537 lines
21 KiB
Python
537 lines
21 KiB
Python
from dataclasses import dataclass, field
|
|
import os
|
|
from pathlib import Path
|
|
from typing import List, Dict, Optional, Set, Tuple, Union
|
|
from safetensors import safe_open, SafetensorError
|
|
import torch
|
|
from loguru import logger
|
|
from huggingface_hub import hf_hub_download
|
|
import json
|
|
from text_generation_server.layers.exl2 import Exl2Weight
|
|
from text_generation_server.layers.gptq import GPTQWeight
|
|
from text_generation_server.utils.log import log_once
|
|
|
|
|
|
class Weights:
|
|
def __init__(
|
|
self,
|
|
filenames: List[Path],
|
|
device,
|
|
dtype,
|
|
process_group,
|
|
aliases: Optional[Dict[str, List[str]]] = None,
|
|
prefix: Optional[str] = None,
|
|
):
|
|
routing = {}
|
|
for filename in filenames:
|
|
with safe_open(filename, framework="pytorch") as f:
|
|
for k in f.keys():
|
|
if k in routing:
|
|
raise RuntimeError(
|
|
f"Key {k} was found in multiple files: {filename} and {routing[k]}"
|
|
)
|
|
routing[k] = filename
|
|
if aliases is None:
|
|
aliases = {}
|
|
self.aliases = aliases
|
|
self.routing = routing
|
|
self.device = device
|
|
self.dtype = dtype
|
|
self.process_group = process_group
|
|
self.prefix = prefix
|
|
self._handles = {}
|
|
|
|
def _get_handle(self, filename):
|
|
if filename not in self._handles:
|
|
f = safe_open(filename, framework="pytorch")
|
|
self._handles[filename] = f
|
|
|
|
return self._handles[filename]
|
|
|
|
def get_filename(self, tensor_name: str) -> (str, str):
|
|
names = [tensor_name]
|
|
if self.prefix is not None:
|
|
prefixed = f"{self.prefix}.{tensor_name}"
|
|
names.append(prefixed)
|
|
for name in names:
|
|
filename = self.routing.get(name, None)
|
|
if filename is not None:
|
|
return str(filename), name
|
|
|
|
aliases = self.aliases.get(name, [])
|
|
for alias in aliases:
|
|
filename = self.routing.get(alias, None)
|
|
if filename is not None:
|
|
return str(filename), alias
|
|
raise RuntimeError(f"weight {tensor_name} does not exist")
|
|
|
|
def _get_slice(self, tensor_name: str):
|
|
filename, tensor_name = self.get_filename(tensor_name)
|
|
f = self._get_handle(filename)
|
|
slice_ = f.get_slice(tensor_name)
|
|
return slice_
|
|
|
|
def get_shape(self, tensor_name: str):
|
|
return self._get_slice(tensor_name).get_shape()
|
|
|
|
def get_tensor(self, tensor_name: str, to_device=True):
|
|
filename, tensor_name = self.get_filename(tensor_name)
|
|
f = self._get_handle(filename)
|
|
tensor = f.get_tensor(tensor_name)
|
|
# Special case for gptq which shouldn't convert
|
|
# u4 which are disguised as int32. Exl2 uses int16
|
|
# as well.
|
|
if tensor.dtype not in [torch.int16, torch.int32, torch.int64]:
|
|
tensor = tensor.to(dtype=self.dtype)
|
|
if to_device:
|
|
tensor = tensor.to(device=self.device)
|
|
return tensor
|
|
|
|
def get_partial_sharded(self, tensor_name: str, dim: int):
|
|
filename, tensor_name = self.get_filename(tensor_name)
|
|
f = self._get_handle(filename)
|
|
slice_ = f.get_slice(tensor_name)
|
|
world_size = self.process_group.size()
|
|
rank = self.process_group.rank()
|
|
|
|
size = slice_.get_shape()[dim]
|
|
block_size = (size + world_size - 1) // world_size
|
|
start = rank * block_size
|
|
stop = (rank + 1) * block_size
|
|
|
|
if dim == 0:
|
|
tensor = slice_[start:stop]
|
|
elif dim == 1:
|
|
tensor = slice_[:, start:stop]
|
|
else:
|
|
raise NotImplementedError("Let's make that generic when needed")
