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"""
Low-level functionality tests for Conv2d operands on SM80
"""
from conv2d_test_utils import *
import cutlass
import logging


cutlass.set_log_level(logging.WARNING)
cc = 80

@unittest.skipIf(device_cc() != cc, 'Device compute capability is invalid for SM80 tests.')
class Conv2dSm80(unittest.TestCase):
    """
    Wrapper class to which tests will be added dynamically in __main__
    """
    pass

conv_problems = get_conv_problems()

# Tests for optimized & analytic
for conv_kind in ["fprop", "wgrad", "dgrad"]:
    # F16, simt
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16, 
        opclass="simt", threadblock_shape=[128, 128, 8], 
        warp_count=[4, 2, 1], stages=2, instruction_shape=[1, 1, 1])
    # F16, tensor op
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16, 
        opclass="tensor_op", threadblock_shape=[128, 128, 64], 
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16])
    # F16, tensor op, analytic iterator
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16, 
        opclass="tensor_op", threadblock_shape=[128, 128, 64], 
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], iterator_algorithm="analytic")
    # F16, tensor op, f32 output
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f32, 
        opclass="tensor_op", threadblock_shape=[128, 128, 64], 
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16])
    # F16, tensor op, different tile description
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16, 
        opclass="tensor_op", threadblock_shape=[128, 64, 32], 
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8])
    # F32, simt
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32, 
        opclass="simt", threadblock_shape=[128, 128, 8], 
        warp_count=[4, 2, 1], stages=4, instruction_shape=[1, 1, 1])
    # Tf32, tensorop
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32,
        opclass="tensor_op", threadblock_shape=[128, 128, 16],
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8]
    )
    # Split-K
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16, 
        opclass="tensor_op", threadblock_shape=[128, 128, 64], 
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode="serial",
        split_k_slices=2)
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16, 
        opclass="tensor_op", threadblock_shape=[128, 128, 64], 
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode="parallel",
        split_k_slices=5)
    # Swizzling functor
    add_test(
        Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16, 
        opclass="tensor_op", threadblock_shape=[128, 64, 32], 
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8], swizzle=4)

# Tests for few channels and fixed channels
# F16, tensor op, few channels
for c, tb, stage, inst in zip([2, 1],
                                [[128, 128, 64], [128, 128, 32]],
                                [3, 2],
                                [[16, 8, 16], [16, 8, 8]]):
    add_test(
        Conv2dSm80, cc, "fprop", conv2d_few_channel_problemsizes(c), cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
        opclass="tensor_op", threadblock_shape=tb, 
        warp_count=[2, 2, 1], stages=stage, instruction_shape=inst, iterator_algorithm="few_channels"
    )
# F16, tensor op, fixed channels
for c in [8, 4, 2]:
    add_test(
        Conv2dSm80, cc, "fprop", conv2d_few_channel_problemsizes(c), cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
        opclass="tensor_op", threadblock_shape=[128, 128, 64], 
        warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], iterator_algorithm="fixed_channels"
    )

# Test activations
for activation in ["relu", "leaky_relu"]:
    for split_k_mode, split_k_slices in zip(["parallel", "serial", "parallel"], [1, 7, 5]):
        add_test(
            Conv2dSm80, cc, "fprop", conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16, 
            opclass="tensor_op", threadblock_shape=[128, 128, 64], 
            warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode=split_k_mode,
            split_k_slices=split_k_slices, activation=activation)
    

if __name__ == '__main__':
    unittest.main()