releaase 2.11 (#703)
This commit is contained in:
Aditya Atluri
@@ -29,290 +29,133 @@
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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################################################################################
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import pycutlass
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from pycutlass import *
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from pycutlass.conv2d_operation import *
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from pycutlass.utils import reference_model
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import torch.nn.functional as F
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"""
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Basic example of using the CUTLASS Python interface to run a 2d convolution
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"""
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import argparse
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import torch
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import numpy as np
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import sys
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# parse the arguments
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parser = argparse.ArgumentParser(description="Launch CUTLASS convolution 2d kernels from python")
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# Operation description
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# math instruction description
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parser.add_argument("-i", "--instruction_shape",
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default=[1, 1, 1], nargs=3, type=int,
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help="This option describes the size of MMA op")
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parser.add_argument("-ta", "--element_a", default="float32", type=str,
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choices=['float64', 'float32', 'float16', 'bfloat16', 'int32', 'int8'],
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help='Data type of elements in input tensor A')
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parser.add_argument("-tb", "--element_b", default="float32", type=str,
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choices=['float64', 'float32', 'float16', 'bfloat16', 'int32', 'int8'],
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help='Data type of elements in input tensor B')
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parser.add_argument("-tc", "--element_c", default="float32", type=str,
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choices=['float64', 'float32', 'float16', 'bfloat16', 'int32', 'int8'],
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help='Data type of elements in input tensor C and output tensor D')
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parser.add_argument("-tacc", "--element_acc", default="float32", type=str,
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choices=['float64', 'float32', 'float16', 'bfloat16', 'int32', 'int8'],
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help='Data type of accumulator')
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parser.add_argument('-m', "--math", default="multiply_add",
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type=str, choices=["multiply_add", "multiply_add_fast_bf16", "multiply_add_fast_f32"], help="math instruction")
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parser.add_argument('-op', "--opcode", default="simt", type=str,
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choices=["Simt", 'TensorOp'],
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help='This option describes whether you want to use tensor \
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cores (TensorOp) or regular SIMT cores (Simt) on GPU SM')
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# tile description
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parser.add_argument("-b", "--threadblock_shape",
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default=[128, 128, 8], nargs=3, type=int,
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help="This option describes the tile size a thread block with compute")
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parser.add_argument("-s", "--stages", default=4,
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type=int, help="Number of pipelines you want to use")
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parser.add_argument("-w", "--warp_count", default=[
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4, 2, 1], nargs=3, type=int,
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help="This option describes the number of warps along M, N, and K of the threadblock")
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parser.add_argument("-cc", "--compute_capability", default=80,
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type=int, help="This option describes CUDA SM architecture number")
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# A
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parser.add_argument('-la', "--layout_a", default="TensorNHWC", type=str, choices=[
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"TensorNHWC", "TensorNC32HW32"],
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help="Memory layout of input tensor A")
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parser.add_argument('-aa', '--alignment_a', default=1,
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type=int, help="Memory alignement of input tensor A")
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# B
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parser.add_argument('-lb', "--layout_b", default="TensorNHWC", type=str, choices=[
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"TensorNHWC", "TensorC32RSK32"],
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help="Memory layout of input tensor B")
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parser.add_argument('-ab', '--alignment_b', default=1,
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type=int, help="Memory alignment of input tensor B")
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# C
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parser.add_argument('-lc', "--layout_c", default="TensorNHWC", type=str, choices=[
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"TensorNHWC", "TensorNC32HW32"],
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help="Memory layout of input tensor C and output tensor D")
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parser.add_argument('-ac', '--alignment_c', default=1,
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type=int, help="Memory alignment of input tensor C and output tensor D")
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# epilogue
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parser.add_argument("-te", "--element_epilogue", default="float32", type=str,
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choices=['float64', 'float32', 'float16', 'bfloat16'],
