add ipex lib

rvc/lib/ipex/__init__.py

@@ -0,0 +1,184 @@
+import contextlib
+import os
+import sys
+
+import intel_extension_for_pytorch as ipex  # pylint: disable=import-error, unused-import
+import torch
+
+from .attention import attention_init
+from .hijacks import ipex_hijacks
+
+
+def ipex_init():
+    try:
+        # Replace cuda with xpu:
+        torch.cuda.current_device = torch.xpu.current_device
+        torch.cuda.current_stream = torch.xpu.current_stream
+        torch.cuda.device = torch.xpu.device
+        torch.cuda.device_count = torch.xpu.device_count
+        torch.cuda.device_of = torch.xpu.device_of
+        torch.cuda.get_device_name = torch.xpu.get_device_name
+        torch.cuda.get_device_properties = torch.xpu.get_device_properties
+        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
+        torch.cuda.Event = torch.xpu.Event
+        torch.cuda.Stream = torch.xpu.Stream
+        torch.cuda.FloatTensor = torch.xpu.FloatTensor
+        torch.Tensor.cuda = torch.Tensor.xpu
+        torch.Tensor.is_cuda = torch.Tensor.is_xpu
+        torch.cuda._initialization_lock = torch.xpu.lazy_init._initialization_lock
+        torch.cuda._initialized = torch.xpu.lazy_init._initialized
+        torch.cuda._lazy_seed_tracker = torch.xpu.lazy_init._lazy_seed_tracker
+        torch.cuda._queued_calls = torch.xpu.lazy_init._queued_calls
+        torch.cuda._tls = torch.xpu.lazy_init._tls
+        torch.cuda.threading = torch.xpu.lazy_init.threading
+        torch.cuda.traceback = torch.xpu.lazy_init.traceback
+        torch.cuda.Optional = torch.xpu.Optional
+        torch.cuda.__cached__ = torch.xpu.__cached__
+        torch.cuda.__loader__ = torch.xpu.__loader__
+        torch.cuda.ComplexFloatStorage = torch.xpu.ComplexFloatStorage
+        torch.cuda.Tuple = torch.xpu.Tuple
+        torch.cuda.streams = torch.xpu.streams
+        torch.cuda._lazy_new = torch.xpu._lazy_new
+        torch.cuda.FloatStorage = torch.xpu.FloatStorage
+        torch.cuda.Any = torch.xpu.Any
+        torch.cuda.__doc__ = torch.xpu.__doc__
+        torch.cuda.default_generators = torch.xpu.default_generators
+        torch.cuda.HalfTensor = torch.xpu.HalfTensor
+        torch.cuda._get_device_index = torch.xpu._get_device_index
+        torch.cuda.__path__ = torch.xpu.__path__
+        torch.cuda.Device = torch.xpu.Device
+        torch.cuda.IntTensor = torch.xpu.IntTensor
+        torch.cuda.ByteStorage = torch.xpu.ByteStorage
+        torch.cuda.set_stream = torch.xpu.set_stream
+        torch.cuda.BoolStorage = torch.xpu.BoolStorage
+        torch.cuda.os = torch.xpu.os
+        torch.cuda.torch = torch.xpu.torch
+        torch.cuda.BFloat16Storage = torch.xpu.BFloat16Storage
+        torch.cuda.Union = torch.xpu.Union
+        torch.cuda.DoubleTensor = torch.xpu.DoubleTensor
+        torch.cuda.ShortTensor = torch.xpu.ShortTensor
+        torch.cuda.LongTensor = torch.xpu.LongTensor
+        torch.cuda.IntStorage = torch.xpu.IntStorage
+        torch.cuda.LongStorage = torch.xpu.LongStorage
+        torch.cuda.__annotations__ = torch.xpu.__annotations__
+        torch.cuda.__package__ = torch.xpu.__package__
+        torch.cuda.__builtins__ = torch.xpu.__builtins__
+        torch.cuda.CharTensor = torch.xpu.CharTensor
+        torch.cuda.List = torch.xpu.List
+        torch.cuda._lazy_init = torch.xpu._lazy_init
+        torch.cuda.BFloat16Tensor = torch.xpu.BFloat16Tensor
+        torch.cuda.DoubleStorage = torch.xpu.DoubleStorage
+        torch.cuda.ByteTensor = torch.xpu.ByteTensor
+        torch.cuda.StreamContext = torch.xpu.StreamContext
+        torch.cuda.ComplexDoubleStorage = torch.xpu.ComplexDoubleStorage
+        torch.cuda.ShortStorage = torch.xpu.ShortStorage
+        torch.cuda._lazy_call = torch.xpu._lazy_call
+        torch.cuda.HalfStorage = torch.xpu.HalfStorage
+        torch.cuda.random = torch.xpu.random
+        torch.cuda._device = torch.xpu._device
+        torch.cuda.classproperty = torch.xpu.classproperty
+        torch.cuda.__name__ = torch.xpu.__name__
+        torch.cuda._device_t = torch.xpu._device_t
+        torch.cuda.warnings = torch.xpu.warnings
+        torch.cuda.__spec__ = torch.xpu.__spec__
+        torch.cuda.BoolTensor = torch.xpu.BoolTensor
+        torch.cuda.CharStorage = torch.xpu.CharStorage
+        torch.cuda.__file__ = torch.xpu.__file__
+        torch.cuda._is_in_bad_fork = torch.xpu.lazy_init._is_in_bad_fork
+        # torch.cuda.is_current_stream_capturing = torch.xpu.is_current_stream_capturing
+
+        # Memory:
+        torch.cuda.memory = torch.xpu.memory
+        if "linux" in sys.platform and "WSL2" in os.popen("uname -a").read():
+            torch.xpu.empty_cache = lambda: None
+        torch.cuda.empty_cache = torch.xpu.empty_cache
+        torch.cuda.memory_stats = torch.xpu.memory_stats
+        torch.cuda.memory_summary = torch.xpu.memory_summary
+        torch.cuda.memory_snapshot = torch.xpu.memory_snapshot
+        torch.cuda.memory_allocated = torch.xpu.memory_allocated
+        torch.cuda.max_memory_allocated = torch.xpu.max_memory_allocated
+        torch.cuda.memory_reserved = torch.xpu.memory_reserved
+        torch.cuda.memory_cached = torch.xpu.memory_reserved
+        torch.cuda.max_memory_reserved = torch.xpu.max_memory_reserved
+        torch.cuda.max_memory_cached = torch.xpu.max_memory_reserved
+        torch.cuda.reset_peak_memory_stats = torch.xpu.reset_peak_memory_stats
+        torch.cuda.reset_max_memory_cached = torch.xpu.reset_peak_memory_stats
+        torch.cuda.reset_max_memory_allocated = torch.xpu.reset_peak_memory_stats
+        torch.cuda.memory_stats_as_nested_dict = torch.xpu.memory_stats_as_nested_dict
+        torch.cuda.reset_accumulated_memory_stats = (
+            torch.xpu.reset_accumulated_memory_stats
+        )
+
+        # RNG:
+        torch.cuda.get_rng_state = torch.xpu.get_rng_state
+        torch.cuda.get_rng_state_all = torch.xpu.get_rng_state_all
+        torch.cuda.set_rng_state = torch.xpu.set_rng_state
+        torch.cuda.set_rng_state_all = torch.xpu.set_rng_state_all
+        torch.cuda.manual_seed = torch.xpu.manual_seed
+        torch.cuda.manual_seed_all = torch.xpu.manual_seed_all
+        torch.cuda.seed = torch.xpu.seed
+        torch.cuda.seed_all = torch.xpu.seed_all
+        torch.cuda.initial_seed = torch.xpu.initial_seed
+
+        # AMP:
+        torch.cuda.amp = torch.xpu.amp
+        if not hasattr(torch.cuda.amp, "common"):
+            torch.cuda.amp.common = contextlib.nullcontext()
+        torch.cuda.amp.common.amp_definitely_not_available = lambda: False
+        try:
+            torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
+        except Exception:
+            try:
+                from .gradscaler import (
+                    gradscaler_init,
+                )
+
+                gradscaler_init()
+                torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
+            except Exception:
+                torch.cuda.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
+
+        # C
+        torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream
+        ipex._C._DeviceProperties.major = 2023
+        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._utils._get_available_device_type = lambda: "xpu"
+        torch.has_cuda = True
+        torch.cuda.has_half = True
+        torch.cuda.is_bf16_supported = lambda *args, **kwargs: True
+        torch.cuda.is_fp16_supported = lambda *args, **kwargs: True
+        torch.version.cuda = "11.7"
+        torch.cuda.get_device_capability = lambda *args, **kwargs: [11, 7]
+        torch.cuda.get_device_properties.major = 11
+        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()
+        attention_init()
+    except Exception as e:
+        return False, e
+    return True, None

rvc/lib/ipex/attention.py

@@ -0,0 +1,207 @@
+import intel_extension_for_pytorch as ipex
+import torch
+
+
+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:
+    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
+    block_size = batch_size_attention * slice_block_size
+
+    split_slice_size = batch_size_attention
+    if block_size > 4:
+        do_split = True
+        # 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:
+                split_slice_size = 1
+                break
+    else:
+        do_split = False
+
+    split_2_slice_size = input_tokens
+    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_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
+                break
+    else:
+        do_split_2 = False
+
+    if do_split:
+        hidden_states = torch.zeros(
+            input.shape[0],
+            input.shape[1],
+            mat2.shape[2],
+            device=input.device,
+            dtype=input.dtype,
+        )
+        for i in range(batch_size_attention // split_slice_size):
+            start_idx = i * split_slice_size
+            end_idx = (i + 1) * split_slice_size
+            if do_split_2:
+                for i2 in range(input_tokens // split_2_slice_size):
+                    start_idx_2 = i2 * split_2_slice_size
+                    end_idx_2 = (i2 + 1) * split_2_slice_size
+                    hidden_states[
+                        start_idx:end_idx, start_idx_2:end_idx_2
+                    ] = original_torch_bmm(
+                        input[start_idx:end_idx, start_idx_2:end_idx_2],
+                        mat2[start_idx:end_idx, start_idx_2:end_idx_2],
+                        out=out,
+                    )
+            else:
+                hidden_states[start_idx:end_idx] = original_torch_bmm(
+                    input[start_idx:end_idx], mat2[start_idx:end_idx], out=out
+                )
+    else:
+        return original_torch_bmm(input, mat2, out=out)
+    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:
+    if len(query.shape) == 3:
+        batch_size_attention, query_tokens, shape_four = query.shape
+        shape_one = 1
+        no_shape_one = True
+    else:
+        shape_one, batch_size_attention, query_tokens, shape_four = query.shape
+        no_shape_one = False
+
+    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 > 4:
+        do_split = True
+        # 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:
+                split_slice_size = 1
+                break
+    else:
+        do_split = False
+
+    split_2_slice_size = query_tokens
+    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 (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
+                break
+    else:
+        do_split_2 = False
+
+    if do_split:
+        hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype)
+        for i in range(batch_size_attention // split_slice_size):
+            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):
+                    start_idx_2 = i2 * split_2_slice_size
+                    end_idx_2 = (i2 + 1) * split_2_slice_size
+                    if no_shape_one:
+                        hidden_states[
+                            start_idx:end_idx, start_idx_2:end_idx_2
+                        ] = original_scaled_dot_product_attention(
+                            query[start_idx:end_idx, start_idx_2:end_idx_2],
+                            key[start_idx:end_idx, start_idx_2:end_idx_2],
+                            value[start_idx:end_idx, start_idx_2:end_idx_2],
+                            attn_mask=attn_mask[
+                                start_idx:end_idx, start_idx_2:end_idx_2
+                            ]
+                            if attn_mask is not None
+                            else attn_mask,
+                            dropout_p=dropout_p,
+                            is_causal=is_causal,
+                        )
+                    else:
+                        hidden_states[
+                            :, start_idx:end_idx, start_idx_2:end_idx_2
+                        ] = original_scaled_dot_product_attention(
+                            query[:, start_idx:end_idx, start_idx_2:end_idx_2],
+                            key[:, start_idx:end_idx, start_idx_2:end_idx_2],
+                            value[:, start_idx:end_idx, start_idx_2:end_idx_2],
+                            attn_mask=attn_mask[
+                                :, start_idx:end_idx, start_idx_2:end_idx_2
+                            ]
+                            if attn_mask is not None
+                            else attn_mask,
+                            dropout_p=dropout_p,
+                            is_causal=is_causal,
+                        )
+            else:
+                if no_shape_one:
+                    hidden_states[
+                        start_idx:end_idx
+                    ] = original_scaled_dot_product_attention(
+                        query[start_idx:end_idx],
+                        key[start_idx:end_idx],
+                        value[start_idx:end_idx],
+                        attn_mask=attn_mask[start_idx:end_idx]
+                        if attn_mask is not None
+                        else attn_mask,
+                        dropout_p=dropout_p,
+                        is_causal=is_causal,
+                    )
+                else:
+                    hidden_states[
+                        :, start_idx:end_idx
+                    ] = original_scaled_dot_product_attention(
+                        query[:, start_idx:end_idx],
+                        key[:, start_idx:end_idx],
+                        value[:, start_idx:end_idx],
+                        attn_mask=attn_mask[:, start_idx:end_idx]
+                        if attn_mask is not None
+                        else attn_mask,
+                        dropout_p=dropout_p,
+                        is_causal=is_causal,
+                    )
+    else:
+        return original_scaled_dot_product_attention(
+            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

rvc/lib/ipex/gradscaler.py

@@ -0,0 +1,185 @@
+from collections import defaultdict
+
+import intel_extension_for_pytorch as ipex
+import intel_extension_for_pytorch._C as core
+import torch
+
+
+OptState = ipex.cpu.autocast._grad_scaler.OptState
+_MultiDeviceReplicator = ipex.cpu.autocast._grad_scaler._MultiDeviceReplicator
+_refresh_per_optimizer_state = (
+    ipex.cpu.autocast._grad_scaler._refresh_per_optimizer_state
+)
+
+
+def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16):
+    per_device_inv_scale = _MultiDeviceReplicator(inv_scale)
+    per_device_found_inf = _MultiDeviceReplicator(found_inf)
+
+    # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
+    # There could be hundreds of grads, so we'd like to iterate through them just once.
+    # However, we don't know their devices or dtypes in advance.
+
+    # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
+    # Google says mypy struggles with defaultdicts type annotations.
+    per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))  # type: ignore[var-annotated]
+    # sync grad to master weight
+    if hasattr(optimizer, "sync_grad"):
+        optimizer.sync_grad()
+    with torch.no_grad():
+        for group in optimizer.param_groups:
+            for param in group["params"]:
+                if param.grad is None:
+                    continue
+                if (not allow_fp16) and param.grad.dtype == torch.float16:
+                    raise ValueError("Attempting to unscale FP16 gradients.")
+                if param.grad.is_sparse:
+                    # is_coalesced() == False means the sparse grad has values with duplicate indices.
+                    # coalesce() deduplicates indices and adds all values that have the same index.
+                    # For scaled fp16 values, there's a good chance coalescing will cause overflow,
+                    # so we should check the coalesced _values().
+                    if param.grad.dtype is torch.float16:
+                        param.grad = param.grad.coalesce()
+                    to_unscale = param.grad._values()
+                else:
+                    to_unscale = param.grad
+
+                # -: is there a way to split by device and dtype without appending in the inner loop?
+                to_unscale = to_unscale.to("cpu")
+                per_device_and_dtype_grads[to_unscale.device][to_unscale.dtype].append(
+                    to_unscale
+                )
+
+        for _, per_dtype_grads in per_device_and_dtype_grads.items():
+            for grads in per_dtype_grads.values():
+                core._amp_foreach_non_finite_check_and_unscale_(
+                    grads,
+                    per_device_found_inf.get("cpu"),
+                    per_device_inv_scale.get("cpu"),
+                )
+
+    return per_device_found_inf._per_device_tensors
+
+
+def unscale_(self, optimizer):
+    """
+    Divides ("unscales") the optimizer's gradient tensors by the scale factor.
+    :meth:`unscale_` is optional, serving cases where you need to
+    :ref:`modify or inspect gradients<working-with-unscaled-gradients>`
+    between the backward pass(es) and :meth:`step`.
+    If :meth:`unscale_` is not called explicitly,  gradients will be unscaled  automatically during :meth:`step`.
+    Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
+        ...
+        scaler.scale(loss).backward()
+        scaler.unscale_(optimizer)
+        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
+        scaler.step(optimizer)
+        scaler.update()
+    Args:
+        optimizer (torch.optim.Optimizer):  Optimizer that owns the gradients to be unscaled.
+    .. warning::
+        :meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
+        and only after all gradients for that optimizer's assigned parameters have been accumulated.
+        Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
+    .. warning::
+        :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
+    """
+    if not self._enabled:
+        return
+
+    self._check_scale_growth_tracker("unscale_")
+
+    optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+    if optimizer_state["stage"] is OptState.UNSCALED:
+        raise RuntimeError(
+            "unscale_() has already been called on this optimizer since the last update()."
+        )
+    elif optimizer_state["stage"] is OptState.STEPPED:
+        raise RuntimeError("unscale_() is being called after step().")
