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| 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 | |
| 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<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: # 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 | |
| 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 | |