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update doc

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Mohit Sharma 2025-01-29 12:46:03 +00:00
parent 0863dd1533
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2 changed files with 5 additions and 3 deletions
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1
docs/source/supported_models.md
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@ -4,6 +4,7 @@
Text Generation Inference enables serving optimized models. The following sections list which models (VLMs & LLMs) are supported. Text Generation Inference enables serving optimized models. The following sections list which models (VLMs & LLMs) are supported.
- [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2) - [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2)
- [Deepseek V3](https://huggingface.co/deepseek-ai/DeepSeek-V3)
- [Idefics 2](https://huggingface.co/HuggingFaceM4/idefics2-8b) (Multimodal) - [Idefics 2](https://huggingface.co/HuggingFaceM4/idefics2-8b) (Multimodal)
- [Idefics 3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (Multimodal) - [Idefics 3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (Multimodal)
- [Llava Next (1.6)](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) (Multimodal) - [Llava Next (1.6)](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) (Multimodal)

7
server/text_generation_server/layers/fp8.py
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@ -5,7 +5,7 @@ from typing import Optional, Tuple, Type, Union, List
import torch import torch
from loguru import logger from loguru import logger
from moe_kernels import w8a8_block_fp8_matmul, per_token_group_quant_fp8 from moe_kernels.fp8_utils import w8a8_block_fp8_matmul, per_token_group_quant_fp8
from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.weights import ( from text_generation_server.utils.weights import (
Weight, Weight,
@ -287,7 +287,9 @@ class HybridFP8UnquantLoader(WeightsLoader):
weights.get_sharded(f"{p}.weight_scale_inv", dim=0, to_device=False) weights.get_sharded(f"{p}.weight_scale_inv", dim=0, to_device=False)
for p in prefixes for p in prefixes
] ]
scale = torch.cat(scale, dim=dim).to(weights.device) scale = torch.cat(scale, dim=dim)
if scale.device == torch.device("cpu"):
scale = scale.to(weights.device)
return Fp8Weight( return Fp8Weight(
weight=w, weight=w,
weight_scale=scale, weight_scale=scale,
@ -489,7 +491,6 @@ class Fp8Linear(torch.nn.Module):
def forward(self, input: torch.Tensor) -> torch.Tensor: def forward(self, input: torch.Tensor) -> torch.Tensor:
if self.weight_block_size is not None: if self.weight_block_size is not None:
qinput, scale = per_token_group_quant_fp8(input, self.weight_block_size[1]) qinput, scale = per_token_group_quant_fp8(input, self.weight_block_size[1])
# logger.info(f"qinput: {qinput.shape} {scale.shape} {self.qweight.shape} {self.scale.shape} {self.weight_block_size}")
output = w8a8_block_fp8_matmul( output = w8a8_block_fp8_matmul(
qinput, qinput,
self.qweight, self.qweight,