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multigpu: shorten _force_supported_compute_dtype docstring

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Amp-Thread-ID: https://ampcode.com/threads/T-019e61db-ffb1-73a6-b2a8-3d23d7b05792
Co-authored-by: Amp <amp@ampcode.com>
This commit is contained in:
Jedrzej Kosinski 2026-05-25 19:56:07 -07:00
parent 4ca4d39076
commit 8969bbbf02
1 changed files with 4 additions and 11 deletions
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15
comfy_extras/nodes_multigpu.py
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@ -49,18 +49,11 @@ class MultiGPUCFGSplitNode(io.ComfyNode):
def _force_supported_compute_dtype(patcher: ModelPatcher, device: torch.device): def _force_supported_compute_dtype(patcher: ModelPatcher, device: torch.device):
"""Ensure the patcher's compute dtype is one the target device actually supports. """Cast compute dtype to one the device supports; no-op if already supported.
Defers to comfy.model_management.unet_manual_cast, which already encodes Uses unet_manual_cast which encodes per-device dtype support (e.g. CPU
per-device dtype support (CPU returns False for fp16/bf16, older GPUs may rejects fp16/bf16, falling back to fp32 to avoid PyTorch's ~500-600x
not support bf16, pre-14 MPS doesn't support bf16, etc.). It returns None slower software emulation)."""
when the weight dtype is already fine and the cast dtype otherwise.
Concrete motivation: PyTorch's CPU conv2d kernels emulate fp16/bf16 in
software (~500-600x slower than fp32), so SelectModelDevice -> CPU on an
fp16 model would otherwise look frozen for hours. Routing through
set_model_compute_dtype leaves the weights as-is and casts at use, so peak
memory does not blow up."""
weight_dtype = patcher.model_dtype() weight_dtype = patcher.model_dtype()
cast_dtype = comfy.model_management.unet_manual_cast(weight_dtype, device) cast_dtype = comfy.model_management.unet_manual_cast(weight_dtype, device)
if cast_dtype is None: if cast_dtype is None: