releaase 2.11 (#703)
This commit is contained in:
Aditya Atluri
+60
-367
@@ -29,417 +29,110 @@
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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 numpy as np
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import pycutlass
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from pycutlass import *
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import cutlass
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from bfloat16 import bfloat16
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"""
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Basic example of using the CUTLASS Python interface to run a GEMM
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"""
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import argparse
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import numpy as np
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import sys
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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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# parse the arguments
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parser = argparse.ArgumentParser(
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description="Launch CUTLASS GEMM kernels from python: 'D = alpha * A * B + beta * C'")
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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=[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="RowMajor", type=str, choices=[
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"RowMajor", "ColumnMajor", "RowMajorInterleaved32", "ColumnMajorInterleaved32"],
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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="RowMajor", type=str, choices=[
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"RowMajor", "ColumnMajor", "RowMajorInterleaved32", "ColumnMajorInterleaved32"],
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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="RowMajor", type=str, choices=[
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"RowMajor", "ColumnMajor", "RowMajorInterleaved32", "ColumnMajorInterleaved32"],
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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'], help='Epilogue datatype')
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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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parser.add_argument("-epv", "--epilogue_visitor", default=None,
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type=str, choices=['RowReduction', 'ColumnReduction', 'RowBroadcast', 'ColumnBroadcast'], help="epilogue visitor for more complex epilogues")
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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", "HorizontalSwizzle", "BatchedIdentitySwizzle"],
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help="This option describes how thread blocks are scheduled on GPU")
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# Argument
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parser.add_argument("-p", "--problem_size",
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default=[128, 128, 128], nargs=3, type=int,
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help="GEMM problem size M, N, K")
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parser.add_argument("-alpha", "--alpha", default=1.0, type=float,
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help="Scaling factor of A * B")
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parser.add_argument("-beta", "--beta", default=0.0, type=float,
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help="Scaling factor of C")
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parser.add_argument("-gm", "--gemm_mode", default="Gemm", type=str,
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choices=["Gemm", "GemmSplitKParallel", "Batched", "Array"],
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help="GEMM mode. Gemm is used for non-splitK or serial-splitK. \
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GemmSplitKParallel 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('-bias', '--bias', action='store_true', help="C is bias vector")
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parser.add_argument('-batch', '--batch', default=1, type=int, help="batch size for batched GEMM")
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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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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(description="Launch a GEMM kernel from Python: 'D = alpha * A * B + beta * C'")
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parser.add_argument("--m", default=128, type=int, help="M dimension of the GEMM")
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parser.add_argument("--n", default=128, type=int, help="N dimension of the GEMM")
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parser.add_argument("--k", default=128, type=int, help="K dimension of the GEMM")
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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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pycutlass.compiler.nvcc()
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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 GEMM example requires compute capability greater than or equal to 70."
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alignment = 8
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assert args.m % alignment == 0, "M dimension of size {} is not divisible by alignment of {}".format(args.m, alignment)
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assert args.n % alignment == 0, "N dimension of size {} is not divisible by alignment of {}".format(args.n, alignment)
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assert args.k % alignment == 0, "K dimension of size {} is not divisible by alignment of {}".format(args.k, alignment)
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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.ColumnMajor, alignment)
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B = TensorDescription(cutlass.float16, cutlass.RowMajor, alignment)
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C = TensorDescription(cutlass.float32, cutlass.ColumnMajor, 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.gemm_mode == "GemmSplitKParallel" 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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swizzling_functor = getattr(cutlass, args.swizzling_functor)
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visitor = args.epilogue_visitor is not None
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if args.epilogue_visitor == "ColumnReduction":
