Fixed reproducibility, more consistant validation

1 files changed, 65 insertions, 27 deletions

diff --git a/training/lora.py b/training/lora.py
index e1c0971..3857d78 100644
--- a/training/lora.py
+++ b/training/lora.py
@@ -1,3 +1,4 @@
+import torch
 import torch.nn as nn
 
 from diffusers import ModelMixin, ConfigMixin
@@ -13,56 +14,93 @@ else:
     xformers = None
 
 
-class LoraAttnProcessor(ModelMixin, ConfigMixin):
-    @register_to_config
-    def __init__(
-        self,
-        cross_attention_dim,
-        inner_dim,
-        r: int = 4
-    ):
+class LoRALinearLayer(nn.Module):
+    def __init__(self, in_features, out_features, rank=4):
         super().__init__()
 
-        if r > min(cross_attention_dim, inner_dim):
+        if rank > min(in_features, out_features):
             raise ValueError(
-                f"LoRA rank {r} must be less or equal than {min(cross_attention_dim, inner_dim)}"
+                f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}"
             )
 
-        self.lora_k_down = nn.Linear(cross_attention_dim, r, bias=False)
-        self.lora_k_up = nn.Linear(r, inner_dim, bias=False)
+        self.lora_down = nn.Linear(in_features, rank, bias=False)
+        self.lora_up = nn.Linear(rank, out_features, bias=False)
+        self.scale = 1.0
 
-        self.lora_v_down = nn.Linear(cross_attention_dim, r, bias=False)
-        self.lora_v_up = nn.Linear(r, inner_dim, bias=False)
+        nn.init.normal_(self.lora_down.weight, std=1 / rank)
+        nn.init.zeros_(self.lora_up.weight)
 
-        self.scale = 1.0
+    def forward(self, hidden_states):
+        down_hidden_states = self.lora_down(hidden_states)
+        up_hidden_states = self.lora_up(down_hidden_states)
 
-        nn.init.normal_(self.lora_k_down.weight, std=1 / r**2)
-        nn.init.zeros_(self.lora_k_up.weight)
+        return up_hidden_states
 
-        nn.init.normal_(self.lora_v_down.weight, std=1 / r**2)
-        nn.init.zeros_(self.lora_v_up.weight)
 
-    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        batch_size, sequence_length, _ = hidden_states.shape
+class LoRACrossAttnProcessor(nn.Module):
+    def __init__(self, hidden_size, cross_attention_dim=None, rank=4):
+        super().__init__()
 
+        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size)
+        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
+        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
+        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size)
+
+    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
+        batch_size, sequence_length, _ = hidden_states.shape
         attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
 
-        query = attn.to_q(hidden_states)
+        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
+        query = attn.head_to_batch_dim(query)
 
         encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
-        key = attn.to_k(encoder_hidden_states) + self.lora_k_up(self.lora_k_down(encoder_hidden_states)) * self.scale
-        value = attn.to_v(encoder_hidden_states) + self.lora_v_up(self.lora_v_down(encoder_hidden_states)) * self.scale
 
+        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
+
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        return hidden_states
+
+
+class LoRAXFormersCrossAttnProcessor(nn.Module):
+    def __init__(self, hidden_size, cross_attention_dim, rank=4):
+        super().__init__()
+
+        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size)
+        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
+        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
+        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size)
+
+    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
+        batch_size, sequence_length, _ = hidden_states.shape
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
+
+        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
         query = attn.head_to_batch_dim(query).contiguous()
+
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+
+        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
+
         key = attn.head_to_batch_dim(key).contiguous()
         value = attn.head_to_batch_dim(value).contiguous()
 
         hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
-        hidden_states = hidden_states.to(query.dtype)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
 
         # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
         # dropout
         hidden_states = attn.to_out[1](hidden_states)