gemm.py

# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""JAX te modules"""

import math
import operator
from collections.abc import Iterable
from typing import Tuple, Sequence, Union
from functools import partial, reduce

import jax
import jax.numpy as jnp
from jax import dtypes
from jax.sharding import NamedSharding, PartitionSpec
from jax.experimental.custom_partitioning import SdyShardingRule

import transformer_engine_jax as tex
from transformer_engine_jax import get_num_compute_streams

from .base import BasePrimitive, register_primitive
from .quantization import grouped_quantize
from ..quantize import (
    ScaledTensor,
    ScaledTensor2x,
    GroupedScaledTensor1x,
    ScalingMode,
    Quantizer,
    GroupedQuantizer,
    QuantizeConfig,
    QuantizerSet,
    QuantizeLayout,
    noop_quantizer_set,
    is_fp8_gemm_with_all_layouts_supported,
    apply_padding_to_scale_inv,
)
from .misc import get_padded_spec


__all__ = [
    "gemm",
    "grouped_gemm",
    "gemm_uses_jax_dot",
    "sanitize_dims",
    "get_non_contracting_dims",
    "transpose_dims",
]


num_cublas_streams = get_num_compute_streams()


def get_cublas_workspace_size_bytes() -> None:
    """Return 32 MiB if using hopper, 4 MiB for all other architectures."""
    if tex.get_device_compute_capability(0) >= 90:
        return 33_554_432
    return 4_194_304


def sanitize_dims(ndim: int, dims: Union[int, Sequence[int]]) -> Sequence[int]:
    """Convert relative (negative) indexes to absolute dimension numbers."""
    dims_ = dims if isinstance(dims, Iterable) else (dims,)
    if len(dims_) == 0:
        return dims_
    return tuple(ndim + dim if dim < 0 else dim for dim in dims_ if dim is not None)


def get_non_contracting_dims(ndim, contracting_dims):
    """Return a tuple of dimensions not included in the contracting dimensions."""
    contracting_dims = sanitize_dims(ndim, contracting_dims)
    return tuple(dim for dim in range(ndim) if dim not in contracting_dims)


def transpose_dims(ndim, dims_to_transpose, flatten_axis=-1):
    """Compute the new dimension numbers after transpose."""
    if len(dims_to_transpose) == 0:
        return dims_to_transpose
    flatten_axis = ndim - flatten_axis if flatten_axis > 0 else flatten_axis
    transposed_dims = (*range(flatten_axis, ndim), *range(flatten_axis))
    return tuple(transposed_dims.index(dim) for dim in dims_to_transpose)


def _compatible_fp8_gemm_dtypes(lhs_dtype, rhs_dtype) -> bool:
    lhs, rhs, e4m3, e5m2 = map(
        dtypes.canonicalize_dtype,
        (
            lhs_dtype,
            rhs_dtype,
            jnp.float8_e4m3fn,
            jnp.float8_e5m2,
        ),
    )

    # FP8 GEMM supports (e4m3 x e4m3), (e4m3 x e5m2) and (e5m2 x e4m3)
    if (lhs is e4m3 and rhs in (e4m3, e5m2)) or (lhs in (e4m3, e5m2) and rhs is e4m3):
        return True

    # Any other combination of data types is not supported
    return False


def _get_gemm_layout(
    operand_ndims: Tuple[int, int], contracting_dims: Tuple[Sequence[int], Sequence[int]]
) -> Tuple[bool, bool]:
    lhs_contracting, rhs_contracting = map(sanitize_dims, operand_ndims, contracting_dims)
    lhs_is_transposed = operand_ndims[0] - 1 not in lhs_contracting
    rhs_is_transposed = operand_ndims[1] - 1 in rhs_contracting
    return lhs_is_transposed, rhs_is_transposed


def _quantize_gemm_operands(lhs, rhs, lhs_quantizer, rhs_quantizer, contracting_dims):
    lhs_q = lhs
    rhs_q = rhs

    if not isinstance(lhs, ScaledTensor) and lhs_quantizer is not None:
        lhs_cdims = sanitize_dims(lhs.ndim, contracting_dims[0])
        lhs_is_transposed = lhs.ndim - 1 not in lhs_cdims
        need_lhs_colwise = lhs_is_transposed and (
            lhs_quantizer.scaling_mode.is_1d_block_scaling()
            or not is_fp8_gemm_with_all_layouts_supported()
        )
        flatten_axis = max(lhs_cdims) + 1 if lhs_is_transposed else min(lhs_cdims)
        lhs_q = lhs_quantizer.quantize(
            lhs,
            is_rowwise=not need_lhs_colwise,
            is_colwise=need_lhs_colwise,
            flatten_axis=flatten_axis,
        )

    if not isinstance(rhs, ScaledTensor) and rhs_quantizer is not None:
        rhs_cdims = sanitize_dims(rhs.ndim, contracting_dims[1])
        rhs_is_transposed = rhs.ndim - 1 in rhs_cdims
        need_rhs_colwise = not rhs_is_transposed and (
            rhs_quantizer.scaling_mode.is_1d_block_scaling()
            or not is_fp8_gemm_with_all_layouts_supported()
        )
        flatten_axis = min(rhs_cdims) if rhs_is_transposed else max(rhs_cdims) + 1
        rhs_q = rhs_quantizer.quantize(
            rhs,
            is_rowwise=not need_rhs_colwise,
            is_colwise=need_rhs_colwise,
            flatten_axis=flatten_axis,
        )

    assert not isinstance(lhs_q, ScaledTensor2x)
    assert not isinstance(rhs_q, ScaledTensor2x)

    return lhs_q, rhs_q


class GemmPrimitive(BasePrimitive):
    """
    Primitive for cuBLAS GEMM
    """

    name = "te_gemm_ffi"
    multiple_results = True
    impl_static_args = (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def abstract(
        lhs,
        lhs_scale_inv,
        rhs,
        rhs_scale_inv,
        bias,
        gelu_input,
        out_dtype,
        contracting_dims,
        batched_dims,
        lhs_quantized_colwise,
        rhs_quantized_colwise,
        scaling_mode,
        fuse_bias,
        fuse_gelu,
        grad,
        use_split_accumulator,
        sequence_parallel_output,
        sequence_dim,
    ):
        del lhs_quantized_colwise, rhs_quantized_colwise, use_split_accumulator
        del (
            sequence_parallel_output,
            sequence_dim,
        )

        def _dims_are_consecutive(dims):
            if len(dims) <= 1:
                return True
            return sorted(dims) == list(range(min(dims), max(dims) + 1))

        # Sanity-check operand layouts and types
        operand_ndims = (lhs.ndim, rhs.ndim)

        (
            lhs_contracting_dims,
            rhs_contracting_dims,
        ) = map(sanitize_dims, operand_ndims, contracting_dims)
        assert _dims_are_consecutive(lhs_contracting_dims), (
            "cuBLAS GEMM expected consecutive contracting dimensions for LHS operand, but got "
            f"{lhs_contracting_dims}."
        )
        assert _dims_are_consecutive(rhs_contracting_dims), (
            "cuBLAS GEMM expected consecutive contracting dimensions for RHS operand, but got "
            f"{rhs_contracting_dims}."
        )

        (
            lhs_batch_dims,
            rhs_batch_dims,
        ) = map(sanitize_dims, operand_ndims, batched_dims)
        assert _dims_are_consecutive(lhs_batch_dims), (
            "cuBLAS GEMM expected consecutive batch dimensions for LHS operand, but got "
            f"{lhs_batch_dims}."
        )
        assert _dims_are_consecutive(rhs_batch_dims), (
            "cuBLAS GEMM expected consecutive batch dimensions for RHS operand, but got "
            f"{rhs_batch_dims}."
        )
        if len(lhs_batch_dims) == 0:
            assert (
                len(rhs_batch_dims) == 0
            ), "cuBLAS GEMM RHS operand cannot be batched if LHS operand is not batched."
        elif len(rhs_batch_dims) != 0:
            assert all(bdim in lhs_contracting_dims for bdim in lhs_batch_dims) and all(
                bdim in rhs_contracting_dims for bdim in rhs_batch_dims
            ), "cuBLAS GEMM batched dimensions must be contracting when both operands are batched."

