cpp_extensions.py 183 KB
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# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
# See LICENSE for license information.
"""JAX te custom call"""
from abc import ABCMeta, abstractmethod
from dataclasses import dataclass
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from typing import Tuple, Sequence, Union, Callable
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from functools import partial, reduce
import operator
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import os
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import warnings

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import numpy as np
import jax.numpy as jnp
from jax.lib import xla_client
from jax import core, dtypes
from jax.interpreters import xla, mlir
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from jax.experimental.custom_partitioning import custom_partitioning
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from jax.interpreters.mlir import ir, dtype_to_ir_type
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from jax.sharding import PartitionSpec, NamedSharding
from jax._src.interpreters import batching
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from jax._src import dispatch

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import transformer_engine_jax
from transformer_engine_jax import DType as TEDType
from transformer_engine_jax import NVTE_Bias_Type
from transformer_engine_jax import NVTE_Mask_Type
from transformer_engine_jax import NVTE_QKV_Layout
from transformer_engine_jax import NVTE_Fused_Attn_Backend
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from transformer_engine_jax import NVTE_Activation_Enum
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from .sharding import all_reduce_max_along_all_axes_except_PP
from .sharding import all_reduce_sum_along_dp_fsdp
from .sharding import get_all_mesh_axes, num_of_devices
from .sharding import get_padded_spec as te_get_padded_spec
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try:
    from jaxlib.hlo_helpers import custom_call
except ImportError:
    # Newer JAX changed its API. But we want to support a few JAX
    # version, so we still need this import.
    pass

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for _name, _value in transformer_engine_jax.registrations().items():
    xla_client.register_custom_call_target(_name, _value, platform="CUDA")


def te_dtype_to_jax_dtype(te_dtype):
    """
    convert TE dtype to jax dtype
    """
    assert isinstance(te_dtype, TEDType)
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    converter = {
        TEDType.kFloat32: jnp.float32,
        TEDType.kFloat16: jnp.float16,
        TEDType.kBFloat16: jnp.bfloat16,
        TEDType.kInt32: jnp.int32,
        TEDType.kInt64: jnp.int64,
        TEDType.kFloat8E4M3: jnp.float8_e4m3fn,
        TEDType.kFloat8E5M2: jnp.float8_e5m2,
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        TEDType.kByte: jnp.uint8
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    }

    if te_dtype not in converter:
        raise ValueError(f"Unsupported {te_dtype=}")

    return converter.get(te_dtype)
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def te_dtype_to_ir_dtype(te_dtype):
    """
    convert TE dtype to MLIR dtype
    """
    return dtype_to_ir_type(np.dtype(te_dtype_to_jax_dtype(te_dtype)))


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def jax_dtype_to_ir_dtype(jax_dtype):
    """
    convert Jax dtype to MLIR dtype
    """
    return dtype_to_ir_type(np.dtype(jax_dtype))


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def jax_dtype_to_te_dtype(jax_dtype):
    """
    convert jax dtype to TE dtype
    """
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    jax_dtype = dtypes.canonicalize_dtype(jax_dtype)
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    converter = {
        jnp.float32.dtype: TEDType.kFloat32,
        jnp.float16.dtype: TEDType.kFloat16,
        jnp.bfloat16.dtype: TEDType.kBFloat16,
        jnp.int32.dtype: TEDType.kInt32,
        jnp.int64.dtype: TEDType.kInt64,
        jnp.float8_e4m3fn.dtype: TEDType.kFloat8E4M3,
        jnp.float8_e5m2.dtype: TEDType.kFloat8E5M2,
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        jnp.uint8.dtype: TEDType.kByte,
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    }
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    if jax_dtype not in converter:
        raise ValueError(f"Unsupported {jax_dtype=}")
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    return converter.get(jax_dtype)
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def get_padded_spec(arg_info):
    """
    Get padded spec for partitioning from arguments' information
    """
    if arg_info.sharding is None:
        return te_get_padded_spec(None, arg_info.ndim)
    ndim, spec = arg_info.ndim, arg_info.sharding.spec
    return te_get_padded_spec(spec, ndim)
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def _check_valid_batch_dims(bdims):
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    """
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    Assert out non-supported bath dims
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    """
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    for dim in bdims:
        assert dim in [0, None], \
            "Currently only support batch_dim in [0, None], " \
            f"but got {dim=}"
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ActivationEnum = {
    ('gelu',): NVTE_Activation_Enum.GELU,
    ('gelu', 'linear'): NVTE_Activation_Enum.GEGLU,
    ('silu',): NVTE_Activation_Enum.SILU,
    ('silu', 'linear'): NVTE_Activation_Enum.SWIGLU
}


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class BasePrimitive(metaclass=ABCMeta):
    """
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    jax primitive
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    """

    @staticmethod
    @abstractmethod
    def abstract():
        """
        to describe computing graph
        """
        return NotImplemented

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    @classmethod
    def outer_abstract(cls, *args, **kwargs):
        """
        optional abstract wrapper to eliminate workspace tensors
        """
        return cls.abstract(*args, **kwargs)

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    @staticmethod
    @abstractmethod
    def lowering():
        """
        to describe MLIR
        """
        return NotImplemented

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    @staticmethod
    @abstractmethod
    def impl():
        """
        to describe implementation
        """
        return NotImplemented

    @staticmethod
    @abstractmethod
    def batcher():
        """
        to describe batch rules for vmap
        """
        return NotImplemented

    @staticmethod
    @abstractmethod
    def infer_sharding_from_operands():
        """
        to describe infer_sharding_from_operands for custom_partitioning
        """
        return NotImplemented

    @staticmethod
    @abstractmethod
    def partition():
        """
        to describe partition for custom_partitioning
        """
        return NotImplemented

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def register_primitive(cls):
    """
    register jax primitive
    """
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    def name_of_wrapper_p():
        return cls.name + "_wrapper"

    inner_p = core.Primitive(cls.name)
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    dispatch.prim_requires_devices_during_lowering.add(inner_p)
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    inner_p.multiple_results = cls.multiple_results
    inner_p.def_impl(partial(xla.apply_primitive, inner_p))
    inner_p.def_abstract_eval(cls.abstract)
    mlir.register_lowering(inner_p, cls.lowering, platform='cuda')
    cls.inner_primitive = inner_p

    outer_p = core.Primitive(name_of_wrapper_p())
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    dispatch.prim_requires_devices_during_lowering.add(outer_p)
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    outer_p.multiple_results = cls.multiple_results
    outer_p.def_impl(cls.impl)
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    outer_p.def_abstract_eval(cls.outer_abstract)
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    batching.primitive_batchers[outer_p] = cls.batcher
    outer_p_lower = custom_partitioning(cls.impl, static_argnums=cls.impl_static_args)
    outer_p_lower.def_partition(infer_sharding_from_operands=cls.infer_sharding_from_operands,
                                partition=cls.partition)
    mlir.register_lowering(outer_p,
                           mlir.lower_fun(outer_p_lower, multiple_results=cls.multiple_results))
    cls.outer_primitive = outer_p
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@dataclass
class CustomCallArgsWrapper:
    """
    wrapper of XLA custom call args
    """

    def __init__(self,
                 output_types,
                 operands,
                 operand_shapes,
                 operand_specific_layouts=None,
                 output_specific_layouts=None):
        self.output_types = output_types
        self.operands = operands
        self.operand_layouts = CustomCallArgsWrapper.generate_layouts(operand_shapes,
                                                                      operand_specific_layouts)
        output_shapes = [x.shape for x in output_types]
        self.output_layouts = CustomCallArgsWrapper.generate_layouts(output_shapes,
                                                                     output_specific_layouts)

    @staticmethod
    def generate_layouts(shapes, specific_layouts):
        """
        setup layouts for XLA custom call
        """

        def default_layout(shape):
            return range(len(shape) - 1, -1, -1)

        if specific_layouts is None:
            specific_layouts = {}

        layouts = []
        for idx, shape in enumerate(shapes):
            if idx in specific_layouts:
                layouts.append(specific_layouts[idx])
            else:
                layouts.append(default_layout(shape))
        return layouts


def custom_caller(name, args, opaque, has_side_effect, **kwargs):
    """
    XLA custom call warpper
    """
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    if hasattr(mlir, "custom_call"):
        out = mlir.custom_call(name,
                               result_types=args.output_types,
                               operands=args.operands,
                               operand_layouts=args.operand_layouts,
                               result_layouts=args.output_layouts,
                               backend_config=opaque,
                               has_side_effect=has_side_effect,
                               **kwargs).results
    else:
        # Need to disable one pylint error as the second function
        # parameter name recenctly in JAX. Otherwise we won't be
        # compatible with multiple JAX version.
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        out = custom_call(    # pylint: disable=too-many-function-args
            name,
            args.output_types,
            operands=args.operands,
            operand_layouts=args.operand_layouts,
            result_layouts=args.output_layouts,
            backend_config=opaque,
            has_side_effect=has_side_effect,
            **kwargs)
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    return out


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class LayerNormFwdPrimitive(BasePrimitive):
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    """
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    Layer Normalization Forward Primitive
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    """
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    name = "te_layernorm_forward"
    multiple_results = True
    impl_static_args = (3, 4)    # zero_centered_gamma, epsilon
    inner_primitive = None
    outer_primitive = None
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    @staticmethod
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    def abstract(x_aval, gamma_aval, beta_aval, **kwargs):
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        """
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        LayerNorm fwd inner primitive abstract
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        """
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        x_dtype = dtypes.canonicalize_dtype(x_aval.dtype)
        assert x_dtype in [jnp.float32, jnp.float16, jnp.bfloat16]

        mu_rsigama_dtype = jnp.float32
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        out_aval = core.raise_to_shaped(x_aval)
        mu_aval = rsigma_aval = out_aval.update(shape=out_aval.shape[:-1], dtype=mu_rsigama_dtype)
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        assert gamma_aval.size == beta_aval.size
        hidden_size = gamma_aval.size
        assert x_aval.size % hidden_size == 0

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        wkspace_info, barrier_info = transformer_engine_jax.get_layernorm_fwd_workspace_sizes(
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            x_aval.size // hidden_size,    # batch size
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            hidden_size,
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            jax_dtype_to_te_dtype(x_aval.dtype),    # in te_dtype
            jax_dtype_to_te_dtype(gamma_aval.dtype),    # weight te_dtype
            jax_dtype_to_te_dtype(x_aval.dtype),    # out te_dtype (same as input for Fp16/Bf16)
            True,
            kwargs['zero_centered_gamma'],
            kwargs['epsilon'])
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        wkspace_aval = out_aval.update(shape=wkspace_info[0],
                                       dtype=te_dtype_to_jax_dtype(wkspace_info[1]))
        barrier_aval = out_aval.update(shape=barrier_info[0],
                                       dtype=te_dtype_to_jax_dtype(barrier_info[1]))

        return out_aval, mu_aval, rsigma_aval, wkspace_aval, barrier_aval

    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
        LayerNorm fwd outer primitive abstract
        """
        out_aval, mu_aval, rsigma_aval, _, _ = \
            LayerNormFwdPrimitive.abstract(*args, **kwargs)
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        return out_aval, mu_aval, rsigma_aval
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    @staticmethod
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    def lowering(ctx, x, gamma, beta, *, zero_centered_gamma, epsilon):
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        """
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        LayerNorm fwd lowering rules
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        """
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        x_aval, gamma_aval, beta_aval = ctx.avals_in
        assert gamma_aval.dtype == beta_aval.dtype
        x_type = ir.RankedTensorType(x.type)
        x_shape = x_type.shape
        g_type = ir.RankedTensorType(gamma.type)
        g_shape = g_type.shape
        b_type = ir.RankedTensorType(beta.type)
        b_shape = b_type.shape
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        assert g_type == b_type
        assert g_shape == b_shape
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        # Output shape is same as the input shape, but the output type is same as the weight type.
        # See ln_api.cpp
        output_type = g_type.element_type
        ir_mu_dtype = ir.F32Type.get()
        ir_rsigma_dtype = ir.F32Type.get()
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        out_shape = x_shape
        hidden_size = reduce(operator.mul, g_shape)
        batch_shape = out_shape[:-1]
        batch_size = reduce(operator.mul, x_shape) // hidden_size
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        wkspace_aval, barrier_aval = ctx.avals_out[-2:]

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        out_types = [
            ir.RankedTensorType.get(out_shape, output_type),
            ir.RankedTensorType.get(batch_shape, ir_mu_dtype),
            ir.RankedTensorType.get(batch_shape, ir_rsigma_dtype),
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            ir.RankedTensorType.get(wkspace_aval.shape, jax_dtype_to_ir_dtype(wkspace_aval.dtype)),
            ir.RankedTensorType.get(barrier_aval.shape, jax_dtype_to_ir_dtype(barrier_aval.dtype))
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        ]
        operands = [x, gamma, beta]
        operand_shapes = [x_shape, g_shape, b_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)
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        sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0"))

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        opaque = transformer_engine_jax.pack_norm_descriptor(
            batch_size,
            hidden_size,
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            wkspace_aval.size,
            barrier_aval.size,
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            (0,),    # no dgamma_part in FWD pass
            (0,),    # no dbeta_part in BWD pass
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            jax_dtype_to_te_dtype(x_aval.dtype),
            jax_dtype_to_te_dtype(gamma_aval.dtype),
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            jax_dtype_to_te_dtype(wkspace_aval.dtype),
            jax_dtype_to_te_dtype(barrier_aval.dtype),
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            TEDType.kByte,    # dummy dgamma_part te_dtype
            TEDType.kByte,    # dummy dbeta_part te_dtype
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            zero_centered_gamma,
            epsilon,
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            sm_margin,
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        )
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        out = custom_caller(LayerNormFwdPrimitive.name, args, opaque, False)
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        return out
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    @staticmethod
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    def impl(x, gamma, beta, zero_centered_gamma, epsilon):
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        """
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        to describe implementation
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        """
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        assert LayerNormFwdPrimitive.inner_primitive is not None
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        out, mu, rsigma, _, _ = LayerNormFwdPrimitive.inner_primitive.bind(
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            x, gamma, beta, zero_centered_gamma=zero_centered_gamma, epsilon=epsilon)
        return out, mu, rsigma

    @staticmethod
    def batcher(batched_args, batch_dims, *, zero_centered_gamma, epsilon):
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        """
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        to describe batch rules for vmap
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        """
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        _check_valid_batch_dims(batch_dims)
        assert LayerNormFwdPrimitive.outer_primitive is not None
        x, gamma, beta = batched_args
        x_bdim, _, _ = batch_dims
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        out_bdims = x_bdim, x_bdim, x_bdim
        return LayerNormFwdPrimitive.outer_primitive.bind(x,
                                                          gamma,
                                                          beta,
                                                          zero_centered_gamma=zero_centered_gamma,
                                                          epsilon=epsilon), out_bdims
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    @staticmethod
    def infer_sharding_from_operands(zero_centered_gamma, epsilon, mesh, arg_infos, result_infos):
        del zero_centered_gamma, epsilon, result_infos
        x_spec = get_padded_spec(arg_infos[0])
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {LayerNormFwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
        mu_sharding = rsigma_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1]))
        return (out_sharding, mu_sharding, rsigma_sharding)
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    @staticmethod
    def partition(zero_centered_gamma, epsilon, mesh, arg_infos, result_infos):
        del result_infos
        x_spec, g_spec, b_spec = map(get_padded_spec, arg_infos)
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {LayerNormFwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
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        if g_spec[-1] is not None:
            warnings.warn(
                f"{LayerNormFwdPrimitive.name} does not support sharding of parameter gamma " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )
        if b_spec[-1] is not None:
            warnings.warn(
                f"{LayerNormFwdPrimitive.name} does not support sharding of parameter beta " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )

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        x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
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        g_sharding = NamedSharding(mesh, PartitionSpec(None))
        b_sharding = NamedSharding(mesh, PartitionSpec(None))
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        out_sharding = x_sharding
        mu_sharding = rsigma_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1]))
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        arg_shardings = (x_sharding, g_sharding, b_sharding)
        out_shardings = (out_sharding, mu_sharding, rsigma_sharding)
        impl = partial(LayerNormFwdPrimitive.impl,
                       zero_centered_gamma=zero_centered_gamma,
                       epsilon=epsilon)
        return mesh, impl, out_shardings, arg_shardings
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register_primitive(LayerNormFwdPrimitive)
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def layernorm_fwd(x: jnp.ndarray, gamma: jnp.ndarray, beta: jnp.ndarray, zero_centered_gamma: bool,
                  epsilon: float):
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    """
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    Wrapper for TE layernorm fwd
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    """
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    return LayerNormFwdPrimitive.outer_primitive.bind(x,
                                                      gamma,
                                                      beta,
                                                      zero_centered_gamma=zero_centered_gamma,
                                                      epsilon=epsilon)
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class LayerNormBwdPrimitive(BasePrimitive):
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    """
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    Layer Normalization Backward Primitive
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    """
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    name = "te_layernorm_backward"
    multiple_results = True
    impl_static_args = (5, 6)    # zero_centered_gamma, epsilon
    inner_primitive = None
    outer_primitive = None
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    @staticmethod
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    def abstract(dz_aval, x_aval, mu_aval, rsigma_aval, gamma_aval, **kwargs):
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        """
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        Layernorm bwd inner primitive abstract
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        """
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        w_dtype = dtypes.canonicalize_dtype(gamma_aval.dtype)
        mu_dtype = dtypes.canonicalize_dtype(mu_aval.dtype)
        rsigma_dtype = dtypes.canonicalize_dtype(rsigma_aval.dtype)

        assert dtypes.canonicalize_dtype(dz_aval.dtype) == w_dtype
        assert dz_aval.shape == x_aval.shape
        assert mu_aval.shape == rsigma_aval.shape == x_aval.shape[:-1]
        assert mu_dtype == rsigma_dtype == jnp.float32
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        dx_aval = core.raise_to_shaped(dz_aval)
        dgamma_aval = dbeta_aval = core.raise_to_shaped(gamma_aval)
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        wkspace_info, barrier_info, dgamma_part_info, dbeta_part_info = \
            transformer_engine_jax.get_layernorm_bwd_workspace_sizes(
                x_aval.size // gamma_aval.size,           # batch size
                gamma_aval.size,                          # hidden size
                jax_dtype_to_te_dtype(x_aval.dtype),      # input te_dtype
                jax_dtype_to_te_dtype(gamma_aval.dtype),  # weight te_dtype
                True, kwargs['zero_centered_gamma'], kwargs['epsilon']
            )
        wkspace_aval = dx_aval.update(shape=wkspace_info[0],
                                      dtype=te_dtype_to_jax_dtype(wkspace_info[1]))
        barrier_aval = dx_aval.update(shape=barrier_info[0],
                                      dtype=te_dtype_to_jax_dtype(barrier_info[1]))
        dgamma_part_aval = dgamma_aval.update(shape=dgamma_part_info[0],
                                              dtype=te_dtype_to_jax_dtype(dgamma_part_info[1]))
        dbeta_part_aval = dbeta_aval.update(shape=dbeta_part_info[0],
547
                                            dtype=te_dtype_to_jax_dtype(dbeta_part_info[1]))
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        return dx_aval, dgamma_aval, dbeta_aval, wkspace_aval, barrier_aval, \
               dgamma_part_aval, dbeta_part_aval

    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
        LayerNorm bwd outer primitive abstract
        """
        dx_aval, dgamma_aval, dbeta_aval, _, _, _, _ = \
            LayerNormBwdPrimitive.abstract(*args, **kwargs)
559
        return dx_aval, dgamma_aval, dbeta_aval
560
561

    @staticmethod
562
    def lowering(ctx, dz, x, mu, rsigma, gamma, *, zero_centered_gamma, epsilon):
563
        """
564
        Layernorm bwd lowering rules
565
        """
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        _, x_aval, _, _, gamma_aval = ctx.avals_in
        x_type = ir.RankedTensorType(x.type)
        x_shape = x_type.shape
        g_type = ir.RankedTensorType(gamma.type)
        g_shape = g_type.shape
        b_type = ir.RankedTensorType(gamma.type)
        b_shape = b_type.shape
        assert g_type == b_type
        assert g_shape == b_shape

        dz_shape = ir.RankedTensorType(dz.type).shape
        mu_shape = ir.RankedTensorType(mu.type).shape
        rsigma_shape = ir.RankedTensorType(rsigma.type).shape

        hidden_size = reduce(operator.mul, g_shape)
        batch_size = reduce(operator.mul, x_shape) // hidden_size
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        out_types = [
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            ir.RankedTensorType.get(output.shape, mlir.dtype_to_ir_type(output.dtype))
            for output in ctx.avals_out
586
        ]
587

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        operands = [dz, mu, rsigma, x, gamma]
        operand_shapes = [dz_shape, mu_shape, rsigma_shape, x_shape, g_shape]
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        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

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        sm_margin = int(os.getenv("NVTE_BWD_LAYERNORM_SM_MARGIN", "0"))

594
        wkspace_aval, barrier_aval, dgamma_part_aval, dbeta_part_aval = ctx.avals_out[-4:]
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        opaque = transformer_engine_jax.pack_norm_descriptor(
            batch_size,
            hidden_size,
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            wkspace_aval.size,
            barrier_aval.size,
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            dgamma_part_aval.shape,
            dbeta_part_aval.shape,
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            jax_dtype_to_te_dtype(x_aval.dtype),
            jax_dtype_to_te_dtype(gamma_aval.dtype),
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            jax_dtype_to_te_dtype(wkspace_aval.dtype),
            jax_dtype_to_te_dtype(barrier_aval.dtype),
            jax_dtype_to_te_dtype(dgamma_part_aval.dtype),
            jax_dtype_to_te_dtype(dbeta_part_aval.dtype),
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            zero_centered_gamma,
            epsilon,
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            sm_margin,
611
        )
612

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        out = custom_caller(LayerNormBwdPrimitive.name, args, opaque, False)
614

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        return out
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    @staticmethod
    def impl(dz, x, mu, rsigma, gamma, zero_centered_gamma, epsilon):
        assert LayerNormBwdPrimitive.inner_primitive is not None
620
        dx, dgamma, dbeta, _, _, _, _ = LayerNormBwdPrimitive.inner_primitive.bind(
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            dz, x, mu, rsigma, gamma, zero_centered_gamma=zero_centered_gamma, epsilon=epsilon)
        return dx, dgamma, dbeta
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    @staticmethod
    def batcher(batched_args, batch_dims, *, zero_centered_gamma, epsilon):
        _check_valid_batch_dims(batch_dims)
        assert LayerNormBwdPrimitive.outer_primitive is not None
        dz, x, mu, rsigma, gamma = batched_args
        _, x_bdim, _, _, gamma_bdim = batch_dims

        out_bdims = x_bdim, gamma_bdim, gamma_bdim
        return LayerNormBwdPrimitive.outer_primitive.bind(dz,
                                                          x,
                                                          mu,
                                                          rsigma,
                                                          gamma,
                                                          zero_centered_gamma=zero_centered_gamma,
                                                          epsilon=epsilon), out_bdims
639

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    @staticmethod
    def infer_sharding_from_operands(zero_centered_gamma, epsilon, mesh, arg_infos, result_infos):
        del zero_centered_gamma, epsilon, result_infos
        x_spec = get_padded_spec(arg_infos[1])
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {LayerNormBwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
        g_b_spec = get_padded_spec(arg_infos[4])
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        if g_b_spec[-1] is not None:
            warnings.warn(
                f"{LayerNormBwdPrimitive.name} does not support sharding of gradients " \
                f"of gamma and beta of Layernorm " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )

658
        dx_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
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        dgamma_sharding = dbeta_sharding = NamedSharding(mesh, PartitionSpec(None))
660
        return dx_sharding, dgamma_sharding, dbeta_sharding
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    @staticmethod
    def partition(zero_centered_gamma, epsilon, mesh, arg_infos, result_infos):
        del result_infos
        x_spec = get_padded_spec(arg_infos[1])
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {LayerNormBwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
        g_b_spec = get_padded_spec(arg_infos[4])
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        if g_b_spec[-1] is not None:
            warnings.warn(
                f"{LayerNormBwdPrimitive.name} does not support sharding of gradients " \
                f"of gamma and beta of Layernorm " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )

680
        dx_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
681
        dgamma_sharding = dbeta_sharding = NamedSharding(mesh, PartitionSpec(None))
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        out_shardings = dx_sharding, dgamma_sharding, dbeta_sharding
        x_shardings = (dx_sharding,) * 2    # dz and x should have the same sharding.
        mu_shardings = (NamedSharding(mesh, PartitionSpec(*x_spec[:-1])),) * 2
685
        arg_shardings = (*x_shardings, *mu_shardings, NamedSharding(mesh, PartitionSpec(None)))
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        def sharded_impl(dz, x, mu, rsigma, gamma):
            local_dx, local_dgamma, local_dbeta = \
                LayerNormBwdPrimitive.impl(dz, x, mu, rsigma, gamma,
                     zero_centered_gamma=zero_centered_gamma,
                     epsilon=epsilon)
            global_dgamma = all_reduce_sum_along_dp_fsdp(local_dgamma)
            global_dbeta = all_reduce_sum_along_dp_fsdp(local_dbeta)
            return local_dx, global_dgamma, global_dbeta

        return mesh, sharded_impl, out_shardings, arg_shardings


register_primitive(LayerNormBwdPrimitive)


def layernorm_bwd(dz: jnp.ndarray, x: jnp.ndarray, mu: jnp.ndarray, rsigma: jnp.ndarray,
                  gamma: jnp.ndarray, zero_centered_gamma: bool, epsilon: float):
704
    """
705
    Wrapper for TE layernorm bwd
706
    """
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    return LayerNormBwdPrimitive.outer_primitive.bind(dz,
                                                      x,
                                                      mu,
                                                      rsigma,
                                                      gamma,
                                                      zero_centered_gamma=zero_centered_gamma,
                                                      epsilon=epsilon)
714
715


716
class RmsNormFwdPrimitive(BasePrimitive):
717
    """
718
    RMS Normalization Forward Primitive
719
    """
720
    name = "te_rmsnorm_forward"
721
    multiple_results = True
722
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724
    impl_static_args = (2,)    # epsilon
    inner_primitive = None
    outer_primitive = None
725
726

    @staticmethod
727
    def abstract(x_aval, gamma_aval, **kwargs):
728
        """
729
        RMSNorm fwd inner primitive abstract
730
        """
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        x_dtype = dtypes.canonicalize_dtype(x_aval.dtype)
        assert x_dtype in [jnp.float32, jnp.float16, jnp.bfloat16]

        rsigama_dtype = jnp.float32

        out_aval = core.raise_to_shaped(x_aval)
        rsigma_aval = out_aval.update(shape=out_aval.shape[:-1], dtype=rsigama_dtype)
738

739
740
        hidden_size = gamma_aval.size
        assert x_aval.size % hidden_size == 0
741

742
        wkspace_info, barrier_info = transformer_engine_jax.get_layernorm_fwd_workspace_sizes(
743
            x_aval.size // hidden_size,    # batch size
744
            hidden_size,
745
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747
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750
            jax_dtype_to_te_dtype(x_aval.dtype),    # in te_dtype
            jax_dtype_to_te_dtype(gamma_aval.dtype),    # weight te_dtype
            jax_dtype_to_te_dtype(x_aval.dtype),    # out te_dtype (same as input for Fp16/Bf16)
            False,
            False,
            kwargs['epsilon'])
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        wkspace_aval = out_aval.update(shape=wkspace_info[0],
                                       dtype=te_dtype_to_jax_dtype(wkspace_info[1]))
        barrier_aval = out_aval.update(shape=barrier_info[0],
                                       dtype=te_dtype_to_jax_dtype(barrier_info[1]))

        return out_aval, rsigma_aval, wkspace_aval, barrier_aval

    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
        RMSNorm fwd outer primitive abstract
        """
        out_aval, rsigma_aval, _, _ = RmsNormFwdPrimitive.abstract(*args, **kwargs)
764
        return out_aval, rsigma_aval
765
766

    @staticmethod
767
    def lowering(ctx, x, gamma, *, epsilon):
768
        """
769
        RMSNorm fwd lowering rules
770
        """
771
772
773
774
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776
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779
780
781
        x_aval, gamma_aval = ctx.avals_in
        x_type = ir.RankedTensorType(x.type)
        x_shape = x_type.shape
        g_type = ir.RankedTensorType(gamma.type)
        g_shape = g_type.shape
        rsigma_element_type = ir.F32Type.get()

        out_shape = x_shape
        hidden_size = reduce(operator.mul, g_shape)
        batch_shape = out_shape[:-1]
        batch_size = reduce(operator.mul, x_shape) // hidden_size
782

783
784
        wkspace_aval, barrier_aval = ctx.avals_out[-2:]

785
        out_types = [
786
787
            ir.RankedTensorType.get(out_shape, x_type.element_type),
            ir.RankedTensorType.get(batch_shape, rsigma_element_type),
788
789
            ir.RankedTensorType.get(wkspace_aval.shape, jax_dtype_to_ir_dtype(wkspace_aval.dtype)),
            ir.RankedTensorType.get(barrier_aval.shape, jax_dtype_to_ir_dtype(barrier_aval.dtype))
790
        ]
791
792
        operands = [x, gamma]
        operand_shapes = [x_shape, g_shape]
793
794
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

795
796
        sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0"))

797
798
799
        opaque = transformer_engine_jax.pack_norm_descriptor(
            batch_size,
            hidden_size,
800
801
            wkspace_aval.size,
            barrier_aval.size,
802
803
            (0,),    # no dgamma_part in FWD pass
            (0,),    # no dbeta_part in BWD pass
804
805
            jax_dtype_to_te_dtype(x_aval.dtype),
            jax_dtype_to_te_dtype(gamma_aval.dtype),
806
807
            jax_dtype_to_te_dtype(wkspace_aval.dtype),
            jax_dtype_to_te_dtype(barrier_aval.dtype),
808
809
            TEDType.kByte,    # dummy dgamma_part te_dtype
            TEDType.kByte,    # dummy dbeta_part te_dtype
810
811
            False,    # RMSNorm doesn't support zero_centered_gamma
            epsilon,
812
            sm_margin,
813
        )
814

815
        out = custom_caller(RmsNormFwdPrimitive.name, args, opaque, False)
816
817
818
819

        return out

    @staticmethod
820
    def impl(x, gamma, epsilon):
821
        """
822
        to describe implementation
823
        """
824
        assert RmsNormFwdPrimitive.inner_primitive is not None
825
        out, rsigma, _, _ = RmsNormFwdPrimitive.inner_primitive.bind(x, gamma, epsilon=epsilon)
826
        return out, rsigma
827
828

    @staticmethod
829
    def batcher(batched_args, batch_dims, *, epsilon):
830
        """
831
        to describe batch rules for vmap
832
        """
833
834
835
836
        _check_valid_batch_dims(batch_dims)
        assert RmsNormFwdPrimitive.outer_primitive is not None
        x, gamma = batched_args
        x_bdim, _ = batch_dims
837