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help='Data type of computation in the epilogue')
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parser.add_argument("-ep", "--epilogue_functor", default="LinearCombination",
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type=str, choices=['LinearCombination', 'FastLinearCombinationClamp', 'LinearCombinationClamp'],
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help="This option describes the epilogue part of the kernel")
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# swizzling
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parser.add_argument("-sw", "--swizzling_functor", default="IdentitySwizzle1", type=str, choices=[
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"IdentitySwizzle1", "IdentitySwizzle2", "IdentitySwizzle4", "IdentitySwizzle8",
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"HorizontalSwizzle", "StridedDgradIdentitySwizzle1", "StridedDgradIdentitySwizzle4",
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"StridedDgradHorizontalSwizzle"],
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help="This option describes how thread blocks are scheduled on GPU")
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# conv related
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parser.add_argument("-co", "--conv_kind", default="fprop", type=str, choices=['fprop', 'dgrad', 'wgrad'],
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help="The type of convolution: forward propagation (fprop), \
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gradient of activation (dgrad), gradient of weight (wgrad)")
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parser.add_argument("-st", "--stride_support", default="Strided", type=str, choices=["Strided", "Unity"],
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)
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parser.add_argument("-ia", "--iterator_algorithm", default="analytic", type=str,
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choices=["analytic", "optimized", "fixed_channels", "few_channels"],
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help="This option describes iterator algorithm")
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# arguments
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parser.add_argument("-sm", "--split_k_mode", default="Serial", type=str, choices=["Serial", "Parallel"],
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help="Split K Mode. Serial is used for non-splitK or serial-splitK.\
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Parallel is used for parallel splitK.")
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parser.add_argument('-k', '--split_k_slices', default=1,
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type=int, help="Number of split-k partitions. (default 1)")
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parser.add_argument("-nhwc", "--nhwc", nargs=4, type=int, help="input size (NHWC)")
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parser.add_argument("-krsc", "--krsc", nargs=4, type=int, help="filter size (KRSC)")
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parser.add_argument("-pad", "--pad", nargs=4, type=int, help="padding (pad_h, _, pad_w, _)")
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parser.add_argument("-stride", "--stride", nargs=2, type=int, help="stride (stride_h, stride_w)")
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parser.add_argument("-dilation", "--dilation", nargs=2, type=int, help="dilation (dilation_h, dilation_w)")
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parser.add_argument("-alpha", "--alpha", default=1.0, type=float, help="alpha")
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parser.add_argument("-beta", "--beta", default=0.0, type=float, help="beta")
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parser.add_argument('-bias', '--bias', action='store_true', help="C is bias vector")
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# Activation function
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parser.add_argument("-activ", "--activation_function", default="identity",
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choices=["identity", "relu", "leaky_relu", "tanh", "sigmoid", "silu", "hardswish", "gelu"], help="activation function")
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parser.add_argument("-activ_arg", "--activation_args", default=[], nargs="+", type=float,
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help="addition arguments for activation")
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import cutlass
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import pycutlass
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from pycutlass import *
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import util
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parser.add_argument('--print_cuda', action="store_true",
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help="print the underlying CUDA kernel")
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parser = argparse.ArgumentParser(
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description=("Launch a 2d convolution kernel from Python. "
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"See https://docs.nvidia.com/deeplearning/performance/dl-performance-convolutional/index.html#convo-intro for notation."))
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parser.add_argument("--n", default=1, type=int, help="N dimension of the convolution")
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parser.add_argument("--c", default=64, type=int, help="C dimension of the convolution")
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parser.add_argument("--h", default=32, type=int, help="H dimension of the convolution")
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parser.add_argument("--w", default=32, type=int, help="W dimension of the convolution")
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parser.add_argument("--k", default=32, type=int, help="N dimension of the convolution")
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parser.add_argument("--r", default=3, type=int, help="R dimension of the convolution")
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parser.add_argument("--s", default=3, type=int, help="S dimension of the convolution")
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parser.add_argument('--print_cuda', action="store_true", help="Print the underlying CUDA kernel")
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try:
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args = parser.parse_args()
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except:
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sys.exit(0)
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pycutlass.get_memory_pool(init_pool_size=2**30, max_pool_size=2**32)
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# Check that the device is of a sufficient compute capability
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cc = util.get_device_cc()
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assert cc >= 70, "The CUTLASS Python Conv2d example requires compute capability greater than or equal to 70."
