IPEX update, concat LoRA

library/ipex/__init__.py

@@ -144,7 +144,7 @@ def ipex_init(): # pylint: disable=too-many-statements
         ipex._C._DeviceProperties.minor = 2
 
         #Fix functions with ipex:
-        torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_allocated(device)), torch.xpu.get_device_properties(device).total_memory]
+        torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_reserved(device)), torch.xpu.get_device_properties(device).total_memory]
         torch._utils._get_available_device_type = lambda: "xpu"
         torch.has_cuda = True
         torch.cuda.has_half = True
@@ -156,7 +156,6 @@ def ipex_init(): # pylint: disable=too-many-statements
         torch.cuda.get_device_properties.minor = 7
         torch.cuda.ipc_collect = lambda *args, **kwargs: None
         torch.cuda.utilization = lambda *args, **kwargs: 0
-        # getDeviceIdListForCard is renamed since https://github.com/intel/intel-extension-for-pytorch/commit/835b41fd5c8b6facf9efee8312f20699850ee592
         if hasattr(torch.xpu, 'getDeviceIdListForCard'):
             torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
             torch.cuda.get_device_id_list_per_card = torch.xpu.getDeviceIdListForCard

library/ipex/attention.py

@@ -10,26 +10,28 @@ def torch_bmm(input, mat2, *, out=None):
 
     #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
     batch_size_attention, input_tokens, mat2_shape = input.shape[0], input.shape[1], mat2.shape[2]
-    block_multiply = 2.4 if input.dtype == torch.float32 else 1.2
-    block_size = (batch_size_attention * input_tokens * mat2_shape) / 1024 * block_multiply #MB
+    block_multiply = input.element_size()
+    slice_block_size = input_tokens * mat2_shape / 1024 / 1024 * block_multiply
+    block_size = batch_size_attention * slice_block_size
+
     split_slice_size = batch_size_attention
-    if block_size >= 4000:
+    if block_size > 4:
         do_split = True
         #Find something divisible with the input_tokens
-        while ((split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply) > 4000:
+        while (split_slice_size * slice_block_size) > 4:
             split_slice_size = split_slice_size // 2
             if split_slice_size <= 1:
                 split_slice_size = 1
                 break
     else:
         do_split = False
 
-    split_block_size = (split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply #MB
     split_2_slice_size = input_tokens
-    if split_block_size >= 4000:
+    if split_slice_size * slice_block_size > 4:
+        slice_block_size2 = split_slice_size * mat2_shape / 1024 / 1024 * block_multiply
         do_split_2 = True
         #Find something divisible with the input_tokens
-        while ((split_slice_size * split_2_slice_size * mat2_shape) / 1024 * block_multiply) > 4000:
+        while (split_2_slice_size * slice_block_size2) > 4:
             split_2_slice_size = split_2_slice_size // 2
             if split_2_slice_size <= 1:
                 split_2_slice_size = 1
@@ -71,26 +73,29 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
     else:
         shape_one, batch_size_attention, query_tokens, shape_four = query.shape
         no_shape_one = False
-    block_multiply = 3.6 if query.dtype == torch.float32 else 1.8
-    block_size = (shape_one * batch_size_attention * query_tokens * shape_four) / 1024 * block_multiply #MB
+
+    block_multiply = query.element_size()
+    slice_block_size = shape_one * query_tokens * shape_four / 1024 / 1024 * block_multiply
+    block_size = batch_size_attention * slice_block_size
+
     split_slice_size = batch_size_attention
-    if block_size >= 4000:
+    if block_size > 4:
         do_split = True
         #Find something divisible with the shape_one
-        while ((shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply) > 4000:
+        while (split_slice_size * slice_block_size) > 4:
             split_slice_size = split_slice_size // 2
             if split_slice_size <= 1:
                 split_slice_size = 1
                 break
     else:
         do_split = False
 
-    split_block_size = (shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply #MB
     split_2_slice_size = query_tokens
-    if split_block_size >= 4000:
+    if split_slice_size * slice_block_size > 4:
+        slice_block_size2 = shape_one * split_slice_size * shape_four / 1024 / 1024 * block_multiply
         do_split_2 = True
         #Find something divisible with the batch_size_attention
-        while ((shape_one * split_slice_size * split_2_slice_size * shape_four) / 1024 * block_multiply) > 4000:
+        while (split_2_slice_size * slice_block_size2) > 4:
             split_2_slice_size = split_2_slice_size // 2
             if split_2_slice_size <= 1:
                 split_2_slice_size = 1

library/ipex/diffusers.py

@@ -55,13 +55,14 @@ def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, a
         )
 
         #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
-        block_multiply = 2.4 if query.dtype == torch.float32 else 1.2
-        block_size = (batch_size_attention * query_tokens * shape_three) / 1024 * block_multiply #MB
+        block_multiply = query.element_size()
+        slice_block_size = self.slice_size * shape_three / 1024 / 1024 * block_multiply
+        block_size = query_tokens * slice_block_size
         split_2_slice_size = query_tokens
-        if block_size >= 4000:
+        if block_size > 4:
             do_split_2 = True
             #Find something divisible with the query_tokens
-            while ((self.slice_size * split_2_slice_size * shape_three) / 1024 * block_multiply) > 4000:
+            while (split_2_slice_size * slice_block_size) > 4:
                 split_2_slice_size = split_2_slice_size // 2
                 if split_2_slice_size <= 1:
                     split_2_slice_size = 1

networks/merge_lora.py

@@ -110,7 +110,7 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
                 module.weight = torch.nn.Parameter(weight)
 
 
-def merge_lora_models(models, ratios, merge_dtype):
+def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
     base_alphas = {}  # alpha for merged model
     base_dims = {}
 
@@ -158,26 +158,43 @@ def merge_lora_models(models, ratios, merge_dtype):
         for key in lora_sd.keys():
             if "alpha" in key:
                 continue
+            if "lora_up" in key and concat:
+                concat_dim = 1
+            elif "lora_down" in key and concat:
+                concat_dim = 0
+            else:
+                concat_dim = None
 
             lora_module_name = key[: key.rfind(".lora_")]
 
             base_alpha = base_alphas[lora_module_name]
             alpha = alphas[lora_module_name]
 
             scale = math.sqrt(alpha / base_alpha) * ratio
+            scale = abs(scale) if "lora_up" in key else scale # マイナスの重みに対応する。
 
             if key in merged_sd:
                 assert (
-                    merged_sd[key].size() == lora_sd[key].size()
+                    merged_sd[key].size() == lora_sd[key].size() or concat_dim is not None
                 ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
-                merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
+                if concat_dim is not None:
+                    merged_sd[key] = torch.cat([merged_sd[key], lora_sd[key] * scale], dim=concat_dim)
+                else:
+                    merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
             else:
                 merged_sd[key] = lora_sd[key] * scale
 
     # set alpha to sd
     for lora_module_name, alpha in base_alphas.items():
         key = lora_module_name + ".alpha"
         merged_sd[key] = torch.tensor(alpha)
+        if shuffle:
+            key_down = lora_module_name + ".lora_down.weight"
+            key_up = lora_module_name + ".lora_up.weight"
+            dim = merged_sd[key_down].shape[0]
+            perm = torch.randperm(dim)
+            merged_sd[key_down] = merged_sd[key_down][perm]
+            merged_sd[key_up] = merged_sd[key_up][:,perm]
 
     print("merged model")
     print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
@@ -256,7 +273,7 @@ def str_to_dtype(p):
             args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, sai_metadata, save_dtype, vae
         )
     else:
-        state_dict, metadata, v2 = merge_lora_models(args.models, args.ratios, merge_dtype)
+        state_dict, metadata, v2 = merge_lora_models(args.models, args.ratios, merge_dtype, args.concat, args.shuffle)
 
         print(f"calculating hashes and creating metadata...")
 