        lhs_contracting_size, rhs_contracting_size = map(
            lambda shape, dims: reduce(operator.mul, [shape[dim] for dim in dims]),
            (lhs.shape, rhs.shape),
            (lhs_contracting_dims, rhs_contracting_dims),
        )
        assert lhs_contracting_size == rhs_contracting_size, (
            "cuBLAS GEMM operands have incompatible contracting dimensions: "
            f"{lhs.shape} @ idx {lhs_contracting_dims} X {rhs.shape} @ idx {rhs_contracting_dims}."
        )

        lhs_is_transposed, rhs_is_transposed = _get_gemm_layout(operand_ndims, contracting_dims)
        if scaling_mode != ScalingMode.NO_SCALING:
            assert _compatible_fp8_gemm_dtypes(lhs.dtype, rhs.dtype), (
                "cuBLAS GEMM quantized operands have incompatible data types: "
                f"{lhs.dtype} x {rhs.dtype}."
            )
            assert (
                lhs_scale_inv.size > 0 and rhs_scale_inv.size > 0
            ), "Quantized cuBLAS GEMM requires inverse scaling factors for both operands."
            if (
                scaling_mode != ScalingMode.MXFP8_1D_SCALING
                and not tex.is_non_nt_fp8_gemm_supported()
            ):
                assert not lhs_is_transposed and rhs_is_transposed, (
                    "cuBLAS FP8 GEMM on devices with compute capability < 10.0 (Hopper) "
                    "require non-transposed LHS and transposed RHS operands "
                    "(`contracting_dims=((-1, ), (-1, ))`)."
                )

        # Determine output shape and dtype
        assert (
            dtypes.canonicalize_dtype(out_dtype).itemsize > 1
        ), "cuBLAS GEMM custom op does not support 8-bit quantized output types."
        lhs_non_contracting_shape, rhs_non_contracting_shape = map(
            lambda shape, dims: [shape[dim] for dim in range(len(shape)) if dim not in dims],
            (lhs.shape, rhs.shape),
            (lhs_contracting_dims, rhs_contracting_dims),
        )
        out_shape = (*lhs_non_contracting_shape, *rhs_non_contracting_shape)
        output = jax.core.ShapedArray(shape=out_shape, dtype=out_dtype)

        # Validate bias
        bias_shape = (0,)
        bias_dtype = out_dtype
        if fuse_bias:
            expected_bias_size = reduce(operator.mul, rhs_non_contracting_shape)
            if not grad:
                assert bias.size == expected_bias_size, (
                    "cuBLAS GEMM bias tensor has incorrect shape, "
                    f"expected ({expected_bias_size}, ) but found {bias.shape}."
                )
                assert bias.dtype == out_dtype, (
                    "cuBLAS GEMM bias tensor has incorrect data type, "
                    f"expected {bias_dtype} but found {bias.dtype}."
                )
                bias_shape = bias.shape
            else:
                bias_shape = rhs_non_contracting_shape
        bias_grad = jax.core.ShapedArray(shape=bias_shape, dtype=bias_dtype)

        # Validate pre-GeLU
        pre_gelu_shape = (0,)
        pre_gelu_dtype = out_dtype
        if fuse_gelu:
            pre_gelu_shape = out_shape
            if grad:
                pre_gelu_ndim = len(pre_gelu_shape)
                assert gelu_input.ndim == pre_gelu_shape and all(
                    gelu_input.shape[i] == pre_gelu_shape[i] for i in range(pre_gelu_ndim)
                ), (
                    "cuBLAS GEMM pre-GeLU tensor has incorrect shape, "
                    f"expected {pre_gelu_shape} but found {gelu_input.shape}."
                )
                assert gelu_input.dtype == out_dtype, (
                    "cuBLAS GEMM pre-GeLU tensor has incorrect data type, "
                    f"expected {pre_gelu_dtype} but found {gelu_input.dtype}."
                )
        pre_gelu_out = jax.core.ShapedArray(shape=pre_gelu_shape, dtype=pre_gelu_dtype)

        # Need extra workspace for swizzled scale factors
        lhs_swizzle_size = 0
        rhs_swizzle_size = 0
        swizzle_dtype = jnp.uint8
        if scaling_mode == ScalingMode.MXFP8_1D_SCALING:
            lhs_swizzle_size = lhs_scale_inv.size
            rhs_swizzle_size = rhs_scale_inv.size
        lhs_swizzle = jax.core.ShapedArray(shape=(lhs_swizzle_size,), dtype=swizzle_dtype)
        rhs_swizzle = jax.core.ShapedArray(shape=(rhs_swizzle_size,), dtype=swizzle_dtype)

        # Declare cuBLAS workspace
        # cuBLAS workspace ptr must be 256 bytes aligned but JAX buffers are not
        # necessarily 256 bytes aligned, we add some padding to ensure alignment.
        workspace_size = get_cublas_workspace_size_bytes() + 256
        workspace = jax.core.ShapedArray(shape=(workspace_size,), dtype=jnp.uint8)

        return output, bias_grad, pre_gelu_out, lhs_swizzle, rhs_swizzle, workspace

    @staticmethod
    def outer_abstract(*args, **kwargs):
        outputs = GemmPrimitive.abstract(*args, **kwargs)
        return outputs[:-3]  # discard workspace arrays

    @staticmethod
    def lowering(
        ctx,
        lhs,
        lhs_scale_inv,
        rhs,
        rhs_scale_inv,
        bias,
        gelu_input,
        out_dtype,
        contracting_dims,
        batched_dims,
        lhs_quantized_colwise,
        rhs_quantized_colwise,
        scaling_mode,
        fuse_bias,
        fuse_gelu,
        grad,
        use_split_accumulator,
        sequence_parallel_output,
        sequence_dim,
    ):
        del batched_dims, lhs_quantized_colwise, rhs_quantized_colwise, out_dtype
        del sequence_parallel_output, sequence_dim

        lhs_aval, _, rhs_aval, *_ = ctx.avals_in
        lhs_cdims, rhs_cdims = map(sanitize_dims, (lhs_aval.ndim, rhs_aval.ndim), contracting_dims)
        lhs_transposed, rhs_transposed = _get_gemm_layout(
            (lhs_aval.ndim, rhs_aval.ndim), (lhs_cdims, rhs_cdims)
        )

        args = (lhs, lhs_scale_inv, rhs, rhs_scale_inv, bias, gelu_input)
        kwargs = {
            "scaling_mode": int(scaling_mode.value),
            "lhs_axis_boundary": max(lhs_cdims) + 1 if lhs_transposed else min(lhs_cdims),
            "rhs_axis_boundary": min(rhs_cdims) if rhs_transposed else max(rhs_cdims) + 1,
            "lhs_transposed": lhs_transposed,
            "rhs_transposed": rhs_transposed,
            "fuse_bias": fuse_bias,
            "fuse_gelu": fuse_gelu,
            "grad": grad,
            "use_split_accumulator": use_split_accumulator,
        }

        operand_output_aliases = {}
        if fuse_bias and not grad:
            operand_output_aliases.update({4: 1})  # bias <-> bias_grad
        if fuse_gelu and grad:
            operand_output_aliases.update({5: 2})  # gelu_input <-> pre_gelu_out

        return jax.ffi.ffi_lowering(
            GemmPrimitive.name,
            operand_output_aliases=operand_output_aliases,
        )(ctx, *args, **kwargs)

