Ethanfel
d642255e70
Integrates GIMM-VFI alongside existing BIM/EMA/SGM models. Key feature: generates all intermediate frames in one forward pass (no recursive 2x passes needed for 4x/8x). - Vendor gimm_vfi_arch/ from kijai/ComfyUI-GIMM-VFI with device fixes - Two variants: RAFT-based (~80MB) and FlowFormer-based (~123MB) - Auto-download checkpoints from HuggingFace (Kijai/GIMM-VFI_safetensors) - Three new nodes: Load GIMM-VFI Model, GIMM-VFI Interpolate, GIMM-VFI Segment Interpolate - single_pass toggle: True=arbitrary timestep (default), False=recursive like other models - ds_factor parameter for high-res input downscaling Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
673 lines
26 KiB
Python
673 lines
26 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# softmax-splatting: https://github.com/sniklaus/softmax-splatting
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# --------------------------------------------------------
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import collections
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import cupy
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import os
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import re
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import torch
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import typing
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##########################################################
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objCudacache = {}
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def cuda_int32(intIn: int):
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return cupy.int32(intIn)
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# end
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def cuda_float32(fltIn: float):
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return cupy.float32(fltIn)
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# end
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def cuda_kernel(strFunction: str, strKernel: str, objVariables: typing.Dict):
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if "device" not in objCudacache:
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objCudacache["device"] = torch.cuda.get_device_name()
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# end
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strKey = strFunction
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for strVariable in objVariables:
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objValue = objVariables[strVariable]
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strKey += strVariable
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if objValue is None:
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continue
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elif type(objValue) == int:
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strKey += str(objValue)
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elif type(objValue) == float:
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strKey += str(objValue)
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elif type(objValue) == bool:
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strKey += str(objValue)
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elif type(objValue) == str:
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strKey += objValue
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elif type(objValue) == torch.Tensor:
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strKey += str(objValue.dtype)
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strKey += str(objValue.shape)
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strKey += str(objValue.stride())
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elif True:
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print(strVariable, type(objValue))
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assert False
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# end
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# end
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strKey += objCudacache["device"]
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if strKey not in objCudacache:
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for strVariable in objVariables:
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objValue = objVariables[strVariable]
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if objValue is None:
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continue
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elif type(objValue) == int:
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strKernel = strKernel.replace("{{" + strVariable + "}}", str(objValue))
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elif type(objValue) == float:
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strKernel = strKernel.replace("{{" + strVariable + "}}", str(objValue))
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elif type(objValue) == bool:
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strKernel = strKernel.replace("{{" + strVariable + "}}", str(objValue))
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elif type(objValue) == str:
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strKernel = strKernel.replace("{{" + strVariable + "}}", objValue)
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elif type(objValue) == torch.Tensor and objValue.dtype == torch.uint8:
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strKernel = strKernel.replace("{{type}}", "unsigned char")
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elif type(objValue) == torch.Tensor and objValue.dtype == torch.float16:
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strKernel = strKernel.replace("{{type}}", "half")
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elif type(objValue) == torch.Tensor and objValue.dtype == torch.float32:
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strKernel = strKernel.replace("{{type}}", "float")
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elif type(objValue) == torch.Tensor and objValue.dtype == torch.float64:
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strKernel = strKernel.replace("{{type}}", "double")
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elif type(objValue) == torch.Tensor and objValue.dtype == torch.int32:
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strKernel = strKernel.replace("{{type}}", "int")
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elif type(objValue) == torch.Tensor and objValue.dtype == torch.int64:
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strKernel = strKernel.replace("{{type}}", "long")
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elif type(objValue) == torch.Tensor:
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print(strVariable, objValue.dtype)
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assert False
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elif True:
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print(strVariable, type(objValue))
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assert False
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# end
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# end
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while True:
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objMatch = re.search(r"(SIZE_)([0-4])(\()([^\)]*)(\))", strKernel)
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if objMatch is None:
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break
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# end
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intArg = int(objMatch.group(2))
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strTensor = objMatch.group(4)