@@ -317,7 +334,19 @@ def setup_parser() -> argparse.ArgumentParser:
         help="do not save sai modelspec metadata (minimum ss_metadata for LoRA is saved) / "
         + "sai modelspecのメタデータを保存しない（LoRAの最低限のss_metadataは保存される）",
     )
-
+    parser.add_argument(
+        "--concat",
+        action="store_true",
+        help="concat lora instead of merge (The dim(rank) of the output LoRA is the sum of the input dims) / "
+        + "マージの代わりに結合する（LoRAのdim(rank)は入力dimの合計になる）",
+    )
+    parser.add_argument(
+        "--shuffle",
+        action="store_true",
+        help="shuffle lora weight./ "
+        + "LoRAの重みをシャッフルする",
+    )
+
     return parser
 
 

networks/sdxl_merge_lora.py

@@ -113,7 +113,7 @@ def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_
                 module.weight = torch.nn.Parameter(weight)
 
 
-def merge_lora_models(models, ratios, merge_dtype):
+def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
     base_alphas = {}  # alpha for merged model
     base_dims = {}
 
@@ -161,26 +161,44 @@ def merge_lora_models(models, ratios, merge_dtype):
         for key in tqdm(lora_sd.keys()):
             if "alpha" in key:
                 continue
+
+            if "lora_up" in key and concat:
+                concat_dim = 1
+            elif "lora_down" in key and concat:
+                concat_dim = 0
+            else:
+                concat_dim = None
 
             lora_module_name = key[: key.rfind(".lora_")]
 
             base_alpha = base_alphas[lora_module_name]
             alpha = alphas[lora_module_name]
 
             scale = math.sqrt(alpha / base_alpha) * ratio
-
+            scale = abs(scale) if "lora_up" in key else scale # マイナスの重みに対応する。
+
             if key in merged_sd:
                 assert (
-                    merged_sd[key].size() == lora_sd[key].size()
+                    merged_sd[key].size() == lora_sd[key].size() or concat_dim is not None
                 ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
-                merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
+                if concat_dim is not None:
+                    merged_sd[key] = torch.cat([merged_sd[key], lora_sd[key] * scale], dim=concat_dim)
+                else:
+                    merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
             else:
                 merged_sd[key] = lora_sd[key] * scale
 
     # set alpha to sd
     for lora_module_name, alpha in base_alphas.items():
         key = lora_module_name + ".alpha"
         merged_sd[key] = torch.tensor(alpha)
+        if shuffle:
+            key_down = lora_module_name + ".lora_down.weight"
+            key_up = lora_module_name + ".lora_up.weight"
+            dim = merged_sd[key_down].shape[0]
+            perm = torch.randperm(dim)
+            merged_sd[key_down] = merged_sd[key_down][perm]
+            merged_sd[key_up] = merged_sd[key_up][:,perm]
 
     print("merged model")
     print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
@@ -252,7 +270,7 @@ def str_to_dtype(p):
             args.save_to, text_model1, text_model2, unet, 0, 0, ckpt_info, vae, logit_scale, sai_metadata, save_dtype
         )
     else:
-        state_dict, metadata = merge_lora_models(args.models, args.ratios, merge_dtype)
+        state_dict, metadata = merge_lora_models(args.models, args.ratios, merge_dtype, args.concat, args.shuffle)
 
         print(f"calculating hashes and creating metadata...")
 
@@ -307,6 +325,18 @@ def setup_parser() -> argparse.ArgumentParser:
         help="do not save sai modelspec metadata (minimum ss_metadata for LoRA is saved) / "
         + "sai modelspecのメタデータを保存しない（LoRAの最低限のss_metadataは保存される）",
     )
+    parser.add_argument(
+        "--concat",
+        action="store_true",
+        help="concat lora instead of merge (The dim(rank) of the output LoRA is the sum of the input dims) / "
+        + "マージの代わりに結合する（LoRAのdim(rank)は入力dimの合計になる）",
+    )
+    parser.add_argument(
+        "--shuffle",
+        action="store_true",
+        help="shuffle lora weight./ "
+        + "LoRAの重みをシャッフルする",
+    )
 
     return parser