|
|
# Special case for gptq which shouldn't convert
|
|
# u4 which are disguised as int32. exl2 uses int16.
|
|
if tensor.dtype not in (torch.int16, torch.int32):
|
|
tensor = tensor.to(dtype=self.dtype)
|
|
tensor = tensor.to(device=self.device)
|
|
return tensor
|
|
|
|
def get_sharded(self, tensor_name: str, dim: int):
|
|
filename, tensor_name = self.get_filename(tensor_name)
|
|
f = self._get_handle(filename)
|
|
slice_ = f.get_slice(tensor_name)
|
|
world_size = self.process_group.size()
|
|
size = slice_.get_shape()[dim]
|
|
assert (
|
|
size % world_size == 0
|
|
), f"The choosen size {size} is not compatible with sharding on {world_size} shards"
|
|
return self.get_partial_sharded(tensor_name, dim)
|
|
|
|
def _get_qweight(self, name: str):
|
|
slice_ = self._get_slice(name)
|
|
total_size = slice_.get_shape()[1]
|
|
assert total_size % 3 == 0, "Prepacked quantized qkv is not divisible by 3"
|
|
single_size = total_size // 3
|
|
world_size = self.process_group.size()
|
|
rank = self.process_group.rank()
|
|
|
|
assert (
|
|
single_size % world_size == 0
|
|
), f"Prepacked quantized qkv cannot be sharded across {world_size} shards"
|
|
block_size = single_size // world_size
|
|
start = rank * block_size
|
|
stop = (rank + 1) * block_size
|
|
q = slice_[:, start:stop]
|
|
k = slice_[:, start + single_size : stop + single_size]
|
|
v = slice_[:, start + 2 * single_size : stop + 2 * single_size]
|
|
weight = torch.cat([q, k, v], dim=1)
|
|
weight = weight.to(device=self.device)
|
|
return weight
|
|
|
|
def get_weights_col_packed_qkv(self, prefix: str, quantize: str):
|
|
return self.get_weights_col_packed(prefix, quantize, 3)
|
|
|
|
def get_weights_col_packed_gate_up(self, prefix: str, quantize: str):
|
|
return self.get_weights_col_packed(prefix, quantize, 2)
|
|
|
|
def get_weights_col_packed(self, prefix: str, quantize: str, blocks: int):
|
|
"""
|
|
Highly specific when the underlying tensor is a simple cat of Q,K,V instead of being
|
|
already alternating Q,K,V within the main tensor
|
|
"""
|
|
if quantize in ["gptq", "awq"]:
|
|
try:
|
|
qweight = self._get_qweight(f"{prefix}.qweight")
|
|
except RuntimeError:
|
|
raise RuntimeError(
|
|
f"Cannot load `{quantize}` weight, make sure the model is already quantized."
|
|
)
|
|
|
|
bits, groupsize, _, quant_method = self._get_gptq_params()
|
|
|
|
qzeros = self._get_qweight(f"{prefix}.qzeros")
|
|
scales = self._get_qweight(f"{prefix}.scales")
|
|
scales = scales.to(dtype=self.dtype)
|
|
|
|
if quantize == "gptq" and quant_method == "gptq":
|
|
g_idx = self.get_tensor(f"{prefix}.g_idx")
|
|
elif quantize == "gptq" and quant_method == "awq":
|
|
log_once(
|
|
logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
|
|
)
|
|
from text_generation_server.layers.awq.conversion_utils import (
|
|
fast_awq_to_gptq,
|
|
)
|
|
|
|
qweight, qzeros = fast_awq_to_gptq(qweight, qzeros)
|
|
g_idx = (
|
|
torch.arange(qweight.shape[0] * (32 // bits), device=qweight.device)
|
|
// groupsize
|
|
).to(dtype=torch.int32)
|
|
else:
|
|
g_idx = None
|
|
|
|
weight = GPTQWeight(
|
|
qweight=qweight,
|
|
qzeros=qzeros,
|
|
scales=scales,
|
|
g_idx=g_idx,
|
|
bits=bits,
|
|
groupsize=groupsize,
|
|
use_exllama=False,
|
|
)
|
|
else:
|
|
slice_ = self._get_slice(f"{prefix}.weight")
|
|
total_size = slice_.get_shape()[0]
|
|
assert total_size % blocks == 0, f"Prepacked is not divisible by {blocks}"
|
|
single_size = total_size // blocks
|
|
world_size = self.process_group.size()
|
|
rank = self.process_group.rank()
|
|
|
|
assert (
|
|
single_size % world_size == 0
|
|
), f"Prepacked qkv cannot be sharded across {world_size} shards"
|
|
block_size = single_size // world_size
|
|
start = rank * block_size
|
|
stop = (rank + 1) * block_size
|
|
tensors = []
|
|
for i in range(blocks):
|
|
tensor = slice_[start + i * single_size : stop + i * single_size]
|
|
tensors.append(tensor)
|
|
weight = torch.cat(tensors, dim=0)
|
|
weight = weight.to(device=self.device)
|
|
weight = weight.to(dtype=self.dtype)
|
|
return weight
|
|
|
|
def get_weights_col(self, prefix: str, quantize: str):
|
|
if quantize == "exl2":
|
|
try:
|
|
q_weight = self.get_tensor(f"{prefix}.q_weight")
|
|
except RuntimeError:
|
|
raise RuntimeError(
|
|
f"Cannot load `exl2`-quantized weight, make sure the model is already quantized."