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class ColumnReduction_(EpilogueVisitTree):
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def __call__(
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self, accum: 'tensor', c: 'tensor',
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alpha: 'scalar', beta: 'scalar'):
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#
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D = alpha * accum + beta * c
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reduction = reduction_op(D, "column", "Add", args.threadblock_shape[0])
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return D, reduction
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epilogue_functor = ColumnReduction_(
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epilogue_functor, tile_description, math_inst.element_accumulator,
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C.alignment, element_epilogue, C.element)
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epilogue_functor.initialize()
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elif args.epilogue_visitor == "RowReduction":
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class RowReduction_(EpilogueVisitTree):
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def __call__(
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self, accum: 'tensor', c: 'tensor',
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alpha: 'scalar', beta: 'scalar'):
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#
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D = alpha * accum + tanh.numpy(beta * c)
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reduction = reduction_op(D, "row", "Add", args.threadblock_shape[1])
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return D, reduction
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epilogue_functor = RowReduction_(
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epilogue_functor, tile_description, math_inst.element_accumulator,
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C.alignment, element_epilogue, C.element)
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epilogue_functor.initialize()
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elif args.epilogue_visitor == "RowBroadcast":
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class RowBroadcast_(EpilogueVisitTree):
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def __call__(
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self, accum: 'tensor', c: 'tensor',
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vector: 'row', alpha: 'scalar', beta: 'scalar'):
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#
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T = accum + vector
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scale_T = alpha * T
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Z = relu.numpy(scale_T + beta * c)
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return Z, T
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epilogue_functor = RowBroadcast_(
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epilogue_functor, tile_description, math_inst.element_accumulator,
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C.alignment, element_epilogue, C.element)
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epilogue_functor.initialize()
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elif args.epilogue_visitor == "ColumnBroadcast":
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class ColumnBroadcast_(EpilogueVisitTree):
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def __call__(
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self, accum: 'tensor', c: 'tensor',
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vector: 'column', alpha: 'scalar', beta: 'scalar'):
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#
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T = accum + vector
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scale_T = leaky_relu.numpy(alpha * T, 0.2)
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Z = scale_T + beta * c
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return Z, T
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epilogue_functor = ColumnBroadcast_(
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epilogue_functor, tile_description, math_inst.element_accumulator,
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C.alignment, element_epilogue, C.element)
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epilogue_functor.initialize()
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else:
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epilogue_functor = epilogue_functor
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epilogue_functor = pycutlass.LinearCombination(C.element, C.alignment, element_acc, element_epilogue)
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operation = GemmOperationUniversal(
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arch=args.compute_capability, tile_description=tile_description,
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arch=cc, tile_description=tile_description,
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A=A, B=B, C=C,
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epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor,
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visitor=visitor
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)
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epilogue_functor=epilogue_functor)
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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.gemm_mode == "GemmSplitKParallel":
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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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# Compile the operation
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pycutlass.compiler.add_module(operations)
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# User-provide inputs
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# Randomly initialize tensors
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tensor_A = np.ceil(np.random.uniform(low=-8.5, high=7.5, size=(args.m * args.k,))).astype(np.float16)
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tensor_B = np.ceil(np.random.uniform(low=-8.5, high=7.5, size=(args.k * args.n,))).astype(np.float16)
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tensor_C = np.ceil(np.random.uniform(low=-8.5, high=7.5, size=(args.m * args.n,))).astype(np.float32)
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tensor_D = np.zeros(shape=(args.m * args.n,)).astype(np.float32)
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problem_size = cutlass.gemm.GemmCoord(
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args.problem_size[0], args.problem_size[1], args.problem_size[2])
