235 lines
No EOL
14 KiB
Python
235 lines
No EOL
14 KiB
Python
import unittest
|
|
from parameterized import parameterized
|
|
import torch
|
|
from auto_gptq.utils.import_utils import dynamically_import_QuantLinear
|
|
|
|
from auto_gptq.nn_modules.qlinear.qlinear_exllama import QuantLinear
|
|
|
|
from exllama_kernels import prepare_buffers, set_tuning_params
|
|
from auto_gptq import AutoGPTQForCausalLM
|
|
from auto_gptq.modeling._utils import autogptq_post_init
|
|
|
|
from transformers import AutoTokenizer
|
|
|
|
def get_diff(a, ref):
|
|
eps = 1e-6
|
|
return f"Maxdiff: {(a - ref).abs().max()}, Mean relative diff: {((a - ref).abs() / (ref.abs() + eps)).mean()}"
|
|
|
|
class TestsQ4Exllama(unittest.TestCase):
|
|
|
|
# reference generated with cuda_old
|
|
REFERENCE = torch.Tensor([5.8398, 6.8555, 7.2734, 6.4219, 6.2070, 5.8203, 6.5664, 6.4219, 6.2148,
|
|
5.3281, 5.7578, 7.5312, 8.1016, 6.1133, 7.2031, 6.6484, 6.5156, 6.0117,
|
|
6.0312, 6.1914, 6.2109, 6.8125, 5.8125, 7.1172, 7.3125, 6.7305, 5.9961,
|
|
6.5117, 6.1914, 5.9648, 7.1680, 6.4766, 7.2070, 6.5469, 6.7734, 6.4219,
|
|
6.8086, 7.0469, 5.9297, 6.4727, 6.2539, 5.9570, 7.2383, 5.8945, 6.0820,
|
|
5.7969, 7.1094, 6.2188, 6.7500, 7.3555, 6.2930, 6.7734, 5.9219, 7.4805,
|
|
6.8750, 6.4102, 6.5898, 6.5469, 7.6016, 6.7461, 5.9492, 7.2227, 5.8164,
|
|
5.4570, 6.2930, 7.3984, 6.0938, 7.3984, 5.9609, 6.3516, 6.5664, 5.7969,
|
|
7.1250, 6.0781, 6.7930, 5.9492, 6.1641, 6.5898, 6.0586, 6.3359, 6.7930,
|
|
7.0469, 6.0664, 6.3320, 5.4414, 6.7617, 5.1641, 7.2891, 6.8516, 6.5312,
|
|
5.6914, 7.3711, 6.8203, 5.9492, 7.0781, 6.3164, 7.1992, 7.1133, 7.4219,
|
|
7.5586, 7.1836, 6.9102, 6.4844, 6.9805, 6.1953, 6.5156, 5.4844, 6.6602,
|
|
6.6719, 7.9844, 6.4727, 6.6367, 6.2227, 6.4531, 5.0625, 6.4609, 6.7031,
|
|
6.6445, 6.5234, 6.8633, 6.6055, 5.6055, 6.4453, 7.2617, 6.3945, 6.6367,
|
|
6.1055, 7.0664, 6.0820, 6.6875, 6.1445, 6.8672, 6.2070, 6.8828, 6.1484,
|
|
6.7070, 6.8516, 6.2734, 7.1055, 7.0586, 6.9648, 5.9727, 6.1016, 6.8750,
|
|
7.0078, 7.1523, 5.7383, 5.9531, 6.5508, 7.5352, 6.1602, 6.2578, 6.3906,
|
|
5.7383, 6.7031, 5.7344, 6.3516, 5.2852, 7.5312, 6.4531, 6.6406, 6.2266,
|
|
6.1094, 5.9102, 5.7617, 6.3789, 7.0508, 6.3750, 6.3320, 6.8555, 6.7266,
|
|
7.0352, 7.7695, 6.3984, 6.5039, 6.8320, 6.1602, 6.0312, 6.3828, 6.9023,
|
|
7.4336, 7.3711, 6.1016, 7.0703, 6.3281, 6.8281, 6.4922, 5.9453, 5.1016,
|
|
6.7188, 6.1406, 6.6289, 7.2695, 6.2070, 6.7070, 7.2930, 7.1836, 6.3828,
|
|
6.1992, 6.7070, 7.8008, 7.7773, 5.6602, 7.0273, 6.6172, 6.0898, 5.3516,
|
|
7.3359, 5.9727, 6.0078, 7.0586, 6.3086, 6.8555, 7.2617, 7.3477, 6.3828,
|
|
7.1133, 6.6328, 7.3516, 6.9141, 7.2031, 6.9805, 6.1719, 6.7812, 8.3047,
|
|
6.5898, 6.3633, 6.2539, 7.2773, 6.5938, 6.4141, 6.8203, 6.8906, 7.8828,
|
|
5.9609, 6.4180, 7.3984, 5.7539, 7.1758, 6.6641, 6.9062, 6.2578, 7.5508,
|
|
6.1719, 6.5742, 5.9375, 6.7891, 6.2109, 6.5039, 6.8750, 6.2031, 6.8828,
|
|
7.1094, 5.9570, 7.2969, 6.6797, 6.8828, 5.5430, 6.9648, 5.8398, 6.5430,
|
|
6.3945, 6.5664, 5.8086, 6.6172, 7.0586, 6.8867, 6.0820, 5.8125, 6.7070,
|
|
7.5742, 6.2578, 6.1328, 6.5391, 5.4531, 6.8242, 6.6953, 6.8008, 6.3398,
|
|
6.4805, 7.2266, 6.3281, 6.6875, 6.4688, 5.9414, 7.4297, 5.8711, 6.0625,
|
|
5.8750, 6.5664, 5.8867, 6.3477, 6.1133, 6.9453, 5.0547, 6.7812, 6.4922,
|
|
7.2422, 5.4688, 6.2109, 7.2148, 6.1758, 5.9297, 7.1953, 5.5195, 6.3203,
|
|
5.9961, 7.9297, 6.2695, 6.4414, 6.7266, 7.1875, 7.3203, 5.4062, 6.0625,
|
|
7.0898, 5.3828, 5.6133, 6.0742, 6.6836, 5.7109, 7.2852, 7.7539, 7.5820,
|
|
6.4258, 5.9336, 6.3750, 6.3555, 7.5469, 6.2539, 6.5898, 6.4102, 7.0469,
|
|
5.7344, 7.2031, 6.7969, 5.6836, 7.6523, 6.9297, 7.8672, 6.4766, 6.3008,
|
|
7.0977, 6.5430, 7.0938, 5.8398, 6.9883, 6.5312, 6.3203, 6.3594, 5.4062,
|
|
6.9688, 5.7930, 6.3164, 6.5547, 7.1992, 5.8750, 6.3008, 6.7930, 6.0391,
|
|
7.4766, 6.6094, 6.5625, 5.9805, 6.2422, 7.2109, 6.6875, 5.3047, 7.6211,
|
|
5.9453, 6.5625, 6.1641, 6.1250, 6.5977, 7.7422, 7.0742, 5.6875, 6.2656,
|
|
6.6250, 6.8945, 5.7070, 6.3203, 5.7500, 6.2695, 6.2773, 6.8516, 6.4883,
|
|
7.0000, 6.7578, 6.1875, 5.9844, 5.5703, 6.7188, 5.5273, 5.3438, 7.2500,
|
|
6.7852, 6.5195, 6.8125, 6.0664, 6.7852, 7.0000, 7.0781, 6.8477, 7.2930,
|
|
6.3438, 7.1523, 6.3281, 6.8047, 7.3203, 5.3359, 6.1484, 6.5586, 7.3828,
|
|
6.2344, 7.1523, 6.4102, 5.5898, 7.0195, 7.1172, 5.8008, 6.5742, 6.2891,
|
|
8.0312, 6.9023, 6.5898, 7.1953, 6.7266, 6.0078, 5.5430, 6.4766, 6.4258,
|
|
5.9648, 8.0859, 5.0547, 7.2188, 7.4375, 6.5156, 5.9922, 6.3281, 6.2852,
|
|
6.7734, 6.2461, 6.9805, 5.4648, 5.8867, 6.8242, 6.3008, 6.3281, 7.3047,
|
|
7.1836, 6.5195, 6.6328, 6.7188, 5.4336, 6.5078, 5.3477, 5.5508, 7.3125,
|
|
5.8750, 6.5195, 6.2383, 6.3594, 6.0898, 6.4141, 5.9844, 6.6250, 7.7109,
|
|
6.0391, 7.2344, 5.9453, 5.9453, 7.0586, 5.6641, 7.2773, 6.5195, 7.2227,
|
|
6.3359, 5.3203, 6.4375, 7.2383, 6.4023, 6.2148, 7.3750, 5.8164, 6.2109,
|
|
6.5430, 5.8164, 6.1680, 6.7656, 6.0820, 6.1094, 6.5312, 6.8906, 6.8320,
|
|
6.1289, 6.3125, 7.6797, 6.3008, 6.0000, 7.3320, 6.7852, 6.9297, 6.6328,
|
|
6.2266, 5.1602, 6.2031, 7.0547, 5.9492, 6.0703, 6.0977, 6.8086, 6.0742,
|
|
6.0195, 7.0625, 6.5781, 5.7461, 6.1562, 7.0430, 6.7148, 6.5312, 6.5820,
|
|
6.4570, 7.5508, 5.6289, 6.0547, 6.5000, 7.3125, 5.8477, 5.9297, 6.2578,
|
|
6.0078, 5.9922, 7.3398, 7.4922, 7.8906, 7.5547, 5.4648, 6.5156, 6.3242,
|
|
