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Support Llama4 fp8 inference (#5194)

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Co-authored-by: laixinn <xielx@shanghaitech.edu.cn>
Co-authored-by: sleepcoo <sleepcoo@gmail.com>
Co-authored-by: zhyncs <me@zhyncs.com>
This commit is contained in:
HandH1998
2025-04-09 20:14:34 +08:00
committed by GitHub
parent 86a876d883
commit 4065248214
14 changed files with 537 additions and 106 deletions
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40
python/sglang/srt/models/deepseek_v2.py
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@@ -55,6 +55,7 @@ from sglang.srt.layers.moe.topk import select_experts
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.fp8_utils import (
block_quant_to_tensor_quant,
channel_quant_to_tensor_quant,
input_to_float8,
normalize_e4m3fn_to_e4m3fnuz,
)
@@ -1411,27 +1412,34 @@ class DeepseekV2ForCausalLM(nn.Module):
w = self_attn.kv_b_proj.weight
# NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
# This may affect the accuracy of fp8 model.
if hasattr(self.quant_config, "weight_block_size") and w.dtype in (
if w.dtype in (
torch.float8_e4m3fn,
torch.float8_e4m3fnuz,
):
weight_block_size = self.quant_config.weight_block_size
if weight_block_size is not None:
assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
if _is_hip:
weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
weight=w,
weight_scale=self_attn.kv_b_proj.weight_scale_inv,
input_scale=None,
)
else:
weight = w
weight_scale = self_attn.kv_b_proj.weight_scale_inv
if hasattr(self.quant_config, "weight_block_size"):
weight_block_size = self.quant_config.weight_block_size
if weight_block_size is not None:
assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
if _is_hip:
weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
weight=w,
weight_scale=self_attn.kv_b_proj.weight_scale_inv,
input_scale=None,
)
else:
weight = w
weight_scale = self_attn.kv_b_proj.weight_scale_inv
w, scale = block_quant_to_tensor_quant(
weight, weight_scale, weight_block_size
)
w, scale = block_quant_to_tensor_quant(
weight, weight_scale, weight_block_size
)
self_attn.w_scale = scale
else:
weight = w
weight_scale = self_attn.kv_b_proj.weight_scale
w, scale = channel_quant_to_tensor_quant(weight, weight_scale)
self_attn.w_scale = scale
if w.dtype == torch.int8:
if hasattr(self.quant_config, "weight_block_size"):
# block-wise int8 need it