838
839
        out_bdims = x_bdim, x_bdim
        return RmsNormFwdPrimitive.outer_primitive.bind(x, gamma, epsilon=epsilon), out_bdims
840

841
842
843
844
845
846
847
848
849
850
851
852
853
    @staticmethod
    def infer_sharding_from_operands(epsilon, mesh, arg_infos, result_infos):
        del epsilon, result_infos
        x_spec = get_padded_spec(arg_infos[0])
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {RmsNormFwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
        rsigma_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1]))
        return (out_sharding, rsigma_sharding)
854

855
856
857
858
859
860
861
862
863
864
    @staticmethod
    def partition(epsilon, mesh, arg_infos, result_infos):
        del result_infos
        x_spec, g_spec = map(get_padded_spec, arg_infos)
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {RmsNormFwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
865
866
867
868
869
870
        if g_spec[-1] is not None:
            warnings.warn(
                f"{RmsNormFwdPrimitive.name} does not support sharding of parameter gamma " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )

871
        x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
872
        g_sharding = NamedSharding(mesh, PartitionSpec(None))
873
874
875
876
877
878
        out_sharding = x_sharding
        rsigma_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1]))
        arg_shardings = (x_sharding, g_sharding)
        out_shardings = (out_sharding, rsigma_sharding)
        impl = partial(RmsNormFwdPrimitive.impl, epsilon=epsilon)
        return mesh, impl, out_shardings, arg_shardings
879
880


881
register_primitive(RmsNormFwdPrimitive)
882
883


884
def rmsnorm_fwd(x: jnp.ndarray, gamma: jnp.ndarray, epsilon: float):
885
    """
886
    Wrapper for TE rmsnorm fwd
887
    """
888
    return RmsNormFwdPrimitive.outer_primitive.bind(x, gamma, epsilon=epsilon)
889
890


891
class RmsNormBwdPrimitive(BasePrimitive):
892
    """
893
    RMS Normalization Backward Primitive
894
    """
895
    name = "te_rmsnorm_backward"
896
    multiple_results = True
897
898
899
    impl_static_args = (4,)    # epsilon
    inner_primitive = None
    outer_primitive = None
900
901

    @staticmethod
902
    def abstract(dz_aval, x_aval, rsigma_aval, gamma_aval, **kwargs):
903
        """
904
        RMSNorm bwd inner primitive abstract
905
        """
906
907
908
909
910
911
912
913
914
915
        w_dtype = dtypes.canonicalize_dtype(gamma_aval.dtype)
        rsigma_dtype = dtypes.canonicalize_dtype(rsigma_aval.dtype)

        assert dtypes.canonicalize_dtype(dz_aval.dtype) == w_dtype
        assert dz_aval.shape == x_aval.shape
        assert rsigma_aval.shape == x_aval.shape[:-1]
        assert rsigma_dtype == jnp.float32

        dx_aval = core.raise_to_shaped(dz_aval)
        dgamma_aval = core.raise_to_shaped(gamma_aval)
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939

        wkspace_info, barrier_info, dgamma_part_info, _ = \
            transformer_engine_jax.get_layernorm_bwd_workspace_sizes(
                x_aval.size // gamma_aval.size,           # batch size
                gamma_aval.size,                          # hidden size
                jax_dtype_to_te_dtype(x_aval.dtype),      # in te_dtype
                jax_dtype_to_te_dtype(gamma_aval.dtype),  # weight te_dtype
                False, False, kwargs['epsilon']
            )
        wkspace_aval = dx_aval.update(shape=wkspace_info[0],
                                      dtype=te_dtype_to_jax_dtype(wkspace_info[1]))
        barrier_aval = dx_aval.update(shape=barrier_info[0],
                                      dtype=te_dtype_to_jax_dtype(barrier_info[1]))
        dgamma_part_aval = dgamma_aval.update(shape=dgamma_part_info[0],
                                              dtype=te_dtype_to_jax_dtype(dgamma_part_info[1]))

        return dx_aval, dgamma_aval, wkspace_aval, barrier_aval, dgamma_part_aval

    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
        RMSNorm bwd outer primitive abstract
        """
        dx_aval, dgamma_aval, _, _, _ = RmsNormBwdPrimitive.abstract(*args, **kwargs)
940
941
942
943
        return dx_aval, dgamma_aval

    @staticmethod
    def lowering(ctx, dz, x, rsigma, gamma, *, epsilon):
944
        """
945
        RMSNorm bwd lowering rules
946
        """
947
948
949
950
951
952
953
954
955
956
        _, x_aval, _, gamma_aval = ctx.avals_in
        x_type = ir.RankedTensorType(x.type)
        x_shape = x_type.shape
        g_type = ir.RankedTensorType(gamma.type)
        g_shape = g_type.shape
        dz_shape = ir.RankedTensorType(dz.type).shape
        rsigma_shape = ir.RankedTensorType(rsigma.type).shape

        hidden_size = reduce(operator.mul, g_shape)
        batch_size = reduce(operator.mul, x_shape) // hidden_size
957

958
959
        wkspace_aval, barrier_aval, dgamma_part_aval = ctx.avals_out[-3:]

960
        out_types = [
961
962
            ir.RankedTensorType.get(x_shape, x_type.element_type),
            ir.RankedTensorType.get(g_shape, g_type.element_type),
963
964
965
966
            ir.RankedTensorType.get(wkspace_aval.shape, jax_dtype_to_ir_dtype(wkspace_aval.dtype)),
            ir.RankedTensorType.get(barrier_aval.shape, jax_dtype_to_ir_dtype(barrier_aval.dtype)),
            ir.RankedTensorType.get(dgamma_part_aval.shape,
                                    jax_dtype_to_ir_dtype(dgamma_part_aval.dtype))
967
        ]
968
969
        operands = [dz, rsigma, x, gamma]
        operand_shapes = [dz_shape, rsigma_shape, x_shape, g_shape]
970
971
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

972
973
        sm_margin = int(os.getenv("NVTE_BWD_LAYERNORM_SM_MARGIN", "0"))

974
975
976
        opaque = transformer_engine_jax.pack_norm_descriptor(
            batch_size,
            hidden_size,
977
978
            wkspace_aval.size,
            barrier_aval.size,
979
980
            dgamma_part_aval.shape,
            (0,),    # no dbeta_part for RMSnorm
981
982
            jax_dtype_to_te_dtype(x_aval.dtype),
            jax_dtype_to_te_dtype(gamma_aval.dtype),
983
984
985
            jax_dtype_to_te_dtype(wkspace_aval.dtype),
            jax_dtype_to_te_dtype(barrier_aval.dtype),
            jax_dtype_to_te_dtype(dgamma_part_aval.dtype),
986
            TEDType.kByte,    # dummy dbeta_part te_dtype
987
988
            False,    # RMSNorm doesn't support zero_centered_gamma
            epsilon,
989
            sm_margin,
990
        )
991

992
        out = custom_caller(RmsNormBwdPrimitive.name, args, opaque, False)
993
994
995

        return out

996
997
998
    @staticmethod
    def impl(dz, x, rsigma, gamma, epsilon):
        assert RmsNormBwdPrimitive.inner_primitive is not None
999
1000
        dx, dgamma, _, _, _ = \
            RmsNormBwdPrimitive.inner_primitive.bind(dz, x, rsigma, gamma, epsilon=epsilon)
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
        return dx, dgamma

    @staticmethod
    def batcher(batched_args, batch_dims, *, epsilon):
        _check_valid_batch_dims(batch_dims)
        assert RmsNormBwdPrimitive.outer_primitive is not None
        dz, x, rsigma, gamma = batched_args
        _, x_bdim, _, gamma_bdim = batch_dims

        out_bdims = x_bdim, gamma_bdim
        return RmsNormBwdPrimitive.outer_primitive.bind(dz, x, rsigma, gamma,
                                                        epsilon=epsilon), out_bdims

    @staticmethod
    def infer_sharding_from_operands(epsilon, mesh, arg_infos, result_infos):
        del epsilon, result_infos
        x_spec = get_padded_spec(arg_infos[1])
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {RmsNormBwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
        g_spec = get_padded_spec(arg_infos[3])
1025
1026
1027
1028
1029
        if g_spec[-1] is not None:
            warnings.warn(
                f"{RmsNormBwdPrimitive.name} does not support sharding of parameter gamma " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )
1030
        dx_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
1031
        dgamma_sharding = NamedSharding(mesh, PartitionSpec(None))
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
        return dx_sharding, dgamma_sharding

    @staticmethod
    def partition(epsilon, mesh, arg_infos, result_infos):
        del result_infos
        x_spec = get_padded_spec(arg_infos[1])
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {RmsNormBwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
        g_spec = get_padded_spec(arg_infos[3])
1045
1046
1047
1048
1049
        if g_spec[-1] is not None:
            warnings.warn(
                f"{RmsNormBwdPrimitive.name} does not support sharding of parameter gamma " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )
1050
        dx_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
1051
        dgamma_sharding = NamedSharding(mesh, PartitionSpec(None))
1052
1053
1054
        out_shardings = dx_sharding, dgamma_sharding
        x_shardings = (dx_sharding,) * 2    # dz and x should have the same sharding.
        rsigma_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1]))
1055
        arg_shardings = (*x_shardings, rsigma_sharding, NamedSharding(mesh, PartitionSpec(None)))
1056
1057
1058
1059
1060
1061
1062
1063
1064

        def sharded_impl(dz, x, rsigma, gamma):
            local_dx, local_dgamma = \
                RmsNormBwdPrimitive.impl(dz, x, rsigma, gamma, epsilon=epsilon)
            global_dgamma = all_reduce_sum_along_dp_fsdp(local_dgamma)
            return local_dx, global_dgamma

        return mesh, sharded_impl, out_shardings, arg_shardings

1065

1066
register_primitive(RmsNormBwdPrimitive)
1067
1068


1069
1070
def rmsnorm_bwd(dz: jnp.ndarray, x: jnp.ndarray, rsigma: jnp.ndarray, gamma: jnp.ndarray,
                epsilon: float):
1071
    """
1072
    Wrapper for TE layernorm bwd
1073
    """
1074
    return RmsNormBwdPrimitive.outer_primitive.bind(dz, x, rsigma, gamma, epsilon=epsilon)
1075
1076


1077
class SoftmaxPrimitive(BasePrimitive):
1078
    """
1079
    Softmax Primitive
1080
    """
1081
    max_k_seqlen_supported = 16384
1082
    name = "te_softmax_internal_placeholder"
1083
1084

    @staticmethod
1085
1086
1087
1088
1089
    @abstractmethod
    def is_kernel_available(batch: int, heads: int, q_seqlen: int, k_seqlen: int,
                            dtype: jnp.dtype) -> bool:
        """Check Softmax kernel availability based on size"""
        raise NotImplementedError
1090

1091
1092
1093
1094
1095
    @staticmethod
    def get_batch_per_block(k_seqlen: int) -> int:
        """Get batch per CTA in Softmax kernels"""
        threads_per_warp = 32
        threads_per_block = 128    # Depends on the kernel implmentation
1096

1097
1098
1099
1100
1101
1102
        pow2 = 1 << (k_seqlen - 1).bit_length()
        warp_size = pow2 if pow2 < threads_per_warp else threads_per_warp
        batches_per_warp = 2 if pow2 <= 128 else 1
        warps_per_block = threads_per_block // warp_size
        batches_per_block = warps_per_block * batches_per_warp
        return batches_per_block
1103
1104

    @staticmethod
1105
    def forward_abstract(logits_aval, scale_factor):
1106
        """
1107
        softmax_forward abstract
1108
        """
1109
1110
1111
1112
1113
1114
1115
1116
1117
        del scale_factor
        i_dtype = dtypes.canonicalize_dtype(logits_aval.dtype)
        assert i_dtype in [jnp.float16, jnp.bfloat16]
        i_shape = logits_aval.shape
        # Assume [...Batch, Head, Q_Seqlen, K_Seqlen]
        q_seqlen = i_shape[-2]
        k_seqlen = i_shape[-1]
        assert k_seqlen <= SoftmaxPrimitive.max_k_seqlen_supported
        assert q_seqlen > 1
1118

1119
1120
        out_aval = core.raise_to_shaped(logits_aval)
        return out_aval
1121

1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
    @staticmethod
    def forward_lowering(name, ctx, logits, *, scale_factor):
        """
        softmax_forward lowering rules
        """
        i_aval, = ctx.avals_in
        i_type = ir.RankedTensorType(logits.type)
        i_shape = i_type.shape
        # Assume [...Batch, Head, Q_Seqlen, K_Seqlen]
        batch = reduce(operator.mul, i_shape[:-3])
        pad_batch = batch
        heads = i_shape[-3]
        q_seqlen = i_shape[-2]
        k_seqlen = i_shape[-1]

        out_types = [ir.RankedTensorType.get(i_shape, i_type.element_type)]
        operands = [logits]
        operand_shapes = [i_shape]
1140
1141
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

1142
1143
1144
1145
        opaque = transformer_engine_jax.pack_softmax_descriptor(batch, pad_batch, heads, q_seqlen,
                                                                k_seqlen,
                                                                jax_dtype_to_te_dtype(i_aval.dtype),
                                                                scale_factor)
1146

1147
        out = custom_caller(name, args, opaque, False)
1148
1149
1150

        return [out]

1151
1152
1153
1154
1155
1156
1157
1158
    @staticmethod
    def forward_impl(primitive, logits, scale_factor):
        """
        softmax_forward implementation
        """
        assert primitive is not None
        output = primitive.bind(logits, scale_factor=scale_factor)
        return output
1159

1160
1161
1162
1163
1164
1165
1166
1167
    @staticmethod
    def forward_batcher(primitive, batched_args, batch_dims, *, scale_factor):
        """
        softmax_forward batcher
        """
        assert primitive is not None
        logits, = batched_args
        logits_bdim, = batch_dims
1168

1169
1170
        out_bdims = logits_bdim
        return primitive.bind(logits, scale_factor=scale_factor), out_bdims
1171

1172
1173
    @classmethod
    def forward_infer_sharding_from_operands(cls, scale_factor, mesh, arg_infos, result_infos):
1174
1175
1176
1177
1178
        """
        softmax_forward infer_sharding_from_operands
        """
        del scale_factor, result_infos    # Unused.
        logits_spec = get_padded_spec(arg_infos[0])
1179
1180
1181
1182
1183
1184
1185
        if logits_spec[-1] is not None:
            warnings.warn(
                f"Sharding the hidden dimension is not supported in {cls.name}! " \
                f"Forcing XLA to not shard the hidden dim, which might introduce extra " \
                f"collective ops and hurt performance."
            )
        out_sharding = NamedSharding(mesh, PartitionSpec(*logits_spec[:-1], None))
1186
        return out_sharding
1187

1188
1189
    @classmethod
    def forward_partition(cls, impl, scale_factor, mesh, arg_infos, result_infos):
1190
        """
1191
        softmax_forward partitioning
1192
        """
1193
        del result_infos
1194
1195
1196
1197
1198
1199
1200
1201
1202
        logits_spec = get_padded_spec(arg_infos[0])
        if logits_spec[-1] is not None:
            warnings.warn(
                f"Sharding the hidden dimension is not supported in {cls.name}! " \
                f"Forcing XLA to not shard the hidden dim, which might introduce extra " \
                f"collective ops and hurt performance."
            )
        out_shardings = NamedSharding(mesh, PartitionSpec(*logits_spec[:-1], None))
        arg_shardings = (out_shardings,)
1203
1204
        impl = partial(impl, scale_factor=scale_factor)
        return mesh, impl, out_shardings, arg_shardings
1205