6.1094, 6.9219, 6.7227, 6.6836, 7.4023, 5.9648, 7.2383, 6.7695, 6.6797,
|
|
7.0547, 6.3047, 6.4688, 6.9961, 6.0391, 5.9727, 6.8398, 6.7422, 5.7656,
|
|
5.4766, 6.7852, 7.0820, 5.3516, 7.6523, 5.1562, 6.6445, 6.1211, 6.2695,
|
|
6.0703, 6.3594, 6.4062, 6.3398, 5.7578, 6.5391, 6.2500, 6.5742, 6.5000,
|
|
7.5625, 7.0117, 6.5547, 7.1250, 6.4453, 6.6094, 6.1875, 6.4219, 6.6172,
|
|
6.4336, 6.5703, 6.1758, 6.4219, 6.6016, 6.7383, 6.7070, 6.1328, 5.5586,
|
|
6.6367, 6.3789, 6.2578, 5.5039, 6.6172, 6.4648, 5.8086, 7.2031, 5.8125,
|
|
6.3711, 7.6758, 7.1289, 5.8086, 6.3008, 6.2109, 6.1602, 6.1797, 7.2305,
|
|
6.7266, 6.2422, 5.6719, 6.7070, 6.9414, 6.8594, 7.4023, 7.2109, 6.0156,
|
|
6.6680, 6.6172, 7.1250, 6.6523, 6.9531, 6.7617, 6.4961, 6.9414, 5.7188,
|
|
7.6367, 6.5469, 6.2305, 6.4414, 7.4648, 5.9102, 6.2461, 6.1367, 6.8203,
|
|
6.5703, 6.8867, 7.0000, 6.7539, 6.1719, 6.5469, 6.2422, 5.4297, 5.7305,
|
|
5.1641, 6.1875, 7.0312, 6.6484, 6.0234, 7.4102, 6.8711, 6.3086, 6.3711,
|
|
6.7344, 6.6992, 5.9766, 7.3906, 7.1875, 6.4883, 6.3984, 7.3438, 6.9688,
|
|
6.9062, 6.4375, 6.7891, 7.0117, 6.4883, 5.7500, 7.0898, 7.0742, 6.7070,
|
|
5.8750, 6.0469, 6.6445, 5.2773, 6.8984, 6.1641, 7.0508, 7.4609, 5.0273,
|
|
6.7734, 6.4531, 5.7656, 6.5312, 7.4648, 6.1250, 6.5625, 7.1367, 6.0625,
|
|
6.1211, 6.9766, 6.6758, 6.3164, 6.8828, 6.8203, 6.7500, 6.5352, 7.3008,
|
|
6.7852, 6.1914, 5.0508, 6.7188, 7.1172, 6.8008, 6.8086, 5.4883, 6.9180,
|
|
6.5742, 6.1719, 7.0469, 7.1523, 5.9492, 5.8594, 6.8320, 6.1719, 6.2031,
|
|
6.8398, 7.3008, 6.6289, 6.4922, 6.0000, 5.4766, 6.3320, 6.5117, 6.2812,
|
|
7.5742, 6.3516, 7.0039, 6.4570, 7.1523, 7.6289, 6.2578, 7.1875, 6.4844,
|
|
5.7930, 6.7070, 7.5508, 7.1797, 6.0430, 6.8711, 6.5742, 7.5781, 6.4766,
|
|
6.5391, 6.9453, 6.1992, 6.6367, 6.2812, 6.0234, 6.6953, 7.0312, 6.2031,
|
|
6.5625, 6.6719, 6.1719, 6.5586, 5.7031, 7.4609, 6.6211, 7.7227, 6.9141,
|
|
6.0469, 6.2500, 5.3828, 6.0078, 5.8164, 5.8867, 6.1523, 6.6523, 6.6953,
|
|
7.3125, 6.4844, 5.9570, 5.9531, 6.2109, 5.5039, 6.5117, 6.8203, 6.6133,
|
|
6.4766, 5.9297, 7.1445, 7.1914, 6.0117, 6.8281, 6.7422, 6.1328, 6.9805,
|
|
6.5625, 6.9180, 7.1133, 7.3359, 5.7617, 5.8711, 6.4961, 6.5859, 6.2422,
|
|
6.5273, 6.7461, 6.6992, 6.7695, 6.6289, 5.9453, 5.9805, 7.1172, 6.6719,
|
|
6.0039, 7.6875, 6.7812, 7.8359, 6.9531, 7.4336, 7.6602, 6.8164, 7.3945,
|
|
7.1602, 6.8789, 5.0078, 6.0547, 6.8086, 6.7070, 6.4688, 6.4492, 6.6172,
|
|
5.5625, 6.6914, 6.4297, 5.7461, 5.3359, 6.8750, 6.4609, 7.4062, 5.2070,
|
|
6.0820, 6.7383, 6.5703, 6.1797, 6.7070, 6.5977, 5.9961, 6.6328, 6.9375,
|
|
6.3906, 6.6484, 4.9609, 6.6445, 6.5898, 7.1875, 7.5195, 6.7969, 6.1367,
|
|
