Analyzing Operator Computing and Transfer Specifications

from mskpp import mmad, Tensor, Chip
def my_mmad(gm_x, gm_y, gm_z):
    # Basic data paths for matrix multiplication:
    # Left matrix x: GM-L1-L0A
    # Right matrix y: GM-L1-L0B
    # Result matrix z: L0C (initialized)-GM
    # Sample mathematical expression: z = x @ y + b
    # Define and allocate variables on L1.
    l1_x = Tensor("L1")
    l1_y = Tensor("L1")
    # Define and allocate variables on L0A and L0B.
    x = Tensor("L0A")
    y = Tensor("L0B")
    # Define and allocate the offset on L0C. Theoretically, the offset should be allocated to the accumulator buffer. Allocating the offset to L0C does not affect the performance.
    b = Tensor("L0C", "FP32", [32, 16], format="NC1HWC0")
    # Move the data on the GM to the memory space corresponding to L1.
    l1_x.load(gm_x)
    l1_y.load(gm_y)
    # Move the left and right matrices on L1 to L0A and L0B.
    x.load(l1_x)
    y.load(l1_y)
    # The current data has been loaded to L0A and L0B. Call the calculation instruction and save the result to L0C. out is the variable allocated by the mmad function in L0C.
    out = mmad(x, y, b, True)()
    # Move the data on L0C to the address space of the GM variable gm_z.
    gm_z.load(out[0])
    return gm_z
if __name__ == '__main__':
    with Chip("Ascendxxxyy") as chip:
        chip.enable_trace() # Enable the operator simulation pipeline chart function to generate the trace.json. file.
        chip.enable_metrics() # Enable single instruction and pipeline information to generate the Instruction_statistic.csv and Pipe_statistic.csv files.
        # Simulate the scenario where a large matrix is split into five small matrices for computation.
        for _ in range(5):
            # Use the operator for AI Core computation.
            in_x = Tensor("GM", "FP16", [32, 48], format="ND")
            in_y = Tensor("GM", "FP16", [48, 16], format="ND")
            in_z = Tensor("GM", "FP32", [32, 16], format="NC1HWC0")
            my_mmad(in_x, in_y, in_z)