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intSizes = objVariables[strTensor].size()
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strKernel = strKernel.replace(
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objMatch.group(),
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str(
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intSizes[intArg]
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if torch.is_tensor(intSizes[intArg]) == False
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else intSizes[intArg].item()
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),
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)
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# end
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while True:
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objMatch = re.search(r"(OFFSET_)([0-4])(\()", strKernel)
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if objMatch is None:
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break
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# end
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intStart = objMatch.span()[1]
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intStop = objMatch.span()[1]
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intParentheses = 1
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while True:
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intParentheses += 1 if strKernel[intStop] == "(" else 0
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intParentheses -= 1 if strKernel[intStop] == ")" else 0
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if intParentheses == 0:
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break
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# end
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intStop += 1
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# end
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intArgs = int(objMatch.group(2))
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strArgs = strKernel[intStart:intStop].split(",")
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assert intArgs == len(strArgs) - 1
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strTensor = strArgs[0]
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intStrides = objVariables[strTensor].stride()
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strIndex = []
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for intArg in range(intArgs):
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strIndex.append(
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"(("
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+ strArgs[intArg + 1].replace("{", "(").replace("}", ")").strip()
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+ ")*"
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+ str(
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intStrides[intArg]
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if torch.is_tensor(intStrides[intArg]) == False
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else intStrides[intArg].item()
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)
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+ ")"
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)
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# end
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strKernel = strKernel.replace(
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"OFFSET_" + str(intArgs) + "(" + strKernel[intStart:intStop] + ")",
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"(" + str.join("+", strIndex) + ")",
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)
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# end
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while True:
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objMatch = re.search(r"(VALUE_)([0-4])(\()", strKernel)
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if objMatch is None:
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break
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# end
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intStart = objMatch.span()[1]
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intStop = objMatch.span()[1]
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intParentheses = 1
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while True:
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intParentheses += 1 if strKernel[intStop] == "(" else 0
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intParentheses -= 1 if strKernel[intStop] == ")" else 0
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if intParentheses == 0:
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break
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# end
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intStop += 1
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# end
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intArgs = int(objMatch.group(2))
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strArgs = strKernel[intStart:intStop].split(",")
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assert intArgs == len(strArgs) - 1
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strTensor = strArgs[0]
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intStrides = objVariables[strTensor].stride()
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strIndex = []
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for intArg in range(intArgs):
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strIndex.append(
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"(("
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+ strArgs[intArg + 1].replace("{", "(").replace("}", ")").strip()
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+ ")*"
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+ str(
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intStrides[intArg]
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if torch.is_tensor(intStrides[intArg]) == False
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else intStrides[intArg].item()
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)
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+ ")"
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)
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# end
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strKernel = strKernel.replace(
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"VALUE_" + str(intArgs) + "(" + strKernel[intStart:intStop] + ")",
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strTensor + "[" + str.join("+", strIndex) + "]",
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)
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# end
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objCudacache[strKey] = {"strFunction": strFunction, "strKernel": strKernel}
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# end
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return strKey
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# end
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@cupy.memoize(for_each_device=True)
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@torch.compiler.disable()
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def cuda_launch(strKey: str):
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try:
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os.environ.setdefault("CUDA_HOME", cupy.cuda.get_cuda_path())
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except Exception:
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if "CUDA_HOME" not in os.environ:
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raise RuntimeError("'CUDA_HOME' not set, unable to find cuda-toolkit installation.")
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strKernel = objCudacache[strKey]["strKernel"]
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strFunction = objCudacache[strKey]["strFunction"]
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return cupy.RawModule(
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code=strKernel,
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options=(
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"-I " + os.environ["CUDA_HOME"],
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"-I " + os.environ["CUDA_HOME"] + "/include",
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),
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).get_function(strFunction)
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##########################################################
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@torch.compiler.disable()
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def softsplat(tenIn, tenFlow, tenMetric, strMode, return_norm=False):
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assert strMode.split("-")[0] in ["sum", "avg", "linear", "softmax"]
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if strMode == "sum":
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assert tenMetric is None
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if strMode == "avg":
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assert tenMetric is None
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if strMode.split("-")[0] == "linear":
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assert tenMetric is not None
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if strMode.split("-")[0] == "softmax":
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assert tenMetric is not None
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if strMode == "avg":
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tenIn = torch.cat(
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[
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tenIn,
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tenIn.new_ones([tenIn.shape[0], 1, tenIn.shape[2], tenIn.shape[3]]),
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],
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1,
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)
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elif strMode.split("-")[0] == "linear":
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tenIn = torch.cat([tenIn * tenMetric, tenMetric], 1)
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elif strMode.split("-")[0] == "softmax":
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tenIn = torch.cat([tenIn * tenMetric.exp(), tenMetric.exp()], 1)
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# end
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if torch.isnan(tenIn).any():
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print("NaN values detected during training in tenIn. Exiting.")
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assert False
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tenOut = softsplat_func.apply(tenIn, tenFlow)
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if torch.isnan(tenOut).any():
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print("NaN values detected during training in tenOut_1. Exiting.")
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assert False
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if strMode.split("-")[0] in ["avg", "linear", "softmax"]:
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tenNormalize = tenOut[:, -1:, :, :]
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if len(strMode.split("-")) == 1:
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tenNormalize = tenNormalize + 0.0000001
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elif strMode.split("-")[1] == "addeps":
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tenNormalize = tenNormalize + 0.0000001
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elif strMode.split("-")[1] == "zeroeps":
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tenNormalize[tenNormalize == 0.0] = 1.0
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elif strMode.split("-")[1] == "clipeps":
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tenNormalize = tenNormalize.clip(0.0000001, None)
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# end
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if return_norm:
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return tenOut[:, :-1, :, :], tenNormalize
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tenOut = tenOut[:, :-1, :, :] / tenNormalize
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if torch.isnan(tenOut).any():
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print("NaN values detected during training in tenOut_2. Exiting.")
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assert False
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# end
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return tenOut
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# end
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class softsplat_func(torch.autograd.Function):
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@staticmethod
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@torch.amp.custom_fwd(device_type="cuda", cast_inputs=torch.float32)
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def forward(self, tenIn, tenFlow):
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tenOut = tenIn.new_zeros(
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[tenIn.shape[0], tenIn.shape[1], tenIn.shape[2], tenIn.shape[3]]
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)
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if tenIn.is_cuda == True:
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cuda_launch(
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cuda_kernel(
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"softsplat_out",
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"""
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extern "C" __global__ void __launch_bounds__(512) softsplat_out(
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const int n,
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const {{type}}* __restrict__ tenIn,
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const {{type}}* __restrict__ tenFlow,
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{{type}}* __restrict__ tenOut
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) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
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const int intN = ( intIndex / SIZE_3(tenOut) / SIZE_2(tenOut) / SIZE_1(tenOut) ) % SIZE_0(tenOut);
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const int intC = ( intIndex / SIZE_3(tenOut) / SIZE_2(tenOut) ) % SIZE_1(tenOut);
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const int intY = ( intIndex / SIZE_3(tenOut) ) % SIZE_2(tenOut);
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const int intX = ( intIndex ) % SIZE_3(tenOut);
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assert(SIZE_1(tenFlow) == 2);
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{{type}} fltX = ({{type}}) (intX) + VALUE_4(tenFlow, intN, 0, intY, intX);
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{{type}} fltY = ({{type}}) (intY) + VALUE_4(tenFlow, intN, 1, intY, intX);
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if (isfinite(fltX) == false) { return; }
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if (isfinite(fltY) == false) { return; }
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{{type}} fltIn = VALUE_4(tenIn, intN, intC, intY, intX);
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int intNorthwestX = (int) (floor(fltX));
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int intNorthwestY = (int) (floor(fltY));
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int intNortheastX = intNorthwestX + 1;
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int intNortheastY = intNorthwestY;
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int intSouthwestX = intNorthwestX;
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int intSouthwestY = intNorthwestY + 1;
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int intSoutheastX = intNorthwestX + 1;
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int intSoutheastY = intNorthwestY + 1;
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{{type}} fltNorthwest = (({{type}}) (intSoutheastX) - fltX) * (({{type}}) (intSoutheastY) - fltY);
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{{type}} fltNortheast = (fltX - ({{type}}) (intSouthwestX)) * (({{type}}) (intSouthwestY) - fltY);
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{{type}} fltSouthwest = (({{type}}) (intNortheastX) - fltX) * (fltY - ({{type}}) (intNortheastY));
