Rework configs to remove redundant code

torchscale/architecture/config.py

@@ -2,57 +2,72 @@
 # Licensed under The MIT License [see LICENSE for details]
 
 
-class EncoderConfig:
+class Config:
     def __init__(self, **kwargs):
+        self.activation_fn = kwargs.pop("activation_fn", "gelu")
+        self.dropout = kwargs.pop("dropout", 0.0)
+        self.drop_path_rate = kwargs.pop("drop_path_rate", 0.0)
+        self.attention_dropout = kwargs.pop("attention_dropout", 0.0)
+        self.activation_dropout = kwargs.pop("activation_dropout", 0.0)
+        self.no_scale_embedding = kwargs.pop("no_scale_embedding", True)
+        self.layernorm_embedding = kwargs.pop("layernorm_embedding", False)
+        self.moe_freq = kwargs.pop("moe_freq", 0)
+        self.moe_top1_expert = kwargs.pop("moe_top1_expert", False)
+        self.moe_expert_count = kwargs.pop("moe_expert_count", 0)
+        self.moe_gating_use_fp32 = kwargs.pop("moe_gating_use_fp32", True)
+        self.moe_eval_capacity_token_fraction = kwargs.pop(
+            "moe_eval_capacity_token_fraction", 0.25
+        )
+        self.moe_second_expert_policy = kwargs.pop("moe_second_expert_policy", "random")
+        self.moe_normalize_gate_prob_before_dropping = kwargs.pop(
+            "moe_normalize_gate_prob_before_dropping", False
+        )
+        self.use_xmoe = kwargs.pop("use_xmoe", False)
+        self.rel_pos_buckets = kwargs.pop("rel_pos_buckets", 0)
+        self.max_rel_pos = kwargs.pop("max_rel_pos", 0)
+        self.deepnorm = kwargs.pop("deepnorm", False)
+        self.subln = kwargs.pop("subln", True)
+        self.bert_init = kwargs.pop("bert_init", False)
+        self.multiway = kwargs.pop("multiway", False)
+        self.no_output_layer = kwargs.pop("no_output_layer", False)
+        self.layernorm_eps = kwargs.pop("layernorm_eps", 1e-5)
+        self.vocab_size = kwargs.pop("vocab_size", -1)
+        self.checkpoint_activations = kwargs.pop("checkpoint_activations", False)
+        self.fsdp = kwargs.pop("fsdp", False)
+        self.ddp_rank = kwargs.pop("ddp_rank", 0)
+        self.xpos_rel_pos = kwargs.pop("xpos_rel_pos", False)
+        self.xpos_scale_base = kwargs.pop("xpos_scale_base", 512)
+
+        if self.use_xmoe:
+            self.moe_normalize_gate_prob_before_dropping = True
+            self.moe_second_expert_policy = "random"
+            assert self.moe_freq > 0 and self.moe_expert_count > 0
+
+    def override(self, args):
+        for hp in self.__dict__.keys():
+            if getattr(args, hp, None) is not None:
+                self.__dict__[hp] = getattr(args, hp, None)
+
+
+class EncoderConfig(Config):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
         self.encoder_embed_dim = kwargs.pop("encoder_embed_dim", 768)
         self.encoder_attention_heads = kwargs.pop("encoder_attention_heads", 12)
         self.encoder_ffn_embed_dim = kwargs.pop("encoder_ffn_embed_dim", 3072)
         self.encoder_layers = kwargs.pop("encoder_layers", 12)
         self.encoder_normalize_before = kwargs.pop("encoder_normalize_before", True)
         self.normalize_output = kwargs.pop("normalize_output", True)
-        self.activation_fn = kwargs.pop("activation_fn", "gelu")
-        self.dropout = kwargs.pop("dropout", 0.0)
-        self.drop_path_rate = kwargs.pop("drop_path_rate", 0.0)
-        self.attention_dropout = kwargs.pop("attention_dropout", 0.0)
-        self.activation_dropout = kwargs.pop("activation_dropout", 0.0)
-        self.no_scale_embedding = kwargs.pop("no_scale_embedding", True)
-        self.layernorm_embedding = kwargs.pop("layernorm_embedding", False)
-        self.moe_freq = kwargs.pop("moe_freq", 0)
-        self.moe_top1_expert = kwargs.pop("moe_top1_expert", False)
-        self.moe_expert_count = kwargs.pop("moe_expert_count", 0)
-        self.moe_gating_use_fp32 = kwargs.pop("moe_gating_use_fp32", True)
-        self.moe_eval_capacity_token_fraction = kwargs.pop(
-            "moe_eval_capacity_token_fraction", 0.25
-        )
-        self.moe_second_expert_policy = kwargs.pop("moe_second_expert_policy", "random")
-        self.moe_normalize_gate_prob_before_dropping = kwargs.pop(
-            "moe_normalize_gate_prob_before_dropping", False
-        )
-        self.use_xmoe = kwargs.pop("use_xmoe", False)
         self.rel_pos_buckets = kwargs.pop("rel_pos_buckets", 0)
-        self.max_rel_pos = kwargs.pop("max_rel_pos", 0)
-        self.deepnorm = kwargs.pop("deepnorm", False)
-        self.subln = kwargs.pop("subln", True)
-        self.bert_init = kwargs.pop("bert_init", False)
-        self.multiway = kwargs.pop("multiway", False)
         self.share_encoder_input_output_embed = kwargs.pop(
             "share_encoder_input_output_embed", False
         )
         self.max_source_positions = kwargs.pop("max_source_positions", 1024)
-        self.no_output_layer = kwargs.pop("no_output_layer", False)
-        self.layernorm_eps = kwargs.pop("layernorm_eps", 1e-5)
-        # Text
-        self.vocab_size = kwargs.pop("vocab_size", -1)
         # Vision
         self.img_size = kwargs.pop("img_size", 224)
         self.patch_size = kwargs.pop("patch_size", 16)
         self.in_chans = kwargs.pop("in_chans", 3)
-        # Fairscale
+        
-        self.checkpoint_activations = kwargs.pop("checkpoint_activations", False)
-        self.fsdp = kwargs.pop("fsdp", False)
-        self.ddp_rank = kwargs.pop("ddp_rank", 0)
-        self.xpos_rel_pos = kwargs.pop("xpos_rel_pos", False)
-        self.xpos_scale_base = kwargs.pop("xpos_scale_base", 512)
 
         if self.deepnorm:
             self.encoder_normalize_before = False
@@ -60,63 +75,22 @@ class EncoderConfig:
         if self.subln:
             self.encoder_normalize_before = True
             self.deepnorm = False
-        if self.use_xmoe:
+        
-            self.moe_normalize_gate_prob_before_dropping = True
-            self.moe_second_expert_policy = "random"
-            assert self.moe_freq > 0 and self.moe_expert_count > 0
-
-    def override(self, args):
-        for hp in self.__dict__.keys():
-            if getattr(args, hp, None) is not None:
-                self.__dict__[hp] = getattr(args, hp, None)
 
 
-class DecoderConfig:
+class DecoderConfig(Config):
     def __init__(self, **kwargs):
+        super().__init__(**kwargs)
         self.decoder_embed_dim = kwargs.pop("decoder_embed_dim", 768)
         self.decoder_attention_heads = kwargs.pop("decoder_attention_heads", 12)
         self.decoder_ffn_embed_dim = kwargs.pop("decoder_ffn_embed_dim", 3072)
         self.decoder_layers = kwargs.pop("decoder_layers", 12)
         self.decoder_normalize_before = kwargs.pop("decoder_normalize_before", True)
-        self.activation_fn = kwargs.pop("activation_fn", "gelu")
-        self.dropout = kwargs.pop("dropout", 0.0)
-        self.drop_path_rate = kwargs.pop("drop_path_rate", 0.0)
-        self.attention_dropout = kwargs.pop("attention_dropout", 0.0)
-        self.activation_dropout = kwargs.pop("activation_dropout", 0.0)
-        self.no_scale_embedding = kwargs.pop("no_scale_embedding", True)
-        self.layernorm_embedding = kwargs.pop("layernorm_embedding", False)
-        self.moe_freq = kwargs.pop("moe_freq", 0)
-        self.moe_top1_expert = kwargs.pop("moe_top1_expert", False)
-        self.moe_expert_count = kwargs.pop("moe_expert_count", 0)
-        self.moe_gating_use_fp32 = kwargs.pop("moe_gating_use_fp32", True)
-        self.moe_eval_capacity_token_fraction = kwargs.pop(
-            "moe_eval_capacity_token_fraction", 0.25
-        )
-        self.moe_second_expert_policy = kwargs.pop("moe_second_expert_policy", "random")
-        self.moe_normalize_gate_prob_before_dropping = kwargs.pop(
-            "moe_normalize_gate_prob_before_dropping", False
-        )
-        self.use_xmoe = kwargs.pop("use_xmoe", False)
         self.rel_pos_buckets = kwargs.pop("rel_pos_buckets", 0)
-        self.max_rel_pos = kwargs.pop("max_rel_pos", 0)
-        self.deepnorm = kwargs.pop("deepnorm", False)
-        self.subln = kwargs.pop("subln", True)
-        self.bert_init = kwargs.pop("bert_init", False)
-        self.multiway = kwargs.pop("multiway", False)
         self.share_decoder_input_output_embed = kwargs.pop(
             "share_decoder_input_output_embed", False
         )
         self.max_target_positions = kwargs.pop("max_target_positions", 1024)
-        self.no_output_layer = kwargs.pop("no_output_layer", False)
-        self.layernorm_eps = kwargs.pop("layernorm_eps", 1e-5)
-        # Text
-        self.vocab_size = kwargs.pop("vocab_size", -1)
-        # Fairscale
-        self.checkpoint_activations = kwargs.pop("checkpoint_activations", False)
-        self.fsdp = kwargs.pop("fsdp", False)
-        self.ddp_rank = kwargs.pop("ddp_rank", 0)
-        self.xpos_rel_pos = kwargs.pop("xpos_rel_pos", False)
-        self.xpos_scale_base = kwargs.pop("xpos_scale_base", 512)
 