    @staticmethod
    def impl(
        lhs,
        lhs_scale_inv,
        rhs,
        rhs_scale_inv,
        bias,
        gelu_input,
        out_dtype,
        contracting_dims,
        batched_dims,
        lhs_quantized_colwise,
        rhs_quantized_colwise,
        scaling_mode,
        fuse_bias,
        fuse_gelu,
        grad,
        use_split_accumulator,
        sequence_parallel_output,
        sequence_dim,
    ):
        lhs_cdims, rhs_cdims = map(sanitize_dims, (lhs.ndim, rhs.ndim), contracting_dims)
        lhs_transposed, rhs_transposed = _get_gemm_layout(
            (lhs.ndim, rhs.ndim), (lhs_cdims, rhs_cdims)
        )
        lhs_scale_inv = apply_padding_to_scale_inv(
            lhs_scale_inv,
            scaling_mode,
            lhs.shape,
            is_colwise=lhs_quantized_colwise,
            flatten_axis=max(lhs_cdims) + 1 if lhs_transposed else min(lhs_cdims),
        )
        rhs_scale_inv = apply_padding_to_scale_inv(
            rhs_scale_inv,
            scaling_mode,
            rhs.shape,
            is_colwise=rhs_quantized_colwise,
            flatten_axis=min(rhs_cdims) if rhs_transposed else max(rhs_cdims) + 1,
        )

        outputs = GemmPrimitive.inner_primitive.bind(
            lhs,
            lhs_scale_inv,
            rhs,
            rhs_scale_inv,
            bias,
            gelu_input,
            out_dtype=out_dtype,
            contracting_dims=contracting_dims,
            batched_dims=batched_dims,
            lhs_quantized_colwise=lhs_quantized_colwise,
            rhs_quantized_colwise=rhs_quantized_colwise,
            scaling_mode=scaling_mode,
            fuse_bias=fuse_bias,
            fuse_gelu=fuse_gelu,
            grad=grad,
            use_split_accumulator=use_split_accumulator,
            sequence_parallel_output=sequence_parallel_output,
            sequence_dim=sequence_dim,
        )
        return outputs[:-3]  # discard workspace arrays

    @staticmethod
    def batcher(
        batched_args,
        jax_batch_dims,
        out_dtype,
        contracting_dims,
        batched_dims,
        lhs_quantized_colwise,
        rhs_quantized_colwise,
        scaling_mode,
        fuse_bias,
        fuse_gelu,
        grad,
        use_split_accumulator,
        sequence_parallel_output,
        sequence_dim,
    ):
        assert GemmPrimitive.outer_primitive is not None
        lhs, _, rhs, *_ = batched_args
        lhs_bdims, _, rhs_bdims, *_ = jax_batch_dims
        arg_lhs_bdims, arg_rhs_bdims = map(sanitize_dims, (lhs.ndim, rhs.ndim), batched_dims)
        arg_lhs_bdims = (None,) if len(arg_lhs_bdims) == 0 else arg_lhs_bdims
        assert all(bdim == arg_bdim for bdim, arg_bdim in zip(lhs_bdims, arg_lhs_bdims)), (
            "User-specified batch dimension(s) for cuBLAS GEMM LHS operand does not match batch "
            f"dimensions inferred by JAX/XLA, expected {lhs_bdims} but got {arg_lhs_bdims}."
        )
        arg_rhs_bdims = (None,) if len(arg_rhs_bdims) == 0 else arg_rhs_bdims
        assert all(bdim == arg_bdim for bdim, arg_bdim in zip(rhs_bdims, arg_rhs_bdims)), (
            "User-specified batch dimension(s) for cuBLAS GEMM RHS operand does not match batch "
            f"dimensions inferred by JAX/XLA, expected {lhs_bdims} but got {arg_lhs_bdims}."
        )

        # Output is batched like the non-contracting batch dimensions of the LHS operand
        lhs_cdims = sanitize_dims(lhs.ndim, contracting_dims)
        lhs_non_contracting_bdims = tuple(dim for dim in lhs_bdims if dim not in lhs_cdims)
        out_bdims = (None,) if len(lhs_non_contracting_bdims) == 0 else lhs_non_contracting_bdims

        # Bias gradient is never batched
        bias_bdims = (None,)

        # Pre-GeLU output, if exists, is batched like GEMM output
        pre_gelu_bdims = (None,)
        if fuse_gelu and not grad:
            pre_gelu_bdims = out_bdims

        return (
            GemmPrimitive.outer_primitive.bind(
                *batched_args,
                out_dtype=out_dtype,
                contracting_dims=contracting_dims,
                batched_dims=batched_dims,
                lhs_quantized_colwise=lhs_quantized_colwise,
                rhs_quantized_colwise=rhs_quantized_colwise,
                scaling_mode=scaling_mode,
                fuse_bias=fuse_bias,
                fuse_gelu=fuse_gelu,
                grad=grad,
                use_split_accumulator=use_split_accumulator,
                sequence_parallel_output=sequence_parallel_output,
                sequence_dim=sequence_dim,
            ),
            (out_bdims, bias_bdims, pre_gelu_bdims),
        )

    @staticmethod
    def _parse_operand_output_specs(
        arg_infos,
        contracting_dims,
        batched_dims,
        sequence_parallel_output,
        sequence_dim,
    ):
        del sequence_dim, sequence_parallel_output, batched_dims
        lhs_specs, _, rhs_specs, *_ = map(get_padded_spec, arg_infos)

        lhs_ndim, rhs_ndim = map(len, (lhs_specs, rhs_specs))
        lhs_cdims, rhs_cdims = map(sanitize_dims, (lhs_ndim, rhs_ndim), contracting_dims)
        lhs_non_cdims, rhs_non_cdims = map(
            lambda ndim, cdims: tuple(i for i in range(ndim) if i not in cdims),
            (lhs_ndim, rhs_ndim),
            (lhs_cdims, rhs_cdims),
        )
        lhs_non_cspecs, lhs_cspecs, rhs_non_cspecs, rhs_cspecs = map(
            lambda specs, dims: tuple(specs[i] for i in dims),
            (lhs_specs, lhs_specs, rhs_specs, rhs_specs),
            (lhs_non_cdims, lhs_cdims, rhs_non_cdims, rhs_cdims),
        )

        reduce_spec = None
        for l in lhs_cspecs:
            for r in rhs_cspecs:
                if l is not None and l == r:
                    assert reduce_spec is None, "Multiple reduce dimension is detected!"
                    reduce_spec = l

        if reduce_spec is not None:
            # Other non-reduce cdims (if exists) need to be unsharded
            lhs_cspecs = tuple(s if s == reduce_spec else None for s in lhs_cspecs)
            rhs_cspecs = tuple(s if s == reduce_spec else None for s in rhs_cspecs)

            # Non-batched non-contracting dims of RHS needs to be unsharded (i.e. FSDP)
            # Check if spec is not the batch-dim is not needed as rhs_non_cspecs never includes batch-dim
            # rhs_specs only includes batch-dim in the Wgrad GEMM, but there batch-dim belongs to rhs_cspecs
            rhs_non_cspecs = tuple(
                None if spec in lhs_non_cspecs else spec for spec in rhs_non_cspecs
            )
        else:
            # Otherwise, require contracting dims of both operands to be unsharded
            lhs_cspecs = (None,) * len(lhs_cspecs)
            rhs_cspecs = (None,) * len(rhs_cspecs)

        # Non-batched non-contracting dims of LHS to be unsharded, i.e gather SP dim
        # The spec for batch_dim in lhs_non_cspecs won't ever appear in the rhs_non_cspecs as
        # rhs_non_cspecs never has batch-dim. Hence, spec for batch_dim of lhs_non_cspecs won't be
        # overwrite
        # Minor note: This causes MaxText TP (= Megatron TP + activation_hidden sharding) gathering x for
        # dW1 = x^T * dY1 which is unexpected. This is a known issue and no solution has found yet.
        lhs_non_cspecs = tuple(None if spec in rhs_non_cspecs else spec for spec in lhs_non_cspecs)

        out_specs = lhs_non_cspecs + rhs_non_cspecs

        # specs = merge(cspecs, non_cspecs)
        lhs_specs, rhs_specs = map(
            lambda cdims, cspecs, non_cspecs: (
                cspecs + non_cspecs if cdims[0] == 0 else non_cspecs + cspecs
            ),
            (lhs_cdims, rhs_cdims),
            (lhs_cspecs, rhs_cspecs),
            (lhs_non_cspecs, rhs_non_cspecs),
        )

        # Bias and Pre-GeLU sharding is based on GEMM output before any scatter
        bias_specs = tuple(list(rhs_non_cspecs).copy())
        gelu_specs = tuple(list(out_specs).copy())

        return (
            (lhs_specs, rhs_specs, bias_specs, gelu_specs),
            (out_specs, bias_specs, gelu_specs),
            reduce_spec,
            0,
        )

    @staticmethod
    def infer_sharding_from_operands(
        out_dtype,
        contracting_dims,
        batched_dims,
        lhs_quantized_colwise,
        rhs_quantized_colwise,
        scaling_mode,
        fuse_bias,
        fuse_gelu,
        grad,
        use_split_accumulator,
        sequence_parallel_output,
        sequence_dim,
        mesh,
        arg_infos,
        result_infos,
    ):
        del (
            out_dtype,
            lhs_quantized_colwise,
            rhs_quantized_colwise,
            scaling_mode,
            grad,
        )
        del use_split_accumulator, result_infos