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alignment = 1
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np.random.seed(0)
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element_a = getattr(cutlass, args.element_a)
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element_b = getattr(cutlass, args.element_b)
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element_c = getattr(cutlass, args.element_c)
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element_acc = getattr(cutlass, args.element_acc)
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math_operation = getattr(MathOperation, args.math)
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opclass = getattr(cutlass.OpClass, args.opcode)
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# Allocate a pool of device memory to be used by the kernel
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pycutlass.get_memory_pool(init_pool_size=2**30, max_pool_size=2**32)
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# Set the compiler to use to NVCC
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pycutlass.compiler.nvcc()
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# Set up A, B, C and accumulator
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A = TensorDescription(cutlass.float16, cutlass.TensorNHWC, alignment)
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B = TensorDescription(cutlass.float16, cutlass.TensorNHWC, alignment)
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C = TensorDescription(cutlass.float32, cutlass.TensorNHWC, alignment)
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element_acc = cutlass.float32
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element_epilogue = cutlass.float32
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math_inst = MathInstruction(
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args.instruction_shape, element_a, element_b,
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element_acc, opclass, math_operation
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[16, 8, 8], # Shape of the Tensor Core instruction
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A.element, B.element, element_acc,
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cutlass.OpClass.TensorOp,
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MathOperation.multiply_add
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)
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tile_description = TileDescription(
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args.threadblock_shape, args.stages, args.warp_count,
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[128, 128, 32], # Threadblock shape
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2, # Number of stages
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[2, 2, 1], # Number of warps within each dimension of the threadblock shape
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math_inst
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)
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layout_a = getattr(cutlass, args.layout_a)
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layout_b = getattr(cutlass, args.layout_b)
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layout_c = getattr(cutlass, args.layout_c)
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A = TensorDescription(
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element_a, layout_a, args.alignment_a
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)
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B = TensorDescription(
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element_b, layout_b, args.alignment_b
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)
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C = TensorDescription(
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element_c, layout_c, args.alignment_c
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)
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element_epilogue = getattr(cutlass, args.element_epilogue)
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if (args.activation_function == "identity"
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or (args.split_k_mode == "Parallel" and args.split_k_slices > 1)):
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#
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epilogue_functor = getattr(pycutlass, args.epilogue_functor)(
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C.element, C.alignment, math_inst.element_accumulator, element_epilogue)
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else:
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epilogue_functor = getattr(pycutlass, "LinearCombinationGeneric")(
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getattr(pycutlass, args.activation_function)(element_epilogue),
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C.element, C.alignment, math_inst.element_accumulator, element_epilogue)
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iterator_algorithm = getattr(cutlass.conv.IteratorAlgorithm, args.iterator_algorithm)
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swizzling_functor = getattr(cutlass, args.swizzling_functor)
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stride_support = getattr(StrideSupport, args.stride_support)
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conv_kind = getattr(cutlass.conv.Operator, args.conv_kind)
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epilogue_functor = pycutlass.LinearCombination(C.element, C.alignment, element_acc, element_epilogue)
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operation = Conv2dOperation(
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conv_kind=conv_kind, iterator_algorithm=iterator_algorithm,
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arch=args.compute_capability, tile_description=tile_description,
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A=A, B=B, C=C, stride_support=stride_support,