6.8906, 7.4297, 6.3633, 6.0508, 6.5000, 6.4648, 6.7539, 6.7109, 5.8086,
|
|
6.6016, 7.1133, 4.8672, 6.6367, 6.1641, 5.1758, 6.9453, 6.3242, 7.0664,
|
|
6.4805, 6.3516, 6.7383, 8.4688, 6.7305, 5.9844, 6.5938, 7.2969, 6.5977,
|
|
7.5898, 6.2969, 6.8672, 6.6680, 7.1289, 6.6875, 5.4258, 8.1875, 8.0391,
|
|
7.7969, 6.6445, 7.0703, 7.3359, 6.9805, 6.6328, 6.5352, 6.2422, 5.5820,
|
|
6.8633, 6.8047, 6.5703, 6.0117, 6.7539, 7.1719, 6.8438, 7.3633, 6.6016,
|
|
7.2070, 6.4727, 5.8008, 7.4062, 7.4805, 6.6445, 5.9023, 6.3984, 6.9961,
|
|
6.6680, 6.8242, 6.7148, 6.6172, 6.9727, 6.8320, 5.9766, 6.6133, 5.5977,
|
|
6.7773, 7.3906, 6.9219, 7.0781, 6.6914, 5.7539, 6.7969, 6.8008, 5.8047,
|
|
7.1055, 6.4961, 6.0352, 5.6211, 7.4414, 7.0703, 6.1172, 6.7461, 6.4492,
|
|
7.7148, 6.4258, 6.0039, 6.5156, 7.2188, 7.4531, 7.4844, 7.5938, 7.4023,
|
|
6.7617, 6.0078, 6.3320, 5.8906, 7.5977, 5.6523, 6.7734, 6.3008, 5.2227,
|
|
7.1719, 7.1289, 6.6602, 5.4609, 7.0312, 6.0820, 6.1719, 6.0000, 6.5547,
|
|
6.6328, 7.0547, 7.0859, 6.2656, 5.5234, 6.0273, 6.7891, 7.1875, 6.9531,
|
|
6.8203, 6.3516, 6.1172, 6.4648, 6.9180, 7.3906, 6.2812, 5.7109, 6.1484,
|
|
6.9102, 6.8711, 7.0156, 6.1445, 5.8867, 6.3828, 5.9961, 6.6914, 6.7891,
|
|
7.0820, 6.6719, 6.9297, 6.3750, 6.7578, 6.4883, 6.2227, 6.2305, 6.0508,
|
|
6.6484, 5.7578, 7.2070, 7.2383, 6.9375, 7.2578, 6.5312, 6.0312, 6.7930,
|
|
6.2578, 7.0625, 7.2148, 6.4961, 7.0703, 6.4727, 7.3906]).to(torch.float16)
|
|
|
|
def test_exllama(self):
|
|
|
|
group_size = 128
|
|
|
|
m = 1
|
|
k = 1024
|
|
n = 1024
|
|
device = torch.device("cuda:0")
|
|
|
|
linear_class = dynamically_import_QuantLinear(use_triton=False, desc_act=False, group_size=group_size, bits=4)
|
|
|
|
linear = linear_class(
|
|
bits=4,
|
|
group_size=group_size,
|
|
infeatures=k,
|
|
outfeatures=n,
|
|
bias=False,
|
|
)
|
|
self.assertTrue(isinstance(linear, QuantLinear))
|
|
|
|
torch.manual_seed(42)
|
|
|
|
linear.qweight = torch.randint(-100, 100, size=linear.qweight.shape, dtype=torch.int32)
|
|
linear.scales = linear.scales + 0.002
|
|
|
|
linear = linear.eval()
|
|
linear = linear.to(device)
|
|
|
|
linear = autogptq_post_init(linear, use_act_order=False)
|
|
|
|
max_inner_outer_dim = max(k, n)
|
|
max_dq_buffer_size = linear.infeatures * linear.outfeatures
|
|
max_input_len = 2048
|
|
buffers = {
|
|
"temp_state": torch.zeros((max_input_len, max_inner_outer_dim), dtype=torch.float16, device=device),
|
|
"temp_dq": torch.zeros((1, max_dq_buffer_size), dtype=torch.float16, device=device)
|
|
}
|
|
|
|
prepare_buffers(device, buffers["temp_state"], buffers["temp_dq"])
|
|
|
|
# Using the default from exllama repo here.
|
|
matmul_recons_thd = 8
|
|
matmul_fused_remap = False
|
|
