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[Kernel] Support Fp8 Checkpoints (Dynamic + Static) (#4332)

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Co-authored-by: Philipp Moritz <pcmoritz@gmail.com>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: mgoin <michael@neuralmagic.com>
Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>
This commit is contained in:
Robert Shaw
2024-04-30 17:46:12 -04:00
committed by GitHub
parent b31a1fb63c
commit 111815d482
3 changed files with 307 additions and 40 deletions
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199
vllm/model_executor/layers/quantization/fp8.py
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@@ -1,23 +1,36 @@
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch.nn import Module
from torch.nn.parameter import Parameter
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig, QuantizeMethodBase)
QuantizationConfig)
from vllm.model_executor.utils import set_weight_attrs
ACTIVATION_SCHEMES = ["static", "dynamic"]
logger = init_logger(__name__)
class Fp8Config(QuantizationConfig):
"""Config class for FP8."""
def __init__(
self,
is_checkpoint_fp8_serialized: bool = False,
activation_scheme: str = "dynamic",
) -> None:
self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
if is_checkpoint_fp8_serialized:
logger.warning("Detected fp8 checkpoint. Please note that the "
"format is experimental and subject to change.")
if activation_scheme not in ACTIVATION_SCHEMES:
raise ValueError(
f"Unsupported activation scheme {activation_scheme}")
self.activation_scheme = activation_scheme
@classmethod
@@ -30,10 +43,7 @@ class Fp8Config(QuantizationConfig):
@classmethod
def get_min_capability(cls) -> int:
# TODO: PyTorch 2.3.0+ is required to run FP8 on
# SM 89 (e.g. Ada) GPUs. Specifically, this PR has to
# be included: https://github.com/pytorch/pytorch/pull/118881
return 90
return 89
@classmethod
def get_config_filenames(cls) -> List[str]:
@@ -41,11 +51,14 @@ class Fp8Config(QuantizationConfig):
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
quant_method = cls.get_from_keys(config, ["quant_method"])
is_checkpoint_fp8_serialized = ("fp8" in quant_method)
activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
return cls(activation_scheme)
return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
activation_scheme=activation_scheme)
def get_quant_method(
self, layer: torch.nn.Module) -> Optional["QuantizeMethodBase"]:
self, layer: torch.nn.Module) -> Optional["Fp8LinearMethod"]:
if isinstance(layer, LinearBase):
return Fp8LinearMethod(self)
return None
@@ -56,8 +69,12 @@ class Fp8Config(QuantizationConfig):
class Fp8LinearMethod(LinearMethodBase):
"""Linear method for FP8.
We now support common FP16/BF16 model checkpoints ONLY. The weight
scaling factor will be initialized after the model weights are loaded.
Supports loading FP8 checkpoints with static weight scale and
dynamic/static activation scale.
Also supports loading quantized FP16/BF16 model checkpoints with dynamic
activation scaling. The weight scaling factor will be initialized after
the model weights are loaded.
Limitations:
1. Only support per-tensor quantization due to torch._scaled_mm support.
@@ -71,6 +88,24 @@ class Fp8LinearMethod(LinearMethodBase):
def __init__(self, quant_config: Fp8Config):
self.quant_config = quant_config
def _create_scale_param(
self,
scale_name: str,
layer: torch.nn.Module,
output_partition_sizes: List[int],
**extra_weight_attrs,
) -> None:
scale = Parameter(torch.empty(len(output_partition_sizes),
dtype=torch.float32),
requires_grad=False)
layer.register_parameter(scale_name, scale)
set_weight_attrs(
scale, {
**extra_weight_attrs,
"fp8_scales_shard_indexer":
self.scales_shard_indexer,
})
def create_weights(
self,
layer: torch.nn.Module,
@@ -81,46 +116,150 @@ class Fp8LinearMethod(LinearMethodBase):
params_dtype: torch.dtype,
**extra_weight_attrs,
):
del input_size, output_size
output_size_per_partition = sum(output_partition_sizes)
layer.process_after_load = True
layer.logical_widths = output_partition_sizes
# WEIGHT
weight_dtype = (torch.float8_e4m3fn
if self.quant_config.is_checkpoint_fp8_serialized else
params_dtype)
weight = Parameter(torch.empty(output_size_per_partition,
input_size_per_partition,
dtype=params_dtype),
dtype=weight_dtype),
requires_grad=False)
layer.register_parameter("weight", weight)
set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
set_weight_attrs(weight, extra_weight_attrs)
set_weight_attrs(weight, {
**extra_weight_attrs,
"input_dim": 1,
"output_dim": 0,
})
w_scale = Parameter(
torch.empty(1, dtype=torch.float32),
requires_grad=False,
)
layer.register_parameter("weight_scaling_factor", w_scale)
