forked from casper-hansen/AutoAWQ
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'casper-hansen:main' into main
- Loading branch information
Showing
19 changed files
with
597 additions
and
60 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,2 @@ | ||
| __version__ = "0.2.2" | ||
| __version__ = "0.2.4" | ||
| from awq.models.auto import AutoAWQForCausalLM |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,149 @@ | ||
| import tqdm | ||
| import torch | ||
| from typing import List, Tuple | ||
| from .base import BaseAWQForCausalLM | ||
| from awq.utils.fused_utils import fuse_qkv | ||
| from awq.modules.fused.block import LlamaLikeBlock | ||
| from awq.modules.fused.model import LlamaLikeModel | ||
| from transformers.models.gemma.modeling_gemma import ( | ||
| GemmaDecoderLayer as OldGemmaDecoderLayer, | ||
| GemmaForCausalLM as OldGemmaForCausalLM, | ||
| ) | ||
| from awq.modules.fused.norm import FasterTransformerRMSNorm | ||
|
|
||
|
|
||
| class GemmaAWQForCausalLM(BaseAWQForCausalLM): | ||
| layer_type = "GemmaDecoderLayer" | ||
| max_new_tokens_key = "max_position_embeddings" | ||
|
|
||
| @staticmethod | ||
| def fuse_layers(model: OldGemmaDecoderLayer): | ||
| fuser = GemmaFuser(model) | ||
| fuser.fuse_transformer() | ||
|
|
||
| @staticmethod | ||
| def get_model_layers(model: OldGemmaForCausalLM): | ||
| return model.model.layers | ||
|
|
||
| @staticmethod | ||
| def get_act_for_scaling(module: OldGemmaDecoderLayer): | ||
| return dict(is_scalable=False) | ||
|
|
||
| @staticmethod | ||
| def move_embed(model: OldGemmaForCausalLM, device: str): | ||
| model.model.embed_tokens = model.model.embed_tokens.to(device) | ||
|
|
||
| @staticmethod | ||
| def get_layers_for_scaling(module: OldGemmaDecoderLayer, input_feat, module_kwargs): | ||
| layers = [] | ||
|
|
||
| # attention input | ||
| layers.append( | ||
| dict( | ||
| prev_op=module.input_layernorm, | ||
| layers=[ | ||
| module.self_attn.q_proj, | ||
| module.self_attn.k_proj, | ||
| module.self_attn.v_proj, | ||
| ], | ||
| inp=input_feat["self_attn.q_proj"], | ||
| module2inspect=module.self_attn, | ||
| kwargs=module_kwargs, | ||
| ) | ||
| ) | ||
|
|
||
| # attention out | ||
| # Please refer to https://github.com/mit-han-lab/llm-awq/pull/67#issue-1850622696 | ||
| if module.self_attn.v_proj.weight.shape == module.self_attn.o_proj.weight.shape: | ||
| layers.append( | ||
| dict( | ||
| prev_op=module.self_attn.v_proj, | ||
| layers=[module.self_attn.o_proj], | ||
| inp=input_feat["self_attn.o_proj"], | ||
| ) | ||
| ) | ||
|
|
||
| # linear 1 | ||
| layers.append( | ||
| dict( | ||
| prev_op=module.post_attention_layernorm, | ||
| layers=[module.mlp.gate_proj, module.mlp.up_proj], | ||
| inp=input_feat["mlp.gate_proj"], | ||
| module2inspect=module.mlp, | ||
| ) | ||
| ) | ||
|
|
||
| # linear 2 | ||
| layers.append( | ||
| dict( | ||
| prev_op=module.mlp.up_proj, | ||
| layers=[module.mlp.down_proj], | ||
| inp=input_feat["mlp.down_proj"], | ||
| ) | ||
| ) | ||
|
|
||
| return layers | ||
|
|
||
|
|
||
| class GemmaFuser: | ||
| def __init__(self, model: OldGemmaForCausalLM): | ||
| self.model = model | ||
|
|
||
| self.Gemma_blocks: List[Tuple[str, OldGemmaDecoderLayer]] = [ | ||
| (name, module) | ||
| for name, module in self.model.named_modules() | ||
| if "GemmaDecoderLayer".lower() in module.__class__.__name__.lower() | ||
| ] | ||
|
|
||
| def fuse_transformer(self): | ||
| blocks = [] | ||
|
|
||
| module: OldGemmaDecoderLayer | ||
| for module in tqdm.tqdm(self.model.model.layers, desc="Fusing layers..."): | ||
| device = next(iter(module.state_dict().values())).device | ||
| qkv = fuse_qkv( | ||
| module, | ||
| module.self_attn.q_proj, | ||
| module.self_attn.k_proj, | ||
| module.self_attn.v_proj, | ||
| ) | ||
| with torch.no_grad(): | ||
| # GemmaRMSNorm is different from Llama's in that it multiplies | ||
| # (1 + weight) to the output, instead of just weight. | ||
| module.input_layernorm.weight += 1 | ||
| module.post_attention_layernorm.weight += 1 | ||
| norm_1 = FasterTransformerRMSNorm( | ||
| module.input_layernorm.weight, module.input_layernorm.eps | ||
| ) | ||
| norm_2 = FasterTransformerRMSNorm( | ||
| module.post_attention_layernorm.weight, | ||
| module.post_attention_layernorm.eps, | ||
| ) | ||
| blocks.append( | ||
| LlamaLikeBlock( | ||
| hidden_size=self.model.config.hidden_size, | ||
| n_heads=self.model.config.num_attention_heads, | ||
| n_kv_heads=self.model.config.num_key_value_heads, | ||
| qkv_layer=qkv, | ||
| o_proj=module.self_attn.o_proj, | ||
| mlp=module.mlp, | ||
| norm_1=norm_1, | ||
| norm_2=norm_2, | ||
| dev=device, | ||
| max_seq_len=self.model.config.max_seq_len, | ||
| rope_theta=self.model.config.rope_theta, | ||
| head_dim=self.model.config.head_dim, | ||
| ) | ||
| ) | ||
|
|
||
| with torch.no_grad(): | ||
| # Normalize Gemma's embedding layer | ||
| self.model.model.embed_tokens.weight *= self.model.config.hidden_size**0.5 | ||
|
|
||
| self.model.model = LlamaLikeModel( | ||
| self.model.config.vocab_size, | ||
| blocks, | ||
| self.model.model.embed_tokens, | ||
| self.model.model.norm, | ||
| ) | ||
| setattr(self.model.model, "blocks", self.model.model.blocks) |
Oops, something went wrong.