matmul_no_half2 = False
|
|
set_tuning_params(matmul_recons_thd, matmul_fused_remap, matmul_no_half2)
|
|
|
|
inp = torch.rand(1, m, k, dtype=torch.float16).to(device)
|
|
|
|
with torch.no_grad():
|
|
res = linear(inp)[0][0]
|
|
|
|
reference = self.REFERENCE.to(device)
|
|
|
|
self.assertTrue(torch.allclose(res, reference, rtol=3e-5, atol=2e-2), get_diff(res, reference))
|
|
|
|
def test_generation_no_act_order(self):
|
|
prompt = "I am in Paris and"
|
|
device = torch.device("cuda:0")
|
|
|
|
# Reference generated with the cuda-old kernel
|
|
reference_output = "<s> I am in Paris and I am going to the Louvre Museum. What time does it open and what is the best way to get there?\nThe Louvre Museum in Paris is open from 9:00 AM to 6:00 PM every day except for Tuesdays. The best way to get"
|
|
|
|
model_id = "TheBloke/WizardLM-7B-uncensored-GPTQ"
|
|
model_basename = "WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order"
|
|
model_q = AutoGPTQForCausalLM.from_quantized(model_id, device="cuda:0", use_triton=False, use_safetensors=True, inject_fused_attention=True, inject_fused_mlp=True, model_basename=model_basename)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
inp = tokenizer(prompt, return_tensors="pt").to(device)
|
|
|
|
res = model_q.generate(**inp, num_beams=1, min_new_tokens=60, max_new_tokens=60)
|
|
|
|
predicted_text = tokenizer.decode(res[0])
|
|
|
|
self.assertEqual(predicted_text, reference_output)
|
|
|
|
def test_generation_with_act_order(self):
|
|
prompt = "I am in Paris and"
|
|
device = torch.device("cuda:0")
|
|
|
|
# Reference generated with the cuda-old kernel
|
|
reference_output = "<s> I am in Paris and it is a beautiful day. I am sitting in a café, drinking coffee and writing this book. I am surrounded by the sights and sounds of the city, and I am filled with a sense of contentment and gratitude.\n\nI am grateful for the opportunity to live and"
|
|
|
|
model_id = "TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g"
|
|
revision = "actorder"
|
|
model_basename = "vicuna-13B-1.1-GPTQ-4bit-128g.latest"
|
|
|
|
model_q = AutoGPTQForCausalLM.from_quantized(model_id, revision=revision, device="cuda:0", use_triton=False, use_safetensors=True, inject_fused_attention=False, inject_fused_mlp=True, model_basename=model_basename, disable_exllama=True)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
inp = tokenizer(prompt, return_tensors="pt").to(device)
|
|
|
|
res = model_q.generate(**inp, num_beams=1, min_new_tokens=60, max_new_tokens=60)
|
|
|
|
predicted_text = tokenizer.decode(res[0])
|
|
|
|
self.assertEqual(predicted_text, reference_output)
|
|
|
|
def test_multigpu(self):
|
|
# TODO
|
|
pass |