        (_, (out_specs, dbias_specs, pre_gelu_specs), *_) = (
            GemmPrimitive._parse_operand_output_specs(
                arg_infos,
                contracting_dims,
                batched_dims,
                sequence_parallel_output,
                sequence_dim,
            )
        )
        out_sharding = NamedSharding(mesh, PartitionSpec(*out_specs))

        # Discard bias gradient spec if there is no bias fusion
        if not fuse_bias:
            dbias_specs = (None,)
        dbias_sharding = NamedSharding(mesh, PartitionSpec(*dbias_specs))

        # Discard pre-GeLU output spec if there is no GeLU fusion
        if not fuse_gelu:
            pre_gelu_specs = (None,)
        pre_gelu_sharding = NamedSharding(mesh, PartitionSpec(*pre_gelu_specs))

        return [out_sharding, dbias_sharding, pre_gelu_sharding]

    @staticmethod
    def partition(
        out_dtype,
        contracting_dims,
        batched_dims,
        lhs_quantized_colwise,
        rhs_quantized_colwise,
        scaling_mode,
        fuse_bias,
        fuse_gelu,
        grad,
        use_split_accumulator,
        sequence_parallel_output,
        sequence_dim,
        mesh,
        arg_infos,
        result_infos,
    ):
        del result_infos

        (
            (lhs_specs, rhs_specs, bias_input_specs, gelu_input_specs),
            (out_specs, dbias_specs, pre_gelu_specs),
            reduce_spec,
            _,
        ) = GemmPrimitive._parse_operand_output_specs(
            arg_infos,
            contracting_dims,
            batched_dims,
            sequence_parallel_output,
            sequence_dim,
        )

        # Assemble argument shardings
        # NOTE: Block scale inverses match their operands, but tensor scale inverses are unsharded.
        none_sharding = NamedSharding(mesh, PartitionSpec(None))
        lhs_sharding = NamedSharding(mesh, PartitionSpec(*lhs_specs))
        rhs_sharding = NamedSharding(mesh, PartitionSpec(*rhs_specs))
        arg_shardings = (
            lhs_sharding,
            lhs_sharding if scaling_mode.is_1d_block_scaling() else none_sharding,
            rhs_sharding,
            rhs_sharding if scaling_mode.is_1d_block_scaling() else none_sharding,
        )

        # Discard bias input spec if there is no bias fusion
        if not fuse_bias:
            bias_input_specs = (None,)
        arg_shardings += (NamedSharding(mesh, PartitionSpec(*bias_input_specs)),)

        # Discard pre-GeLU input spec if there is no GeLU fusion
        if not fuse_gelu:
            gelu_input_specs = (None,)
        arg_shardings += (NamedSharding(mesh, PartitionSpec(*gelu_input_specs)),)

        # Assemble output shardings
        out_shardings = [NamedSharding(mesh, PartitionSpec(*out_specs))]

        # Discard bias gradient spec if there is no bias fusion
        if not fuse_bias:
            dbias_specs = (None,)
        out_shardings.append(NamedSharding(mesh, PartitionSpec(*dbias_specs)))

        # Discard pre-GeLU output spec if there is no GeLU fusion
        if not fuse_gelu:
            pre_gelu_specs = (None,)
        out_shardings.append(NamedSharding(mesh, PartitionSpec(*pre_gelu_specs)))

        def _sharded_impl(lhs, lhs_scale_inv, rhs, rhs_scale_inv, bias, gelu_input):
            outputs = GemmPrimitive.impl(
                lhs,
                lhs_scale_inv,
                rhs,
                rhs_scale_inv,
                bias,
                gelu_input,
                out_dtype=out_dtype,
                contracting_dims=contracting_dims,
                batched_dims=batched_dims,
                lhs_quantized_colwise=lhs_quantized_colwise,
                rhs_quantized_colwise=rhs_quantized_colwise,
                scaling_mode=scaling_mode,
                fuse_bias=fuse_bias,
                fuse_gelu=fuse_gelu,
                grad=grad,
                use_split_accumulator=use_split_accumulator,
                sequence_parallel_output=sequence_parallel_output,
                sequence_dim=sequence_dim,
            )

            # All-Reduce/Reduce-Scatter GEMM output
            if reduce_spec is not None:
                outputs[0] = jax.lax.psum(outputs[0], reduce_spec)

            return outputs

        return mesh, _sharded_impl, out_shardings, arg_shardings

    @staticmethod
    def shardy_sharding_rule(
        out_dtype,
        contracting_dims,
        batched_dims,
        lhs_quantized_colwise,
        rhs_quantized_colwise,
        scaling_mode,
        fuse_bias,
        fuse_gelu,
        grad,
        use_split_accumulator,
        sequence_parallel_output,
        sequence_dim,
        mesh,
        operand_types,
        result_types,
    ):
        del lhs_quantized_colwise, rhs_quantized_colwise, out_dtype, grad, use_split_accumulator
        del sequence_parallel_output, sequence_dim, mesh, result_types

        prefix = "GemmPrimitive_"

        def _generate_operand_rules(name, ndim, cdims, bdims):
            specs = []
            ldims = tuple(i for i in range(ndim) if i not in bdims + cdims)
            for i in range(ndim):
                dim_name = None
                if i in bdims:
                    dim_idx = bdims.index(i) if len(bdims) > 1 else ""
                    dim_name = f"b{dim_idx}"
                elif i in cdims:
                    dim_idx = cdims.index(i) if len(cdims) > 1 else ""
                    dim_name = f"k{dim_idx}"
                else:
                    dim_idx = ldims.index(i) if len(ldims) > 1 else ""
                    dim_name = f"{name}_l{dim_idx}"
                specs.append(prefix + dim_name)
            return specs

        lhs, _, rhs, *_ = operand_types
        operand_ndims = (len(lhs.shape), len(rhs.shape))
        (lhs_cdims, rhs_cdims), (lhs_bdims, rhs_bdims) = map(
            lambda dims: map(sanitize_dims, operand_ndims, dims),
            (contracting_dims, batched_dims),
        )
        lhs_specs, rhs_specs = map(
            _generate_operand_rules,
            ("lhs", "rhs"),
            operand_ndims,
            (lhs_cdims, rhs_cdims),
            (lhs_bdims, rhs_bdims),
        )
        lhs_scale_specs = ("…1",)
        rhs_scale_specs = ("…2",)
        if scaling_mode.is_1d_block_scaling():
            # Shardy rules for MXFP8 scales cannot be related to the operands because of the
            # global-unpadding and local-padding workflow. This can potentially insert expensive
            # re-shards in the partition call later if the scales are not already sharded correctly.
            lhs_scale_specs, rhs_scale_specs = map(
                lambda specs: tuple(spec.replace(prefix, prefix + "scale_inv_") for spec in specs),
                (lhs_specs, rhs_specs),
            )

        lhs_non_cspec = tuple(lhs_specs[i] for i in range(operand_ndims[0]) if i not in lhs_cdims)
        rhs_non_cspec = tuple(rhs_specs[i] for i in range(operand_ndims[1]) if i not in rhs_cdims)
        out_spec = (*lhs_non_cspec, *rhs_non_cspec)
        bias_spec = rhs_non_cspec if fuse_bias else ("…4",)
        gelu_spec = out_spec if fuse_gelu else ("…5",)

        return SdyShardingRule(
            operand_mappings=(
                lhs_specs,
                lhs_scale_specs,
                rhs_specs,
                rhs_scale_specs,
                bias_spec,
                gelu_spec,
            ),
            result_mappings=(
                out_spec,
                bias_spec,
                gelu_spec,
            ),
        )


register_primitive(GemmPrimitive)


def gemm_uses_jax_dot() -> bool:
    """Check if the GEMM call directs to the TE custom cuBLAS call or native JAX dot."""
    return not GemmPrimitive.enabled()


def _te_gemm(
    lhs: Union[jax.Array, ScaledTensor],
    rhs: Union[jax.Array, ScaledTensor],
    bias: jax.Array = None,
    gelu_input: jax.Array = None,
    lhs_quantizer: Quantizer = None,
    rhs_quantizer: Quantizer = None,
    contracting_dims: Tuple[Sequence[int], Sequence[int]] = ((-1,), (0,)),
    batched_dims: Tuple[Sequence[int], Sequence[int]] = ((), ()),
    fuse_bias: bool = False,
    fuse_gelu: bool = False,
    grad: bool = False,
    use_split_accumulator: bool = QuantizeConfig.FP8_2X_ACC_FPROP,
    sequence_parallel_output: bool = False,
    sequence_dim: int = None,
) -> Tuple[jax.Array, ...]:

    # Prepare non-quantized GEMM operands
    lhs_data = lhs
    rhs_data = rhs
    lhs_scale_inv = jnp.empty(0, dtype=jnp.float32)
    rhs_scale_inv = jnp.empty(0, dtype=jnp.float32)
    scaling_mode = ScalingMode.NO_SCALING
    lhs_is_transposed, rhs_is_transposed = _get_gemm_layout((lhs.ndim, rhs.ndim), contracting_dims)
    lhs_cdims, rhs_cdims = map(sanitize_dims, (lhs.ndim, rhs.ndim), contracting_dims)
    lhs_bdims, rhs_bdims = map(sanitize_dims, (lhs.ndim, rhs.ndim), batched_dims)

    # Quantize operands (if necessary)
    lhs_q, rhs_q = _quantize_gemm_operands(lhs, rhs, lhs_quantizer, rhs_quantizer, contracting_dims)

    # Extract GEMM custom op inputs from quantized operands
    if isinstance(lhs_q, ScaledTensor):
        assert isinstance(rhs_q, ScaledTensor) or rhs_quantizer is not None, (
            "cuBLAS GEMM with quantized LHS and non-quantized RHS operands requires a valid "
            "`Quantizer` object to quantize the RHS operand."
        )
        if isinstance(lhs_q, ScaledTensor2x):
            # Choose the quantization of the contracting dimension(s)
            lhs_q = lhs_q.get_colwise_tensor() if lhs_is_transposed else lhs_q.get_rowwise_tensor()
        scaling_mode = lhs_q.scaling_mode
        lhs_data = lhs_q.data
        lhs_scale_inv = lhs_q.scale_inv
        if lhs_q.data_layout == "T":
            lhs_cdims = transpose_dims(lhs_q.ndim, lhs_cdims, flatten_axis=lhs_q.flatten_axis)
            lhs_bdims = transpose_dims(lhs_q.ndim, lhs_bdims, flatten_axis=lhs_q.flatten_axis)

    if isinstance(rhs_q, ScaledTensor):
        assert isinstance(lhs_q, ScaledTensor) or lhs_quantizer is not None, (
            "cuBLAS GEMM with non-quantized LHS and quantized RHS operands requires a valid "
            "`Quantizer` object to quantize the LHS operand."
        )
        if isinstance(rhs_q, ScaledTensor2x):
            # Choose the quantization of the contracting dimension(s)
            rhs_q = rhs_q.get_rowwise_tensor() if rhs_is_transposed else rhs_q.get_colwise_tensor()
        assert rhs_q.scaling_mode == lhs_q.scaling_mode, (
            "cuBLAS GEMM quantized operands have mismatched scaling types, "
            f"LHS:{lhs_q.scaling_mode} x RHS:{rhs_q.scaling_mode}."
        )
        rhs_data = rhs_q.data
        rhs_scale_inv = rhs_q.scale_inv
        if rhs_q.data_layout == "T":
            rhs_cdims = transpose_dims(rhs_q.ndim, rhs_cdims, flatten_axis=rhs_q.flatten_axis)
            rhs_bdims = transpose_dims(rhs_q.ndim, rhs_bdims, flatten_axis=rhs_q.flatten_axis)

    # Dummy empties for bias and gelu
    out_dtype = lhs_q.dq_dtype if isinstance(lhs_q, ScaledTensor) else lhs_data.dtype
    if bias is None or not (fuse_bias and not grad):
        bias = jnp.empty(0, dtype=out_dtype)
    if gelu_input is None or not (fuse_gelu and grad):
        gelu_input = jnp.empty(0, dtype=out_dtype)

    return GemmPrimitive.outer_primitive.bind(
        lhs_data,
        lhs_scale_inv,
        rhs_data,
        rhs_scale_inv,
        bias,
        gelu_input,
        out_dtype=out_dtype,
        contracting_dims=(lhs_cdims, rhs_cdims),
        batched_dims=(lhs_bdims, rhs_bdims),
        lhs_quantized_colwise=lhs_q.is_colwise if isinstance(lhs_q, ScaledTensor) else False,
        rhs_quantized_colwise=rhs_q.is_colwise if isinstance(rhs_q, ScaledTensor) else False,
        scaling_mode=scaling_mode,
        fuse_bias=fuse_bias,
        fuse_gelu=fuse_gelu,
        grad=grad,
        use_split_accumulator=use_split_accumulator,
        sequence_parallel_output=sequence_parallel_output,
        sequence_dim=sequence_dim,
    )


class GroupedGemmPrimitive(BasePrimitive):
    """
    Primitive for grouped GEMM
    """

    name = "te_grouped_gemm_ffi"
    multiple_results = True
    impl_static_args = (7, 8, 9, 10, 11, 12, 13, 14, 15)
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def abstract(
        lhs_data_aval,
        lhs_scale_inv_aval,
        rhs_data_aval,
        rhs_scale_inv_aval,
        bias_aval,
        group_sizes_aval,
        group_offset_aval,
        *,
        M,
        N,
        K,
        lhs_is_trans,
        rhs_is_trans,
        scaling_mode,
        out_dtype,
        has_bias,
        is_grouped_dense_wgrad,
    ):
        """
        Grouped GEMM operation.

        Args:
            lhs_data: Left-hand side input matrix data, 1D flattened array
            lhs_scale_inv: Left-hand side input scale_inv matrix, 1D flattened array
            rhs_data: Right-hand side input matrix data, 1D flattened array
            rhs_scale_inv: Right-hand side input scale_inv matrix, 1D flattened array
            bias: Bias matrix of shape (G, N)
            group_sizes: 1D array containing the sizes of each group
            group_offset: 1D array containing offsets for each group (not yet implemented)
            M: Number of rows in the output matrix
            N: Number of columns in the output matrix
            K: Number of columns in the left-hand side matrix
            lhs_is_trans: Boolean indicating if the left-hand side matrix is transposed
            rhs_is_trans: Boolean indicating if the right-hand side matrix is transposed
            scaling_mode: Scaling mode for the GEMM operations
            out_dtype: Data type of the output tensors
            has_bias: Boolean indicating if bias tensors are provided
            is_grouped_dense_wgrad: Boolean indicating if this is a grouped dense wgrad operation
                                    where both lhs and rhs are 2D matrices and output is (G, M, N)

        Returns:
            A jnp.ndarray containing the result of the grouped GEMM operation
        """
        del lhs_data_aval, rhs_data_aval, bias_aval, group_offset_aval
        del K, lhs_is_trans, rhs_is_trans, has_bias
        # TODO(Phuong): move some shape checks from Cpp to here
        workspace_size = get_cublas_workspace_size_bytes() * num_cublas_streams
        workspace_alignment_padding = 256
        tensor_scaling_sinv_aligment = 16
        mxfp8_scaling_sinv_alignment_padding = 256
        # cuBLAS workspace ptr must be 256 bytes aligned but JAX buffers are not
        # necessarily 256 bytes aligned, we add some padding to ensure alignment.
        workspace_size += workspace_alignment_padding
        if scaling_mode in (
            ScalingMode.DELAYED_TENSOR_SCALING.value,
            ScalingMode.CURRENT_TENSOR_SCALING.value,
        ):
            # For tensor scaling, each matrix has a single scale value, but it
            # needs to be aligned to 16 bytes for CUDA 12.9.1 and later.
            workspace_size += lhs_scale_inv_aval.size * tensor_scaling_sinv_aligment
            workspace_size += rhs_scale_inv_aval.size * tensor_scaling_sinv_aligment
        elif scaling_mode == ScalingMode.MXFP8_1D_SCALING.value:
            # We also pad scale_inv swizzle buffers size for 256 bytes alignment.
            workspace_size += lhs_scale_inv_aval.size + mxfp8_scaling_sinv_alignment_padding
            workspace_size += rhs_scale_inv_aval.size + mxfp8_scaling_sinv_alignment_padding
        workspace_aval = jax.core.ShapedArray(shape=(workspace_size,), dtype=jnp.uint8)

        out_shape = (M, N)
        if is_grouped_dense_wgrad:
            out_shape = (group_sizes_aval.size, M, N)
        out_aval = jax.core.ShapedArray(shape=out_shape, dtype=out_dtype)
        return (out_aval, workspace_aval)

