(data_qtype, bool):
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if self.ftype == gguf.LlamaFileType.ALL_F32:
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data_qtype = gguf.GGMLQuantizationType.F32
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elif self.ftype == gguf.LlamaFileType.MOSTLY_F16:
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data_qtype = gguf.GGMLQuantizationType.F16
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elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
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data_qtype = gguf.GGMLQuantizationType.BF16
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elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
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data_qtype = gguf.GGMLQuantizationType.Q8_0
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else:
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raise ValueError(f"Unknown file type: {self.ftype.name}")
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try:
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data = gguf.quants.quantize(data, data_qtype)
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except gguf.QuantError as e:
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logger.warning("%s, %s", e, "falling back to F16")
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data_qtype = gguf.GGMLQuantizationType.F16
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data = gguf.quants.quantize(data, data_qtype)
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shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape
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# reverse shape to make it similar to the internal ggml dimension order
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@ -1765,7 +1752,7 @@ class DbrxModel(Model):
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return [(new_name, data_torch)]
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def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
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del name, new_name, bid # unused
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return n_dims > 1
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@ -2786,18 +2773,22 @@ class MambaModel(Model):
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return [(new_name, data_torch)]
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def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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del n_dims # unused
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return bid is not None and new_name in (
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self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [
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def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
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if bid is not None and new_name in (
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self.format_tensor_name(
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n, bid, ".weight" if name.endswith(".weight") else ""
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)
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for n in [
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gguf.MODEL_TENSOR.SSM_CONV1D,
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gguf.MODEL_TENSOR.SSM_X,
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gguf.MODEL_TENSOR.SSM_DT,
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gguf.MODEL_TENSOR.SSM_A,
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gguf.MODEL_TENSOR.SSM_D,
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]
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)
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):
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return gguf.GGMLQuantizationType.F32
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return super().tensor_force_quant(name, new_name, bid, n_dims)
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@Model.register("CohereForCausalLM")
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@ -1146,6 +1146,9 @@ class GGMLQuantizationType(IntEnum):
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F64 = 28
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IQ1_M = 29
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BF16 = 30
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Q4_0_4_4 = 31
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Q4_0_4_8 = 32
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Q4_0_8_8 = 33
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# TODO: add GGMLFileType from ggml_ftype in ggml.h
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@ -1158,7 +1161,7 @@ class LlamaFileType(IntEnum):
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MOSTLY_F16 = 1 # except 1d tensors
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MOSTLY_Q4_0 = 2 # except 1d tensors
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MOSTLY_Q4_1 = 3 # except 1d tensors
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MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16
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# MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16
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# MOSTLY_Q4_2 = 5 # support has been removed
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# MOSTLY_Q4_3 = 6 # support has been removed
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MOSTLY_Q8_0 = 7 # except 1d tensors
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@ -1187,6 +1190,9 @@ class LlamaFileType(IntEnum):
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MOSTLY_IQ4_XS = 30 # except 1d tensors
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MOSTLY_IQ1_M = 31 # except 1d tensors
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MOSTLY_BF16 = 32 # except 1d tensors
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MOSTLY_Q4_0_4_4 = 33 # except 1d tensors
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MOSTLY_Q4_0_4_8 = 34 # except 1d tensors
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MOSTLY_Q4_0_8_8 = 35 # except 1d tensors
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GUESSED = 1024 # not specified in the model file
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@ -1260,6 +1266,9 @@ GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
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GGMLQuantizationType.F64: (1, 8),
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GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32),
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GGMLQuantizationType.BF16: (1, 2),
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GGMLQuantizationType.Q4_0_4_4:(32, 2 + 16),
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GGMLQuantizationType.Q4_0_4_8:(32, 2 + 16),
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GGMLQuantizationType.Q4_0_8_8:(32, 2 + 16),
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}
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@ -191,6 +191,8 @@ class LazyBase(ABC, metaclass=LazyMeta):
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class LazyNumpyTensor(LazyBase):
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_tensor_type = np.ndarray
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shape: tuple[int, ...] # Makes the type checker happy in quants.py
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@classmethod
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def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: tuple[int, ...]) -> np.ndarray[Any, Any]:
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# The initial idea was to use np.nan as the fill value,
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@ -1,5 +1,6 @@
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from __future__ import annotations
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from typing import Callable, Sequence
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Sequence
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from numpy.typing import DTypeLike
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@ -9,32 +10,22 @@ from .lazy import LazyNumpyTensor
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import numpy as np
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def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType):
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def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]:
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block_size, type_size = GGML_QUANT_SIZES[quant_type]
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if shape[-1] % block_size != 0:
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raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})")
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return (*shape[:-1], shape[-1] // block_size * type_size)
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def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType):
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def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]:
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block_size, type_size = GGML_QUANT_SIZES[quant_type]
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if shape[-1] % type_size != 0:
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raise ValueError(f"Quantized tensor bytes per row ({shape[-1]}) is not a multiple of {quant_type.name} type size ({type_size})")
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return (*shape[:-1], shape[-1] // type_size * block_size)
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# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
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def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray:
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n = n.astype(np.float32, copy=False).view(np.uint32)
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# force nan to quiet
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n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n)
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# round to nearest even
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n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16
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return n.astype(np.uint16)
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# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
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def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
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def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
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rows = arr.reshape((-1, arr.shape[-1]))
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osize = 1
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for dim in oshape:
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@ -46,27 +37,6 @@ def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.