1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
    @staticmethod
    def backward_abstract(dz_aval, softmax_out_aval, scale_factor=None):    # pylint: disable=unused-argument
        """
        softmax_backward abstract
        """
        dz_dtype = dtypes.canonicalize_dtype(dz_aval.dtype)
        softmax_out_dtype = dtypes.canonicalize_dtype(softmax_out_aval.dtype)
        assert dz_dtype == softmax_out_dtype
        assert dz_dtype in [jnp.float16, jnp.bfloat16]
        assert softmax_out_dtype in [jnp.float16, jnp.bfloat16]
1216

1217
        assert dz_aval.shape == softmax_out_aval.shape
1218

1219
        dx_aval = core.raise_to_shaped(dz_aval)
1220
        return dx_aval
1221
1222

    @staticmethod
1223
    def backward_lowering(name, ctx, dz, softmax_out, *, scale_factor):
1224
        """
1225
        softmax_backward lowering rules
1226
        """
1227
        dz_aval, _ = ctx.avals_in
1228

1229
1230
        dz_type = ir.RankedTensorType(dz.type)
        dz_shape = dz_type.shape
1231

1232
1233
1234
1235
1236
1237
        # Assume [...Batch, Head, Q_Seqlen, K_Seqlen]
        batch = reduce(operator.mul, dz_shape[:-3])
        pad_batch = batch    # unused
        heads = dz_shape[-3]
        q_seqlen = dz_shape[-2]
        k_seqlen = dz_shape[-1]
1238

1239
1240
        softmax_out_type = ir.RankedTensorType(softmax_out.type)
        softmax_out_shape = softmax_out_type.shape
1241

1242
        out_types = [ir.RankedTensorType.get(dz_shape, dz_type.element_type)]
1243
1244
        operands = [dz, softmax_out]
        operand_shapes = [dz_shape, softmax_out_shape]
1245
1246
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

1247
1248
1249
        opaque = transformer_engine_jax.pack_softmax_descriptor(
            batch, pad_batch, heads, q_seqlen, k_seqlen, jax_dtype_to_te_dtype(dz_aval.dtype),
            scale_factor)
1250

1251
        out = custom_caller(name, args, opaque, False)
1252

1253
        return [out]
1254
1255

    @staticmethod
1256
    def backward_impl(primitive, dz, softmax_out, scale_factor):
1257
        """
1258
        softmax_backward implementation
1259
        """
1260
1261
1262
        assert primitive is not None
        dx = primitive.bind(dz, softmax_out, scale_factor=scale_factor)
        return dx
1263

1264
1265
1266
1267
1268
1269
1270
1271
    @staticmethod
    def backward_batcher(primitive, batched_args, batch_dims, *, scale_factor):
        """
        softmax_backward batcher
        """
        assert primitive is not None
        dz, softmax_out = batched_args
        _, softmax_out_bdim = batch_dims
1272

1273
1274
        out_bdims = softmax_out_bdim
        return primitive.bind(dz, softmax_out, scale_factor=scale_factor), out_bdims
1275

1276
1277
    @classmethod
    def backward_infer_sharding_from_operands(cls, scale_factor, mesh, arg_infos, result_infos):
1278
        """
1279
        softmax_backward infer_sharding_from_operands
1280
        """
1281
        del scale_factor, result_infos    # Unused.
1282
1283
1284
1285
1286
1287
1288
1289
        dz_spec = get_padded_spec(arg_infos[0])
        if dz_spec[-1] is not None:
            warnings.warn(
                f"Sharding the hidden dimension is not supported in {cls.name}! " \
                f"Forcing XLA to not shard the hidden dim, which might introduce extra " \
                f"collective ops and hurt performance."
            )
        dx_sharding = NamedSharding(mesh, PartitionSpec(*dz_spec[:-1], None))
1290
        return dx_sharding
1291

1292
1293
    @classmethod
    def backward_partition(cls, impl, scale_factor, mesh, arg_infos, result_infos):
1294
1295
1296
1297
        """
        softmax_backward partition
        """
        del result_infos
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313

        dz_spec = get_padded_spec(arg_infos[0])
        softmax_out_spec = get_padded_spec(arg_infos[1])
        if dz_spec[-1] is not None or softmax_out_spec[-1] is not None:
            warnings.warn(
                f"Sharding the hidden dimension is not supported in {cls.name}! " \
                f"Forcing XLA to not shard the hidden dim, which might introduce extra " \
                f"collective ops and hurt performance."
            )

        dz_sharding = NamedSharding(mesh, PartitionSpec(*dz_spec[:-1], None))
        softmax_out_sharding = NamedSharding(mesh, PartitionSpec(*softmax_out_spec[:-1], None))
        dx_sharding = dz_sharding
        arg_shardings = (dz_sharding, softmax_out_sharding)
        out_shardings = dx_sharding

1314
1315
        impl = partial(impl, scale_factor=scale_factor)
        return mesh, impl, out_shardings, arg_shardings
1316
1317


1318
1319
1320
1321
1322
1323
1324
1325
1326
class ScaledSoftmaxFwdPrimitive(SoftmaxPrimitive):
    """
    Scaled Softmax Fwd Primitive
    """
    name = "te_scaled_softmax_forward"
    multiple_results = False
    impl_static_args = (1,)    # scale_factor
    inner_primitive = None
    outer_primitive = None
1327

1328
1329
1330
1331
1332
    @staticmethod
    def is_kernel_available(batch: int, heads: int, q_seqlen: int, k_seqlen: int,
                            dtype: jnp.dtype) -> bool:
        """Check Softmax kernel availability based on size"""
        attn_batches = batch * heads
1333

1334
1335
        dtype = dtypes.canonicalize_dtype(dtype)
        if (dtype in [jnp.float16, jnp.bfloat16]
1336
                and 16 <= k_seqlen <= SoftmaxPrimitive.max_k_seqlen_supported
1337
1338
1339
1340
1341
1342
1343
                and q_seqlen % 4 == 0    # q_seqlen must be divisor of 4
                and attn_batches % 4 == 0    # batch * heads must be divisor of 4
           ):
            if 0 <= k_seqlen <= SoftmaxPrimitive.max_k_seqlen_supported:
                batch_per_block = SoftmaxPrimitive.get_batch_per_block(k_seqlen)
                return q_seqlen % batch_per_block == 0
        return False
1344

1345
1346
1347
1348
1349
1350
    @staticmethod
    def abstract(logits_aval, scale_factor):    # pylint: disable=unused-argument
        """
        te_scaled_softmax_forward abstract
        """
        return SoftmaxPrimitive.forward_abstract(logits_aval, scale_factor)
1351

1352
1353
1354
1355
1356
1357
1358
1359
1360
    @staticmethod
    def lowering(ctx, logits, *, scale_factor):
        """
        te_scaled_softmax_forward lowering rules
        """
        return SoftmaxPrimitive.forward_lowering(ScaledSoftmaxFwdPrimitive.name,
                                                 ctx,
                                                 logits,
                                                 scale_factor=scale_factor)
1361

1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
    @staticmethod
    def impl(logits, scale_factor):
        return SoftmaxPrimitive.forward_impl(ScaledSoftmaxFwdPrimitive.inner_primitive, logits,
                                             scale_factor)

    @staticmethod
    def batcher(batched_args, batch_dims, *, scale_factor):
        _check_valid_batch_dims(batch_dims)
        return SoftmaxPrimitive.forward_batcher(ScaledSoftmaxFwdPrimitive.outer_primitive,
                                                batched_args,
                                                batch_dims,
                                                scale_factor=scale_factor)
1374

1375
1376
    @staticmethod
    def infer_sharding_from_operands(scale_factor, mesh, arg_infos, result_infos):
1377
        return ScaledSoftmaxFwdPrimitive.forward_infer_sharding_from_operands(
1378
            scale_factor, mesh, arg_infos, result_infos)
1379
1380
1381

    @staticmethod
    def partition(scale_factor, mesh, arg_infos, result_infos):
1382
1383
1384
        return ScaledSoftmaxFwdPrimitive.forward_partition(ScaledSoftmaxFwdPrimitive.impl,
                                                           scale_factor, mesh, arg_infos,
                                                           result_infos)
1385
1386


1387
register_primitive(ScaledSoftmaxFwdPrimitive)
1388

1389
1390

def scaled_softmax_fwd(logits: jnp.ndarray, scale_factor: float) -> jnp.ndarray:
1391
    """
1392
1393
    scaled_softmax_forward wrapper
    Return FP16/BF16 tensor
1394
    """
1395
    return ScaledSoftmaxFwdPrimitive.outer_primitive.bind(logits, scale_factor=scale_factor)
1396
1397


1398
class ScaledSoftmaxBwdPrimitive(SoftmaxPrimitive):
1399
    """
1400
    Scaled Softmax Bwd Primitive
1401
    """
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
    name = "te_scaled_softmax_backward"
    multiple_results = False
    impl_static_args = (2,)    # scale_factor
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def is_kernel_available(batch: int, heads: int, q_seqlen: int, k_seqlen: int,
                            dtype: jnp.dtype) -> bool:
        """Check Softmax kernel availability based on size"""
        return ScaledSoftmaxFwdPrimitive.is_kernel_available(batch, heads, q_seqlen, k_seqlen,
                                                             dtype)
1414
1415

    @staticmethod
1416
    def abstract(dz_aval, softmax_out_aval, scale_factor):
1417
        """
1418
        te_scaled_softmax_backward abstract
1419
        """
1420
        return SoftmaxPrimitive.backward_abstract(dz_aval, softmax_out_aval, scale_factor)
1421

1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
    @staticmethod
    def lowering(ctx, dz, softmax_out, *, scale_factor):
        """
        te_scaled_softmax_backward lowering rules
        """
        out = SoftmaxPrimitive.backward_lowering(ScaledSoftmaxBwdPrimitive.name,
                                                 ctx,
                                                 dz,
                                                 softmax_out,
                                                 scale_factor=scale_factor)
1432

1433
        return out
1434

1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
    @staticmethod
    def impl(dz, softmax_out, scale_factor):
        return SoftmaxPrimitive.backward_impl(ScaledSoftmaxBwdPrimitive.inner_primitive,
                                              dz,
                                              softmax_out,
                                              scale_factor=scale_factor)

    @staticmethod
    def batcher(batched_args, batch_dims, *, scale_factor):
        _check_valid_batch_dims(batch_dims)
        return SoftmaxPrimitive.backward_batcher(ScaledSoftmaxBwdPrimitive.outer_primitive,
                                                 batched_args,
                                                 batch_dims,
                                                 scale_factor=scale_factor)

    @staticmethod
    def infer_sharding_from_operands(scale_factor, mesh, arg_infos, result_infos):
1452
        return ScaledSoftmaxBwdPrimitive.backward_infer_sharding_from_operands(
1453
            scale_factor, mesh, arg_infos, result_infos)
1454
1455

    @staticmethod
1456
    def partition(scale_factor, mesh, arg_infos, result_infos):
1457
1458
1459
        return ScaledSoftmaxBwdPrimitive.backward_partition(ScaledSoftmaxBwdPrimitive.impl,
                                                            scale_factor, mesh, arg_infos,
                                                            result_infos)
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493


register_primitive(ScaledSoftmaxBwdPrimitive)


def scaled_softmax_bwd(dz: jnp.ndarray, softmax_out: jnp.ndarray,
                       scale_factor: float) -> jnp.ndarray:
    """
    scaled_backward wrapper
    Return FP16/BF16 tensor
    """
    return ScaledSoftmaxBwdPrimitive.outer_primitive.bind(dz,
                                                          softmax_out,
                                                          scale_factor=scale_factor)


class ScaledMaskedSoftmaxFwdPrimitive(SoftmaxPrimitive):
    """
    Scaled Masked Softmax Fwd Primitive
    """
    name = "te_scaled_masked_softmax_forward"
    multiple_results = False
    impl_static_args = (2,)    # scale_factor
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def is_kernel_available(batch: int, heads: int, q_seqlen: int, k_seqlen: int,
                            dtype: jnp.dtype) -> bool:
        """Check Softmax kernel availability based on size"""
        attn_batches = batch * heads

        dtype = dtypes.canonicalize_dtype(dtype)
        if (dtype in [jnp.float16, jnp.bfloat16]
1494
                and 16 <= k_seqlen <= SoftmaxPrimitive.max_k_seqlen_supported
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
                and q_seqlen % 4 == 0    # q_seqlen must be divisor of 4
                and attn_batches % 4 == 0    # batch * heads must be divisor of 4
           ):
            if 0 <= k_seqlen <= SoftmaxPrimitive.max_k_seqlen_supported:
                batch_per_block = SoftmaxPrimitive.get_batch_per_block(k_seqlen)
                return q_seqlen % batch_per_block == 0
        return False

    @staticmethod
    def abstract(logits_aval, mask_aval, scale_factor):    # pylint: disable=unused-argument
1505
        """
1506
        te_scaled_masked_softmax_forward abstract
1507
1508
        """

1509
1510
1511
        i_dtype = dtypes.canonicalize_dtype(logits_aval.dtype)
        assert i_dtype in [jnp.float16, jnp.bfloat16]
        i_shape = logits_aval.shape
1512

1513
1514
1515
1516
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        # Assume [...Batch, Head, Q_Seqlen, K_Seqlen]
        batch = reduce(operator.mul, i_shape[:-3])
        q_seqlen = i_shape[-2]
        k_seqlen = i_shape[-1]
        assert k_seqlen <= SoftmaxPrimitive.max_k_seqlen_supported
        assert q_seqlen > 1
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        mask_dtype = dtypes.canonicalize_dtype(mask_aval.dtype)
        assert mask_dtype in [
            jnp.uint8,
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        ]
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        mask_shape = mask_aval.shape
        pad_batch = batch = reduce(operator.mul, mask_shape[:-3])
        assert pad_batch in (1, batch)    # 1 means broadcast
        assert mask_shape[-3] == 1    # 1 means broadcast
        assert mask_shape[-2] == q_seqlen
        assert mask_shape[-1] == k_seqlen

        out_aval = core.raise_to_shaped(logits_aval)
        return out_aval

    @staticmethod
    def lowering(ctx, logits, mask, *, scale_factor):
        """
        te_scaled_masked_softmax_forward lowering rules
        """

        logits_aval, _ = ctx.avals_in
        i_type = ir.RankedTensorType(logits.type)
        i_shape = i_type.shape
        # Assume [...Batch, Head, Q_Seqlen, K_Seqlen]
        batch = reduce(operator.mul, i_shape[:-3])
        heads = i_shape[-3]
        q_seqlen = i_shape[-2]
        k_seqlen = i_shape[-1]

        mask_type = ir.RankedTensorType(mask.type)
        mask_shape = mask_type.shape
        pad_batch = reduce(operator.mul, mask_shape[:-3])

        out_types = [ir.RankedTensorType.get(i_shape, i_type.element_type)]
        operands = [logits, mask]
        operand_shapes = [i_shape, mask_shape]
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        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

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        opaque = transformer_engine_jax.pack_softmax_descriptor(
            batch, pad_batch, heads, q_seqlen, k_seqlen, jax_dtype_to_te_dtype(logits_aval.dtype),
            scale_factor)
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        out = custom_caller(ScaledMaskedSoftmaxFwdPrimitive.name, args, opaque, False)

        return [out]

    @staticmethod
    def impl(logits, mask, scale_factor):
        assert ScaledMaskedSoftmaxFwdPrimitive.inner_primitive is not None
        output = ScaledMaskedSoftmaxFwdPrimitive.inner_primitive.bind(logits,
                                                                      mask,
                                                                      scale_factor=scale_factor)
        return output