# If checkpoint is serialized fp8, load them.
# Otherwise, wait until process_weights_after_loading.
if self.quant_config.is_checkpoint_fp8_serialized:
# WEIGHT SCALE
self._create_scale_param(
scale_name="weight_scale",
layer=layer,
output_partition_sizes=output_partition_sizes,
**extra_weight_attrs)
# ACTIVATION SCALE
if self.quant_config.activation_scheme == "static":
self._create_scale_param(
scale_name="act_scale",
layer=layer,
output_partition_sizes=output_partition_sizes,
**extra_weight_attrs)
def scales_shard_indexer(
self, param: torch.Tensor, loaded_weight: torch.Tensor,
shard_id: Union[str, int]) -> Tuple[torch.Tensor, torch.Tensor]:
qkv_idxs = {"q": 0, "k": 1, "v": 2}
if isinstance(shard_id, int):
pass
elif isinstance(shard_id, str):
if shard_id not in qkv_idxs:
raise ValueError(f"Unknown shard_id: {shard_id}")
shard_id = qkv_idxs[shard_id]
else:
ValueError(f"Shard id must be int or str but got {type(shard_id)}")
return param[shard_id], loaded_weight
def process_weights_after_loading(self, layer: Module) -> None:
# Although the quant_method is propagated to all layers,
# only linear layers invoke "create_weights". So we check
# whether "weight_scaling_facor" is registered to determine
# whether the layer is a linear layer that requires quantization.
if not hasattr(layer, "weight_scaling_factor"):
if (not hasattr(layer, "process_after_load")
or not layer.process_after_load):
return
qweight, weight_scale = ops.scaled_fp8_quant(layer.weight)
# torch._scaled_mm requires column-major in the second
# input (weight), so we transpose the quantized weight.
layer.weight = Parameter(qweight.t(), requires_grad=False)
layer.weight_scaling_factor.data.copy_(weight_scale)
# If checkpoint is fp/bf16 (not serialized fp8), quantize the weights.
if not self.quant_config.is_checkpoint_fp8_serialized:
qweight, weight_scale = ops.scaled_fp8_quant(layer.weight,
scale=None)
layer.weight = Parameter(qweight.t(), requires_grad=False)
layer.weight_scale = Parameter(weight_scale, requires_grad=False)
layer.logical_widths = None
layer.act_scale = None
return
# If checkpoint is fp8, requantize the separately quantized logical
# weights into a single fp8 weight with a single weight scale.
else:
# WEIGHT_SCALE / WEIGHT
# Loop over logical weights, requantizing with single scale.
max_w_scale = layer.weight_scale.max()
start = 0
for idx, logical_width in enumerate(layer.logical_widths):
end = start + logical_width
weight_dq = per_tensor_dequantize(layer.weight[start:end, :],
layer.weight_scale[idx])
layer.weight[start:end, :] = per_tensor_quantize(
weight_dq, layer.weight_scale.max())
start = end
layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
# WEIGHT
# Transpose weight for passing to torch._scaled_mm
weight = layer.weight
layer.weight = Parameter(weight.t(), requires_grad=False)
# ACT_SCALE
# Dynamic: set to None (required input to ops.scaled_fp8_quant).
# Static: set to max of the act_scales (since they are equal).
if self.quant_config.activation_scheme == "dynamic":
layer.act_scale = None
elif self.quant_config.activation_scheme == "static":
if not all_close_1d(layer.act_scale):
raise ValueError(
"All the act_scales for the logical weights of a layer "
f"must be equal. But got {layer.act_scale}")
layer.act_scale = Parameter(layer.act_scale.max(),
requires_grad=False)
else:
raise ValueError(
f"Unknown scheme {self.quant_config.activation_scheme}")
def apply(self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
qinput, x_scale = ops.scaled_fp8_quant(x)
# ops.scaled_fp8_quant supports both dynamic and static quant.
# If dynamic, layer.act_scale is None and x_scale computed from x.
# If static, layer.act_scale is scalar and x_scale set to act_scale.
qinput, x_scale = ops.scaled_fp8_quant(x, layer.act_scale)
# Fused GEMM_DQ
output, _ = torch._scaled_mm(
qinput,
layer.weight,
out_dtype=x.dtype,
scale_a=x_scale,
scale_b=layer.weight_scaling_factor,
scale_b=layer.weight_scale,
bias=bias,
)
return output
def all_close_1d(x: torch.Tensor) -> bool:
assert len(x.shape) == 1
return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0]))
def per_tensor_quantize(tensor: torch.Tensor,
inv_scale: float) -> torch.Tensor:
finfo = torch.finfo(torch.float8_e4m3fn)
qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max)
return qweight.to(torch.float8_e4m3fn)
def per_tensor_dequantize(tensor: torch.Tensor,
inv_scale: float) -> torch.Tensor:
fake_qweight = tensor.to(torch.float16)
dq_weight = fake_qweight * inv_scale
return dq_weight