|
|
)
|
|
|
|
q_scale = self.get_tensor(f"{prefix}.q_scale")
|
|
q_invperm = self.get_tensor(f"{prefix}.q_invperm")
|
|
q_scale_max = self.get_tensor(f"{prefix}.q_scale_max")
|
|
q_groups = self.get_tensor(f"{prefix}.q_groups")
|
|
|
|
return Exl2Weight(
|
|
q_weight=q_weight,
|
|
q_scale=q_scale,
|
|
q_invperm=q_invperm,
|
|
q_scale_max=q_scale_max,
|
|
q_groups=q_groups,
|
|
)
|
|
|
|
return self.get_multi_weights_col([prefix], quantize, 0)
|
|
|
|
def get_multi_weights_col(self, prefixes: List[str], quantize: str, dim: int):
|
|
if quantize == "exl2":
|
|
raise ValueError("get_multi_weights_col is not supported for exl2")
|
|
elif quantize in ["gptq", "awq"]:
|
|
try:
|
|
qweight = torch.cat(
|
|
[self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1
|
|
)
|
|
except RuntimeError:
|
|
raise RuntimeError(
|
|
f"Cannot load `{quantize}` weight, make sure the model is already quantized"
|
|
)
|
|
|
|
qzeros = torch.cat(
|
|
[self.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1
|
|
)
|
|
scales = torch.cat(
|
|
[self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1
|
|
)
|
|
|
|
bits, groupsize, desc_act, quant_method = self._get_gptq_params()
|
|
|
|
from text_generation_server.layers.gptq import HAS_EXLLAMA
|
|
|
|
use_exllama = (
|
|
bits == 4 and HAS_EXLLAMA and quantize == "gptq" and not desc_act
|
|
)
|
|
|
|
if quantize == "gptq" and quant_method == "gptq":
|
|
w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
|
|
for w2 in w[1:]:
|
|
torch.testing.assert_close(w2, w[0])
|
|
g_idx = w[0]
|
|
elif quantize == "gptq" and quant_method == "awq":
|
|
log_once(
|
|
logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
|
|
)
|
|
from text_generation_server.layers.awq.conversion_utils import (
|
|
fast_awq_to_gptq,
|
|
)
|
|
|
|
qweight, qzeros = fast_awq_to_gptq(qweight, qzeros)
|
|
if use_exllama:
|
|
g_idx = None
|
|
else:
|
|
g_idx = (
|
|
torch.arange(
|
|
qweight.shape[0] * (32 // bits), device=qweight.device
|
|
)
|
|
// groupsize
|
|
).to(dtype=torch.int32)
|
|
else:
|
|
g_idx = None
|
|
|
|
weight = GPTQWeight(
|
|
qweight=qweight,
|
|
qzeros=qzeros,
|
|
scales=scales,
|
|
g_idx=g_idx,
|
|
bits=bits,
|
|
groupsize=groupsize,
|
|
use_exllama=use_exllama,
|
|
)
|
|
else:
|
|
w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
|
|
weight = torch.cat(w, dim=dim)
|
|
return weight
|
|
|
|
def get_tensor_shard(self, var, dim):
|
|
world_size = self.process_group.size()
|
|
rank = self.process_group.rank()
|
|
block_size = var.size()[dim] // world_size
|
|
start = rank * block_size
|
|
stop = (rank + 1) * block_size
|
|
if dim == 0:
|
|
tensor = var[start:stop]
|
|
elif dim == 1:
|
|
tensor = var[:, start:stop]
|
|
else:
|
|
raise NotImplementedError("Let's make that generic when needed")
|
|
tensor = tensor.to(dtype=self.dtype)
|
|
tensor = tensor.to(device=self.device)
|
|
return tensor
|
|
|
|
def get_multi_weights_row(self, prefix: str, quantize: str):
|
|
if quantize == "exl2":
|
|
try:
|
|
q_weight = self.get_tensor(f"{prefix}.q_weight")
|
|
except RuntimeError:
|
|
raise RuntimeError(
|
|
f"Cannot load `exl2`-quantized weight, make sure the model is already quantized."
