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ixformer_sdk/train/speedformer/policy/llama.py

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+import warnings
+import types
+from abc import ABC, abstractmethod
+from functools import partial
+from typing import Callable, Dict, List, Union
+
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import Module
+
+from ixformer.train.speedformer.policy.utils import SubModuleReplacementDescription
+from ixformer.train.speedformer.policy.replacer import Replacer
+
+from ixformer.train.speedformer.layers.normalization import APEXFusedRMSNorm, IXFFusedRMSNorm
+from ixformer.train.speedformer.layers.llama.attention import LlamaAttention as IXF_LlamaAttention
+from ixformer.train.speedformer.layers.llama.mlp import IXFLlamaMLP
+from ixformer.train.speedformer.layers.llama.llama_method import LlamaModel_forward, LlamaForCausalLM_forward
+from ixformer.train.speedformer.layers.fast_lora.fast_lora import apply_lora_mlp_swiglu
+
+from peft import PeftType
+
+
+class LlamaReplacer(Replacer):
+    def __init__(self):
+        self.policy = {}
+
+    def module_policy(self) -> Dict[Union[str, nn.Module], List[SubModuleReplacementDescription]]:
+        self.append_or_create_submodule_replacement(
+            description=[
+                SubModuleReplacementDescription(
+                    suffix="input_layernorm",
+                    target_module=APEXFusedRMSNorm,
+                    kwargs={}
+                ),
+                SubModuleReplacementDescription(
+                    suffix="post_attention_layernorm",
+                    target_module=APEXFusedRMSNorm,
+                    kwargs={},
+                ),
+                SubModuleReplacementDescription(
+                    suffix="self_attn",
+                    target_module=IXF_LlamaAttention,
+                    kwargs={}
+                ),
+                # SubModuleReplacementDescription(
+                #     suffix="mlp",
+                #     target_module=IXFLlamaMLP,
+                #     kwargs={}
+                # ),
+            ],
+            target_key="LlamaDecoderLayer"
+        )
+
+        self.append_or_create_submodule_replacement(
+            description=[
+                SubModuleReplacementDescription(
+                    suffix="norm",
+                    target_module=APEXFusedRMSNorm,
+                    kwargs={}
+                ),
+            ],
+            target_key="LlamaModel"
+        )
+
+        self.append_or_create_method_replacement(
+            description=[
+                {"forward": LlamaModel_forward()}
+            ],
+            target_key="LlamaModel"
+        )
+        self.append_or_create_method_replacement(
+            description=[
+                {"forward": LlamaForCausalLM_forward()}
+            ],
+            target_key="LlamaForCausalLM"
+        )
+
+    def post_process(self, model: nn.Module):
+        if model.peft_type != PeftType.LORA:
+            return
+        peft_config = model.peft_config
+        active_adapter = model.active_adapters[0] if \
+            hasattr(model, "active_adapters") else model.active_adapter
+        target_modules = peft_config[active_adapter].target_modules
+
+        # for now, fast_lora only support lora_dropout=0 and bias=None
+        lora_dropout = model.peft_config[active_adapter].lora_dropout
+        bias = model.peft_config[active_adapter].bias
+
+        # 首先判断是否可以使用fast_lora
+        check = lora_dropout == 0 and bias == "none"
+
+        # 其次确定mlp的3个线性层是否在target_modules
+        mlp_use_fastlora = "gate_proj" in target_modules and "up_proj" in target_modules and "up_proj" in target_modules
+
+        n_mlp = 0
+        if check:
+            if mlp_use_fastlora:
+                for layer in model.model.model.layers:
+                    layer.mlp.forward = types.MethodType(
+                        apply_lora_mlp_swiglu, layer.mlp)
+                    n_mlp += 1
+
+        print(f"{len(model.model.model.layers)} layers replace mlp with fast_lora mlp")