IPEX support for Torch 2.1 and fix dtype erros

library/ipex/__init__.py

@@ -156,20 +156,9 @@ 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
-        if hasattr(torch.xpu, 'getDeviceIdListForCard'):
-            torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
-            torch.cuda.get_device_id_list_per_card = torch.xpu.getDeviceIdListForCard
-        else:
-            torch.cuda.getDeviceIdListForCard = torch.xpu.get_device_id_list_per_card
-            torch.cuda.get_device_id_list_per_card = torch.xpu.get_device_id_list_per_card
 
         ipex_hijacks()
         if not torch.xpu.has_fp64_dtype():
-            try:
-                from .attention import attention_init
-                attention_init()
-            except Exception: # pylint: disable=broad-exception-caught
-                pass
             try:
                 from .diffusers import ipex_diffusers
                 ipex_diffusers()

library/ipex/attention.py

@@ -4,11 +4,8 @@ import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unuse
 # pylint: disable=protected-access, missing-function-docstring, line-too-long
 
 original_torch_bmm = torch.bmm
-def torch_bmm(input, mat2, *, out=None):
-    if input.dtype != mat2.dtype:
-        mat2 = mat2.to(input.dtype)
-
-    #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
+def torch_bmm_32_bit(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 = input.element_size()
     slice_block_size = input_tokens * mat2_shape / 1024 / 1024 * block_multiply
@@ -17,7 +14,7 @@ def torch_bmm(input, mat2, *, out=None):
     split_slice_size = batch_size_attention
     if block_size > 4:
         do_split = True
-        #Find something divisible with the input_tokens
+        # Find something divisible with the input_tokens
         while (split_slice_size * slice_block_size) > 4:
             split_slice_size = split_slice_size // 2
             if split_slice_size <= 1:
@@ -30,7 +27,7 @@ def torch_bmm(input, mat2, *, out=None):
     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
+        # Find something divisible with the input_tokens
         while (split_2_slice_size * slice_block_size2) > 4:
             split_2_slice_size = split_2_slice_size // 2
             if split_2_slice_size <= 1:
@@ -64,8 +61,8 @@ def torch_bmm(input, mat2, *, out=None):
     return hidden_states
 
 original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
-def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
-    #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
+def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
+    # ARC GPUs can't allocate more than 4GB to a single block, Slice it:
     if len(query.shape) == 3:
         batch_size_attention, query_tokens, shape_four = query.shape
         shape_one = 1
@@ -74,11 +71,6 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
         shape_one, batch_size_attention, query_tokens, shape_four = query.shape
         no_shape_one = False
 
-    if query.dtype != key.dtype:
-        key = key.to(dtype=query.dtype)
-    if query.dtype != value.dtype:
-        value = value.to(dtype=query.dtype)
-
     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
@@ -86,7 +78,7 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
     split_slice_size = batch_size_attention
     if block_size > 4:
         do_split = True
-        #Find something divisible with the shape_one
+        # Find something divisible with the shape_one
         while (split_slice_size * slice_block_size) > 4:
             split_slice_size = split_slice_size // 2
             if split_slice_size <= 1:
@@ -99,7 +91,7 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
     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
+        # Find something divisible with the batch_size_attention
         while (split_2_slice_size * slice_block_size2) > 4:
             split_2_slice_size = split_2_slice_size // 2
             if split_2_slice_size <= 1:
@@ -155,8 +147,3 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
             query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal
         )
     return hidden_states
-
-def attention_init():
-    #ARC GPUs can't allocate more than 4GB to a single block:
-    torch.bmm = torch_bmm
-    torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention

library/ipex/hijacks.py

@@ -117,6 +117,31 @@ def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name
     else:
         return original_linalg_solve(A, B, *args, **kwargs)
 
+if torch.xpu.has_fp64_dtype():
+    original_torch_bmm = torch.bmm
+    original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
+else:
+    # 64 bit attention workarounds for Alchemist:
+    try:
+        from .attention import torch_bmm_32_bit as original_torch_bmm
+        from .attention import scaled_dot_product_attention_32_bit as original_scaled_dot_product_attention
+    except Exception: # pylint: disable=broad-exception-caught
+        original_torch_bmm = torch.bmm
+        original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
+
+# dtype errors:
+def torch_bmm(input, mat2, *, out=None):
+    if input.dtype != mat2.dtype:
+        mat2 = mat2.to(input.dtype)
+    return original_torch_bmm(input, mat2, out=out)
+
+def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
+    if query.dtype != key.dtype:
+        key = key.to(dtype=query.dtype)
+    if query.dtype != value.dtype:
+        value = value.to(dtype=query.dtype)
+    return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
+
 @property
 def is_cuda(self):
     return self.device.type == 'xpu'
@@ -156,10 +181,10 @@ def ipex_hijacks():
         lambda orig_func, f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs:
         orig_func(orig_func, f, map_location=return_xpu(map_location), pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs),
         lambda orig_func, f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs: check_device(map_location))
-
-    CondFunc('torch.Generator',
-        lambda orig_func, device=None: torch.xpu.Generator(return_xpu(device)),
-        lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
+    if hasattr(torch.xpu, "Generator"):
+        CondFunc('torch.Generator',
+            lambda orig_func, device=None: torch.xpu.Generator(return_xpu(device)),
+            lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
 
     # TiledVAE and ControlNet:
     CondFunc('torch.batch_norm',
@@ -208,11 +233,16 @@ def ipex_hijacks():
         lambda orig_func, *args, **kwargs: True)
 
     # Functions that make compile mad with CondFunc:
-    torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
     torch.nn.DataParallel = DummyDataParallel
+    torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
+
     torch.autocast = ipex_autocast
-    torch.cat = torch_cat
-    torch.linalg.solve = linalg_solve
-    torch.UntypedStorage.is_cuda = is_cuda
-    torch.nn.functional.interpolate = interpolate
     torch.backends.cuda.sdp_kernel = return_null_context
+    torch.UntypedStorage.is_cuda = is_cuda
+
+    torch.nn.functional.interpolate = interpolate
+    torch.linalg.solve = linalg_solve
+
+    torch.bmm = torch_bmm
+    torch.cat = torch_cat
+    torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention