from collections import defaultdict import torch import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import import intel_extension_for_pytorch._C as core # pylint: disable=import-error, unused-import # pylint: disable=protected-access, missing-function-docstring, line-too-long device_supports_fp64 = torch.xpu.has_fp64_dtype() 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): # pylint: disable=unused-argument 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` 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: # pylint: disable=no-else-raise 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 if device_supports_fp64: inv_scale = self._scale.double().reciprocal().float() else: 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