    @staticmethod
    def outer_abstract(*args, **kwargs):
        (out_aval, _) = GroupedGemmPrimitive.abstract(*args, **kwargs)
        return (out_aval,)

    @staticmethod
    def lowering(
        ctx,
        *args,
        M,
        N,
        K,
        lhs_is_trans,
        rhs_is_trans,
        scaling_mode,
        out_dtype,
        has_bias,
        is_grouped_dense_wgrad,
    ):
        del out_dtype
        return jax.ffi.ffi_lowering(GroupedGemmPrimitive.name)(
            ctx,
            *args,
            M=M,
            N=N,
            K=K,
            lhs_is_trans=lhs_is_trans,
            rhs_is_trans=rhs_is_trans,
            scaling_mode=scaling_mode.value,
            has_bias=has_bias,
            is_grouped_dense_wgrad=is_grouped_dense_wgrad,
        )

    @staticmethod
    def impl(
        lhs_data,
        lhs_scale_inv,
        rhs_data,
        rhs_scale_inv,
        bias,
        group_sizes,
        group_offset,
        M,
        N,
        K,
        lhs_is_trans,
        rhs_is_trans,
        scaling_mode,
        out_dtype,
        has_bias,
        is_grouped_dense_wgrad,
    ):
        assert GroupedGemmPrimitive.inner_primitive is not None
        (out, _) = GroupedGemmPrimitive.inner_primitive.bind(
            lhs_data,
            lhs_scale_inv,
            rhs_data,
            rhs_scale_inv,
            bias,
            group_sizes,
            group_offset,
            M=M,
            N=N,
            K=K,
            lhs_is_trans=lhs_is_trans,
            rhs_is_trans=rhs_is_trans,
            scaling_mode=scaling_mode,
            out_dtype=out_dtype,
            has_bias=has_bias,
            is_grouped_dense_wgrad=is_grouped_dense_wgrad,
        )
        return (out,)


register_primitive(GroupedGemmPrimitive)


def _shape_normalization(x, dimension_numbers, already_transposed: bool = False):
    orig_order = list(range(x.ndim))
    contracting_dims, batch_dims = dimension_numbers
    contracting_order = [d for d in orig_order if d in contracting_dims]
    batch_order = [d for d in orig_order if d in batch_dims]
    non_contracting_order = [
        d for d in orig_order if d not in contracting_dims and d not in batch_dims
    ]
    batch_shape = [x.shape[d] for d in batch_order]
    rows_shape = [x.shape[d] for d in non_contracting_order]
    cols_shape = [x.shape[d] for d in contracting_order]
    new_order = batch_order + non_contracting_order + contracting_order
    rows, cols, batches = (
        reduce(operator.mul, rows_shape, 1),
        reduce(operator.mul, cols_shape, 1),
        reduce(operator.mul, batch_shape, 1),
    )
    # Remove this transpose when non-TN dot is supported
    if not already_transposed:
        t = jnp.transpose(x, new_order)
    else:
        t = x
    return jnp.reshape(t, (batches, rows, cols))


def _calculate_remaining_shape(shape, contracting_dims):
    contracting_dims_ = sanitize_dims(len(shape), contracting_dims)
    return tuple(shape[dim] for dim in range(len(shape)) if dim not in contracting_dims_)


# Apply jit to guarantee correctness of FP8 GEMM.
@partial(jax.jit, static_argnums=(2, 3))
def _jax_gemm_tensor_scaling_fp8(lhs, rhs, dim_nums, precision):
    (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dim_nums
    if lhs.data_layout == "T":
        lhs_contract = transpose_dims(lhs.data.ndim, lhs_contract, flatten_axis=lhs.flatten_axis)
        lhs_batch = transpose_dims(lhs.data.ndim, lhs_batch, flatten_axis=lhs.flatten_axis)
    if rhs.data_layout == "T":
        rhs_contract = transpose_dims(rhs.data.ndim, rhs_contract, flatten_axis=rhs.flatten_axis)
        rhs_batch = transpose_dims(rhs.data.ndim, rhs_batch, flatten_axis=rhs.flatten_axis)

    dim_nums = (lhs_contract, rhs_contract), (lhs_batch, rhs_batch)

    out_fp8 = jax.lax.dot_general(
        lhs.data, rhs.data, dim_nums, precision=precision, preferred_element_type=lhs.dq_dtype
    )
    scale_inv = lhs.scale_inv * rhs.scale_inv
    out = (out_fp8 * scale_inv).astype(lhs.dq_dtype)

    return out


@partial(jax.jit, static_argnums=(2,))
def _jax_gemm_mxfp8_1d(
    lhs: ScaledTensor, rhs: ScaledTensor, dim_nums: Tuple[Tuple[Sequence[int], Sequence[int]]]
):
    """
    JAX GEMM for MXFP8 via scaled_matmul
    """
    assert (
        rhs.scaling_mode == ScalingMode.MXFP8_1D_SCALING
    ), "rhs does not have MXFP8 1D scaling mode"

    (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dim_nums

    expected_lhs_is_colwise = lhs_contract[-1] != lhs.data.ndim - 1
    expected_rhs_is_colwise = rhs_contract[-1] != rhs.data.ndim - 1
    assert lhs.is_colwise is expected_lhs_is_colwise, (
        f"LHS with unexpected quantize dimension.\nExpect is_colwise={expected_lhs_is_colwise}, got"
        f" {lhs.is_colwise}"
    )
    assert rhs.is_colwise is expected_rhs_is_colwise, (
        f"RHS with unexpected quantize dimension.\nExpect is_colwise={expected_rhs_is_colwise}, got"
        f" {rhs.is_colwise}"
    )

    # Reshape + Transpose (if needed)
    # [..., M, K] -> [1, reduce(..., M), K]
    # [..., K, M] -> [1, reduce(..., M), K]
    lhs_3d = _shape_normalization(lhs.data, (lhs_contract, lhs_batch))
    rhs_3d = _shape_normalization(rhs.data, (rhs_contract, rhs_batch))
    lhs_scale_3d = _shape_normalization(lhs.scale_inv, (lhs_contract, lhs_batch))
    rhs_scale_3d = _shape_normalization(rhs.scale_inv, (rhs_contract, rhs_batch))

    # JAX scaled_matmul only supports NT now (TN-gemm)
    # * Expected shape:
    # * lhs_data  (B, M, K)           * rhs_data  (B, N, K)
    # * lhs_scale (B, M, K_block)     * rhs_scale (B, N, K_block)
    out_3d = jax.nn.scaled_matmul(
        lhs_3d, rhs_3d, lhs_scale_3d, rhs_scale_3d, preferred_element_type=lhs.dq_dtype
    )
    # Reshape [1, reduce(..., M), N] -> [..., M, N]
    lhs_remain_shape = tuple(
        lhs.data.shape[dim] for dim in range(len(lhs.data.shape)) if dim not in lhs_contract
    )
    rhs_remain_shape = tuple(
        rhs.data.shape[dim] for dim in range(len(rhs.data.shape)) if dim not in rhs_contract
    )
    out = out_3d.reshape(*lhs_remain_shape, *rhs_remain_shape)
    return out


def _jax_gemm(
    lhs: Union[jnp.ndarray, ScaledTensor],
    rhs: Union[jnp.ndarray, ScaledTensor],
    contracting_dims: Tuple[Sequence[int], Sequence[int]] = ((1,), (0,)),
    lhs_quantizer: Quantizer = None,
    rhs_quantizer: Quantizer = None,
) -> jnp.ndarray:
    """
    FP8 GEMM via JAX
    """
    dim_nums = (contracting_dims, ((), ()))

    def _jax_gemm_fp8_impl(lhs, rhs):
        if lhs.scaling_mode.is_tensor_scaling():
            assert (
                rhs.scaling_mode == lhs.scaling_mode
            ), f"rhs.scaling_mode={rhs.scaling_mode} != lhs.scaling_mode={lhs.scaling_mode}"
            precision = (
                jax.lax.Precision.HIGHEST
                if QuantizeConfig.FP8_2X_ACC_FPROP
                else jax.lax.Precision.DEFAULT
            )
            return _jax_gemm_tensor_scaling_fp8(lhs, rhs, dim_nums, precision)