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{{type}} fltSoutheast = (fltX - ({{type}}) (intNorthwestX)) * (fltY - ({{type}}) (intNorthwestY));
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if ((intNorthwestX >= 0) && (intNorthwestX < SIZE_3(tenOut)) && (intNorthwestY >= 0) && (intNorthwestY < SIZE_2(tenOut))) {
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atomicAdd(&tenOut[OFFSET_4(tenOut, intN, intC, intNorthwestY, intNorthwestX)], fltIn * fltNorthwest);
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}
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if ((intNortheastX >= 0) && (intNortheastX < SIZE_3(tenOut)) && (intNortheastY >= 0) && (intNortheastY < SIZE_2(tenOut))) {
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atomicAdd(&tenOut[OFFSET_4(tenOut, intN, intC, intNortheastY, intNortheastX)], fltIn * fltNortheast);
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}
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if ((intSouthwestX >= 0) && (intSouthwestX < SIZE_3(tenOut)) && (intSouthwestY >= 0) && (intSouthwestY < SIZE_2(tenOut))) {
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atomicAdd(&tenOut[OFFSET_4(tenOut, intN, intC, intSouthwestY, intSouthwestX)], fltIn * fltSouthwest);
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}
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if ((intSoutheastX >= 0) && (intSoutheastX < SIZE_3(tenOut)) && (intSoutheastY >= 0) && (intSoutheastY < SIZE_2(tenOut))) {
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atomicAdd(&tenOut[OFFSET_4(tenOut, intN, intC, intSoutheastY, intSoutheastX)], fltIn * fltSoutheast);
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}
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} }
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""",
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{"tenIn": tenIn, "tenFlow": tenFlow, "tenOut": tenOut},
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)
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)(
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grid=tuple([int((tenOut.nelement() + 512 - 1) / 512), 1, 1]),
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block=tuple([512, 1, 1]),
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args=[
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cuda_int32(tenOut.nelement()),
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tenIn.data_ptr(),
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tenFlow.data_ptr(),
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tenOut.data_ptr(),
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],
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stream=collections.namedtuple("Stream", "ptr")(
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torch.cuda.current_stream().cuda_stream
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),
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)
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elif tenIn.is_cuda != True:
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assert False
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# end
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self.save_for_backward(tenIn, tenFlow)
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return tenOut
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# end
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@staticmethod
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@torch.compiler.disable()
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@torch.amp.custom_bwd(device_type="cuda")
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def backward(self, tenOutgrad):
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tenIn, tenFlow = self.saved_tensors
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tenOutgrad = tenOutgrad.contiguous()
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assert tenOutgrad.is_cuda == True
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tenIngrad = (
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tenIn.new_zeros(
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[tenIn.shape[0], tenIn.shape[1], tenIn.shape[2], tenIn.shape[3]]
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)
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if self.needs_input_grad[0] == True
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else None
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)
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tenFlowgrad = (
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tenFlow.new_zeros(
