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Format code (#1193)

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Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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github-actions[bot]
2023-09-14 09:34:30 +09:00
committed by GitHub
parent 72a18e66b6
commit a6456f6d46
15 changed files with 562 additions and 237 deletions
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30
infer/modules/ipex/gradscaler.py
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@@ -1,15 +1,20 @@
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
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
_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
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)
@@ -43,9 +48,9 @@ def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16): # pylint
# -: 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)
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():
@@ -57,6 +62,7 @@ def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16): # pylint
return per_device_found_inf._per_device_tensors
def unscale_(self, optimizer):
"""
Divides ("unscales") the optimizer's gradient tensors by the scale factor.
@@ -87,7 +93,7 @@ def unscale_(self, optimizer):
optimizer_state = self._per_optimizer_states[id(optimizer)]
if optimizer_state["stage"] is OptState.UNSCALED: # pylint: disable=no-else-raise
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()."
)
@@ -96,16 +102,17 @@ def unscale_(self, optimizer):
# 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
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.
@@ -171,6 +178,7 @@ def update(self, new_scale=None):
# 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_