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Add sageattn++ as selectable mode for easier testing

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kijai 2025-07-01 12:26:27 +03:00
parent 6363376a17
commit ff49e1b01f
1 changed files with 9 additions and 3 deletions
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12
nodes/model_optimization_nodes.py
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@ -8,6 +8,7 @@ import folder_paths
import comfy.model_management as mm
from comfy.cli_args import args
sageattn_modes = ["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda", "sageattn_qk_int8_pv_fp8_cuda++"]
_initialized = False
_original_functions = {}
@ -49,6 +50,11 @@ class BaseLoaderKJ:
def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp32", tensor_layout=tensor_layout)
return func
elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda++":
from sageattention import sageattn_qk_int8_pv_fp8_cuda
def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp16", tensor_layout=tensor_layout)
return func
sage_func = set_sage_func(sage_attention)
@ -136,7 +142,7 @@ class PathchSageAttentionKJ(BaseLoaderKJ):
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"sage_attention": (["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda"], {"default": False, "tooltip": "Global patch comfy attention to use sageattn, once patched to revert back to normal you would need to run this node again with disabled option."}),
"sage_attention": (sageattn_modes, {"default": False, "tooltip": "Global patch comfy attention to use sageattn, once patched to revert back to normal you would need to run this node again with disabled option."}),
}}
RETURN_TYPES = ("MODEL", )
@ -167,7 +173,7 @@ class CheckpointLoaderKJ(BaseLoaderKJ):
"weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],),
"compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "default", "tooltip": "The compute dtype to use for the model."}),
"patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}),
"sage_attention": (["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda"], {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
"sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
"enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}),
}}
@ -309,7 +315,7 @@ class DiffusionModelLoaderKJ(BaseLoaderKJ):
"weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],),
"compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "default", "tooltip": "The compute dtype to use for the model."}),
"patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}),
"sage_attention": (["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda"], {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
"sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
"enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}),
}}