DeepGEMM/deep_gemm/jit_kernels/m_grouped_gemm.py

import torch
from typing import Tuple

from .gemm import get_best_configs
from .runtime import (
    FP8GemmRuntime, GemmType,
    make_2d_tma_a_desc, make_2d_tma_b_desc,
    make_2d_tma_d_desc, make_2d_tma_scales_a_desc)
from .tuner import jit_tuner
from .utils import get_col_major_tma_aligned_tensor, get_num_sms


def m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(lhs: Tuple[torch.Tensor, torch.Tensor],
                                              rhs: Tuple[torch.Tensor, torch.Tensor],
                                              out: torch.Tensor, m_indices: torch.Tensor) -> None:
    """
    Do a grouped GEMM (contiguous format) with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
    LHS, RHS, RHS scaling factors, and output tensors must be in contiguous format.
    RHS and RHS scaling factors are required to be transposed.
    The LHS scaling tensor requires a TMA-aligned transposed format, if your input does not match the requirement,
        this function will do a transposing with a set of slow PyTorch operations.
    On the M axis, inputs are grouped into several batches, of which batch sizes aligned to
        `get_m_alignment_for_contiguous_layout()` (128).

    Arguments:
        lhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[m_sum, k]`,
             the second element is an FP32 1x128 scaling tensor for LHS of shape `[m_sum, ⌈k / 128⌉]`.
        rhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[num_groups, n, k]`,
             the second element is an FP32 128x128 scaling tensor for RHS of shape `[num_groups, ⌈n / 128⌉, ⌈k / 128⌉]`.
        out: the BF16 output tensor of shape `[m_sum, n]`, representing the result.
        m_indices: a tensor of shape `[m_sum]` with type `torch.int`.
            `m_indices[i]` records the group which the i-th row of the LHS belongs to,
            which means that the i-th row of the LHS matrix will be multiplied with `rhs[m_indices[i]]`.
            Values of `m_indices` in every-m-alignment-block must also be the same.
    """
    lhs, lhs_scales = lhs
    rhs, rhs_scales = rhs
    m, k = lhs.shape
    num_groups, n, k_ = rhs.shape
    m_, n_ = out.shape
    m__ = m_indices.numel()

    # Type and shape checks
    assert m == m_ == m__ and k == k_ and n == n_
    assert lhs_scales.shape == (m, (k + 127) // 128)
    assert rhs_scales.shape == (num_groups, (n + 127) // 128, (k + 127) // 128)
    assert lhs.dtype == torch.float8_e4m3fn and lhs_scales.dtype == torch.float32
    assert rhs.dtype == torch.float8_e4m3fn and rhs_scales.dtype == torch.float32
    assert out.dtype == torch.bfloat16
    assert m_indices.dtype == torch.int32
    assert lhs.is_contiguous() and rhs.is_contiguous()
    assert out.is_contiguous() and m_indices.is_contiguous()

    # LHS scales must be transposed for TMA load, but not for RHS scales
    lhs_scales = get_col_major_tma_aligned_tensor(lhs_scales)
    assert rhs_scales.is_contiguous()

    # Do nothing if `m` is zero
    if m == 0:
        return

    # Auto-tuning with compilation
    num_sms = get_num_sms()
    num_sms, block_m, block_n, num_stages, tma_multicast_config, smem_config = get_best_configs(
        m, n, k, 1, num_sms, is_grouped_contiguous=True)
    block_k = 128
    num_tma_threads = 128
    num_math_threads_per_group = 128

    tensor_map_a = make_2d_tma_a_desc(
        GemmType.GroupedContiguous, lhs, m, k, block_m, block_k, num_groups)
    tensor_map_b = make_2d_tma_b_desc(
        GemmType.GroupedContiguous, rhs, k, n, block_k, block_n, num_groups)
    tensor_map_d = make_2d_tma_d_desc(
        GemmType.GroupedContiguous, out, m, n, block_m, block_n, num_groups, smem_config[1])
    tensor_map_scales_a = make_2d_tma_scales_a_desc(
        GemmType.GroupedContiguous, lhs_scales, m, k, block_m, block_k, num_groups)

    kwargs = {
        'NUM_TMA_THREADS': num_tma_threads,
        'NUM_MATH_THREADS_PER_GROUP': num_math_threads_per_group,
        'M': m,
        'BLOCK_K': block_k,
        'GMEM_D': out,
        'SCALES_B': rhs_scales,
        'GROUPED_LAYOUT': m_indices,
        'NUM_SMS': num_sms,
        'SMEM_SIZE': smem_config[0],
        'TENSOR_MAP_A': tensor_map_a,
        'TENSOR_MAP_B': tensor_map_b,
        'TENSOR_MAP_SCALES_A': tensor_map_scales_a,
        'TENSOR_MAP_D': tensor_map_d,
        'STREAM': torch.cuda.current_stream().cuda_stream,
    }

    runtime, best_keys = jit_tuner.compile_and_tune(
        name='m_grouped_gemm_fp8_fp8_bf16_nt',
        keys={'N': n, 'K': k, 'BLOCK_M': block_m, 'BLOCK_N': block_n,
              'SWIZZLE_D_MODE': smem_config[1],
              'BLOCK_N_PADDING': smem_config[2],
              'NUM_GROUPS': num_groups,
              'NUM_STAGES': num_stages,
              'NUM_TMA_MULTICAST': tma_multicast_config[0],
              'IS_TMA_MULTICAST_ON_A': tma_multicast_config[1],
              'GEMM_TYPE': GemmType.GroupedContiguous},
        space=(),
        kwargs=kwargs,
        runtime_cls=FP8GemmRuntime,
    )

    # Run the kernel
    runtime(**best_keys, **kwargs)


def m_grouped_gemm_fp8_fp8_bf16_nt_masked(lhs: Tuple[torch.Tensor, torch.Tensor],
                                          rhs: Tuple[torch.Tensor, torch.Tensor],
                                          out: torch.Tensor, masked_m: torch.Tensor, expected_m: int) -> None:
    """
    Do a grouped GEMM (masked format) with FP8 inputs and BF16 output, with 1x128 LHS scaling and 128x128 RHS scaling.
    LHS, RHS, RHS scaling factors, and output tensors must be in contiguous format.
    RHS and RHS scaling factors are required to be transposed.
    The LHS scaling tensor requires a TMA-aligned transposed format, if your input does not match the requirement,
        this function will do a transposing with a set of slow PyTorch operations.
    Moreover, this alignment requirement is different with the contiguous-format kernel, as we require that each batch
        should be separately transposed.