+
+    # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
+    assert self._scale is not None
+    inv_scale = (
+        self._scale.to("cpu").double().reciprocal().float().to(self._scale.device)
+    )
+    found_inf = torch.full((1,), 0.0, dtype=torch.float32, device=self._scale.device)
+
+    optimizer_state["found_inf_per_device"] = self._unscale_grads_(
+        optimizer, inv_scale, found_inf, False
+    )
+    optimizer_state["stage"] = OptState.UNSCALED
+
+
+def update(self, new_scale=None):
+    """
+    Updates the scale factor.
+    If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
+    to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
+    the scale is multiplied by ``growth_factor`` to increase it.
+    Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
+    used directly, it's used to fill GradScaler's internal scale tensor. So if
+    ``new_scale`` was a tensor, later in-place changes to that tensor will not further
+    affect the scale GradScaler uses internally.)
+    Args:
+        new_scale (float or :class:`torch.FloatTensor`, optional, default=None):  New scale factor.
+    .. warning::
+        :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
+        been invoked for all optimizers used this iteration.
+    """
+    if not self._enabled:
+        return
+
+    _scale, _growth_tracker = self._check_scale_growth_tracker("update")
+
+    if new_scale is not None:
+        # Accept a new user-defined scale.
+        if isinstance(new_scale, float):
+            self._scale.fill_(new_scale)  # type: ignore[union-attr]
+        else:
+            reason = "new_scale should be a float or a 1-element torch.FloatTensor with requires_grad=False."
+            assert isinstance(new_scale, torch.FloatTensor), reason  # type: ignore[attr-defined]
+            assert new_scale.numel() == 1, reason
+            assert new_scale.requires_grad is False, reason
+            self._scale.copy_(new_scale)  # type: ignore[union-attr]
+    else:
+        # Consume shared inf/nan data collected from optimizers to update the scale.
+        # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
+        found_infs = [
+            found_inf.to(device="cpu", non_blocking=True)
+            for state in self._per_optimizer_states.values()
+            for found_inf in state["found_inf_per_device"].values()
+        ]
+
+        assert len(found_infs) > 0, "No inf checks were recorded prior to update."
+
+        found_inf_combined = found_infs[0]
+        if len(found_infs) > 1:
+            for i in range(1, len(found_infs)):
+                found_inf_combined += found_infs[i]
+
+        to_device = _scale.device
+        _scale = _scale.to("cpu")
+        _growth_tracker = _growth_tracker.to("cpu")
+
+        core._amp_update_scale_(
+            _scale,
+            _growth_tracker,
+            found_inf_combined,
+            self._growth_factor,
+            self._backoff_factor,
+            self._growth_interval,
+        )
+
+        _scale = _scale.to(to_device)
+        _growth_tracker = _growth_tracker.to(to_device)
+    # To prepare for next iteration, clear the data collected from optimizers this iteration.
+    self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
+
+
+def gradscaler_init():
+    torch.xpu.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
+    torch.xpu.amp.GradScaler._unscale_grads_ = _unscale_grads_
+    torch.xpu.amp.GradScaler.unscale_ = unscale_
+    torch.xpu.amp.GradScaler.update = update
+    return torch.xpu.amp.GradScaler

rvc/lib/ipex/hijacks.py

@@ -0,0 +1,352 @@
+import contextlib
+import importlib
+
+import intel_extension_for_pytorch as ipex
+import torch
+
+
+class CondFunc:
+    def __new__(cls, orig_func, sub_func, cond_func):
+        self = super(CondFunc, cls).__new__(cls)
+        if isinstance(orig_func, str):
+            func_path = orig_func.split(".")
