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Add torchao quant (int4/int8/fp8) to llama models #1341

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merged 8 commits into from
Sep 9, 2024
Merged

Add torchao quant (int4/int8/fp8) to llama models #1341

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0b095c8
Add torchao quant to sgl llama model for testing
jerryzh168 Sep 6, 2024
b3696b5
add torchao-config
jerryzh168 Sep 7, 2024
281c19b
add fp8
jerryzh168 Sep 7, 2024
01cc04e
Merge branch 'main' into enable-torchao
merrymercy Sep 9, 2024
6712230
lint
merrymercy Sep 9, 2024
8ae193b
move utils to torchao_utils.py
merrymercy Sep 9, 2024
69d2bb5
update
merrymercy Sep 9, 2024
b76dc72
update test cases
merrymercy Sep 9, 2024
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25 changes: 23 additions & 2 deletions python/sglang/srt/models/llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.sampler import Sampler
from sglang.srt.model_executor.forward_batch_info import InputMetadata
ENABLE_TORCHAO = True


class LlamaMLP(nn.Module):
Expand Down Expand Up @@ -350,8 +351,17 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
if hasattr(param, "weight_loader"):
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weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
if ENABLE_TORCHAO and name.endswith("proj.weight") and param.ndim == 2:
from torchao.quantization import quantize_, int4_weight_only
dummy_linear = torch.nn.Linear(param.shape[1], param.shape[0], bias=False)
dummy_linear.weight.data = param.data
quantize_(dummy_linear, int4_weight_only())
params_dict[name] = dummy_linear.weight

# TODO
break
else:
# Skip loading extra bias for GPTQ models.
Expand All @@ -361,6 +371,17 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)

if ENABLE_TORCHAO and name.endswith("proj.weight") and param.ndim == 2:
from torchao.quantization import quantize_, int4_weight_only
dummy_linear = torch.nn.Linear(param.shape[1], param.shape[0], bias=False)
dummy_linear.weight.data = param.data
quantize_(dummy_linear, int4_weight_only())
params_dict[name] = dummy_linear.weight

self.load_state_dict(params_dict, assign=True)




class Phi3ForCausalLM(LlamaForCausalLM):
pass
Expand Down
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