    Arguments:
        lhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[num_groups, m_max, k]`,
             the second element is an FP32 1x128 scaling tensor for LHS of shape `[num_groups, m_max, ⌈k / 128⌉]`.
        rhs: the first element is an FP8 tensor (typed `torch.float8_e4m3fn`) of shape `[num_groups, n, k]`.
             The second element is an FP32 128x128 scaling tensor for RHS of shape `[num_groups, ⌈n / 128⌉, ⌈k / 128⌉]`.
        out: the BF16 output tensor of shape `[num_groups, m_max, n]`, representing the result.
        masked_m: a tensor of shape `[num_groups]`, `masked_m[i]` records actual rows of the `lhs[i]` matrix to compute
            in the i-th group.
        expected_m: a value hint (which is a value on CPU) for the M expectation of each batch,
            correctly setting this value may lead to better performance.
    """
    lhs, lhs_scales = lhs
    rhs, rhs_scales = rhs
    num_groups, m, k = lhs.shape
    num_groups_, n, k_ = rhs.shape
    num_groups__, m_, n_ = out.shape
    num_groups___ = masked_m.numel()

    # Type and shape checks
    assert num_groups == num_groups_ == num_groups__ == num_groups___
    assert m == m_ and n == n_ and k == k_
    assert expected_m > 0 and m > 0 and n > 0 and k > 0 and num_groups > 0
    assert lhs_scales.shape == (num_groups, m, (k + 127) // 128)
    assert rhs_scales.shape == (num_groups, (n + 127) // 128, (k + 127) // 128)
    assert lhs.dtype == torch.float8_e4m3fn and lhs_scales.dtype == torch.float32
    assert rhs.dtype == torch.float8_e4m3fn and rhs_scales.dtype == torch.float32
    assert out.dtype == torch.bfloat16
    assert masked_m.dtype == torch.int32
    assert lhs.is_contiguous() and rhs.is_contiguous()
    assert out.is_contiguous() and masked_m.is_contiguous()

    # LHS scales must be transposed for TMA load, but not for RHS scales
    lhs_scales = get_col_major_tma_aligned_tensor(lhs_scales)
    assert rhs_scales.is_contiguous()

    # Auto-tuning with compilation
    num_sms = get_num_sms()
    num_sms, block_m, block_n, num_stages, tma_multicast_config, smem_config = get_best_configs(
        expected_m, n, k, num_groups, num_sms, is_grouped_masked=True)

    # Extra checks for TMA store
    if num_groups > 1 and m > block_m:
        assert m % block_m == 0, f'For masked grouped GEMM, shape M should be multiple of the block M (current block M: {block_m})'

    block_k = 128
    num_tma_threads = 128
    num_math_threads_per_group = 128

    tensor_map_a = make_2d_tma_a_desc(
        GemmType.GroupedMasked, lhs, m, k, block_m, block_k, num_groups)
    tensor_map_b = make_2d_tma_b_desc(
        GemmType.GroupedMasked, rhs, k, n, block_k, block_n, num_groups)
    tensor_map_d = make_2d_tma_d_desc(
        GemmType.GroupedMasked, out, m, n, block_m, block_n, num_groups, smem_config[1])
    tensor_map_scales_a = make_2d_tma_scales_a_desc(
        GemmType.GroupedMasked, lhs_scales, m, k, block_m, block_k, num_groups)

    kwargs = {
        'NUM_TMA_THREADS': num_tma_threads,
        'NUM_MATH_THREADS_PER_GROUP': num_math_threads_per_group,
        'M': m,
        'BLOCK_K': block_k,
        'GMEM_D': out,
        'SCALES_B': rhs_scales,
        'GROUPED_LAYOUT': masked_m,
        'NUM_SMS': num_sms,
        'SMEM_SIZE': smem_config[0],
        'TENSOR_MAP_A': tensor_map_a,
        'TENSOR_MAP_B': tensor_map_b,
        'TENSOR_MAP_SCALES_A': tensor_map_scales_a,
        'TENSOR_MAP_D': tensor_map_d,
        'STREAM': torch.cuda.current_stream().cuda_stream,
    }

    runtime, best_keys = jit_tuner.compile_and_tune(
        name='m_grouped_gemm_fp8_fp8_bf16_nt',
        keys={'N': n, 'K': k, 'BLOCK_M': block_m, 'BLOCK_N': block_n,
              'SWIZZLE_D_MODE': smem_config[1],
              'BLOCK_N_PADDING': smem_config[2],
              'NUM_GROUPS': num_groups,
              'NUM_STAGES': num_stages,
              'NUM_TMA_MULTICAST': tma_multicast_config[0],
              'IS_TMA_MULTICAST_ON_A': tma_multicast_config[1],
              'GEMM_TYPE': GemmType.GroupedMasked},
        space=(),
        kwargs=kwargs,
        runtime_cls=FP8GemmRuntime,
    )

    # Run the kernel
    runtime(**best_keys, **kwargs)