    @staticmethod
    def batcher(batched_args, batch_dims, *, scale_factor):
        _check_valid_batch_dims(batch_dims)
        assert ScaledMaskedSoftmaxFwdPrimitive.outer_primitive is not None
        logits, mask = batched_args
        logits_bdim, _ = batch_dims

        out_bdims = logits_bdim
        return ScaledMaskedSoftmaxFwdPrimitive.outer_primitive.bind(
            logits, mask, scale_factor=scale_factor), out_bdims

    @staticmethod
    def infer_sharding_from_operands(scale_factor, mesh, arg_infos, result_infos):
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        return ScaledMaskedSoftmaxFwdPrimitive.forward_infer_sharding_from_operands(
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            scale_factor, mesh, arg_infos, result_infos)
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    @staticmethod
    def partition(scale_factor, mesh, arg_infos, result_infos):
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        return ScaledMaskedSoftmaxFwdPrimitive.backward_partition(
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            ScaledMaskedSoftmaxFwdPrimitive.impl, scale_factor, mesh, arg_infos, result_infos)
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register_primitive(ScaledMaskedSoftmaxFwdPrimitive)


def scaled_masked_softmax_fwd(logits: jnp.ndarray, mask: jnp.ndarray,
                              scale_factor: float) -> jnp.ndarray:
    """
    scaled_masked_softmax_forward wrapper
    Return FP16/BF16 tensor
    """
    return ScaledMaskedSoftmaxFwdPrimitive.outer_primitive.bind(logits,
                                                                mask,
                                                                scale_factor=scale_factor)


class ScaledMaskedSoftmaxBwdPrimitive(SoftmaxPrimitive):
    """
    Scaled Masked Softmax Bwd Primitive
    """
    name = "te_scaled_masked_softmax_backward"
    multiple_results = False
    impl_static_args = (2,)    # scale_factor
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def is_kernel_available(batch: int, heads: int, q_seqlen: int, k_seqlen: int,
                            dtype: jnp.dtype) -> bool:
        """Check Softmax kernel availability based on size"""
        return ScaledSoftmaxFwdPrimitive.is_kernel_available(batch, heads, q_seqlen, k_seqlen,
                                                             dtype)

    @staticmethod
    def abstract(dz_aval, softmax_out_aval, *, scale_factor):
        """
        te_scaled_upper_triang_masked_backward abstract
        """
        return SoftmaxPrimitive.backward_abstract(dz_aval, softmax_out_aval, scale_factor)

    @staticmethod
    def lowering(ctx, dz, softmax_out, *, scale_factor):
        """
        te_scaled_upper_triang_masked_backward lowering rules
        """
        out = SoftmaxPrimitive.backward_lowering(ScaledMaskedSoftmaxBwdPrimitive.name,
                                                 ctx,
                                                 dz,
                                                 softmax_out,
                                                 scale_factor=scale_factor)

        return out

    @staticmethod
    def impl(dz, softmax_out, scale_factor):
        return SoftmaxPrimitive.backward_impl(ScaledMaskedSoftmaxBwdPrimitive.inner_primitive,
                                              dz,
                                              softmax_out,
                                              scale_factor=scale_factor)

    @staticmethod
    def batcher(batched_args, batch_dims, *, scale_factor):
        _check_valid_batch_dims(batch_dims)
        return SoftmaxPrimitive.backward_batcher(ScaledMaskedSoftmaxBwdPrimitive.outer_primitive,
                                                 batched_args,
                                                 batch_dims,
                                                 scale_factor=scale_factor)

    @staticmethod
    def infer_sharding_from_operands(scale_factor, mesh, arg_infos, result_infos):
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        return ScaledMaskedSoftmaxBwdPrimitive.backward_infer_sharding_from_operands(
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            scale_factor, mesh, arg_infos, result_infos)
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    @staticmethod
    def partition(scale_factor, mesh, arg_infos, result_infos):
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        return ScaledMaskedSoftmaxBwdPrimitive.backward_partition(
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            ScaledMaskedSoftmaxBwdPrimitive.impl, scale_factor, mesh, arg_infos, result_infos)
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register_primitive(ScaledMaskedSoftmaxBwdPrimitive)


def scaled_masked_softmax_bwd(dz: jnp.ndarray, softmax_out: jnp.ndarray,
                              scale_factor: float) -> jnp.ndarray:
    """
    scaled_masked_backward wrapper
    Return FP16/BF16 tensor
    """
    return ScaledMaskedSoftmaxBwdPrimitive.outer_primitive.bind(dz,
                                                                softmax_out,
                                                                scale_factor=scale_factor)


class ScaledUpperTriangMaskedSoftmaxFwdPrimitive(SoftmaxPrimitive):
    """
    Scaled Upper Triang Masked Softmax Fwd Primitive
    """
    name = "te_scaled_upper_triang_masked_softmax_forward"
    multiple_results = False
    impl_static_args = (1,)    # scale_factor
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def is_kernel_available(batch: int, heads: int, q_seqlen: int, k_seqlen: int,
                            dtype: jnp.dtype) -> bool:
        """Check Softmax kernel availability based on size"""
        attn_batches = batch * heads

        dtype = dtypes.canonicalize_dtype(dtype)
        if (dtype in [jnp.float16, jnp.bfloat16]
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                and 16 <= k_seqlen <= SoftmaxPrimitive.max_k_seqlen_supported
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                and q_seqlen % 4 == 0    # q_seqlen must be divisor of 4
                and attn_batches % 4 == 0    # batch * heads must be divisor of 4
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                and k_seqlen == q_seqlen):
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            if 0 <= k_seqlen <= SoftmaxPrimitive.max_k_seqlen_supported:
                batch_per_block = SoftmaxPrimitive.get_batch_per_block(k_seqlen)
                return attn_batches % batch_per_block == 0
        return False

    @staticmethod
    def abstract(logits_aval, scale_factor):    # pylint: disable=unused-argument
        """
        te_scaled_upper_triang_masked_softmax_forward abstract
        """
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        q_seqlen = logits_aval.shape[-2]
        k_seqlen = logits_aval.shape[-1]
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        assert q_seqlen == k_seqlen
        return SoftmaxPrimitive.forward_abstract(logits_aval, scale_factor)

    @staticmethod
    def lowering(ctx, logits, *, scale_factor):
        """
        te_scaled_upper_triang_masked_softmax_forward lowering rules
        """
        return SoftmaxPrimitive.forward_lowering(ScaledUpperTriangMaskedSoftmaxFwdPrimitive.name,
                                                 ctx,
                                                 logits,
                                                 scale_factor=scale_factor)

    @staticmethod
    def impl(logits, scale_factor):
        return SoftmaxPrimitive.forward_impl(
            ScaledUpperTriangMaskedSoftmaxFwdPrimitive.inner_primitive, logits, scale_factor)

    @staticmethod
    def batcher(batched_args, batch_dims, *, scale_factor):
        _check_valid_batch_dims(batch_dims)
        return SoftmaxPrimitive.forward_batcher(
            ScaledUpperTriangMaskedSoftmaxFwdPrimitive.outer_primitive,
            batched_args,
            batch_dims,
            scale_factor=scale_factor)

    @staticmethod
    def infer_sharding_from_operands(scale_factor, mesh, arg_infos, result_infos):
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        return ScaledUpperTriangMaskedSoftmaxFwdPrimitive.forward_infer_sharding_from_operands(
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            scale_factor, mesh, arg_infos, result_infos)
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    @staticmethod
    def partition(scale_factor, mesh, arg_infos, result_infos):
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        return ScaledUpperTriangMaskedSoftmaxFwdPrimitive.forward_partition(
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            ScaledUpperTriangMaskedSoftmaxFwdPrimitive.impl, scale_factor, mesh, arg_infos,
            result_infos)
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register_primitive(ScaledUpperTriangMaskedSoftmaxFwdPrimitive)


def scaled_upper_triang_masked_softmax_fwd(logits: jnp.ndarray, scale_factor: float) -> jnp.ndarray:
    """
    scaled_upper_triang_masked_softmax_forward wrapper
    Return FP16/BF16 tensor
    """
    return ScaledUpperTriangMaskedSoftmaxFwdPrimitive.outer_primitive.bind(
        logits, scale_factor=scale_factor)


class ScaledUpperTriangMaskedSoftmaxBwdPrimitive(SoftmaxPrimitive):
    """
    Scaled Upper Triang Masked Softmax Bwd Primitive
    """
    name = "te_scaled_upper_triang_masked_softmax_backward"
    multiple_results = False
    impl_static_args = (2,)    # scale_factor
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def is_kernel_available(batch: int, heads: int, q_seqlen: int, k_seqlen: int,
                            dtype: jnp.dtype) -> bool:
        """Check Softmax kernel availability based on size"""
        return ScaledUpperTriangMaskedSoftmaxFwdPrimitive.is_kernel_available(
            batch, heads, q_seqlen, k_seqlen, dtype)

    @staticmethod
    def abstract(dz_aval, softmax_out_aval, *, scale_factor):
        """
        te_scaled_upper_triang_masked_backward abstract
        """
        return SoftmaxPrimitive.backward_abstract(dz_aval, softmax_out_aval, scale_factor)

    @staticmethod
    def lowering(ctx, dz, softmax_out, *, scale_factor):
        """
        te_scaled_upper_triang_masked_backward lowering rules
        """
        out = SoftmaxPrimitive.backward_lowering(ScaledUpperTriangMaskedSoftmaxBwdPrimitive.name,
                                                 ctx,
                                                 dz,
                                                 softmax_out,
                                                 scale_factor=scale_factor)
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        return out

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    @staticmethod
    def impl(dz, softmax_out, scale_factor):
        return SoftmaxPrimitive.backward_impl(
            ScaledUpperTriangMaskedSoftmaxBwdPrimitive.inner_primitive,
            dz,
            softmax_out,
            scale_factor=scale_factor)
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    @staticmethod
    def batcher(batched_args, batch_dims, *, scale_factor):
        _check_valid_batch_dims(batch_dims)
        return SoftmaxPrimitive.backward_batcher(
            ScaledUpperTriangMaskedSoftmaxBwdPrimitive.outer_primitive,
            batched_args,
            batch_dims,
            scale_factor=scale_factor)
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    @staticmethod
    def infer_sharding_from_operands(scale_factor, mesh, arg_infos, result_infos):
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    @staticmethod
    def partition(scale_factor, mesh, arg_infos, result_infos):
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        return ScaledUpperTriangMaskedSoftmaxBwdPrimitive.backward_partition(
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            result_infos)
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register_primitive(ScaledUpperTriangMaskedSoftmaxBwdPrimitive)


def scaled_upper_triang_masked_softmax_bwd(dz: jnp.ndarray, softmax_out: jnp.ndarray,
                                           scale_factor: float) -> jnp.ndarray:
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    """
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    scaled_upper_triang_masked_backward wrapper
    Return FP16/BF16 tensor
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    """
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    return ScaledUpperTriangMaskedSoftmaxBwdPrimitive.outer_primitive.bind(
        dz, softmax_out, scale_factor=scale_factor)
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@dataclass(frozen=True)
class FusedAttnHelper:
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    """
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    Helper for the fused attention backend
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    """
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    q_dtype: jnp.dtype
    kv_dtype: jnp.dtype
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    qkv_layout: NVTE_QKV_Layout
    attn_bias_type: NVTE_Bias_Type
    attn_mask_type: NVTE_Mask_Type
    dropout_probability: float
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    q_num_heads: int
    kv_num_heads: int
    q_max_seqlen: int
    kv_max_seqlen: int
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    head_dim: int

    def is_fused_attn_kernel_available(self):
        """Check if there is available fused attention kernel"""
        return self.get_fused_attn_backend() != NVTE_Fused_Attn_Backend.NVTE_No_Backend

    def get_fused_attn_backend(self):
        """Get the fused attention kernel backend"""
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        return transformer_engine_jax.get_fused_attn_backend(
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            jax_dtype_to_te_dtype(self.q_dtype), jax_dtype_to_te_dtype(self.kv_dtype),
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            self.qkv_layout, self.attn_bias_type, self.attn_mask_type, self.dropout_probability,
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            self.q_num_heads, self.kv_num_heads, self.q_max_seqlen, self.kv_max_seqlen,
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            self.head_dim)
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    @staticmethod
    def parse_qkv_aval(q_aval, k_aval, v_aval, qkv_layout):
        """Parse qkv aval"""
        match qkv_layout:
            case NVTE_QKV_Layout.NVTE_BS3HD:
                *q_batch_shape, q_max_seqlen, nqkv, attn_heads, q_head_dim = q_aval.shape
                kv_batch_shape = q_batch_shape
                kv_max_seqlen = q_max_seqlen
                num_gqa_groups = attn_heads
                kv_head_dim = q_head_dim
                assert nqkv == 3
            case NVTE_QKV_Layout.NVTE_BSHD_BS2HD:
                *q_batch_shape, q_max_seqlen, attn_heads, q_head_dim = q_aval.shape
                *kv_batch_shape, kv_max_seqlen, nkv, num_gqa_groups, kv_head_dim = k_aval.shape
                assert nkv == 2
            case NVTE_QKV_Layout.NVTE_BSHD_BSHD_BSHD:
                *q_batch_shape, q_max_seqlen, attn_heads, q_head_dim = q_aval.shape
                *kv_batch_shape, kv_max_seqlen, num_gqa_groups, kv_head_dim = k_aval.shape
                assert k_aval.shape == v_aval.shape
            case _:
                raise ValueError(f"Unexpected {qkv_layout=}")
        assert q_batch_shape == kv_batch_shape
        assert q_head_dim == kv_head_dim
        assert q_aval.dtype == k_aval.dtype == v_aval.dtype

        return (q_batch_shape, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups, q_head_dim)

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@dataclass(frozen=True)
class _FusedAttnRNGStateChecker:
    """
    Checker for guarding the fused attention rng state.
    The fused attention backend requires a 64 bits seed and a 64 bits offset.
    However, JAX doesn't enable 64 bits by default,
    so we have to emulate seed as two 32 bits array.
    The offset calculation is maintained in the backend.
    """
    rng_state_dtype: jnp.dtype = jnp.uint32
    # (seed,) with internal dtype int64
    seed_size: int = 2
    # (seed, offset) with internal dtype int64
    rng_state_size: int = 2 * 2

    def check_seed(self, seed, dropout_probability, is_training):
        """
        Check the seed and convert the data type of seed if possible.
        """
        # Jax can't bind None, create a dummy tensor for None
        if seed is None:
            dropout_enabled = dropout_probability > 0 and is_training
            assert not dropout_enabled, "seed is not allowed to be None when dropout is enabled."
            seed = jnp.zeros(2, dtype=self.rng_state_dtype)
            seed = jnp.repeat(seed, num_of_devices())

        if seed.dtype != self.rng_state_dtype:
            warnings.warn(
                f"Requested {seed.dtype=} is not available, and will be "
                f"casted to dtype {self.rng_state_dtype}. "
                f"Please use threefry/rbg/unsafe_rbg PRNG implementations to remove this warning.")
            seed = seed.astype(self.rng_state_dtype)

        assert seed.dtype == self.rng_state_dtype
        # Backend takes an int64_t seed, so only the first two u32 elements are taken
        assert seed.size >= self.seed_size

        return seed


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def generate_cu_seqlen(actual_seqlen):
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    """
    Generating cumsum seqlen for a batch
    """
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    cu_seqlen = jnp.cumsum(actual_seqlen)
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    cu_seqlen = jnp.hstack((0, cu_seqlen))
    return cu_seqlen


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class FusedAttnFwdPrimitive(BasePrimitive):
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    """
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    Fused Attention Forward Primitive
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    """
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    name = "te_fused_attn_forward"
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    multiple_results = True
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    impl_static_args = (7, 8, 9, 10, 11, 12)
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    inner_primitive = None
    outer_primitive = None
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    @staticmethod
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    def abstract(q_aval, k_aval, v_aval, bias_aval, q_seqlen_or_cu_seqlen_aval,
                 kv_seqlen_or_cu_seqlen_aval, seed_aval, *, attn_bias_type, attn_mask_type,
                 qkv_layout, scaling_factor, dropout_probability, is_training):
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        """
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        Fused attention fwd abstract
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        """
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        q_dtype = dtypes.canonicalize_dtype(q_aval.dtype)
        k_dtype = dtypes.canonicalize_dtype(k_aval.dtype)
        v_dtype = dtypes.canonicalize_dtype(v_aval.dtype)
        bias_dtype = dtypes.canonicalize_dtype(bias_aval.dtype)
        assert q_dtype == k_dtype == v_dtype == bias_dtype
        assert q_seqlen_or_cu_seqlen_aval.dtype == kv_seqlen_or_cu_seqlen_aval.dtype

        batch_shape, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups, head_dim = \
            FusedAttnHelper.parse_qkv_aval(q_aval, k_aval, v_aval, qkv_layout)
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        output_shape = (*batch_shape, q_max_seqlen, attn_heads, head_dim)
        out_aval = q_aval.update(shape=output_shape, dtype=q_dtype)
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        # backend determines the softmax buffer shape/dtype
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        backend = FusedAttnHelper(q_dtype, k_dtype, qkv_layout, attn_bias_type, attn_mask_type,
                                  dropout_probability, attn_heads, num_gqa_groups, q_max_seqlen,
                                  kv_max_seqlen, head_dim).get_fused_attn_backend()
1992

1993
        if backend == NVTE_Fused_Attn_Backend.NVTE_F16_max512_seqlen:
1994
1995
            softmax_shape = (*batch_shape, attn_heads, q_max_seqlen, kv_max_seqlen)
            softmax_dtype = q_dtype
1996
        elif backend == NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen:
1997
            softmax_shape = (*batch_shape, attn_heads, q_max_seqlen, 1)
1998
1999
            softmax_dtype = dtypes.canonicalize_dtype(jnp.float32)
        else:
2000
            raise ValueError(f'Unsupported {backend=}')
2001
        softmax_aux_aval = q_aval.update(shape=softmax_shape, dtype=softmax_dtype)
2002

2003
2004
        # JAX does not enable 64-bit int by default so we get XLA to allocate x8 memory with
        # 32-bit unsigned int to get the buffer size we need in the C++ kernel
2005
2006
2007
2008
        checker = _FusedAttnRNGStateChecker()
        seed_dtype = dtypes.canonicalize_dtype(seed_aval.dtype)
        assert seed_dtype == checker.rng_state_dtype
        rng_state_shape = (seed_aval.shape[0], checker.rng_state_size)
2009
2010
        rng_state_aval = seed_aval.update(shape=rng_state_shape, dtype=checker.rng_state_dtype)

2011
2012
2013
2014
2015
2016
        if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
            bias_batch = bias_heads = 0
        else:
            *bias_batch_shape, bias_heads, _, _ = bias_aval.shape
            bias_batch = reduce(operator.mul, bias_batch_shape)

2017
2018
        # do a dummy kernel call here to get workspace buffer shapes/dtypes that XLA needs to
        # prepare for the active fused-attn backend
2019
2020
2021
2022
2023
2024
2025
        input_batch = reduce(operator.mul, batch_shape)
        wkspace_info = transformer_engine_jax.get_fused_attn_fwd_workspace_sizes(
            input_batch, bias_batch, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups,
            bias_heads, head_dim, scaling_factor, dropout_probability, attn_bias_type,
            attn_mask_type, qkv_layout, jax_dtype_to_te_dtype(q_aval.dtype), is_training)
        wkspace_aval = q_aval.update(shape=wkspace_info[0],
                                     dtype=te_dtype_to_jax_dtype(wkspace_info[1]))
2026
2027

        return out_aval, softmax_aux_aval, rng_state_aval, wkspace_aval
2028

2029
2030
2031
    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
2032
        Fused attention fwd outer primitive abstract
2033
2034
        """
        out_aval, softmax_aux_aval, rng_state_aval, _ = \
2035
            FusedAttnFwdPrimitive.abstract(*args, **kwargs)
2036
        return out_aval, softmax_aux_aval, rng_state_aval
2037
2038

    @staticmethod
2039
2040
    def lowering(ctx, q, k, v, bias, q_cu_seqlen, kv_cu_seqlen, seed, *, attn_bias_type,
                 attn_mask_type, qkv_layout, scaling_factor, dropout_probability, is_training):
2041
        """
2042
        Fused attention fwd lowering rules
2043
        """
2044
        operands = [q, k, v, bias, q_cu_seqlen, kv_cu_seqlen, seed]
2045
        operand_shapes = map(lambda x: x.type.shape, operands)
2046
        out_types = [
2047
2048
            ir.RankedTensorType.get(output.shape, mlir.dtype_to_ir_type(output.dtype))
            for output in ctx.avals_out
2049
2050
        ]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)
2051

2052
2053
2054
2055
2056
2057
        q_aval, k_aval, v_aval, bias_aval, *_ = ctx.avals_in

        batch_shape, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups, head_dim = \
            FusedAttnHelper.parse_qkv_aval(q_aval, k_aval, v_aval, qkv_layout)

        input_batch = reduce(operator.mul, batch_shape)
2058
2059
2060
2061
2062
2063

        if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
            bias_batch = bias_heads = 0
        else:
            *bias_batch_shape, bias_heads, _, _ = bias_aval.shape
            bias_batch = reduce(operator.mul, bias_batch_shape)
2064
2065
2066

        wkspace_aval = ctx.avals_out[-1]

2067
        opaque = transformer_engine_jax.pack_fused_attn_descriptor(
2068
2069
2070
2071
            input_batch, bias_batch, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups,
            bias_heads, head_dim, wkspace_aval.size, scaling_factor, dropout_probability,
            attn_bias_type, attn_mask_type, qkv_layout, jax_dtype_to_te_dtype(q_aval.dtype),
            jax_dtype_to_te_dtype(wkspace_aval.dtype), is_training)
2072

2073
        out = custom_caller(FusedAttnFwdPrimitive.name, args, opaque, has_side_effect=False)
2074

2075
2076
2077
        return out

    @staticmethod
2078
2079
2080
    def impl(q, k, v, bias, q_seqlen, kv_seqlen, seed, attn_bias_type, attn_mask_type, qkv_layout,
             scaling_factor, dropout_probability, is_training):
        assert FusedAttnFwdPrimitive.inner_primitive is not None
2081

2082
2083
        q_cu_seqlen = generate_cu_seqlen(q_seqlen)
        kv_cu_seqlen = generate_cu_seqlen(kv_seqlen)
2084

2085
2086
2087
2088
        output, softmax_aux, rng_state, _ = FusedAttnFwdPrimitive.inner_primitive.bind(
            q,
            k,
            v,
2089
            bias,
2090
2091
            q_cu_seqlen,
            kv_cu_seqlen,
2092
2093
2094
            seed,
            attn_bias_type=attn_bias_type,
            attn_mask_type=attn_mask_type,
2095
            qkv_layout=qkv_layout,
2096
2097
2098
2099
            scaling_factor=scaling_factor,
            dropout_probability=dropout_probability,
            is_training=is_training)
        return output, softmax_aux, rng_state
2100

2101
    @staticmethod
2102
2103
    def batcher(batched_args, batch_dims, *, attn_bias_type, attn_mask_type, qkv_layout,
                scaling_factor, dropout_probability, is_training):
2104
        _check_valid_batch_dims(batch_dims)
2105
2106
        assert FusedAttnFwdPrimitive.outer_primitive is not None
        q_bdim, *_, seed_bdim = batch_dims
2107

2108
2109
2110
2111
2112
2113
2114
2115
        out_bdims = q_bdim, q_bdim, seed_bdim
        return FusedAttnFwdPrimitive.outer_primitive.bind(*batched_args,
                                                          attn_bias_type=attn_bias_type,
                                                          attn_mask_type=attn_mask_type,
                                                          qkv_layout=qkv_layout,
                                                          scaling_factor=scaling_factor,
                                                          dropout_probability=dropout_probability,
                                                          is_training=is_training), out_bdims
2116

2117
    @staticmethod
2118
    def infer_sharding_from_operands(attn_bias_type, attn_mask_type, qkv_layout, scaling_factor,
2119
2120
2121
2122
                                     dropout_probability, is_training, mesh, arg_infos,
                                     result_infos):
        del attn_bias_type, attn_mask_type, scaling_factor
        del dropout_probability, is_training, result_infos
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
        q_spec = get_padded_spec(arg_infos[0])
        k_spec = get_padded_spec(arg_infos[1])
        match qkv_layout:
            case NVTE_QKV_Layout.NVTE_BS3HD:
                # q_spec = (...batch, q_seqlen, head, hidden)
                out_sharding = NamedSharding(mesh, PartitionSpec(*q_spec[:-3], *q_spec[-2:]))
                softmax_aux_sharding = NamedSharding(
                    mesh, PartitionSpec(*q_spec[:-4], q_spec[-2], q_spec[-4], None))
            case NVTE_QKV_Layout.NVTE_BSHD_BS2HD:
                # q_spec = (...batch, q_seqlen, head, hidden)
                # k_spec = (...batch, kv_seqlen, 2, num_gqa_groups, hidden)
                out_sharding = NamedSharding(mesh, PartitionSpec(*q_spec))
                softmax_aux_sharding = NamedSharding(
                    mesh, PartitionSpec(*q_spec[:-3], q_spec[-2], q_spec[-3], k_spec[-4]))
            case NVTE_QKV_Layout.NVTE_BSHD_BSHD_BSHD:
                # q_spec = (...batch, q_seqlen, head, hidden)
                # k_spec = (...batch, kv_seqlen, num_gqa_groups, hidden)
                out_sharding = NamedSharding(mesh, PartitionSpec(*q_spec))
                softmax_aux_sharding = NamedSharding(
                    mesh, PartitionSpec(*q_spec[:-3], q_spec[-2], q_spec[-3], k_spec[-3]))
            case _:
                raise ValueError(f"Unsupported {qkv_layout=}")
2145
2146
        rng_state_sharding = NamedSharding(mesh, PartitionSpec(get_all_mesh_axes(), None))
        return (out_sharding, softmax_aux_sharding, rng_state_sharding)
2147

2148
    @staticmethod
2149
2150
2151
2152
2153
2154
2155
    def partition(attn_bias_type, attn_mask_type, qkv_layout, scaling_factor, dropout_probability,
                  is_training, mesh, arg_infos, result_infos):
        out_sharding = result_infos[0].sharding
        softmax_aux_sharding = result_infos[1].sharding
        rng_state_sharding = seed_sharding = NamedSharding(mesh,
                                                           PartitionSpec(get_all_mesh_axes(), None))
        arg_shardings = tuple([arg_i.sharding for arg_i in arg_infos[:-1]] + [seed_sharding])
2156
        out_shardings = (out_sharding, softmax_aux_sharding, rng_state_sharding)
2157
        impl = partial(FusedAttnFwdPrimitive.impl,
2158
2159
                       attn_bias_type=attn_bias_type,
                       attn_mask_type=attn_mask_type,
2160
                       qkv_layout=qkv_layout,
2161
2162
2163
2164
2165
2166
                       scaling_factor=scaling_factor,
                       dropout_probability=dropout_probability,
                       is_training=is_training)
        return mesh, impl, out_shardings, arg_shardings


2167
register_primitive(FusedAttnFwdPrimitive)
2168
2169


2170
class FusedAttnBwdPrimitive(BasePrimitive):
2171
    """
2172
    Fused Attention Backward Primitive
2173
    """
2174
    name = "te_fused_attn_backward"
2175
    multiple_results = True
2176
    impl_static_args = (10, 11, 12, 13, 14, 15)
2177
2178
    inner_primitive = None
    outer_primitive = None
2179
2180

    @staticmethod
2181
2182
2183
    def abstract(q_aval, k_aval, v_aval, bias_aval, softmax_aux_aval, rng_state_aval, output_aval,
                 doutput_aval, q_cu_seqlen_aval, kv_cu_seqlen_aval, *, attn_bias_type,
                 attn_mask_type, qkv_layout, scaling_factor, dropout_probability, is_training):
2184
        """
2185
        Fused attention bwd abstract
2186
        """
2187
        del softmax_aux_aval, rng_state_aval, output_aval
2188

2189
2190
2191
        q_dtype = dtypes.canonicalize_dtype(q_aval.dtype)
        k_dtype = dtypes.canonicalize_dtype(k_aval.dtype)
        v_dtype = dtypes.canonicalize_dtype(v_aval.dtype)
2192
        bias_dtype = dtypes.canonicalize_dtype(bias_aval.dtype)
2193
2194
2195
2196
2197
2198
        doutput_dtype = dtypes.canonicalize_dtype(doutput_aval.dtype)
        assert q_dtype == k_dtype == v_dtype == bias_dtype == doutput_dtype
        assert q_cu_seqlen_aval.dtype == kv_cu_seqlen_aval.dtype

        batch_shape, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups, head_dim = \
            FusedAttnHelper.parse_qkv_aval(q_aval, k_aval, v_aval, qkv_layout)
2199

2200
2201
2202
2203
2204
2205
        if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
            bias_batch = bias_heads = 0
        else:
            *bias_batch_shape, bias_heads, _, _ = bias_aval.shape
            bias_batch = reduce(operator.mul, bias_batch_shape)

2206
        input_batch = reduce(operator.mul, batch_shape)
2207
        wkspace_shape, wkspace_dtype = \
2208
2209
2210
2211
            transformer_engine_jax.get_fused_attn_bwd_workspace_sizes(
                input_batch, bias_batch, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups,
                bias_heads, head_dim, scaling_factor, dropout_probability, attn_bias_type,
                attn_mask_type, qkv_layout, jax_dtype_to_te_dtype(q_aval.dtype), is_training)
2212

2213
2214
2215
        dq_aval = q_aval.update(shape=q_aval.shape, dtype=q_dtype)
        dk_aval = k_aval.update(shape=k_aval.shape, dtype=k_dtype)
        dv_aval = v_aval.update(shape=v_aval.shape, dtype=v_dtype)
2216
        dbias_aval = bias_aval.update(shape=bias_aval.shape, dtype=bias_dtype)
2217
2218
        wkspace_aval = q_aval.update(shape=wkspace_shape,
                                     dtype=te_dtype_to_jax_dtype(wkspace_dtype))
2219

2220
        return dq_aval, dk_aval, dv_aval, dbias_aval, wkspace_aval
2221
2222
2223
2224

    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
2225
        Fused attention fwd outer primitive abstract
2226
        """
2227
2228
2229
        dq_aval, dk_aval, dv_aval, dbias_aval, _ = \
            FusedAttnBwdPrimitive.abstract(*args, **kwargs)
        return dq_aval, dk_aval, dv_aval, dbias_aval
2230
2231