+            for i in range(len(func_path) - 1, -1, -1):
+                try:
+                    resolved_obj = importlib.import_module(".".join(func_path[:i]))
+                    break
+                except ImportError:
+                    pass
+            for attr_name in func_path[i:-1]:
+                resolved_obj = getattr(resolved_obj, attr_name)
+            orig_func = getattr(resolved_obj, func_path[-1])
+            setattr(
+                resolved_obj,
+                func_path[-1],
+                lambda *args, **kwargs: self(*args, **kwargs),
+            )
+        self.__init__(orig_func, sub_func, cond_func)
+        return lambda *args, **kwargs: self(*args, **kwargs)
+
+    def __init__(self, orig_func, sub_func, cond_func):
+        self.__orig_func = orig_func
+        self.__sub_func = sub_func
+        self.__cond_func = cond_func
+
+    def __call__(self, *args, **kwargs):
+        if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
+            return self.__sub_func(self.__orig_func, *args, **kwargs)
+        else:
+            return self.__orig_func(*args, **kwargs)
+
+
+_utils = torch.utils.data._utils
+
+
+def _shutdown_workers(self):
+    if (
+        torch.utils.data._utils is None
+        or torch.utils.data._utils.python_exit_status is True
+        or torch.utils.data._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:
+                w.join(timeout=torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL)
+            for q in self._index_queues:
+                q.cancel_join_thread()
+                q.close()
+        finally:
+            if self._worker_pids_set:
+                torch.utils.data._utils.signal_handling._remove_worker_pids(id(self))
+                self._worker_pids_set = False
+            for w in self._workers:
+                if w.is_alive():
+                    w.terminate()
+
+
+class DummyDataParallel(torch.nn.Module):
+    def __new__(cls, module, device_ids=None, output_device=None, dim=0):
+        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):
+    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)
+    )
+
+
+def return_xpu(device):
+    return (
+        f"xpu:{device[-1]}"
+        if isinstance(device, str) and ":" in device
+        else f"xpu:{device}"
+        if isinstance(device, int)
+        else torch.device("xpu")
+        if isinstance(device, torch.device)
+        else "xpu"
+    )
+
+
+def ipex_no_cuda(orig_func, *args, **kwargs):
+    torch.cuda.is_available = lambda: False
+    orig_func(*args, **kwargs)
+    torch.cuda.is_available = torch.xpu.is_available
+
+
+original_autocast = torch.autocast
+
+
+def ipex_autocast(*args, **kwargs):
+    if len(args) > 0 and args[0] == "cuda":
+        return original_autocast("xpu", *args[1:], **kwargs)
+    else:
+        return original_autocast(*args, **kwargs)
+
+
+original_torch_cat = torch.cat
+
+
+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(tensor, *args, **kwargs)
+
+
+original_interpolate = torch.nn.functional.interpolate
+
+
+def interpolate(
+    tensor,
+    size=None,
+    scale_factor=None,
+    mode="nearest",
+    align_corners=None,
+    recompute_scale_factor=None,
+    antialias=False,
+):
+    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(
+            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(A, B, *args, **kwargs):
+    if A.device != torch.device("cpu") or B.device != torch.device("cpu"):
+        return_device = A.device
+        return original_linalg_solve(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(
+            return_device
+        )
+    else:
+        return original_linalg_solve(A, B, *args, **kwargs)
+
+
+def ipex_hijacks():
+    CondFunc(
+        "torch.Tensor.to",
+        lambda orig_func, self, device=None, *args, **kwargs: orig_func(
+            self, return_xpu(device), *args, **kwargs
+        ),
+        lambda orig_func, self, device=None, *args, **kwargs: check_device(device),
+    )
+    CondFunc(
+        "torch.Tensor.cuda",
+        lambda orig_func, self, device=None, *args, **kwargs: orig_func(
+            self, return_xpu(device), *args, **kwargs
+        ),
+        lambda orig_func, self, device=None, *args, **kwargs: check_device(device),
+    )
+    CondFunc(
+        "torch.empty",
+        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.load",
+        lambda orig_func, *args, map_location=None, **kwargs: orig_func(
+            *args, return_xpu(map_location), **kwargs
+        ),
+        lambda orig_func, *args, map_location=None, **kwargs: map_location is None
+        or check_device(map_location),
+    )
+    CondFunc(
+        "torch.randn",
+        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.ones",
+        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.zeros",
+        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.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=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,
+            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"),
+    )
+    CondFunc(
+        "torch.instance_norm",
+        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"),
+    )
+
+    # 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.nn.modules.conv.Conv2d.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.functional.layer_norm",
+        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs: orig_func(
+            input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs
+        ),
+        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs: weight
+        is not None
+        and input.dtype != weight.data.dtype,
+    )
+
+    # Diffusers Float64 (ARC GPUs doesn't support double or Float64):
+    if not torch.xpu.has_fp64_dtype():
+        CondFunc(
+            "torch.from_numpy",
+            lambda orig_func, ndarray: orig_func(ndarray.astype("float32")),
+            lambda orig_func, ndarray: ndarray.dtype == float,
+        )
+
+    # Broken functions when torch.cuda.is_available is True:
+    CondFunc(
+        "torch.utils.data.dataloader._BaseDataLoaderIter.__init__",
+        lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
+        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