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[Quantization] Fp8 Channelwise Dynamic Per Token GroupedGEMM (#15587)

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Signed-off-by: ElizaWszola <eliza@neuralmagic.com>
Signed-off-by: ElizaWszola <ewszola@redhat.com>
Signed-off-by: rshaw@neuralmagic.com <robertgshaw2@gmail.com>
Co-authored-by: ElizaWszola <eliza@neuralmagic.com>
Co-authored-by: Lucas Wilkinson <wilkinson.lucas@gmail.com>
Co-authored-by: ElizaWszola <ewszola@redhat.com>
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Robert Shaw 2025-03-27 02:47:25 -04:00 committed by GitHub
parent f4c98b4d4c
commit 43ed4143c4
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2 changed files with 66 additions and 65 deletions
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26
vllm/model_executor/layers/fused_moe/layer.py
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@ -885,32 +885,6 @@ class FusedMoE(torch.nn.Module):
]
]
def _load_fp8_scale(self, param: torch.nn.Parameter,
loaded_weight: torch.Tensor, weight_name: str,
shard_id: str, expert_id: int) -> None:
param_data = param.data
# Input scales can be loaded directly and should be equal.
if "input_scale" in weight_name:
if param_data[expert_id] != 1 and (param_data[expert_id] -
loaded_weight).abs() > 1e-5:
raise ValueError(
"input_scales of w1 and w3 of a layer "
f"must be equal. But got {param_data[expert_id]} "
f"vs. {loaded_weight}")
param_data[expert_id] = loaded_weight
# Weight scales
elif "weight_scale" in weight_name:
# If we are in merged column case (gate_up_proj)
if shard_id in ("w1", "w3"):
# We have to keep the weight scales of w1 and w3 because
# we need to re-quantize w1/w3 weights after weight loading.
idx = 0 if shard_id == "w1" else 1
param_data[expert_id][idx] = loaded_weight
# If we are in the row parallel case (down_proj)
else:
param_data[expert_id] = loaded_weight
def extra_repr(self) -> str:
s = (