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epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor
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conv_kind=cutlass.conv.Operator.fprop,
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iterator_algorithm=cutlass.conv.IteratorAlgorithm.optimized,
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arch=cc, tile_description=tile_description,
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A=A, B=B, C=C, stride_support=StrideSupport.Strided,
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epilogue_functor=epilogue_functor
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)
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if args.print_cuda:
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print(operation.rt_module.emit())
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operations = [operation,]
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if args.split_k_mode == "Parallel" and args.split_k_slices > 1:
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if (args.activation_function == "identity"):
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epilogue_functor_reduction = getattr(pycutlass, args.epilogue_functor)(
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C.element, C.alignment, math_inst.element_accumulator, element_epilogue)
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else:
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epilogue_functor_reduction = getattr(pycutlass, "LinearCombinationGeneric")(
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getattr(pycutlass, args.activation_function)(element_epilogue),
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C.element, C.alignment, math_inst.element_accumulator, element_epilogue)
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reduction_operation = ReductionOperation(
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shape=cutlass.MatrixCoord(4, 32 * C.alignment),
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C=C, element_accumulator=element_acc,
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element_compute=element_epilogue,
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epilogue_functor=epilogue_functor_reduction,
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count=C.alignment
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)
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operations.append(reduction_operation)
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operations = [operation, ]
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# Compile the operation
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pycutlass.compiler.add_module(operations)
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# Randomly initialize tensors
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problem_size = cutlass.conv.Conv2dProblemSize(
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cutlass.Tensor4DCoord(args.nhwc[0], args.nhwc[1], args.nhwc[2], args.nhwc[3]),
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cutlass.Tensor4DCoord(args.krsc[0], args.krsc[1], args.krsc[2], args.krsc[3]),
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cutlass.Tensor4DCoord(args.pad[0], args.pad[1], args.pad[2], args.pad[3]),
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cutlass.MatrixCoord(args.stride[0], args.stride[1]),
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cutlass.MatrixCoord(args.dilation[0], args.dilation[1]),
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cutlass.Tensor4DCoord(args.n, args.h, args.c, args.w),
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cutlass.Tensor4DCoord(args.k, args.r, args.s, args.c),
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cutlass.Tensor4DCoord(0, 0, 0, 0), # Padding
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cutlass.MatrixCoord(1, 1), # Strides
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cutlass.MatrixCoord(1, 1), # Dilation
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cutlass.conv.Mode.cross_correlation,
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args.split_k_slices, 1
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1, # Split k slices
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1 # Groups
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)
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tensor_A_size = cutlass.conv.implicit_gemm_tensor_a_size(operation.conv_kind, problem_size)
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tensor_B_size = cutlass.conv.implicit_gemm_tensor_b_size(operation.conv_kind, problem_size)
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tensor_C_size = cutlass.conv.implicit_gemm_tensor_c_size(operation.conv_kind, problem_size)
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# User-provide inputs
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tensor_A_size = cutlass.conv.implicit_gemm_tensor_a_size(
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conv_kind, problem_size
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)
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tensor_B_size = cutlass.conv.implicit_gemm_tensor_b_size(
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conv_kind, problem_size
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)
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if args.bias:
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tensor_C_size = cutlass.conv.implicit_gemm_tensor_c_extent(
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conv_kind, problem_size
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).at(3)
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else:
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tensor_C_size = cutlass.conv.implicit_gemm_tensor_c_size(
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conv_kind, problem_size
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)
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tensor_A = torch.ceil(torch.empty(size=(tensor_A_size,), dtype=torch.float16, device="cuda").uniform_(-8.5, 7.5))