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return out.reshape(oshape)
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def __quantize_bf16_array(n: np.ndarray) -> np.ndarray:
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return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.uint16, oshape=n.shape)
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__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.uint16)
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def quantize_bf16(n: np.ndarray):
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if type(n) is LazyNumpyTensor:
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return __quantize_bf16_lazy(n)
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else:
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return __quantize_bf16_array(n)
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__q8_block_size, __q8_type_size = GGML_QUANT_SIZES[GGMLQuantizationType.Q8_0]
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def can_quantize_to_q8_0(n: np.ndarray) -> bool:
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return n.shape[-1] % __q8_block_size == 0
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# round away from zero
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# ref: https://stackoverflow.com/a/59143326/22827863
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def np_roundf(n: np.ndarray) -> np.ndarray:
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@ -76,46 +46,168 @@ def np_roundf(n: np.ndarray) -> np.ndarray:
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return np.sign(n) * b
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def __quantize_q8_0_shape_change(s: tuple[int, ...]) -> tuple[int, ...]:
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return (*s[:-1], s[-1] // __q8_block_size * __q8_type_size)
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class QuantError(Exception): ...
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# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
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def __quantize_q8_0_rows(n: np.ndarray) -> np.ndarray:
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shape = n.shape
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assert shape[-1] % __q8_block_size == 0
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n_blocks = n.size // __q8_block_size
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blocks = n.reshape((n_blocks, __q8_block_size)).astype(np.float32, copy=False)
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d = abs(blocks).max(axis=1, keepdims=True) / 127
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with np.errstate(divide="ignore"):
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id = np.where(d == 0, 0, 1 / d)
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qs = np_roundf(blocks * id)
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# (n_blocks, 2)
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d = d.astype(np.float16).view(np.uint8)
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# (n_blocks, block_size)
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qs = qs.astype(np.int8).view(np.uint8)
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assert d.shape[1] + qs.shape[1] == __q8_type_size
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return np.concatenate([d, qs], axis=1).reshape(__quantize_q8_0_shape_change(shape))
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_type_traits: dict[GGMLQuantizationType, type[__Quant]] = {}
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def __quantize_q8_0_array(n: np.ndarray) -> np.ndarray:
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return __apply_over_grouped_rows(__quantize_q8_0_rows, arr=n, otype=np.uint8, oshape=__quantize_q8_0_shape_change(n.shape))
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__quantize_q8_0_lazy = LazyNumpyTensor._wrap_fn(
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__quantize_q8_0_array,
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meta_noop=(np.uint8, __quantize_q8_0_shape_change),
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)
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def quantize_q8_0(data: np.ndarray):
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if type(data) is LazyNumpyTensor:
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return __quantize_q8_0_lazy(data)
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def quantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
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if qtype == GGMLQuantizationType.F32:
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return data.astype(np.float32, copy=False)
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elif qtype == GGMLQuantizationType.F16:
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return data.astype(np.float16, copy=False)
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elif (q := _type_traits.get(qtype)) is not None:
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return q.quantize(data)
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else:
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return __quantize_q8_0_array(data)
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raise NotImplementedError(f"Quantization for {qtype.name} is not yet implemented")
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def dequantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
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if qtype == GGMLQuantizationType.F32 or qtype == GGMLQuantizationType.F16:
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return data.astype(np.float32, copy=False)
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elif (q := _type_traits.get(qtype)) is not None:
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return q.dequantize(data)
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else:
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raise NotImplementedError(f"Dequantization for {qtype.name} is not yet implemented")
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class __Quant(ABC):
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qtype: GGMLQuantizationType
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block_size: int
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type_size: int
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def __init__(self):
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return TypeError("Quant conversion classes can't have instances")
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def __init_subclass__(cls, qtype: GGMLQuantizationType) -> None:
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cls.qtype = qtype
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cls.block_size, cls.type_size = GGML_QUANT_SIZES[qtype]
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cls.__quantize_lazy = LazyNumpyTensor._wrap_fn(
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cls.__quantize_array,
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meta_noop=(np.uint8, cls.__shape_to_bytes)
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)
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cls.__dequantize_lazy = LazyNumpyTensor._wrap_fn(
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cls.__dequantize_array,
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meta_noop=(np.float32, cls.__shape_from_bytes)
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)
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assert qtype not in _type_traits
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_type_traits[qtype] = cls
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@classmethod
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@abstractmethod
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def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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raise NotImplementedError
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@classmethod
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@abstractmethod
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def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
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raise NotImplementedError
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@classmethod
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def quantize_rows(cls, rows: np.ndarray) -> np.ndarray:
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rows = rows.astype(np.float32, copy=False)
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shape = rows.shape
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n_blocks = rows.size // cls.block_size
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blocks = rows.reshape((n_blocks, cls.block_size))
|
||||
blocks = cls.quantize_blocks(blocks)
|
||||
assert blocks.dtype == np.uint8
|
||||
assert blocks.shape[-1] == cls.type_size
|
||||
return blocks.reshape(cls.__shape_to_bytes(shape))
|
||||
|
||||
@classmethod
|
||||
def dequantize_rows(cls, rows: np.ndarray) -> np.ndarray:
|
||||
rows = rows.view(np.uint8)
|
||||
shape = rows.shape
|
||||
n_blocks = rows.size // cls.type_size
|
||||
blocks = rows.reshape((n_blocks, cls.type_size))
|
||||
blocks = cls.dequantize_blocks(blocks)
|
||||
assert blocks.dtype == np.float32
|
||||
assert blocks.shape[-1] == cls.block_size
|
||||
return blocks.reshape(cls.__shape_from_bytes(shape))
|
||||
|
||||
@classmethod
|
||||
def __shape_to_bytes(cls, shape: Sequence[int]):
|
||||
return quant_shape_to_byte_shape(shape, cls.qtype)
|
||||
|
||||
@classmethod
|
||||
def __shape_from_bytes(cls, shape: Sequence[int]):
|
||||
return quant_shape_from_byte_shape(shape, cls.qtype)
|
||||
|
||||
@classmethod
|
||||
def __quantize_array(cls, array: np.ndarray) -> np.ndarray:
|
||||
return _apply_over_grouped_rows(cls.quantize_rows, arr=array, otype=np.uint8, oshape=cls.__shape_to_bytes(array.shape))
|
||||
|
||||
@classmethod
|
||||
def __dequantize_array(cls, array: np.ndarray) -> np.ndarray:
|
||||
return _apply_over_grouped_rows(cls.dequantize_rows, arr=array, otype=np.float32, oshape=cls.__shape_from_bytes(array.shape))
|
||||
|
||||
@classmethod
|
||||
def __quantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any:
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def __dequantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any:
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def can_quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> bool:
|
||||
return tensor.shape[-1] % cls.block_size == 0
|
||||
|
||||
@classmethod
|
||||
def quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray:
|
||||
if not cls.can_quantize(tensor):
|
||||
raise QuantError(f"Can't quantize tensor with shape {tensor.shape} to {cls.qtype.name}")
|
||||
if isinstance(tensor, LazyNumpyTensor):
|
||||
return cls.__quantize_lazy(tensor)
|
||||
else:
|
||||
return cls.__quantize_array(tensor)
|
||||
|
||||
@classmethod
|
||||
def dequantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray:
|
||||
if isinstance(tensor, LazyNumpyTensor):
|
||||
return cls.__dequantize_lazy(tensor)
|
||||
else:
|
||||
return cls.__dequantize_array(tensor)
|
||||
|
||||
|
||||
class BF16(__Quant, qtype=GGMLQuantizationType.BF16):
|
||||
@classmethod
|
||||
# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
|
||||
def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
n = blocks.view(np.uint32)
|
||||
# force nan to quiet
|
||||
n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n)
|
||||
# round to nearest even
|
||||
n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16
|
||||
return n.astype(np.uint16).view(np.uint8)
|
||||
|
||||
@classmethod
|
||||
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
return (blocks.view(np.int16).astype(np.int32) << 16).view(np.float32)
|
||||
|
||||
|
||||
class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0):
|
||||
@classmethod
|
||||
# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
|
||||
def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
|
||||
d = abs(blocks).max(axis=1, keepdims=True) / 127
|
||||
with np.errstate(divide="ignore"):
|
||||
id = np.where(d == 0, 0, 1 / d)
|
||||
qs = np_roundf(blocks * id)
|
||||
|
||||
# (n_blocks, 2)
|
||||
d = d.astype(np.float16).view(np.uint8)
|
||||
# (n_blocks, block_size)
|
||||
qs = qs.astype(np.int8).view(np.uint8)
|
||||
|
||||
return np.concatenate([d, qs], axis=1)
|
||||
|
||||
@classmethod
|
||||
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
|
||||
d, x = np.split(blocks, [2], axis=1)
|
||||
d = d.view(np.float16).astype(np.float32)
|
||||
x = x.view(np.int8).astype(np.float32)
|
||||
|
||||
return (x * d)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user