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[tenFlow.shape[0], tenFlow.shape[1], tenFlow.shape[2], tenFlow.shape[3]]
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)
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if self.needs_input_grad[1] == True
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else None
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)
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if tenIngrad is not None:
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cuda_launch(
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cuda_kernel(
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"softsplat_ingrad",
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"""
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extern "C" __global__ void __launch_bounds__(512) softsplat_ingrad(
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const int n,
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const {{type}}* __restrict__ tenIn,
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const {{type}}* __restrict__ tenFlow,
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const {{type}}* __restrict__ tenOutgrad,
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{{type}}* __restrict__ tenIngrad,
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{{type}}* __restrict__ tenFlowgrad
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) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
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const int intN = ( intIndex / SIZE_3(tenIngrad) / SIZE_2(tenIngrad) / SIZE_1(tenIngrad) ) % SIZE_0(tenIngrad);
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const int intC = ( intIndex / SIZE_3(tenIngrad) / SIZE_2(tenIngrad) ) % SIZE_1(tenIngrad);
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const int intY = ( intIndex / SIZE_3(tenIngrad) ) % SIZE_2(tenIngrad);
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const int intX = ( intIndex ) % SIZE_3(tenIngrad);
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assert(SIZE_1(tenFlow) == 2);
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{{type}} fltIngrad = 0.0f;
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{{type}} fltX = ({{type}}) (intX) + VALUE_4(tenFlow, intN, 0, intY, intX);
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{{type}} fltY = ({{type}}) (intY) + VALUE_4(tenFlow, intN, 1, intY, intX);
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if (isfinite(fltX) == false) { return; }
|
|
if (isfinite(fltY) == false) { return; }
|
|
|
|
int intNorthwestX = (int) (floor(fltX));
|
|
int intNorthwestY = (int) (floor(fltY));
|
|
int intNortheastX = intNorthwestX + 1;
|
|
int intNortheastY = intNorthwestY;
|
|
int intSouthwestX = intNorthwestX;
|
|
int intSouthwestY = intNorthwestY + 1;
|
|
int intSoutheastX = intNorthwestX + 1;
|
|
int intSoutheastY = intNorthwestY + 1;
|
|
|
|
{{type}} fltNorthwest = (({{type}}) (intSoutheastX) - fltX) * (({{type}}) (intSoutheastY) - fltY);
|
|
{{type}} fltNortheast = (fltX - ({{type}}) (intSouthwestX)) * (({{type}}) (intSouthwestY) - fltY);
|
|
{{type}} fltSouthwest = (({{type}}) (intNortheastX) - fltX) * (fltY - ({{type}}) (intNortheastY));
|
|
{{type}} fltSoutheast = (fltX - ({{type}}) (intNorthwestX)) * (fltY - ({{type}}) (intNorthwestY));
|
|
|
|
if ((intNorthwestX >= 0) && (intNorthwestX < SIZE_3(tenOutgrad)) && (intNorthwestY >= 0) && (intNorthwestY < SIZE_2(tenOutgrad))) {
|
|
fltIngrad += VALUE_4(tenOutgrad, intN, intC, intNorthwestY, intNorthwestX) * fltNorthwest;
|
|
}
|
|
|
|
if ((intNortheastX >= 0) && (intNortheastX < SIZE_3(tenOutgrad)) && (intNortheastY >= 0) && (intNortheastY < SIZE_2(tenOutgrad))) {
|
|
fltIngrad += VALUE_4(tenOutgrad, intN, intC, intNortheastY, intNortheastX) * fltNortheast;
|
|
}
|
|
|
|
if ((intSouthwestX >= 0) && (intSouthwestX < SIZE_3(tenOutgrad)) && (intSouthwestY >= 0) && (intSouthwestY < SIZE_2(tenOutgrad))) {
|
|
fltIngrad += VALUE_4(tenOutgrad, intN, intC, intSouthwestY, intSouthwestX) * fltSouthwest;
|
|
}
|
|
|
|
if ((intSoutheastX >= 0) && (intSoutheastX < SIZE_3(tenOutgrad)) && (intSoutheastY >= 0) && (intSoutheastY < SIZE_2(tenOutgrad))) {
|
|
fltIngrad += VALUE_4(tenOutgrad, intN, intC, intSoutheastY, intSoutheastX) * fltSoutheast;
|
|
}
|
|
|
|
tenIngrad[intIndex] = fltIngrad;
|
|
} }
|
|
""",
|
|
{
|
|
"tenIn": tenIn,
|
|
"tenFlow": tenFlow,
|
|
"tenOutgrad": tenOutgrad,
|
|
"tenIngrad": tenIngrad,
|
|
"tenFlowgrad": tenFlowgrad,
|
|
},
|
|
)
|
|
)(
|
|