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tensor_B = torch.ceil(torch.empty(size=(tensor_B_size,), dtype=torch.float16, device="cuda").uniform_(-8.5, 7.5))
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tensor_C = torch.ceil(torch.empty(size=(tensor_C_size,), dtype=torch.float32, device="cuda").uniform_(-8.5, 7.5))
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tensor_D = torch.ones(size=(tensor_C_size,), dtype=torch.float32, device="cuda")
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tensor_D_size = cutlass.conv.implicit_gemm_tensor_c_size(
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conv_kind, problem_size
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)
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if args.element_a != "int8":
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tensor_A = torch.ceil(torch.empty(size=(tensor_A_size,), dtype=getattr(torch, args.element_a), device="cuda").uniform_(-8.5, 7.5))
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else:
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tensor_A = torch.empty(size=(tensor_A_size,), dtype=getattr(torch, args.element_a), device="cuda").uniform_(-2, 2)
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if args.element_b != "int8":
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tensor_B = torch.ceil(torch.empty(size=(tensor_B_size,), dtype=getattr(torch, args.element_b), device="cuda").uniform_(-8.5, 7.5))
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else:
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tensor_B = torch.empty(size=(tensor_B_size,), dtype=getattr(torch, args.element_b), device="cuda").uniform_(-2, 2)
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if args.element_c != "int8":
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tensor_C = torch.ceil(torch.empty(size=(tensor_C_size,), dtype=getattr(torch, args.element_c), device="cuda").uniform_(-8.5, 7.5))
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else:
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tensor_C = torch.empty(size=(tensor_C_size,), dtype=getattr(torch, args.element_c), device="cuda").uniform_(-2, 2)
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tensor_D = torch.ones(size=(tensor_D_size,), dtype=getattr(torch, args.element_c), device="cuda")
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alpha = 1.
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beta = 0.
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arguments = Conv2dArguments(
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operation=operation, problem_size=problem_size, A=tensor_A,
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B=tensor_B, C=tensor_C, D=tensor_D,
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output_op = operation.epilogue_type(*([args.alpha, args.beta] + args.activation_args)),
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split_k_mode=getattr(cutlass.conv.SplitKMode, args.split_k_mode),
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split_k_slices=problem_size.split_k_slices
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operation=operation, problem_size=problem_size,
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A=tensor_A, B=tensor_B, C=tensor_C, D=tensor_D,
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output_op=operation.epilogue_type(alpha, beta)
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)
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if args.split_k_mode == "Parallel" and args.split_k_slices > 1:
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implicit_gemm_size = cutlass.conv.implicit_gemm_problem_size(conv_kind, arguments.problem_size)
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reduction_arguments = ReductionArguments(
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reduction_operation,
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problem_size=[implicit_gemm_size.m(), implicit_gemm_size.n()],
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partitions=problem_size.split_k_slices,
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workspace=arguments.ptr_D,
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destination=tensor_D,
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source=tensor_C,
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output_op = reduction_operation.epilogue_type(*([args.alpha, args.beta] + args.activation_args)),
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bias = arguments.bias
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)
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# Run the operation
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operation.run(arguments)
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arguments.sync()
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if args.split_k_mode == "Parallel" and args.split_k_slices > 1:
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reduction_operation.run(reduction_arguments)
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reduction_arguments.sync()
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else:
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arguments.sync()
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reference_model = Conv2dReferenceModule(A, B, C, conv_kind)
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tensor_D_ref = reference_model.run(tensor_A, tensor_B, tensor_C, arguments.problem_size, args.alpha, args.beta, args.bias)
|
||||
if (args.activation_function != "identity"):
|
||||
tensor_D_ref = getattr(F, args.activation_function)(*([tensor_D_ref,] + args.activation_args))
|
||||
# Run the host reference module and compare to the CUTLASS result
|
||||
reference = Conv2dReferenceModule(A, B, C, operation.conv_kind)
|
||||
tensor_D_ref = reference.run(tensor_A, tensor_B, tensor_C, problem_size, alpha, beta)
|
||||
|
||||
try:
|
||||
assert torch.equal(tensor_D, tensor_D_ref)
|
||||
except:
|
||||
assert torch.allclose(tensor_D, tensor_D_ref, rtol=1e-2)
|
||||
|
||||
print("Passed.")
|
||||
|
||||
Reference in New Issue
Block a user