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

import argparse
import numpy as np
import sys

import cutlass
import pycutlass
from pycutlass import *
from pycutlass.utils.device import device_cc


parser = argparse.ArgumentParser(description="Launch a grouped GEMM kernel from Python")
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 grouped GEMM example requires compute capability greater than or equal to 70."

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
alignment = 1
A = TensorDescription(cutlass.float16, cutlass.ColumnMajor, alignment)
B = TensorDescription(cutlass.float16, cutlass.RowMajor, alignment)
C = TensorDescription(cutlass.float32, cutlass.ColumnMajor, alignment)
element_acc = cutlass.float32
element_epilogue = cutlass.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:
    instruction_shape = [16, 8, 16]

math_inst = MathInstruction(
    instruction_shape,
    A.element, B.element, element_acc,
    cutlass.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 = GemmOperationGrouped(
    arch=cc, tile_description=tile_description,
    A=A, B=B, C=C,
    epilogue_functor=epilogue_functor,
    precompute_mode=SchedulerMode.Device)

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

operations = [operation, ]

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

# Initialize tensors for each problem in the group
problem_sizes = [
    cutlass.gemm.GemmCoord(128, 128, 64),
    cutlass.gemm.GemmCoord(512, 256, 128)
]
problem_count = len(problem_sizes)

alpha = 1.
beta = 0.

tensor_As = []
tensor_Bs = []
tensor_Cs = []
tensor_Ds = []
tensor_D_refs = []

reference = ReferenceModule(A, B, C)

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

    tensor_As.append(tensor_A)
    tensor_Bs.append(tensor_B)
    tensor_Cs.append(tensor_C)
    tensor_Ds.append(tensor_D)

    # Run the reference GEMM
    tensor_D_ref = reference.run(tensor_A, tensor_B, tensor_C, problem_size, alpha, beta)
    tensor_D_refs.append(tensor_D_ref)

arguments = GemmGroupedArguments(
    operation, problem_sizes, tensor_As, tensor_Bs, tensor_Cs, tensor_Ds,
    output_op=operation.epilogue_type(alpha, beta)
)

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

# Compare the CUTLASS result to the host reference result
for tensor_d, tensor_d_ref in zip(tensor_Ds, tensor_D_refs):
    try:
        assert np.array_equal(tensor_d, tensor_d_ref)
    except:
        assert np.allclose(tensor_d, tensor_d_ref, rtol=1e-5)

print("Passed.")
