133 lines
3.8 KiB
Python
133 lines
3.8 KiB
Python
import torch
|
|
from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params
|
|
|
|
# Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
|
|
none_tensor = torch.empty((1, 1), device="meta")
|
|
|
|
|
|
def ext_make_q4(qweight, qzeros, scales, g_idx, device):
|
|
"""Construct Q4Matrix, return handle"""
|
|
return make_q4(
|
|
qweight, qzeros, scales, g_idx if g_idx is not None else none_tensor, device
|
|
)
|
|
|
|
|
|
def ext_q4_matmul(x, q4, q4_width):
|
|
"""Matrix multiplication, returns x @ q4"""
|
|
outshape = x.shape[:-1] + (q4_width,)
|
|
x = x.view(-1, x.shape[-1])
|
|
output = torch.empty((x.shape[0], q4_width), dtype=torch.float16, device=x.device)
|
|
|
|
q4_matmul(x, q4, output)
|
|
|
|
return output.view(outshape)
|
|
|
|
|
|
MAX_DQ = 1
|
|
MAX_INNER = 1
|
|
ACT_ORDER = False
|
|
DEVICE = None
|
|
|
|
TEMP_STATE = None
|
|
TEMP_DQ = None
|
|
|
|
|
|
def set_device(device):
|
|
global DEVICE
|
|
DEVICE = device
|
|
|
|
|
|
def create_exllama_buffers(max_total_tokens: int):
|
|
global MAX_DQ, MAX_INNER, ACT_ORDER, DEVICE, TEMP_STATE, TEMP_DQ
|
|
|
|
assert DEVICE is not None, "call set_device first"
|
|
|
|
if not ACT_ORDER:
|
|
max_total_tokens = 1
|
|
|
|
# This temp_state buffer is required to reorder X in the act-order case.
|
|
temp_state = torch.zeros(
|
|
(max_total_tokens, MAX_INNER), dtype=torch.float16, device=DEVICE
|
|
)
|
|
temp_dq = torch.zeros((1, MAX_DQ), dtype=torch.float16, device=DEVICE)
|
|
|
|
# This temp_dq buffer is required to dequantize weights when using cuBLAS, typically for the prefill.
|
|
prepare_buffers(DEVICE, temp_state, temp_dq)
|
|
|
|
matmul_recons_thd = 8
|
|
matmul_fused_remap = False
|
|
matmul_no_half2 = False
|
|
set_tuning_params(matmul_recons_thd, matmul_fused_remap, matmul_no_half2)
|
|
|
|
TEMP_STATE, TEMP_DQ = temp_state, temp_dq
|
|
|
|
|
|
class Ex4bitLinear(torch.nn.Module):
|
|
"""Linear layer implementation with per-group 4-bit quantization of the weights"""
|
|
|
|
def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
|
|
super().__init__()
|
|
global MAX_DQ, MAX_INNER, ACT_ORDER, DEVICE
|
|
assert bits == 4
|
|
|
|
self.device = qweight.device
|
|
self.qweight = qweight
|
|
self.qzeros = qzeros
|
|
self.scales = scales
|
|
self.g_idx = g_idx.cpu() if g_idx is not None else None
|
|
self.bias = bias if bias is not None else None
|
|
|
|
if self.g_idx is not None and (
|
|
(self.g_idx == 0).all()
|
|
or torch.equal(
|
|
g_idx.cpu(),
|
|
torch.tensor(
|
|
[i // groupsize for i in range(g_idx.shape[0])], dtype=torch.int32
|
|
),
|
|
)
|
|
):
|
|
self.empty_g_idx = True
|
|
self.g_idx = None
|
|
|
|
assert self.device.type == "cuda"
|
|
assert self.device.index is not None
|
|
|
|
self.q4 = ext_make_q4(
|
|
self.qweight, self.qzeros, self.scales, self.g_idx, self.device.index
|
|
)
|
|
|
|
self.height = qweight.shape[0] * 8
|
|
self.width = qweight.shape[1]
|
|
|
|
# Infer groupsize from height of qzeros
|
|
self.groupsize = None
|
|
if self.qzeros.shape[0] > 1:
|
|
self.groupsize = (self.qweight.shape[0] * 8) // (self.qzeros.shape[0])
|
|
|
|
if self.groupsize is not None:
|
|
assert groupsize == self.groupsize
|
|
|
|
# Handle act-order matrix
|
|
if self.g_idx is not None:
|
|
if self.groupsize is None:
|
|
raise ValueError("Found group index but no groupsize. What do?")
|
|
self.act_order = True
|
|
else:
|
|
self.act_order = False
|
|
|
|
DEVICE = self.qweight.device
|
|
|
|
MAX_DQ = max(MAX_DQ, self.qweight.numel() * 8)
|
|
|
|
if self.act_order:
|
|
MAX_INNER = max(MAX_INNER, self.height, self.width)
|
|
|
|
ACT_ORDER = True
|
|
|
|
def forward(self, x):
|
|
out = ext_q4_matmul(x, self.q4, self.width)
|
|
|
|
if self.bias is not None:
|
|
out.add_(self.bias)
|
|
return out
|