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clean up

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jiawenliu64 2025-04-09 14:55:30 -07:00
parent c8a0b110c0
commit 5ecedc12e7
2 changed files with 2 additions and 18 deletions
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2
llama_stack/models/llama/llama4/quantization/loader.py
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@ -10,7 +10,7 @@ from typing import Callable, Optional
import torch
from fairscale.nn.model_parallel.initialize import get_model_parallel_rank
from torch import nn, Tensor
from torch import Tensor, nn
from torch.nn import functional as F
from ...datatypes import QuantizationMode

18
llama_stack/models/llama/quantize_impls.py
View file

@ -65,7 +65,7 @@ class Int4Weights(
Int4ScaledWeights,
collections.namedtuple(
"Int4Weights",
["weight", "scale", "zero_point", "shape", "activation_scale_ub"],
["weight", "scale", "zero_point", "shape"],
),
):
pass
@ -184,20 +184,13 @@ def quantize_fp8(
@torch.inference_mode()
def quantize_int4(
w: Tensor,
fp8_activation_scale_ub: float,
output_device: Optional[torch.device] = None,
) -> Int4Weights:
"""Quantize [n, k/2] weight tensor.
Args:
w (Tensor): [n, k/2] input high precision tensor to quantize.
fp8_activation_scale_ub (float): Upper bound for activation max.
"""
activation_scale_ub = torch.tensor(
[fp8_activation_scale_ub],
dtype=torch.float,
device=output_device,
)
if w.ndim >= 3:
wq, scale, zero_point = zip(*[int4_row_quantize(i) for i in w], strict=False)
wq = torch.stack([pack_int4(i) for i in wq], dim=0)
@ -212,7 +205,6 @@ def quantize_int4(
scale=scale.to(output_device),
zero_point=zero_point.to(output_device),
shape=wq.shape,
activation_scale_ub=activation_scale_ub,
)
@ -247,26 +239,18 @@ def load_int4(
w: Tensor,
scale: Tensor,
zero_point: Tensor,
fp8_activation_scale_ub: float,
output_device: Optional[torch.device] = None,
) -> Int4Weights:
"""Load INT4 [n, k/2] weight tensor.
Args:
w (Tensor): [n, k/2] input INT4.
fp8_activation_scale_ub (float): Upper bound for activation max.
"""
activation_scale_ub = torch.tensor(
[fp8_activation_scale_ub],
dtype=torch.float,
device=output_device,
)
return Int4Weights(
weight=w.to(torch.int8).to(device=output_device),
scale=scale.to(device=output_device),
zero_point=zero_point.to(device=output_device),
shape=w.shape,
activation_scale_ub=activation_scale_ub,
)