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tensor_a_size = args.batch * problem_size.m() * problem_size.k()
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if args.element_a != "int8":
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if args.element_a == "bfloat16":
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tensor_A = np.ceil(
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np.random.uniform(low=-8.5, high=7.5, size=(tensor_a_size,))
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).astype(bfloat16)
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else:
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tensor_A = np.ceil(
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np.random.uniform(low=-8.5, high=7.5, size=(tensor_a_size,))
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).astype(getattr(np, args.element_a))
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else:
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tensor_A = np.random.uniform(
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low=-2, high=2,size=(tensor_a_size,)
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).astype(getattr(np, args.element_a))
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tensor_b_size = args.batch * problem_size.k() * problem_size.n()
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if args.element_b != "int8":
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if args.element_b == "bfloat16":
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tensor_B = np.ceil(
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np.random.uniform(low=-8.5, high=7.5, size=(tensor_b_size,))
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).astype(bfloat16)
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else:
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tensor_B = np.ceil(
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np.random.uniform(low=-8.5, high=7.5, size=(tensor_b_size,))
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).astype(getattr(np, args.element_b))
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else:
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tensor_B = np.random.uniform(
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low=-2, high=2, size=(tensor_b_size,)
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).astype(getattr(np, args.element_b))
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if args.element_c != "int8":
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if args.bias:
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if args.layout_c == "RowMajor":
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tensor_c_size = args.batch * problem_size.n()
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elif args.layout_c == "ColumnMajor":
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tensor_c_size = args.batch * problem_size.m()
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else:
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raise ValueError(args.layout_c)
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else:
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tensor_c_size = args.batch * problem_size.m() * problem_size.n()
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if args.element_c == "bfloat16":
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tensor_C = np.ceil(
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np.random.uniform(low=-8.5, high=7.5, size=(tensor_c_size,))
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).astype(bfloat16)
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else:
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tensor_C = np.ceil(
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np.random.uniform(low=-8.5, high=7.5, size=(tensor_c_size,))
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).astype(getattr(np, args.element_c))
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else:
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tensor_C = np.random.uniform(
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low=-2, high=2, size=(args.batch * problem_size.m() * problem_size.n(),)
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).astype(getattr(np, args.element_c))
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tensor_D = np.zeros(
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shape=(args.batch * problem_size.m() * problem_size.n(),)
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).astype(getattr(np, args.element_c))
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if args.epilogue_visitor == "RowReduction":
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cta_n = args.threadblock_shape[1]
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num_cta_n = (problem_size.n() + cta_n - 1) // cta_n
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reduction = np.zeros(shape=(args.batch * problem_size.m() * num_cta_n,), dtype=getattr(np, args.element_c))
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output_op = operation.epilogue_type(
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D=tensor_D, alpha=args.alpha, beta=args.beta, c=tensor_C, reduction=reduction, problem_size=[problem_size.m(), problem_size.n()]
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)
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elif args.epilogue_visitor == "ColumnReduction":
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cta_m = args.threadblock_shape[0]
|
||||
num_cta_m = (problem_size.m() + cta_m - 1) // cta_m
|
||||
reduction = np.zeros(shape=(args.batch * problem_size.n() * num_cta_m,), dtype=getattr(np, args.element_c))
|
||||
output_op = operation.epilogue_type(
|
||||
D=tensor_D, alpha=args.alpha, beta=args.beta, c=tensor_C, reduction=reduction, problem_size=[problem_size.m(), problem_size.n()]
|
||||
)
|
||||
elif args.epilogue_visitor == "RowBroadcast":
|
||||
vector = np.ceil(
|
||||
np.random.uniform(low=-8.5, high=7.5, size=(args.batch, 1, problem_size.n()))
|
||||
).astype(getattr(np, args.element_c))
|
||||
tensor_t = np.empty_like(tensor_D)
|
||||
output_op = operation.epilogue_type(
|
||||
c=tensor_C, vector=vector, alpha=args.alpha, beta=args.beta, Z=tensor_D, T=tensor_t, problem_size=[problem_size.m(), problem_size.n()]
|
||||
)
|
||||
elif args.epilogue_visitor == "ColumnBroadcast":
|
||||
vector = np.ceil(
|
||||
np.random.uniform(low=-8.5, high=7.5, size=(args.batch, problem_size.m(), 1))
|
||||
).astype(getattr(np, args.element_c))
|
||||
tensor_t = np.empty_like(tensor_D)
|
||||
output_op = operation.epilogue_type(
|
||||
c=tensor_C, vector=vector, alpha=args.alpha, beta=args.beta, Z=tensor_D, T=tensor_t, problem_size=[problem_size.m(), problem_size.n()]
|
||||
)
|
||||
else:
|
||||
output_op = operation.epilogue_type(*([args.alpha, args.beta] + args.activation_args))
|
||||
problem_size = cutlass.gemm.GemmCoord(args.m, args.n, args.k)
|
||||
alpha = 1.
|
||||
beta = 0.
