VTimeLLM/flash-attention/csrc/cutlass/examples/40_cutlass_py/gemm.py

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"""
Basic example of using the CUTLASS Python interface to run a GEMM
"""

import sys
print("This example is deprecated. Please see examples/python for examples of using "
      "the CUTLASS Python interface.")
sys.exit(0)

import argparse
import numpy as np

import cutlass_bindings
import cutlass.backend as pycutlass
from cutlass.backend import *
from cutlass.backend.utils.device import device_cc


parser = argparse.ArgumentParser(description="Launch a GEMM kernel from Python: 'D = alpha * A * B + beta * C'")
parser.add_argument("--m", default=128, type=int, help="M dimension of the GEMM")
parser.add_argument("--n", default=128, type=int, help="N dimension of the GEMM")
parser.add_argument("--k", default=128, type=int, help="K dimension of the GEMM")
parser.add_argument('--print_cuda', action="store_true", help="Print the underlying CUDA kernel")

try:
    args = parser.parse_args()
except:
    sys.exit(0)

# Check that the device is of a sufficient compute capability
cc = device_cc()
assert cc >= 70, "The CUTLASS Python GEMM example requires compute capability greater than or equal to 70."

alignment = 8
assert args.m % alignment == 0, "M dimension of size {} is not divisible by alignment of {}".format(args.m, alignment)
assert args.n % alignment == 0, "N dimension of size {} is not divisible by alignment of {}".format(args.n, alignment)
assert args.k % alignment == 0, "K dimension of size {} is not divisible by alignment of {}".format(args.k, alignment)

np.random.seed(0)

# Allocate a pool of device memory to be used by the kernel
pycutlass.get_memory_pool(init_pool_size=2**30, max_pool_size=2**32)

# Set the compiler to use to NVCC
pycutlass.compiler.nvcc()

# Set up A, B, C and accumulator
A = TensorDescription(cutlass_bindings.float16, cutlass_bindings.ColumnMajor, alignment)
B = TensorDescription(cutlass_bindings.float16, cutlass_bindings.RowMajor, alignment)
C = TensorDescription(cutlass_bindings.float32, cutlass_bindings.ColumnMajor, alignment)
element_acc = cutlass_bindings.float32
element_epilogue = cutlass_bindings.float32

# Select instruction shape based on the Tensor Core instructions supported
# by the device on which we are running
if cc == 70:
    instruction_shape = [8, 8, 4]
elif cc == 75:
    instruction_shape = [16, 8, 8]
else:
    # Use CUTLASS kernels for CC 80 by default (e.g., for cases in which SM86 is used)
    cc = 80
    instruction_shape = [16, 8, 16]

math_inst = MathInstruction(
    instruction_shape,
    A.element, B.element, element_acc,
    cutlass_bindings.OpClass.TensorOp,
    MathOperation.multiply_add
)

tile_description = TileDescription(
    [128, 128, 32],   # Threadblock shape
    2,                # Number of stages
    [2, 2, 1],        # Number of warps within each dimension of the threadblock shape
    math_inst
)

epilogue_functor = pycutlass.LinearCombination(C.element, C.alignment, element_acc, element_epilogue)

operation = GemmOperationUniversal(
    arch=cc, tile_description=tile_description,
    A=A, B=B, C=C,
    epilogue_functor=epilogue_functor)

if args.print_cuda:
    print(operation.rt_module.emit())

operations = [operation, ]

# Compile the operation
pycutlass.compiler.add_module(operations)

# Randomly initialize tensors
tensor_A = np.ceil(np.random.uniform(low=-8.5, high=7.5, size=(args.m * args.k,))).astype(np.float16)
tensor_B = np.ceil(np.random.uniform(low=-8.5, high=7.5, size=(args.k * args.n,))).astype(np.float16)
tensor_C = np.ceil(np.random.uniform(low=-8.5, high=7.5, size=(args.m * args.n,))).astype(np.float32)
tensor_D = np.zeros(shape=(args.m * args.n,)).astype(np.float32)

problem_size = cutlass_bindings.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=operation.epilogue_type(alpha, beta))

# Run the operation
operation.run(arguments)
arguments.sync()

# 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, 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.")