|
|
)
|
|
|
|
q_scale = self.get_tensor(f"{prefix}.q_scale")
|
|
q_invperm = self.get_tensor(f"{prefix}.q_invperm")
|
|
q_scale_max = self.get_tensor(f"{prefix}.q_scale_max")
|
|
q_groups = self.get_tensor(f"{prefix}.q_groups")
|
|
|
|
return Exl2Weight(
|
|
q_weight=q_weight,
|
|
q_scale=q_scale,
|
|
q_invperm=q_invperm,
|
|
q_scale_max=q_scale_max,
|
|
q_groups=q_groups,
|
|
)
|
|
|
|
elif quantize == "gptq":
|
|
use_exllama = True
|
|
bits, groupsize, desc_act, quant_method = self._get_gptq_params()
|
|
|
|
if bits != 4:
|
|
use_exllama = False
|
|
|
|
if desc_act:
|
|
log_once(logger.warning, "Disabling exllama because desc_act=True")
|
|
use_exllama = False
|
|
|
|
try:
|
|
qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
|
|
except RuntimeError:
|
|
raise RuntimeError(
|
|
"Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
|
|
)
|
|
|
|
if quant_method == "gptq":
|
|
g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)
|
|
elif quant_method == "awq":
|
|
g_idx = None
|
|
|
|
if self.process_group.size() > 1:
|
|
if g_idx is not None:
|
|
if (
|
|
not torch.equal(
|
|
g_idx.cpu(),
|
|
torch.tensor(
|
|
[i // groupsize for i in range(g_idx.shape[0])],
|
|
dtype=torch.int32,
|
|
),
|
|
)
|
|
and not (g_idx == 0).all()
|
|
):
|
|
# Exllama implementation does not support row tensor parallelism with act-order, as
|
|
# it would require to reorder input activations that are split unto several GPUs
|
|
use_exllama = False
|
|
|
|
from text_generation_server.layers.gptq import HAS_EXLLAMA, CAN_EXLLAMA
|
|
|
|
if use_exllama:
|
|
if not HAS_EXLLAMA:
|
|
if CAN_EXLLAMA:
|
|
log_once(
|
|
logger.warning,
|
|
"Exllama GPTQ cuda kernels (which are faster) could have been used, but are not currently installed, try using BUILD_EXTENSIONS=True",
|
|
)
|
|
use_exllama = False
|
|
else:
|
|
log_once(logger.info, f"Using exllama kernels v{HAS_EXLLAMA}")
|
|
|
|
if use_exllama and groupsize != -1:
|
|
qzeros = self.get_sharded(f"{prefix}.qzeros", dim=0)
|
|
scales = self.get_sharded(f"{prefix}.scales", dim=0)
|
|
else:
|
|
qzeros = self.get_tensor(f"{prefix}.qzeros")
|
|
scales = self.get_tensor(f"{prefix}.scales")
|
|
|
|
if use_exllama and g_idx is not None:
|
|
g_idx = g_idx - g_idx[0]
|
|
|
|
if quant_method == "awq":
|
|
log_once(
|
|
logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
|
|
)
|
|
from text_generation_server.layers.awq.conversion_utils import (
|
|
fast_awq_to_gptq,
|
|
)
|
|
|
|
qweight, qzeros = fast_awq_to_gptq(qweight, qzeros)
|
|
if use_exllama:
|
|
g_idx = None
|
|
else:
|
|
g_idx = (
|
|
torch.arange(
|
|
qweight.shape[0] * (32 // bits), device=qweight.device
|
|
)
|
|
// groupsize
|
|
).to(dtype=torch.int32)
|
|
|
|
weight = GPTQWeight(
|
|
qweight=qweight,
|
|
qzeros=qzeros,
|
|
scales=scales,
|
|
g_idx=g_idx,
|
|
bits=bits,
|
|
groupsize=groupsize,
|
|
use_exllama=use_exllama,
|
|
)
|
|
elif quantize == "awq":
|
|
bits, groupsize, _, _ = self._get_gptq_params()
|
|
|
|
try:
|
|
qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
|
|
except RuntimeError:
|
|
raise RuntimeError(
|
|
"Cannot load `awq` weight, make sure the model is already quantized"
|
|
)
|
|
|
|
qzeros = self.get_sharded(f"{prefix}.qzeros", dim=0)
|
|
scales = self.get_sharded(f"{prefix}.scales", dim=0)
|
|
g_idx = None
|
|
use_exllama = False
|
|
|
|
weight = GPTQWeight(
|
|
qweight=qweight,
|
|
qzeros=qzeros,
|
|
scales=scales,
|
|
g_idx=g_idx,
|
|
bits=bits,
|
|
groupsize=groupsize,
|
|
use_exllama=use_exllama,
|
|
)
|
|
else:
|
|
weight = self.get_sharded(f"{prefix}.weight", dim=1)
|
|
return weight
|
|
|
|
def _get_gptq_params(self) -> Tuple[int, int, int, str]:
|
|
try:
|
|
bits = self.get_tensor("gptq_bits").item()
|
|
groupsize = self.get_tensor("gptq_groupsize").item()
|
|
desc_act = False
|
|
quant_method = "gptq"
|
|
except (SafetensorError, RuntimeError) as e:
|
|
try:
|
|
bits = self.gptq_bits
|
|
groupsize = self.gptq_groupsize
|
|
desc_act = getattr(self, "gptq_desc_act", False)
|
|
quant_method = getattr(self, "quant_method", "gptq")
|
|
except Exception:
|
|
raise e
|
|
|
|
return bits, groupsize, desc_act, quant_method
|
|
|
|
def _set_gptq_params(self, model_id, revision):
|
|
filename = "config.json"
|
|
try:
|
|
if os.path.exists(os.path.join(model_id, filename)):
|
|
filename = os.path.join(model_id, filename)
|
|
else:
|
|
filename = hf_hub_download(
|
|
model_id, filename=filename, revision=revision
|
|
)
|
|
with open(filename, "r") as f:
|
|
data = json.load(f)
|
|
self.gptq_bits = data["quantization_config"]["bits"]
|
|
self.gptq_groupsize = data["quantization_config"]["group_size"]
|
|
# Order is important here, desc_act is missing on some real models
|
|
self.quant_method = data["quantization_config"]["quant_method"]
|
|
self.gptq_desc_act = data["quantization_config"]["desc_act"]
|
|
except Exception:
|
|
filename = "quantize_config.json"
|
|
try:
|
|
if os.path.exists(os.path.join(model_id, filename)):
|
|
filename = os.path.join(model_id, filename)
|
|
else:
|
|
filename = hf_hub_download(
|
|
model_id, filename=filename, revision=revision
|
|
)
|
|
with open(filename, "r") as f:
|
|
data = json.load(f)
|
|
self.gptq_bits = data["bits"]
|
|
self.gptq_groupsize = data["group_size"]
|
|
self.gptq_desc_act = data["desc_act"]
|
|
if "version" in data and data["version"] == "GEMM":
|
|
self.quant_method = "awq"
|
|
except Exception:
|
|
filename = "quant_config.json"
|
|
try:
|
|
if os.path.exists(os.path.join(model_id, filename)):
|
|
filename = os.path.join(model_id, filename)
|
|
else:
|
|
filename = hf_hub_download(
|
|
model_id, filename=filename, revision=revision
|
|
)
|
|
with open(filename, "r") as f:
|
|
data = json.load(f)
|
|
self.gptq_bits = data["w_bit"]
|
|
self.gptq_groupsize = data["q_group_size"]
|
|
self.gptq_desc_act = data["desc_act"]
|
|
if "version" in data and data["version"] == "GEMM":
|
|
self.quant_method = "awq"
|
|
except Exception:
|
|
pass
|