|
||||
|
||||
arguments = GemmArguments(
|
||||
operation=operation, problem_size=problem_size,
|
||||
A=tensor_A, B=tensor_B, C=tensor_C, D=tensor_D,
|
||||
output_op=output_op,
|
||||
gemm_mode=getattr(cutlass.gemm.Mode, args.gemm_mode),
|
||||
split_k_slices=args.split_k_slices, batch=args.batch
|
||||
)
|
||||
|
||||
if args.gemm_mode == "GemmSplitKParallel":
|
||||
reduction_arguments = ReductionArguments(
|
||||
operation=reduction_operation,
|
||||
problem_size=[problem_size.m(), problem_size.n()],
|
||||
partitions=args.split_k_slices, workspace=arguments.ptr_D,
|
||||
destination=tensor_D, source=tensor_C,
|
||||
output_op=reduction_operation.epilogue_type(*([args.alpha, args.beta] + args.activation_args)),
|
||||
bias = arguments.bias
|
||||
)
|
||||
output_op=operation.epilogue_type(alpha, beta))
|
||||
|
||||
# Run the operation
|
||||
operation.run(arguments)
|
||||
arguments.sync()
|
||||
|
||||
if args.gemm_mode == "GemmSplitKParallel":
|
||||
reduction_operation.run(reduction_arguments)
|
||||
reduction_arguments.sync()
|
||||
else:
|
||||
arguments.sync()
|
||||
|
||||
# run the host reference module
|
||||
# Run the host reference module and compare to the CUTLASS result
|
||||
reference = ReferenceModule(A, B, C)
|
||||
tensor_D_ref = reference.run(
|
||||
tensor_A, tensor_B, tensor_C, problem_size, args.alpha, args.beta, args.bias, args.batch)
|
||||
|
||||
if args.epilogue_visitor in ["RowBroadcast", "ColumnBroadcast"]:
|
||||
tensor_D_ref = (tensor_D_ref.reshape((args.batch, problem_size.m(), problem_size.n())) + vector).flatten()
|
||||
tensor_D_ref = getattr(pycutlass, args.activation_function).numpy(*([tensor_D_ref,] + args.activation_args))
|
||||
|
||||
if args.epilogue_visitor in ["RowReduction", "ColumnReduction"]:
|
||||
output_op.sync()
|
||||
accum_ref = reference.run(
|
||||
tensor_A, tensor_B, tensor_C, problem_size, 1.0, 0.0, args.bias, args.batch)
|
||||
tensor_D_ref, reduction_ref = epilogue_functor(
|
||||
accum_ref.reshape((args.batch, problem_size.m(), problem_size.n())),
|
||||
tensor_C.reshape((args.batch, problem_size.m(), problem_size.n())),
|
||||
args.alpha, args.beta
|
||||
)
|
||||
tensor_D_ref = tensor_D_ref.flatten()
|
||||
reduction_ref = reduction_ref.flatten()
|
||||
assert np.allclose(reduction_ref, reduction, atol=1e-2)
|
||||
|
||||
elif args.epilogue_visitor in ["RowBroadcast", "ColumnBroadcast"]:
|
||||
output_op.sync()
|
||||
accum_ref = reference.run(
|
||||
tensor_A, tensor_B, tensor_C, problem_size, 1.0, 0.0, args.bias, args.batch)
|
||||
|
||||
tensor_D_ref, tensor_T_ref = epilogue_functor(
|
||||
accum_ref.reshape((args.batch, problem_size.m(), problem_size.n())),
|
||||
tensor_C.reshape((args.batch, problem_size.m(), problem_size.n())),
|
||||
vector, args.alpha, args.beta)
|
||||
|
||||
tensor_D_ref = tensor_D_ref.flatten()
|
||||
tensor_T_ref = tensor_T_ref.flatten()
|
||||
|
||||
assert np.array_equal(tensor_t, tensor_T_ref)
|
||||
tensor_D_ref = reference.run(tensor_A, tensor_B, tensor_C, problem_size, alpha, beta)
|
||||
|
||||
try:
|
||||
assert np.array_equal(tensor_D, tensor_D_ref)
|
||||
except:
|
||||
assert np.allclose(tensor_D, tensor_D_ref, atol=1e-5)
|
||||
|
||||
print("Passed.")
|
||||
|
||||
Reference in New Issue
Block a user