        if lhs.scaling_mode == ScalingMode.MXFP8_1D_SCALING:
            return _jax_gemm_mxfp8_1d(lhs, rhs, dim_nums)

        raise NotImplementedError("Unsupported ScalingMode: {lhs.scaling_mode}")

    lhs_q, rhs_q = _quantize_gemm_operands(lhs, rhs, lhs_quantizer, rhs_quantizer, contracting_dims)

    if isinstance(lhs_q, ScaledTensor) and isinstance(rhs_q, ScaledTensor):
        return _jax_gemm_fp8_impl(lhs_q, rhs_q)

    if (
        isinstance(lhs, jnp.ndarray)
        and isinstance(rhs, jnp.ndarray)
        and lhs_quantizer is None
        and rhs_quantizer is None
    ):
        return jax.lax.dot_general(lhs, rhs, dim_nums, preferred_element_type=lhs.dtype)

    raise NotImplementedError("Not supporting multiplication of ScaledTensor and jnp.array")


def gemm(
    lhs: Union[jnp.ndarray, ScaledTensor],
    rhs: Union[jnp.ndarray, ScaledTensor],
    contracting_dims: Tuple[Sequence[int], Sequence[int]] = ((-1,), (0,)),
    batched_dims: Tuple[Sequence[int], Sequence[int]] = ((), ()),
    lhs_quantizer: Quantizer = None,
    rhs_quantizer: Quantizer = None,
    **kwargs,
) -> Tuple[jnp.ndarray, ...]:
    r"""General matrix multiplication with optional quantization.

    Parameters
    ----------
    lhs: Union[jax.Array, ScaledTensor]
        Left-hand side operand in the matrix multiplication.
    rhs: Union[jax.Array, ScaledTensor]
        Right-hand side operand in the matrix multiplication.
    lhs_quantizer: Quantizer, default = None
        Object for down-casting the LHS operand for quantized GEMM.
    rhs_quantizer: Quantizer, default = None
        Object for down-casting the RHS operand for quantized GEMM.
    contracting_dims: Tuple[Sequence[int], Sequence[int]], default = ((-1, ), (0, ))
        Tuple of sequences representing the contracting dimensions of the operands.
    batched_dims: Tuple[Sequence[int], Sequence[int]], default = ((), ()),
        Tuple of sequences representing the batched dimensions of the operands. This is *not* used
        to perform a batched matrix multiplication, but it is required for TE's custom cuBLAS GEMM
        call to avoid a potentially undesirable reduction in any batched contracting dimensions
        when invoked with sharded operands (e.g. when computing weight gradients in a Flax module).
    bias: jax.Array, default = None
        Optional additive bias term, required for forward GEMM with bias fusion. Only supported
        with TE's custom call to cuBLAS GEMM.
    gelu_input: jax.Array, default = None
        Pre-GeLU output from forward GEMM, required for backward/grad GEMM with dGeLU fusion. Only
        supported with TE's custom call to cuBLAS GEMM.
    fuse_bias: bool, default = False
        Enable bias addition in forward GEMM or bias gradient in backward GEMM. Only supported with
        TE's custom call to cuBLAS GEMM.
    fuse_gelu: bool, default = False
        Enable GeLU activation in forward GEMM or GeLU gradient in backward GEMM. Only supported
        with TE's custom call to cuBLAS GEMM.
    grad: bool, default = False
        Flag for switching bias and GeLU fusions from forward to backward mode. Only supported with
        TE's custom call to cuBLAS GEMM.
    use_split_accumulator: bool, default = True
        Enable promoting some intermediate sums to higher precision when accumulating the result in
        the cuBLAS GEMM kernel. Disabling this trades off numerical accuracy for speed. Only
        supported with TE's custom call to cuBLAS GEMM.
    sequence_parallel_output: bool, default = False
        Produces an output with the first non-batched non-contracting dimension sharded with the
        same spec as operand contracting dimensions. This effectively converts the `jax.lax.psum`
        for the GEMM output into a `jax.lax.psum_scatter`. Only supported with TE's custom call to
        cuBLAS GEMM.
    sequence_dim: int, default = None
        Index of the sequence dimension for the LHS operand. This controls which dimension of the
        GEMM output is scattered when `sequence_parallel_output=True`. When `None`, the first
        non-batched non-contracting dimension is assumed to be the sequence dimension.

    Returns
    -------
    jax.Array:
        Result of the operation. For TE's custom call to cuBLAS GEMM, this result can include the
        GeLU application when `fuse_gelu=True` and `grad=False`, the GeLU gradient contribution
        when `fuse_gelu=True` and `grad=True`, and the additive bias when `fuse_bias=True` and
        `grad=False`.
    Optional[jax.Array]:
        Bias gradient when `fuse_bias=True` and `grad=True`. Only supported with TE's custom call
        to cuBLAS GEMM.
    Optional[jax.Array]:
        Pre-GeLU GEMM output when `fuse_gelu=True` and `grad=False`. This is required as an input
        to `_te_gemm()` with `fuse_gelu=True` and `grad=True` in the backward pass in order to
        compute the GeLU contribution to the gradient. Only supported with TE's custom call to
        cuBLAS GEMM.
    """
    # Try to get LHS and RHS quantizers from a quantizer set for backward compatibility
    if lhs_quantizer is None or rhs_quantizer is None:
        quantizer_set = kwargs.get("quantizer_set", None)
        if quantizer_set is not None:
            lhs_quantizer = quantizer_set.x
            rhs_quantizer = quantizer_set.kernel

    # Fall back on a native JAX implementation when the custom call to cuBLAS GEMM is disabled
    fuse_bias = kwargs.get("fuse_bias", False)
    fuse_gelu = kwargs.get("fuse_gelu", False)
    if not GemmPrimitive.enabled():
        assert kwargs.get("bias", None) is None and not fuse_gelu, (
            "TE GEMM was invoked with bias fusion options that are not supported by the "
            "`jax.lax.dot_general` and `jax.nn.scaled_matmul` backends used when the custom cuBLAS "
            "GEMM primitive is disabled."
        )
        assert kwargs.get("gelu_input", None) is None and not fuse_bias, (
            "TE GEMM was invoked with GeLU fusion options that are not supported by the "
            "`jax.lax.dot_general` and `jax.nn.scaled_matmul` backends used when the custom cuBLAS "
            "GEMM primitive is disabled."
        )
        assert (
            not kwargs.get("sequence_parallel_output", False)
            and kwargs.get("sequence_dim", None) is None
        ), (
            "TE GEMM was invoked with sequence-parallelism options that are not supported by the "
            "`jax.lax.dot_general` and `jax.nn.scaled_matmul` backedns used when the custom cuBLAS "
            "GEMM primitive is disabled."
        )
        return _jax_gemm(lhs, rhs, contracting_dims, lhs_quantizer, rhs_quantizer)

    outputs = _te_gemm(
        lhs,
        rhs,
        lhs_quantizer=lhs_quantizer,
        rhs_quantizer=rhs_quantizer,
        contracting_dims=contracting_dims,
        batched_dims=batched_dims,
        **kwargs,
    )

    # Discard empty outputs
    grad = kwargs.get("grad", False)
    clean_outputs = outputs[0]  # first output is the final result and is never empty
    if (fuse_bias and grad) or (fuse_gelu and not grad):
        clean_outputs = (outputs[0],)
        if fuse_bias and grad:  # only return bias gradient if it exists
            clean_outputs += (outputs[1],)
        if fuse_gelu and not grad:  # only return pre-GeLU output if it exists
            clean_outputs += (outputs[2],)
    return clean_outputs


def grouped_gemm(
    lhs: Union[jnp.ndarray, GroupedScaledTensor1x],
    rhs: Union[jnp.ndarray, GroupedScaledTensor1x],
    group_sizes: jnp.ndarray,
    contracting_dims: Tuple[Sequence[int], Sequence[int]] = ((1,), (2,)),
    bias: jnp.ndarray = None,
    precision: jax.lax.Precision = jax.lax.Precision.DEFAULT,
    preferred_element_type: jnp.dtype = None,
    group_offset: jnp.array = None,
    quantizer_set: QuantizerSet = noop_quantizer_set,
) -> jnp.ndarray:
    """
    Grouped GEMM operation.

    Args:
        lhs: Left-hand side input matrix, can be a jnp.ndarray or GroupedScaledTensor1x
        rhs: Right-hand side input matrix, can be a jnp.ndarray or GroupedScaledTensor1x
        group_sizes: 1D array containing the sizes of each group
        contracting_dims: Tuple of two sequences representing the contracting dimensions
        bias: Bias tensor of shape (G, N)
        precision: JAX precision for the GEMM operation
        preferred_element_type: Preferred data type for the output tensor
        group_offset: 1D array containing offsets for each group (not yet implemented)
        quantizer_set: Set of quantizers for FP8 quantization of the input and output

    Returns:
        A jnp.ndarray containing the result of the grouped GEMM operation

    Note:
        Tested shapes:
        lhs: [M, K] or [K, N]
        rhs: [G, N, K] or [G, K, N] or [G * K, N] or [N, G * K]
    """
    # TODO(Phuong): implement the group_offset
    group_offset = group_offset or jnp.zeros((1,), jnp.int32)

    # TODO(Phuong): implement the precision
    del precision

    if isinstance(lhs, jnp.ndarray):
        assert isinstance(rhs, jnp.ndarray)
        out_dtype = lhs.dtype
        lhs_shape = lhs.shape
        rhs_shape = rhs.shape
        lhs_data = lhs
        rhs_data = rhs
        lhs_scale_inv = rhs_scale_inv = jnp.empty((0,), jnp.float32)
        scaling_mode = ScalingMode.NO_SCALING
    elif isinstance(lhs, GroupedScaledTensor1x):
        assert isinstance(rhs, GroupedScaledTensor1x)
        out_dtype = lhs.dq_dtype
        lhs_shape = lhs.original_shape
        rhs_shape = rhs.original_shape
        lhs_data = lhs.data
        rhs_data = rhs.data
        lhs_scale_inv = lhs.scale_inv
        rhs_scale_inv = rhs.scale_inv
        assert lhs.scaling_mode == rhs.scaling_mode
        scaling_mode = lhs.scaling_mode
    else:
        raise TypeError("Unsupported lhs type object!")

    out_dtype = preferred_element_type or out_dtype

    lhs_contract_dim, rhs_contract_dim = contracting_dims

    lhs_is_trans = lhs_contract_dim[-1] != len(lhs_shape) - 1
    lhs_flatten_axis = len(lhs_contract_dim) * (1 if lhs_is_trans else -1)

    # rhs_shape [G, K, N]
    rhs_is_trans = rhs_contract_dim[0] != 1
    rhs_flatten_axis = -len(rhs_contract_dim) if rhs_is_trans else 1 + len(rhs_contract_dim)

    is_grouped_dense_wgrad = False
    if len(rhs_shape) == 2:
        rhs_is_trans = rhs_contract_dim[0] != 0
        is_grouped_dense_wgrad = True

    # TODO(Hua): thses are for fp16 dense wgrad, any better way to handle this?
    if (
        is_grouped_dense_wgrad
        and not isinstance(lhs, ScaledTensor)
        and not isinstance(rhs, ScaledTensor)
    ):
        lhs_is_trans = True
        rhs_is_trans = False
        lhs_flatten_axis = 1
        rhs_flatten_axis = 1

    if (
        not isinstance(lhs, ScaledTensor)
        and not isinstance(rhs, ScaledTensor)
        and quantizer_set != noop_quantizer_set
    ):
        assert isinstance(quantizer_set.x, GroupedQuantizer)
        assert type(quantizer_set.x) is type(quantizer_set.kernel)
        scaling_mode = quantizer_set.x.scaling_mode
        if (
            quantizer_set.x.scaling_mode.is_tensor_scaling()
            and is_fp8_gemm_with_all_layouts_supported()
        ):
            lhs_is_rowwise = rhs_is_rowwise = True
        else:
            lhs_is_rowwise = not lhs_is_trans
            rhs_is_rowwise = rhs_is_trans
        quantizer_set.x.q_layout = (
            QuantizeLayout.ROWWISE if lhs_is_rowwise else QuantizeLayout.COLWISE
        )
        quantizer_set.kernel.q_layout = (
            QuantizeLayout.ROWWISE if rhs_is_rowwise else QuantizeLayout.COLWISE
        )
        lhs_q = grouped_quantize(lhs, quantizer_set.x, group_sizes, lhs_flatten_axis)
        rhs_q = grouped_quantize(
            rhs, quantizer_set.kernel, group_sizes=None, flatten_axis=rhs_flatten_axis
        )
        lhs_data = lhs_q.data
        rhs_data = rhs_q.data
        lhs_scale_inv = lhs_q.scale_inv
        rhs_scale_inv = rhs_q.scale_inv
        lhs_shape = lhs_q.original_shape
        rhs_shape = rhs_q.original_shape

    assert not (
        lhs_data.dtype == jnp.float8_e5m2 and rhs_data.dtype == jnp.float8_e5m2
    ), "FP8 GEMM does not support E5M2 * E5M2"

    # Only support FP8 GEMM with NT layout on Hopper and other earlier GPUs
    # thus additional transpose is required
    if scaling_mode.is_tensor_scaling() and not is_fp8_gemm_with_all_layouts_supported():
        if isinstance(lhs, ScaledTensor) and isinstance(rhs, ScaledTensor):
            lhs_layout_is_T = lhs.data_layout == "T"
            rhs_layout_is_T = rhs.data_layout == "T"
        else:
            lhs_layout_is_T = lhs_q.data_layout == "T"
            rhs_layout_is_T = rhs_q.data_layout == "T"
        # we can't apply _shape_normalization on the grouped input
        # thus we need to ensure that lhs is in N and rhs is in T
        assert (
            lhs_is_trans == lhs_layout_is_T
        ), "lhs input must be transposed before calling grouped_gemm"
        assert (
            not rhs_is_trans == rhs_layout_is_T
        ), "rhs input must be transposed before calling grouped_gemm"
        lhs_is_trans = False
        rhs_is_trans = True
        lhs_ndim = len(lhs_shape)
        rhs_ndim = len(rhs_shape)
        if lhs_layout_is_T:
            lhs_contract_dim = tuple((lhs_ndim - 1 - i) % lhs_ndim for i in lhs_contract_dim)
        if rhs_layout_is_T:
            # For rhs [G, K, N], need to exclude the G dim from contract_dim
            if group_sizes.size == rhs_shape[0]:
                rhs_contract_dim = tuple(
                    (rhs_ndim - 1 - i) % (rhs_ndim - 1) + 1 for i in rhs_contract_dim
                )
            else:
                rhs_contract_dim = tuple((rhs_ndim - 1 - i) % rhs_ndim for i in rhs_contract_dim)

    # Calling GroupedGEMM Custom Call
    K_lhs = math.prod(lhs_shape[i] for i in lhs_contract_dim)
    K_rhs = math.prod(rhs_shape[i] for i in rhs_contract_dim)
    assert K_lhs == K_rhs
    M = math.prod(_calculate_remaining_shape(lhs_shape, lhs_contract_dim))
    N = math.prod(_calculate_remaining_shape(rhs_shape, rhs_contract_dim)[1:])  # Exclude G

    if is_grouped_dense_wgrad:
        N = math.prod(_calculate_remaining_shape(rhs_shape, rhs_contract_dim))
    else:
        assert group_sizes.size == rhs_shape[0]

    assert group_offset.size == 1

    has_bias = bias is not None
    assert not has_bias or bias.shape == (group_sizes.size, N)
    bias = jnp.empty((), jnp.float32) if bias is None else bias

    (out,) = GroupedGemmPrimitive.outer_primitive.bind(
        lhs_data,
        lhs_scale_inv,
        rhs_data,
        rhs_scale_inv,
        bias,
        group_sizes,
        group_offset,
        M=M,
        N=N,
        K=K_lhs,
        lhs_is_trans=lhs_is_trans,
        rhs_is_trans=rhs_is_trans,
        scaling_mode=scaling_mode.value,
        out_dtype=out_dtype,
        has_bias=has_bias,
        is_grouped_dense_wgrad=is_grouped_dense_wgrad,
    )
    return out