         if self.deepnorm:
             self.decoder_normalize_before = False
@@ -124,85 +98,10 @@ class DecoderConfig:
         if self.subln:
             self.decoder_normalize_before = True
             self.deepnorm = False
-        if self.use_xmoe:
-            self.moe_normalize_gate_prob_before_dropping = True
-            self.moe_second_expert_policy = "random"
-            assert self.moe_freq > 0 and self.moe_expert_count > 0
-
-    def override(self, args):
-        for hp in self.__dict__.keys():
-            if getattr(args, hp, None) is not None:
-                self.__dict__[hp] = getattr(args, hp, None)
 
 
-class EncoderDecoderConfig:
+
+class EncoderDecoderConfig(EncoderConfig, DecoderConfig):
     def __init__(self, **kwargs):
-        self.encoder_embed_dim = kwargs.pop("encoder_embed_dim", 768)
+        super().__init__(**kwargs)
-        self.encoder_attention_heads = kwargs.pop("encoder_attention_heads", 12)
-        self.encoder_ffn_embed_dim = kwargs.pop("encoder_ffn_embed_dim", 3072)
-        self.encoder_layers = kwargs.pop("encoder_layers", 12)
-        self.encoder_normalize_before = kwargs.pop("encoder_normalize_before", True)
-        self.decoder_embed_dim = kwargs.pop("decoder_embed_dim", 768)
-        self.decoder_attention_heads = kwargs.pop("decoder_attention_heads", 12)
-        self.decoder_ffn_embed_dim = kwargs.pop("decoder_ffn_embed_dim", 3072)
-        self.decoder_layers = kwargs.pop("decoder_layers", 12)
-        self.decoder_normalize_before = kwargs.pop("decoder_normalize_before", True)
-        self.activation_fn = kwargs.pop("activation_fn", "gelu")
-        self.dropout = kwargs.pop("dropout", 0.0)
-        self.drop_path_rate = kwargs.pop("drop_path_rate", 0.0)
-        self.attention_dropout = kwargs.pop("attention_dropout", 0.0)
-        self.activation_dropout = kwargs.pop("activation_dropout", 0.0)
-        self.no_scale_embedding = kwargs.pop("no_scale_embedding", True)
-        self.layernorm_embedding = kwargs.pop("layernorm_embedding", False)
-        self.moe_freq = kwargs.pop("moe_freq", 0)
-        self.moe_top1_expert = kwargs.pop("moe_top1_expert", False)
-        self.moe_expert_count = kwargs.pop("moe_expert_count", 0)
-        self.moe_gating_use_fp32 = kwargs.pop("moe_gating_use_fp32", True)
-        self.moe_eval_capacity_token_fraction = kwargs.pop(
-            "moe_eval_capacity_token_fraction", 0.25
-        )
-        self.moe_second_expert_policy = kwargs.pop("moe_second_expert_policy", "random")
-        self.moe_normalize_gate_prob_before_dropping = kwargs.pop(
-            "moe_normalize_gate_prob_before_dropping", False
-        )
-        self.use_xmoe = kwargs.pop("use_xmoe", False)
-        self.rel_pos_buckets = kwargs.pop("rel_pos_buckets", 0)
-        self.max_rel_pos = kwargs.pop("max_rel_pos", 0)
-        self.deepnorm = kwargs.pop("deepnorm", False)
-        self.subln = kwargs.pop("subln", True)
-        self.bert_init = kwargs.pop("bert_init", False)
-        self.multiway = kwargs.pop("multiway", False)
         self.share_all_embeddings = kwargs.pop("share_all_embeddings", False)
-        self.share_decoder_input_output_embed = kwargs.pop(
-            "share_decoder_input_output_embed", False
-        )
-        self.max_source_positions = kwargs.pop("max_source_positions", 1024)
-        self.max_target_positions = kwargs.pop("max_target_positions", 1024)
-        self.no_output_layer = kwargs.pop("no_output_layer", False)
-        self.layernorm_eps = kwargs.pop("layernorm_eps", 1e-5)
-        # Text
-        self.vocab_size = kwargs.pop("vocab_size", -1)
-        # Fairscale
-        self.checkpoint_activations = kwargs.pop("checkpoint_activations", False)
-        self.fsdp = kwargs.pop("fsdp", False)
-        self.ddp_rank = kwargs.pop("ddp_rank", 0)
-        self.xpos_rel_pos = kwargs.pop("xpos_rel_pos", False)
-        self.xpos_scale_base = kwargs.pop("xpos_scale_base", 512)
-
-        if self.deepnorm:
-            self.encoder_normalize_before = False
-            self.decoder_normalize_before = False
-            self.subln = False
-        if self.subln:
-            self.encoder_normalize_before = True
-            self.decoder_normalize_before = True
-            self.deepnorm = False
-        if self.use_xmoe:
-            self.moe_normalize_gate_prob_before_dropping = True
-            self.moe_second_expert_policy = "random"
-            assert self.moe_freq > 0 and self.moe_expert_count > 0
-
-    def override(self, args):
-        for hp in self.__dict__.keys():
-            if getattr(args, hp, None) is not None:
-                self.__dict__[hp] = getattr(args, hp, None)

torchscale/architecture/decoder.py

@@ -9,12 +9,14 @@ import torch.nn as nn
 from fairscale.nn import checkpoint_wrapper, wrap
 
 from torchscale.architecture.utils import init_bert_params
+from torchscale.architecture.config import DecoderConfig, EncoderConfig
 from torchscale.component.droppath import DropPath
 from torchscale.component.feedforward_network import FeedForwardNetwork, make_experts
 from torchscale.component.multihead_attention import MultiheadAttention
 from torchscale.component.relative_position_bias import RelativePositionBias
 from torchscale.component.xmoe.moe_layer import MOELayer
 from torchscale.component.xmoe.routing import Top1Gate, Top2Gate
+
 try:
     from apex.normalization import FusedLayerNorm as LayerNorm
 except ModuleNotFoundError:
@@ -23,7 +25,7 @@ except ModuleNotFoundError:
 class DecoderLayer(nn.Module):
     def __init__(
         self,
-        args,
+        args: DecoderConfig,
         depth,
         is_moe_layer=False,
         is_encoder_decoder=False,
@@ -209,7 +211,7 @@ class DecoderLayer(nn.Module):
 class Decoder(nn.Module):
     def __init__(
         self,
-        args,
+        args: DecoderConfig,
         embed_tokens=None,
         embed_positions=None,
         output_projection=None,

torchscale/architecture/encoder.py

@@ -13,6 +13,7 @@ except ModuleNotFoundError:
     from torch.nn import LayerNorm
 
 from torchscale.architecture.utils import init_bert_params
+from torchscale.architecture.config import EncoderConfig
 from torchscale.component.droppath import DropPath
 from torchscale.component.feedforward_network import FeedForwardNetwork, make_experts
 from torchscale.component.multihead_attention import MultiheadAttention
@@ -23,7 +24,11 @@ from torchscale.component.xmoe.routing import Top1Gate, Top2Gate
 
 
 class EncoderLayer(nn.Module):
-    def __init__(self, args, depth, is_moe_layer=False, is_encoder_decoder=False):
+    def __init__(self, 
+                 args: EncoderConfig, 
+                 depth, 
+                 is_moe_layer: bool = False, 
+                 is_encoder_decoder: bool = False):
         super().__init__()
         self.args = args
         self.embed_dim = args.encoder_embed_dim
@@ -165,11 +170,11 @@ class EncoderLayer(nn.Module):
 class Encoder(nn.Module):
     def __init__(
         self,
-        args,
+        args: EncoderConfig,
         embed_tokens=None,
         embed_positions=None,
         output_projection=None,
-        is_encoder_decoder=False,
+        is_encoder_decoder: bool = False,
         **kwargs
     ):
         self.args = args

torchscale/architecture/encoder_decoder.py

@@ -3,6 +3,7 @@
 
 import torch.nn as nn
 
+from torchscale.architecture.config import EncoderDecoderConfig
 from torchscale.architecture.decoder import Decoder
 from torchscale.architecture.encoder import Encoder