Merge pull request #457 from Disty0/united

IPEX fixes

modeling/ipex/__init__.py

@@ -2,22 +2,12 @@ import os
 import sys
 import contextlib
 import torch
-import intel_extension_for_pytorch as ipex
-from .diffusers import ipex_diffusers
+import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
 from .hijacks import ipex_hijacks
-from logger import logger
 
-#ControlNet depth_leres++
-class DummyDataParallel(torch.nn.Module):
-    def __new__(cls, module, device_ids=None, output_device=None, dim=0):
-        if type(device_ids) is list and len(device_ids) > 1:
-            logger.info("IPEX backend doesn't support DataParallel on multiple XPU devices")
-        return module.to("xpu")
+# pylint: disable=protected-access, missing-function-docstring, line-too-long
 
-def return_null_context(*args, **kwargs):
-    return contextlib.nullcontext()
-
-def ipex_init():
+def ipex_init(): # pylint: disable=too-many-statements
     #Replace cuda with xpu:
     torch.cuda.current_device = torch.xpu.current_device
     torch.cuda.current_stream = torch.xpu.current_stream
@@ -30,6 +20,7 @@ def ipex_init():
     torch.cuda.init = torch.xpu.init
     torch.cuda.is_available = torch.xpu.is_available
     torch.cuda.is_initialized = torch.xpu.is_initialized
+    torch.cuda.is_current_stream_capturing = lambda: False
     torch.cuda.set_device = torch.xpu.set_device
     torch.cuda.stream = torch.xpu.stream
     torch.cuda.synchronize = torch.xpu.synchronize
@@ -138,8 +129,13 @@ def ipex_init():
     torch.cuda.amp.common.amp_definitely_not_available = lambda: False
     try:
         torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
-    except Exception:
-        torch.cuda.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
+    except Exception: # pylint: disable=broad-exception-caught
+        try:
+            from .gradscaler import gradscaler_init # pylint: disable=import-outside-toplevel, import-error
+            gradscaler_init()
+            torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
+        except Exception: # pylint: disable=broad-exception-caught
+            torch.cuda.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
 
     #C
     torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream
@@ -156,10 +152,12 @@ def ipex_init():
     torch.cuda.get_device_capability = lambda: [11,7]
     torch.cuda.get_device_properties.major = 11
     torch.cuda.get_device_properties.minor = 7
-    torch.backends.cuda.sdp_kernel = return_null_context
-    torch.nn.DataParallel = DummyDataParallel
     torch.cuda.ipc_collect = lambda: None
     torch.cuda.utilization = lambda: 0
 
     ipex_hijacks()
-    ipex_diffusers()
+    try:
+        from .diffusers import ipex_diffusers # pylint: disable=import-outside-toplevel, import-error
+        ipex_diffusers()
+    except Exception: # pylint: disable=broad-exception-caught
+        pass

modeling/ipex/diffusers.py

@@ -1,11 +1,13 @@
 import torch
-import intel_extension_for_pytorch as ipex
-import torch.nn.functional as F
-import diffusers #0.20.2
+import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
+import torch.nn.functional as F # pylint: disable=ungrouped-imports
+import diffusers #0.20.2 # pylint: disable=import-error
+
+# pylint: disable=protected-access, missing-function-docstring, line-too-long
 
 Attention = diffusers.models.attention_processor.Attention
 
-class SlicedAttnProcessor:
+class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
     r"""
     Processor for implementing sliced attention.
 
@@ -18,7 +20,7 @@ class SlicedAttnProcessor:
     def __init__(self, slice_size):
         self.slice_size = slice_size
 
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
+    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): # pylint: disable=too-many-statements, too-many-locals, too-many-branches
         residual = hidden_states
 
         input_ndim = hidden_states.ndim
@@ -74,7 +76,7 @@ class SlicedAttnProcessor:
             end_idx = (i + 1) * self.slice_size
 
             if do_split_2:
-                for i2 in range(query_tokens // split_2_slice_size):
+                for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name
                     start_idx_2 = i2 * split_2_slice_size
                     end_idx_2 = (i2 + 1) * split_2_slice_size
 
@@ -114,7 +116,7 @@ class SlicedAttnProcessor:
 
         return hidden_states
 
-class AttnProcessor2_0:
+class AttnProcessor2_0: # pylint: disable=too-few-public-methods, invalid-name
     r"""
     Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
     """
@@ -123,7 +125,7 @@ class AttnProcessor2_0:
         if not hasattr(F, "scaled_dot_product_attention"):
             raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
 
-    def __call__(
+    def __call__( # pylint: disable=too-many-arguments, too-many-statements, too-many-locals, too-many-branches
         self,
         attn: Attention,
         hidden_states,
@@ -208,7 +210,7 @@ class AttnProcessor2_0:
                 start_idx = i * split_slice_size
                 end_idx = (i + 1) * split_slice_size
                 if do_split_2:
-                    for i2 in range(query_tokens // split_2_slice_size):
+                    for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name
                         start_idx_2 = i2 * split_2_slice_size
                         end_idx_2 = (i2 + 1) * split_2_slice_size
 

modeling/ipex/hijacks.py

@@ -1,8 +1,11 @@
-import torch
-import intel_extension_for_pytorch as ipex
+import contextlib
 import importlib
+import torch
+import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
 