105
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
View File

@ -268,14 +268,23 @@ class CompressedTensorsW8A8Fp8MoECutlassMethod(CompressedTensorsMoEMethod):
self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
"input_activations")
if not (self.weight_quant.strategy == QuantizationStrategy.TENSOR
and self.input_quant.strategy == QuantizationStrategy.TENSOR):
per_tensor = (self.weight_quant.strategy == QuantizationStrategy.TENSOR
and self.input_quant.strategy
== QuantizationStrategy.TENSOR)
per_channel = (
self.weight_quant.strategy == QuantizationStrategy.CHANNEL
and self.input_quant.strategy == QuantizationStrategy.TOKEN)
if not (per_tensor or per_channel):
raise ValueError(
"For FP8 Fused MoE layers, only per-tensor scales "
"for weights and activations are supported. Found "
"For FP8 Fused MoE layers, we require per tensor "
"or channelwise, dynamic per token quantization. Found "
f"{self.weight_quant}, {self.input_quant}")
self.static_input_scales = not self.input_quant.dynamic
if self.static_input_scales and per_channel:
raise ValueError(
"For FP8 Fused MoE layer, we require either per tensor or "
"channelwise, dynamic per token quantization.")
def create_weights(self, layer: torch.nn.Module, num_experts: int,
hidden_size: int, intermediate_size_per_partition: int,
@ -303,24 +312,40 @@ class CompressedTensorsW8A8Fp8MoECutlassMethod(CompressedTensorsMoEMethod):
set_weight_attrs(w2_weight, extra_weight_attrs)
# WEIGHT_SCALES
# Allocate 2 scales for w1 and w3 respectively.
# They will be combined to a single scale after weight loading.
w13_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
2,
dtype=torch.float32),
requires_grad=False)
layer.register_parameter("w13_weight_scale", w13_weight_scale)
if self.weight_quant.strategy == QuantizationStrategy.TENSOR:
# Allocate 2 scales for w1 and w3 respectively.
# They are combined to a single scale after weight loading.
w13_weight_scale = torch.nn.Parameter(torch.ones(
num_experts, 2, dtype=torch.float32),
requires_grad=False)
layer.register_parameter("w13_weight_scale", w13_weight_scale)
w2_weight_scale = torch.nn.Parameter(torch.ones(
num_experts, dtype=torch.float32),
requires_grad=False)
layer.register_parameter("w2_weight_scale", w2_weight_scale)
# Add PER-TENSOR quantization for FusedMoE.weight_loader.
extra_weight_attrs.update(
{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
dtype=torch.float32),
requires_grad=False)
layer.register_parameter("w2_weight_scale", w2_weight_scale)
# Add the quantization method used (per tensor/grouped/channel)
# to ensure the weight scales are loaded in properly
extra_weight_attrs.update(
{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
elif self.weight_quant.strategy == QuantizationStrategy.CHANNEL:
w13_weight_scale = torch.nn.Parameter(torch.ones(
num_experts,
2 * intermediate_size_per_partition,
1,
dtype=torch.float32),
requires_grad=False)
layer.register_parameter("w13_weight_scale", w13_weight_scale)
w2_weight_scale = torch.nn.Parameter(torch.ones(
num_experts, hidden_size, 1, dtype=torch.float32),
requires_grad=False)
layer.register_parameter("w2_weight_scale", w2_weight_scale)
# Add PER-CHANNEL quantization for FusedMoE.weight_loader.
extra_weight_attrs.update(
{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value})
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
# INPUT_SCALES
if self.static_input_scales:
@ -362,6 +387,7 @@ class CompressedTensorsW8A8Fp8MoECutlassMethod(CompressedTensorsMoEMethod):
# Fp8 moe kernels require a single activation scale.
# We take the max of all the scales in case they differ.
if self.static_input_scales:
assert self.input_quant.strategy == QuantizationStrategy.TENSOR
if (layer.w13_input_scale is None or layer.w2_input_scale is None):
raise ValueError(
"QuantConfig has static quantization, but found "
@ -377,24 +403,25 @@ class CompressedTensorsW8A8Fp8MoECutlassMethod(CompressedTensorsMoEMethod):
layer.w2_input_scale = torch.nn.Parameter(
layer.w2_input_scale.max(), requires_grad=False)
# Fp8 moe kernel needs single weight scale for w13 per expert.
# We take the max then dequant and requant each expert.
assert layer.w13_weight_scale is not None
shard_size = layer.intermediate_size_per_partition
max_w13_scales = layer.w13_weight_scale.max(dim=1).values
for expert_id in range(layer.local_num_experts):
start = 0
for shard_id in range(2):
dq_weight = per_tensor_dequantize(
layer.w13_weight[expert_id][start:start + shard_size, :],
layer.w13_weight_scale[expert_id][shard_id])
layer.w13_weight[expert_id][
start:start + shard_size, :], _ = ops.scaled_fp8_quant(
dq_weight, max_w13_scales[expert_id])
start += shard_size
layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
requires_grad=False)
# For Per-TENSOR case, Fp8 moe kernel needs single weight scale
# for w13 per expert. Use max then dequant and requant each expert.
if self.weight_quant.strategy == QuantizationStrategy.TENSOR:
assert layer.w13_weight_scale is not None
shard_size = layer.intermediate_size_per_partition
max_w13_scales = layer.w13_weight_scale.max(dim=1).values
for expert_id in range(layer.local_num_experts):
start = 0
for shard_id in range(2):
dq_weight = per_tensor_dequantize(
layer.w13_weight[expert_id][start:start +
shard_size, :],
layer.w13_weight_scale[expert_id][shard_id])
layer.w13_weight[expert_id][
start:start + shard_size, :], _ = ops.scaled_fp8_quant(
dq_weight, max_w13_scales[expert_id])
start += shard_size
layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
requires_grad=False)
def apply(
self,