grid=tuple([int((tenIngrad.nelement() + 512 - 1) / 512), 1, 1]),
|
|
block=tuple([512, 1, 1]),
|
|
args=[
|
|
cuda_int32(tenIngrad.nelement()),
|
|
tenIn.data_ptr(),
|
|
tenFlow.data_ptr(),
|
|
tenOutgrad.data_ptr(),
|
|
tenIngrad.data_ptr(),
|
|
None,
|
|
],
|
|
stream=collections.namedtuple("Stream", "ptr")(
|
|
torch.cuda.current_stream().cuda_stream
|
|
),
|
|
)
|
|
# end
|
|
|
|
if tenFlowgrad is not None:
|
|
cuda_launch(
|
|
cuda_kernel(
|
|
"softsplat_flowgrad",
|
|
"""
|
|
extern "C" __global__ void __launch_bounds__(512) softsplat_flowgrad(
|
|
const int n,
|
|
const {{type}}* __restrict__ tenIn,
|
|
const {{type}}* __restrict__ tenFlow,
|
|
const {{type}}* __restrict__ tenOutgrad,
|
|
{{type}}* __restrict__ tenIngrad,
|
|
{{type}}* __restrict__ tenFlowgrad
|
|
) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
|
|
const int intN = ( intIndex / SIZE_3(tenFlowgrad) / SIZE_2(tenFlowgrad) / SIZE_1(tenFlowgrad) ) % SIZE_0(tenFlowgrad);
|
|
const int intC = ( intIndex / SIZE_3(tenFlowgrad) / SIZE_2(tenFlowgrad) ) % SIZE_1(tenFlowgrad);
|
|
const int intY = ( intIndex / SIZE_3(tenFlowgrad) ) % SIZE_2(tenFlowgrad);
|
|
const int intX = ( intIndex ) % SIZE_3(tenFlowgrad);
|
|
|
|
assert(SIZE_1(tenFlow) == 2);
|
|
|
|
{{type}} fltFlowgrad = 0.0f;
|
|
|
|
{{type}} fltX = ({{type}}) (intX) + VALUE_4(tenFlow, intN, 0, intY, intX);
|
|
{{type}} fltY = ({{type}}) (intY) + VALUE_4(tenFlow, intN, 1, intY, intX);
|
|
|
|
if (isfinite(fltX) == false) { return; }
|
|
if (isfinite(fltY) == false) { return; }
|
|
|
|
int intNorthwestX = (int) (floor(fltX));
|
|
int intNorthwestY = (int) (floor(fltY));
|
|
int intNortheastX = intNorthwestX + 1;
|
|
int intNortheastY = intNorthwestY;
|
|
int intSouthwestX = intNorthwestX;
|
|
int intSouthwestY = intNorthwestY + 1;
|
|
int intSoutheastX = intNorthwestX + 1;
|
|
int intSoutheastY = intNorthwestY + 1;
|
|
|
|
{{type}} fltNorthwest = 0.0f;
|
|
{{type}} fltNortheast = 0.0f;
|
|
{{type}} fltSouthwest = 0.0f;
|
|
{{type}} fltSoutheast = 0.0f;
|
|
|
|
if (intC == 0) {
|
|
fltNorthwest = (({{type}}) (-1.0f)) * (({{type}}) (intSoutheastY) - fltY);
|
|
fltNortheast = (({{type}}) (+1.0f)) * (({{type}}) (intSouthwestY) - fltY);
|
|
fltSouthwest = (({{type}}) (-1.0f)) * (fltY - ({{type}}) (intNortheastY));
|
|
fltSoutheast = (({{type}}) (+1.0f)) * (fltY - ({{type}}) (intNorthwestY));
|
|
|
|
} else if (intC == 1) {
|
|
fltNorthwest = (({{type}}) (intSoutheastX) - fltX) * (({{type}}) (-1.0f));
|
|
fltNortheast = (fltX - ({{type}}) (intSouthwestX)) * (({{type}}) (-1.0f));
|
|
fltSouthwest = (({{type}}) (intNortheastX) - fltX) * (({{type}}) (+1.0f));
|
|
fltSoutheast = (fltX - ({{type}}) (intNorthwestX)) * (({{type}}) (+1.0f));
|
|
|
|
}
|
|
|
|
for (int intChannel = 0; intChannel < SIZE_1(tenOutgrad); intChannel += 1) {
|
|
{{type}} fltIn = VALUE_4(tenIn, intN, intChannel, intY, intX);
|
|
|
|
if ((intNorthwestX >= 0) && (intNorthwestX < SIZE_3(tenOutgrad)) && (intNorthwestY >= 0) && (intNorthwestY < SIZE_2(tenOutgrad))) {
|
|
fltFlowgrad += VALUE_4(tenOutgrad, intN, intChannel, intNorthwestY, intNorthwestX) * fltIn * fltNorthwest;
|
|
}
|
|
|
|
if ((intNortheastX >= 0) && (intNortheastX < SIZE_3(tenOutgrad)) && (intNortheastY >= 0) && (intNortheastY < SIZE_2(tenOutgrad))) {
|
|
fltFlowgrad += VALUE_4(tenOutgrad, intN, intChannel, intNortheastY, intNortheastX) * fltIn * fltNortheast;
|
|
}
|
|
|
|
if ((intSouthwestX >= 0) && (intSouthwestX < SIZE_3(tenOutgrad)) && (intSouthwestY >= 0) && (intSouthwestY < SIZE_2(tenOutgrad))) {
|
|
fltFlowgrad += VALUE_4(tenOutgrad, intN, intChannel, intSouthwestY, intSouthwestX) * fltIn * fltSouthwest;
|
|
}
|
|
|
|
if ((intSoutheastX >= 0) && (intSoutheastX < SIZE_3(tenOutgrad)) && (intSoutheastY >= 0) && (intSoutheastY < SIZE_2(tenOutgrad))) {
|
|
fltFlowgrad += VALUE_4(tenOutgrad, intN, intChannel, intSoutheastY, intSoutheastX) * fltIn * fltSoutheast;
|
|
}
|
|
}
|
|
|
|
tenFlowgrad[intIndex] = fltFlowgrad;
|
|
} }
|
|
""",
|
|
{
|
|
"tenIn": tenIn,
|
|
"tenFlow": tenFlow,
|
|
"tenOutgrad": tenOutgrad,
|
|
"tenIngrad": tenIngrad,
|
|
"tenFlowgrad": tenFlowgrad,
|
|
},
|
|
)
|
|
)(
|
|
grid=tuple([int((tenFlowgrad.nelement() + 512 - 1) / 512), 1, 1]),
|
|
block=tuple([512, 1, 1]),
|
|
args=[
|
|
cuda_int32(tenFlowgrad.nelement()),
|
|
tenIn.data_ptr(),
|
|
tenFlow.data_ptr(),
|
|
tenOutgrad.data_ptr(),
|
|
None,
|
|
tenFlowgrad.data_ptr(),
|
|
],
|
|
stream=collections.namedtuple("Stream", "ptr")(
|
|
torch.cuda.current_stream().cuda_stream
|
|
),
|
|
)
|
|
# end
|
|
|
|
return tenIngrad, tenFlowgrad
|
|
|
|
# end
|
|
|
|
|
|
# end
|