-class CondFunc:
+# pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return
+
+class CondFunc: # pylint: disable=missing-class-docstring
     def __new__(cls, orig_func, sub_func, cond_func):
         self = super(CondFunc, cls).__new__(cls)
         if isinstance(orig_func, str):
@@ -29,6 +32,45 @@ class CondFunc:
         else:
             return self.__orig_func(*args, **kwargs)
 
+_utils = torch.utils.data._utils
+def _shutdown_workers(self):
+    if _utils is None or _utils.python_exit_status is True or _utils.python_exit_status is None:
+        return
+    if hasattr(self, "_shutdown") and not self._shutdown:
+        self._shutdown = True
+        try:
+            if hasattr(self, '_pin_memory_thread'):
+                self._pin_memory_thread_done_event.set()
+                self._worker_result_queue.put((None, None))
+                self._pin_memory_thread.join()
+                self._worker_result_queue.cancel_join_thread()
+                self._worker_result_queue.close()
+            self._workers_done_event.set()
+            for worker_id in range(len(self._workers)):
+                if self._persistent_workers or self._workers_status[worker_id]:
+                    self._mark_worker_as_unavailable(worker_id, shutdown=True)
+            for w in self._workers: # pylint: disable=invalid-name
+                w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
+            for q in self._index_queues: # pylint: disable=invalid-name
+                q.cancel_join_thread()
+                q.close()
+        finally:
+            if self._worker_pids_set:
+                _utils.signal_handling._remove_worker_pids(id(self))
+                self._worker_pids_set = False
+            for w in self._workers: # pylint: disable=invalid-name
+                if w.is_alive():
+                    w.terminate()
+
+class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods
+    def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument
+        if isinstance(device_ids, list) and len(device_ids) > 1:
+            print("IPEX backend doesn't support DataParallel on multiple XPU devices")
+        return module.to("xpu")
+
+def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
+    return contextlib.nullcontext()
+
 def check_device(device):
     return bool((isinstance(device, torch.device) and device.type == "cuda") or (isinstance(device, str) and "cuda" in device) or isinstance(device, int))
 
@@ -51,25 +93,25 @@ def ipex_autocast(*args, **kwargs):
         return original_autocast(*args, **kwargs)
 
 original_torch_cat = torch.cat
-def torch_cat(input, *args, **kwargs):
-    if len(input) == 3 and (input[0].dtype != input[1].dtype or input[2].dtype != input[1].dtype):
-        return original_torch_cat([input[0].to(input[1].dtype), input[1], input[2].to(input[1].dtype)], *args, **kwargs)
+def torch_cat(tensor, *args, **kwargs):
+    if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype):
+        return original_torch_cat([tensor[0].to(tensor[1].dtype), tensor[1], tensor[2].to(tensor[1].dtype)], *args, **kwargs)
     else:
-        return original_torch_cat(input, *args, **kwargs)
+        return original_torch_cat(tensor, *args, **kwargs)
 
 original_interpolate = torch.nn.functional.interpolate
-def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False):
-    if antialias:
-        return_device = input.device
-        return_dtype = input.dtype
-        return original_interpolate(input.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode,
+def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments
+    if antialias or align_corners is not None:
+        return_device = tensor.device
+        return_dtype = tensor.dtype
+        return original_interpolate(tensor.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode,
         align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(return_device, dtype=return_dtype)
     else:
-        return original_interpolate(input, size=size, scale_factor=scale_factor, mode=mode,
+        return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode,
         align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias)
 
 original_linalg_solve = torch.linalg.solve
-def linalg_solve(orig_func, A, B, *args, **kwargs):
+def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name
     if A.device != torch.device("cpu") or B.device != torch.device("cpu"):
         return_device = A.device
         original_linalg_solve(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(return_device)
@@ -101,10 +143,13 @@ def ipex_hijacks():
     CondFunc('torch.tensor',
         lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
         lambda orig_func, *args, device=None, **kwargs: check_device(device))
+    CondFunc('torch.linspace',
+        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
+        lambda orig_func, *args, device=None, **kwargs: check_device(device))
 
     CondFunc('torch.Generator',
-        lambda orig_func, device: torch.xpu.Generator(device),
-        lambda orig_func, device: device != torch.device("cpu") and device != "cpu")
+        lambda orig_func, device=None: torch.xpu.Generator(device),
+        lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
 
     CondFunc('torch.batch_norm',
         lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
@@ -115,12 +160,15 @@ def ipex_hijacks():
         lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
         weight if weight is not None else torch.ones(input.size()[1], device=input.device),
         bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
-        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))    
+        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
 
     #Functions with dtype errors:
     CondFunc('torch.nn.modules.GroupNorm.forward',
         lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
         lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
+    CondFunc('torch.nn.modules.linear.Linear.forward',
+        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
+        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
     CondFunc('torch.bmm',
         lambda orig_func, input, mat2, *args, **kwargs: orig_func(input, mat2.to(input.dtype), *args, **kwargs),
         lambda orig_func, input, mat2, *args, **kwargs: input.dtype != mat2.dtype)
@@ -142,7 +190,10 @@ 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.autocast = ipex_autocast
     torch.cat = torch_cat
     torch.linalg.solve = linalg_solve
     torch.nn.functional.interpolate = interpolate
+    torch.backends.cuda.sdp_kernel = return_null_context