    @staticmethod
2232
2233
2234
    def lowering(ctx, q, k, v, bias, softmax_aux, rng_state, output, doutput, q_cu_seqlen,
                 kv_cu_seqlen, *, attn_bias_type, attn_mask_type, qkv_layout, scaling_factor,
                 dropout_probability, is_training):
2235
        """
2236
        Fused attention bwd lowering rules
2237
        """
2238
2239
2240
        operands = [
            q, k, v, bias, softmax_aux, rng_state, output, doutput, q_cu_seqlen, kv_cu_seqlen
        ]
2241
        operand_shapes = map(lambda x: x.type.shape, operands)
2242
        out_types = [
2243
2244
            ir.RankedTensorType.get(output.shape, mlir.dtype_to_ir_type(output.dtype))
            for output in ctx.avals_out
2245
        ]
2246

2247
2248
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

2249
2250
2251
2252
2253
2254
        q_aval, k_aval, v_aval, bias_aval, *_ = ctx.avals_in

        batch_shape, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups, head_dim = \
            FusedAttnHelper.parse_qkv_aval(q_aval, k_aval, v_aval, qkv_layout)

        input_batch = reduce(operator.mul, batch_shape)
2255
2256
2257
2258
2259
2260

        if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
            bias_batch = bias_heads = 0
        else:
            *bias_batch_shape, bias_heads, _, _ = bias_aval.shape
            bias_batch = reduce(operator.mul, bias_batch_shape)
2261
2262
2263

        wkspace_aval = ctx.avals_out[-1]

2264
        opaque = transformer_engine_jax.pack_fused_attn_descriptor(
2265
2266
2267
2268
            input_batch, bias_batch, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups,
            bias_heads, head_dim, wkspace_aval.size, scaling_factor, dropout_probability,
            attn_bias_type, attn_mask_type, qkv_layout, jax_dtype_to_te_dtype(q_aval.dtype),
            jax_dtype_to_te_dtype(wkspace_aval.dtype), is_training)
2269

2270
        out = custom_caller(FusedAttnBwdPrimitive.name, args, opaque, has_side_effect=False)
2271
2272
2273

        return out

2274
    @staticmethod
2275
2276
2277
2278
    def impl(q, k, v, bias, softmax_aux, rng_state, output, doutput, q_seqlen, kv_seqlen,
             attn_bias_type, attn_mask_type, qkv_layout, scaling_factor, dropout_probability,
             is_training):
        assert FusedAttnBwdPrimitive.inner_primitive is not None
2279

2280
2281
        q_cu_seqlen = generate_cu_seqlen(q_seqlen)
        kv_cu_seqlen = generate_cu_seqlen(kv_seqlen)
2282

2283
2284
2285
2286
        dq, dk, dv, dbias, _ = FusedAttnBwdPrimitive.inner_primitive.bind(
            q,
            k,
            v,
2287
            bias,
2288
2289
2290
2291
            softmax_aux,
            rng_state,
            output,
            doutput,
2292
2293
            q_cu_seqlen,
            kv_cu_seqlen,
2294
2295
            attn_bias_type=attn_bias_type,
            attn_mask_type=attn_mask_type,
2296
            qkv_layout=qkv_layout,
2297
2298
2299
            scaling_factor=scaling_factor,
            dropout_probability=dropout_probability,
            is_training=is_training)
2300
        return dq, dk, dv, dbias
2301

2302
    @staticmethod
2303
2304
    def batcher(batched_args, batch_dims, *, attn_bias_type, attn_mask_type, qkv_layout,
                scaling_factor, dropout_probability, is_training):
2305
        _check_valid_batch_dims(batch_dims)
2306
2307
        assert FusedAttnBwdPrimitive.outer_primitive is not None
        q_bdim, k_bdim, v_bdim, *_ = batch_dims
2308

2309
2310
2311
2312
2313
2314
2315
2316
        out_bdims = q_bdim, k_bdim, v_bdim, q_bdim
        return FusedAttnBwdPrimitive.outer_primitive.bind(*batched_args,
                                                          attn_bias_type=attn_bias_type,
                                                          attn_mask_type=attn_mask_type,
                                                          qkv_layout=qkv_layout,
                                                          scaling_factor=scaling_factor,
                                                          dropout_probability=dropout_probability,
                                                          is_training=is_training), out_bdims
2317

2318
    @staticmethod
2319
    def infer_sharding_from_operands(attn_bias_type, attn_mask_type, qkv_layout, scaling_factor,
2320
2321
                                     dropout_probability, is_training, mesh, arg_infos,
                                     result_infos):
2322
2323
2324
2325
2326
2327
2328
2329
2330
        del attn_bias_type, attn_mask_type, qkv_layout, scaling_factor
        del dropout_probability, is_training, result_infos
        q_spec = get_padded_spec(arg_infos[0])
        k_spec = get_padded_spec(arg_infos[1])
        v_spec = get_padded_spec(arg_infos[2])
        bias_spec = get_padded_spec(arg_infos[3])
        dq_sharding = NamedSharding(mesh, PartitionSpec(*q_spec))
        dk_sharding = NamedSharding(mesh, PartitionSpec(*k_spec))
        dv_sharding = NamedSharding(mesh, PartitionSpec(*v_spec))
2331
        dbias_sharding = NamedSharding(mesh, PartitionSpec(*bias_spec))
2332
        return (dq_sharding, dk_sharding, dv_sharding, dbias_sharding)
2333
2334

    @staticmethod
2335
2336
    def partition(attn_bias_type, attn_mask_type, qkv_layout, scaling_factor, dropout_probability,
                  is_training, mesh, arg_infos, result_infos):
2337
        del result_infos
2338
2339
2340
2341
2342
2343
2344
        q_spec = get_padded_spec(arg_infos[0])
        k_spec = get_padded_spec(arg_infos[1])
        v_spec = get_padded_spec(arg_infos[2])
        bias_spec = get_padded_spec(arg_infos[3])
        dq_sharding = NamedSharding(mesh, PartitionSpec(*q_spec))
        dk_sharding = NamedSharding(mesh, PartitionSpec(*k_spec))
        dv_sharding = NamedSharding(mesh, PartitionSpec(*v_spec))
2345
        dbias_sharding = NamedSharding(mesh, PartitionSpec(*bias_spec))
2346
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
2347
        out_shardings = (dq_sharding, dk_sharding, dv_sharding, dbias_sharding)
2348

2349
2350
2351
2352
2353
2354
        def sharded_impl(q, k, v, bias, softmax_aux, rng_state, output, doutput, q_cu_seqlen,
                         kv_cu_seqlen):
            local_dq, local_dk, local_dv, local_dbias = FusedAttnBwdPrimitive.impl(
                q,
                k,
                v,
2355
                bias,
2356
2357
2358
2359
                softmax_aux,
                rng_state,
                output,
                doutput,
2360
2361
                q_cu_seqlen,
                kv_cu_seqlen,
2362
2363
                attn_bias_type=attn_bias_type,
                attn_mask_type=attn_mask_type,
2364
                qkv_layout=qkv_layout,
2365
2366
2367
2368
2369
2370
                scaling_factor=scaling_factor,
                dropout_probability=dropout_probability,
                is_training=is_training)
            global_dbias = local_dbias
            if attn_bias_type is not NVTE_Bias_Type.NVTE_NO_BIAS:
                global_dbias = all_reduce_sum_along_dp_fsdp(local_dbias)
2371
            return local_dq, local_dk, local_dv, global_dbias
2372
2373
2374
2375

        return mesh, sharded_impl, out_shardings, arg_shardings


2376
register_primitive(FusedAttnBwdPrimitive)
2377
2378


2379
2380
2381
2382
def fused_attn_fwd_qkvpacked(qkv: jnp.ndarray, bias: jnp.ndarray, seqlen: jnp.ndarray,
                             seed: jnp.ndarray, attn_bias_type: NVTE_Bias_Type,
                             attn_mask_type: NVTE_Mask_Type, scaling_factor: float,
                             dropout_probability: float, is_training: bool):
2383
    """
2384
2385
    Wrapper for TE self fused attention fwd
    Return BMM1 -> (PreBias) -> ScaleMaskSoftmax -> (PostBias) -> (Dropout) -> BMM2
2386
    """
2387
2388
2389
    checker = _FusedAttnRNGStateChecker()
    seed = checker.check_seed(seed, dropout_probability, is_training)

2390
2391
2392
    if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
        assert bias is None
        bias = jnp.zeros(0, dtype=qkv.dtype)
2393

2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
    _not_used = jnp.zeros(0, qkv.dtype)
    return FusedAttnFwdPrimitive.outer_primitive.bind(qkv,
                                                      _not_used,
                                                      _not_used,
                                                      bias,
                                                      seqlen,
                                                      seqlen,
                                                      seed,
                                                      attn_bias_type=attn_bias_type,
                                                      attn_mask_type=attn_mask_type,
                                                      qkv_layout=NVTE_QKV_Layout.NVTE_BS3HD,
                                                      scaling_factor=scaling_factor,
                                                      dropout_probability=dropout_probability,
                                                      is_training=is_training)
2408
2409


2410
2411
2412
2413
2414
2415
2416
2417
def fused_attn_bwd_qkvpacked(qkv: jnp.ndarray, bias: jnp.ndarray, softmax_aux: jnp.ndarray,
                             rng_state: jnp.ndarray, output: jnp.ndarray, doutput: jnp.ndarray,
                             seqlen: jnp.ndarray, attn_bias_type: NVTE_Bias_Type,
                             attn_mask_type: NVTE_Mask_Type, scaling_factor: float,
                             dropout_probability: float, is_training: bool):
    """
    Wrapper for TE self fused attention bwd
    Return the gradients of self fused attention with packed qkv input
2418
    """
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
    if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
        assert bias is None
        bias = jnp.zeros(0, dtype=qkv.dtype)
    dummy_input = jnp.zeros(0, dtype=qkv.dtype)
    dqkv, *_, dbias = FusedAttnBwdPrimitive.outer_primitive.bind(
        qkv,
        dummy_input,
        dummy_input,
        bias,
        softmax_aux,
        rng_state,
        output,
        doutput,
        seqlen,
        seqlen,
        attn_bias_type=attn_bias_type,
        attn_mask_type=attn_mask_type,
        qkv_layout=NVTE_QKV_Layout.NVTE_BS3HD,
        scaling_factor=scaling_factor,
        dropout_probability=dropout_probability,
        is_training=is_training)
    return dqkv, dbias


def fused_attn_fwd_kvpacked(q: jnp.ndarray, kv: jnp.ndarray, bias: jnp.ndarray,
                            q_seqlen: jnp.ndarray, kv_seqlen: jnp.ndarray, seed: jnp.ndarray,
                            attn_bias_type: NVTE_Bias_Type, attn_mask_type: NVTE_Mask_Type,
                            scaling_factor: float, dropout_probability: float, is_training: bool):
    """
    Wrapper for TE fused attention fwd with kvpacked inputs
    Return BMM1 -> (PreBias) -> ScaleMaskSoftmax -> (PostBias) -> (Dropout) -> BMM2
2450
    """
2451
2452
    checker = _FusedAttnRNGStateChecker()
    seed = checker.check_seed(seed, dropout_probability, is_training)
2453

2454
2455
2456
    if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
        assert bias is None
        bias = jnp.zeros(0, dtype=q.dtype)
2457

2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
    return FusedAttnFwdPrimitive.outer_primitive.bind(q,
                                                      kv,
                                                      jnp.zeros(0, q.dtype),
                                                      bias,
                                                      q_seqlen,
                                                      kv_seqlen,
                                                      seed,
                                                      attn_bias_type=attn_bias_type,
                                                      attn_mask_type=attn_mask_type,
                                                      qkv_layout=NVTE_QKV_Layout.NVTE_BSHD_BS2HD,
                                                      scaling_factor=scaling_factor,
                                                      dropout_probability=dropout_probability,
                                                      is_training=is_training)
2471

2472

2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
def fused_attn_bwd_kvpacked(q: jnp.ndarray, kv: jnp.ndarray, bias: jnp.ndarray,
                            softmax_aux: jnp.ndarray, rng_state: jnp.ndarray, output: jnp.ndarray,
                            doutput: jnp.ndarray, q_seqlen: jnp.ndarray, kv_seqlen: jnp.ndarray,
                            attn_bias_type: NVTE_Bias_Type, attn_mask_type: NVTE_Mask_Type,
                            scaling_factor: float, dropout_probability: float, is_training: bool):
    """
    Wrapper for TE fused attention bwd with kvpacked inputs
    Return the gradients of fused attention with packed kv input
    """
    if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
        assert bias is None
        bias = jnp.zeros(0, dtype=q.dtype)
    dummy_input = jnp.zeros(0, q.dtype)
    dq, dkv, _, dbias = FusedAttnBwdPrimitive.outer_primitive.bind(
        q,
        kv,
        dummy_input,
        bias,
        softmax_aux,
        rng_state,
        output,
        doutput,
        q_seqlen,
        kv_seqlen,
        attn_bias_type=attn_bias_type,
        attn_mask_type=attn_mask_type,
        qkv_layout=NVTE_QKV_Layout.NVTE_BSHD_BS2HD,
        scaling_factor=scaling_factor,
        dropout_probability=dropout_probability,
        is_training=is_training)
    return dq, dkv, dbias
2504
2505
2506
2507
2508
2509
2510
2511


def fused_attn_fwd(q: jnp.ndarray, k: jnp.ndarray, v: jnp.ndarray, bias: jnp.ndarray,
                   q_seqlen: jnp.ndarray, kv_seqlen: jnp.ndarray, seed: jnp.ndarray,
                   attn_bias_type: NVTE_Bias_Type, attn_mask_type: NVTE_Mask_Type,
                   scaling_factor: float, dropout_probability: float, is_training: bool):
    """
    Wrapper for TE fused attention fwd, where query, key, value are seperated tensors
2512
    Return BMM1 -> (PreBias) -> ScaleMaskSoftmax -> (PostBias) -> (Dropout) -> BMM2
2513
2514
2515
2516
2517
2518
2519
2520
    """
    checker = _FusedAttnRNGStateChecker()
    seed = checker.check_seed(seed, dropout_probability, is_training)

    if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
        assert bias is None
        bias = jnp.zeros(0, dtype=q.dtype)

2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
    return FusedAttnFwdPrimitive.outer_primitive.bind(
        q,
        k,
        v,
        bias,
        q_seqlen,
        kv_seqlen,
        seed,
        attn_bias_type=attn_bias_type,
        attn_mask_type=attn_mask_type,
        qkv_layout=NVTE_QKV_Layout.NVTE_BSHD_BSHD_BSHD,
        scaling_factor=scaling_factor,
        dropout_probability=dropout_probability,
        is_training=is_training)
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548


def fused_attn_bwd(q: jnp.ndarray, k: jnp.ndarray, v: jnp.ndarray, bias: jnp.ndarray,
                   softmax_aux: jnp.ndarray, rng_state: jnp.ndarray, output: jnp.ndarray,
                   doutput: jnp.ndarray, q_seqlen: jnp.ndarray, kv_seqlen: jnp.ndarray,
                   attn_bias_type: NVTE_Bias_Type, attn_mask_type: NVTE_Mask_Type,
                   scaling_factor: float, dropout_probability: float, is_training: bool):
    """
    Wrapper for TE fused attention bwd
    Return the gradients of fused attention with seperated query, key, value tensors
    """
    if attn_bias_type == NVTE_Bias_Type.NVTE_NO_BIAS:
        assert bias is None
        bias = jnp.zeros(0, dtype=q.dtype)
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
    return FusedAttnBwdPrimitive.outer_primitive.bind(
        q,
        k,
        v,
        bias,
        softmax_aux,
        rng_state,
        output,
        doutput,
        q_seqlen,
        kv_seqlen,
        attn_bias_type=attn_bias_type,
        attn_mask_type=attn_mask_type,
        qkv_layout=NVTE_QKV_Layout.NVTE_BSHD_BSHD_BSHD,
        scaling_factor=scaling_factor,
        dropout_probability=dropout_probability,
        is_training=is_training)
2566
2567


2568
class ActLuPrimitive(BasePrimitive):
2569
    """
2570
    Activation Forward Primitive
2571
    """
2572
    name = "te_act_lu"
2573
2574
2575
    multiple_results = False
    inner_primitive = None
    outer_primitive = None
2576
    impl_static_args = (1,)
2577
2578

    @staticmethod
2579
    def abstract(x_aval, *, act_enum):  # pylint: disable=unused-argument
2580
        """
2581
        act_lu abstract
2582
2583
2584
2585
        """
        dtype = dtypes.canonicalize_dtype(x_aval.dtype)
        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]

2586
        x_shape = x_aval.shape
2587
        assert (x_shape[-2] == 2 or x_shape[-2] == 1)
2588
2589
2590
2591
2592
        hidden_size = x_shape[-1]
        batch_shapes = x_shape[:-2]
        out_aval = core.raise_to_shaped(x_aval)
        out_shape = (batch_shapes) + (hidden_size,)
        out_aval = out_aval.update(shape=out_shape, dtype=dtype)
2593

2594
        return out_aval
2595
2596

    @staticmethod
2597
    def lowering(ctx, x, *, act_enum):
2598
        """
2599
        act_lu lowering rules
2600
        """
2601
2602
2603
2604
2605
        (x_aval,) = ctx.avals_in
        assert x_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        ir_x_type = ir.RankedTensorType(x.type)
        ir_x_shape = ir_x_type.shape
        out_shape = ir_x_shape[:-2] + [ir_x_shape[-1]]
2606

2607
2608
2609
2610
2611
2612
        out_types = [
            ir.RankedTensorType.get(out_shape, ir_x_type.element_type),
        ]
        operands = [x]
        operand_shapes = [ir_x_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)
2613

2614
2615
2616
        hidden_size = ir_x_shape[-1]
        batch_size = reduce(operator.mul, ir_x_shape[:-2])
        in_dtype = jax_dtype_to_te_dtype(x_aval.dtype)
2617
2618
        opaque = transformer_engine_jax.pack_common_descriptor(
            (batch_size, hidden_size), in_dtype, in_dtype, act_enum)
2619

2620
        out = custom_caller(ActLuPrimitive.name, args, opaque, False)
2621

2622
        return [out]
2623

2624
    @staticmethod
2625
2626
2627
    def impl(x, act_enum):
        assert ActLuPrimitive.inner_primitive is not None
        out = ActLuPrimitive.inner_primitive.bind(x, act_enum=act_enum)
2628
        return out
2629

2630
    @staticmethod
2631
    def batcher(batched_args, batch_dims, *, act_enum):
2632
        """
2633
        act_lu batcher
2634
2635
        """
        _check_valid_batch_dims(batch_dims)
2636
        assert ActLuPrimitive.outer_primitive is not None
2637
2638
        inputs, = batched_args
        inputs_bdim, = batch_dims
2639

2640
        out_bdims = inputs_bdim
2641
        return ActLuPrimitive.outer_primitive.bind(inputs, act_enum=act_enum), out_bdims
2642

2643
    @staticmethod
2644
    def infer_sharding_from_operands(act_enum, mesh, arg_infos, result_infos):
2645
        """
2646
        act_lu infer_sharding_from_operands
2647
        """
2648
        del result_infos, act_enum    # Unused.
2649
2650
2651
        x_spec = get_padded_spec(arg_infos[0])
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-2], x_spec[-1]))
        return out_sharding
2652

2653
    @staticmethod
2654
    def partition(act_enum, mesh, arg_infos, result_infos):
2655
        """
2656
        act_lu partitioning
2657
        """
2658
        del result_infos
2659
2660
2661
        x_spec = get_padded_spec(arg_infos[0])
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-2], x_spec[-1]))
2662
2663
2664
2665
2666

        def sharded_impl(x):
            return ActLuPrimitive.impl(x, act_enum=act_enum)

        return mesh, sharded_impl, out_sharding, arg_shardings
2667
2668


2669
register_primitive(ActLuPrimitive)
2670

2671

2672
def act_lu(inputs: jnp.ndarray, activation_type: Sequence[Union[str, Callable]]) -> jnp.ndarray:
2673
    """
2674
2675
2676
2677
    act_lu wrapper
    Return act_lu(inputs)
    Input shape: (N, 1, H) for non-gated activations
                 (N, 2, H) for gated activations
2678
    """
2679
2680
    act_type_id = ActivationEnum[activation_type]
    return ActLuPrimitive.outer_primitive.bind(inputs, act_enum=act_type_id)
2681
2682


2683
class DActLuPrimitive(BasePrimitive):
2684
    """
2685
    Dgated ActLu Primitive
2686
    """
2687
    name = "te_dact_lu"
2688
2689
2690
    multiple_results = False
    inner_primitive = None
    outer_primitive = None
2691
    impl_static_args = (2,)
2692
2693

    @staticmethod
2694
    def abstract(dz_aval, x_aval, *, act_enum):  # pylint: disable=unused-argument
2695
        """
2696
        dact_lu abstract
2697
        """
2698
2699
2700
2701
2702
        dtype = dtypes.canonicalize_dtype(dz_aval.dtype)
        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert x_aval.dtype == dtype
        for axis in range(len(dz_aval.shape) - 1):
            assert dz_aval.shape[axis] == x_aval.shape[axis]
2703
        assert (x_aval.shape[-2] == 2 or x_aval.shape[-2] == 1)
2704

2705
2706
2707
2708
        i_hidden_size = dz_aval.shape[-1]
        g_hidden_size = x_aval.shape[-1]
        assert i_hidden_size == g_hidden_size
        out_aval = core.raise_to_shaped(x_aval)
2709

2710
        return out_aval
2711
2712

    @staticmethod
2713
    def lowering(ctx, dz, x, *, act_enum):
2714
        """
2715
        dact_lu lowering rules
2716
        """
2717
2718
2719
2720
2721
2722
2723
2724
2725
        in_aval, gi_aval = ctx.avals_in
        assert in_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert gi_aval.dtype == in_aval.dtype
        ir_in_type = ir.RankedTensorType(dz.type)
        ir_in_shape = ir_in_type.shape
        gi_type = ir.RankedTensorType(x.type)
        gi_shape = gi_type.shape
        for axis in range(len(ir_in_shape) - 1):
            assert ir_in_shape[axis] == gi_shape[axis]
2726

2727
2728
2729
2730
2731
2732
        ir_batch_size = reduce(operator.mul, ir_in_shape[:-1])
        i_hidden_size = ir_in_shape[-1]
        g_hidden_size = gi_shape[-1]
        assert i_hidden_size == g_hidden_size
        out_dtype = ir_in_type.element_type
        out_shape = gi_shape
2733
2734

        out_types = [
2735
            ir.RankedTensorType.get(out_shape, out_dtype),
2736
        ]
2737
2738
        operands = [dz, x]
        operand_shapes = [ir_in_shape, gi_shape]
2739
2740
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

2741
2742
        in_dtype = jax_dtype_to_te_dtype(in_aval.dtype)
        opaque = transformer_engine_jax.pack_common_descriptor((ir_batch_size, i_hidden_size),
2743
                                                               in_dtype, in_dtype, act_enum)
2744

2745
        out = custom_caller(DActLuPrimitive.name, args, opaque, False)
2746
2747
2748
2749

        return [out]

    @staticmethod
2750
    def impl(dz, x, act_enum):
2751
        """
2752
        dact_lu implementation
2753
        """
2754
2755
        assert DActLuPrimitive.inner_primitive is not None
        dx = DActLuPrimitive.inner_primitive.bind(dz, x, act_enum=act_enum)
2756
        return dx
2757
2758

    @staticmethod
2759
    def batcher(batched_args, batch_dims, *, act_enum):
2760
        """
2761
        dact_lu batcher
2762
        """
2763
        _check_valid_batch_dims(batch_dims)
2764
        assert DActLuPrimitive.outer_primitive is not None
2765
2766
        dz, x = batched_args
        _, x_bdim = batch_dims
2767

2768
        out_bdims = x_bdim
2769
        return DActLuPrimitive.outer_primitive.bind(dz, x, act_enum=act_enum), out_bdims
2770
2771

    @staticmethod
2772
    def infer_sharding_from_operands(act_enum, mesh, arg_infos, result_infos):
2773
        """
2774
        dact_lu infer_sharding_from_operands
2775
        """
2776
2777
2778
        del result_infos, act_enum    # Unused.
        act_lu_out_spec = get_padded_spec(arg_infos[1])
        dx_sharding = NamedSharding(mesh, PartitionSpec(*act_lu_out_spec))
2779
        return dx_sharding
2780

2781
    @staticmethod
2782
    def partition(act_enum, mesh, arg_infos, result_infos):
2783
        """
2784
        dact_lu partition
2785
        """
2786
        del result_infos
2787
2788
2789
        dx_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_shardings = dx_sharding
2790
2791
2792
2793
2794

        def sharded_impl(dz, x):
            return DActLuPrimitive.impl(dz, x, act_enum=act_enum)

        return mesh, sharded_impl, out_shardings, arg_shardings
2795
2796


2797
register_primitive(DActLuPrimitive)
2798
2799


2800
2801
def dact_lu(inputs: jnp.ndarray, act_lu_inputs: jnp.ndarray,
            activation_type: Sequence[Union[str, Callable]]) -> jnp.ndarray:
2802
    """
2803
2804
    dact_lu fusion wrapper
    Return dgated_act_lu(inputs)
2805
    """
2806
2807
    act_type_id = ActivationEnum[activation_type]
    return DActLuPrimitive.outer_primitive.bind(inputs, act_lu_inputs, act_enum=act_type_id)
2808
2809


2810
2811
def _normalize_axis_boundary(axis, ndim):
    return axis if axis >= 0 else ndim + axis
2812
2813


2814
def _multidim_transpose(shape, static_axis_boundary, transpose_axis_boundary):
2815
    """
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
    te_cast_transpose_p multi-dims transpose

    static_axis_boundary: int, Indicate those axes <= static_axis_boundary would not be
        involved into transpose, -1 means all axes involve into transpose.
    transpose_axis_boundary: int, Indicate how to split multi-dimensions tensors to 2D matrix for
        transpose. Note, transpose_axis_boundary should be greater than static_axis_boundary

    examples:
        X in shape (dim0, dim1, dim2, dim3, dim4)

        static_axis_boundary == -1, transpose_axis_boundary == 2
            Xt = (dim2, dim3, dim4, dim0, dim1)

        static_axis_boundary == 0, transpose_axis_boundary == 2
            Xt = (dim0, dim2, dim3, dim4, dim1)

        static_axis_boundary == 0, transpose_axis_boundary == 3
            Xt = (dim0, dim3, dim4, dim1. dim2)
2834
    """
2835
2836
2837
2838
2839
2840
2841
2842
    if static_axis_boundary < 0:
        static_axis_boundary = -1    # means no static axes
    assert static_axis_boundary < len(shape) - 2    # at least 2 remaining for transpose.
    transpose_start_idx = static_axis_boundary + 1
    transpose_axis_boundary = _normalize_axis_boundary(transpose_axis_boundary, len(shape))
    assert transpose_start_idx < transpose_axis_boundary
    return (*shape[:transpose_start_idx], *shape[transpose_axis_boundary:],
            *shape[transpose_start_idx:transpose_axis_boundary])
2843
2844


2845
class CastTransposePrimitive(BasePrimitive):
2846
    """
2847
    Cast Transpose Primitive
2848
    """
2849
2850
2851
2852
2853
    name = "te_cast_transpose"
    multiple_results = True
    impl_static_args = (4, 5, 6)
    inner_primitive = None
    outer_primitive = None
2854
2855

    @staticmethod
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
    def abstract(x_aval, amax_aval, scale_aval, scale_inv_aval, *, out_dtype, static_axis_boundary,
                 transpose_axis_boundary):
        """
        te_cast_transpose_p abstract
        """
        dtype = dtypes.canonicalize_dtype(x_aval.dtype)
        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32

        transposed_x_shape = _multidim_transpose(x_aval.shape, static_axis_boundary,
                                                 transpose_axis_boundary)

        casted_x_aval = x_aval.update(shape=x_aval.shape, dtype=out_dtype)
        casted_xt_aval = x_aval.update(shape=transposed_x_shape, dtype=out_dtype)
        updated_amax_aval = amax_aval.update(shape=amax_aval.shape, dtype=amax_aval.dtype)

        return casted_x_aval, casted_xt_aval, updated_amax_aval
2875
2876

    @staticmethod
2877
2878
    def lowering(ctx, x, amax, scale, scale_inv, *, out_dtype, static_axis_boundary,
                 transpose_axis_boundary):
2879
        """
2880
        te_cast_transpose_p lowering rules
2881
        """
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
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2915
2916
2917
2918
2919
2920
2921
2922
2923
        x_aval, amax_aval, scale_aval, scale_inv_aval = ctx.avals_in
        assert x_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
        ir_x_type = ir.RankedTensorType(x.type)
        ir_x_shape = ir_x_type.shape
        if static_axis_boundary >= 0:
            for i in range(static_axis_boundary + 1):
                assert ir_x_shape[i] == 1
        ir_out_dtype = jax_dtype_to_ir_dtype(out_dtype)
        ir_amax_type = ir.RankedTensorType(amax.type)
        ir_amax_dtype = ir_amax_type.element_type
        ir_amax_shape = ir_amax_type.shape
        ir_scale_shape = ir_amax_shape
        ir_scale_inv_shape = ir_amax_shape

        transposed_x_shape = _multidim_transpose(ir_x_shape, static_axis_boundary,
                                                 transpose_axis_boundary)

        out_types = [
            ir.RankedTensorType.get(ir_x_shape, ir_out_dtype),
            ir.RankedTensorType.get(transposed_x_shape, ir_out_dtype),
            ir.RankedTensorType.get(ir_amax_shape, ir_amax_dtype),
        ]
        operands = [x, amax, scale, scale_inv]
        operand_shapes = [ir_x_shape, ir_amax_shape, ir_scale_shape, ir_scale_inv_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

        contracted_x_shape = (reduce(operator.mul, ir_x_shape[:transpose_axis_boundary]),
                              reduce(operator.mul, ir_x_shape[transpose_axis_boundary:]))
        opaque = transformer_engine_jax.pack_common_descriptor(contracted_x_shape,
                                                               jax_dtype_to_te_dtype(x_aval.dtype),
                                                               jax_dtype_to_te_dtype(out_dtype))

        out = custom_caller(CastTransposePrimitive.name,
                            args,
                            opaque,
                            False,
                            operand_output_aliases={1: 2})

        return out
2924
2925

    @staticmethod
2926
    def impl(x, amax, scale, scale_inv, out_dtype, static_axis_boundary, transpose_axis_boundary):
2927
        """
2928
        te_cast_transpose implementation
2929
        """
2930
2931
2932
2933
2934
2935
2936
        assert CastTransposePrimitive.inner_primitive is not None
        casted_x, casted_transposed_x, updated_amax = \
            CastTransposePrimitive.inner_primitive.bind(
                x, amax, scale, scale_inv, out_dtype=out_dtype,
                static_axis_boundary=static_axis_boundary,
                transpose_axis_boundary=transpose_axis_boundary)
        return casted_x, casted_transposed_x, updated_amax
2937

2938
2939
2940
2941
2942
2943
    @staticmethod
    def batcher(batched_args, batch_dims, *, out_dtype, static_axis_boundary,
                transpose_axis_boundary):
        _check_valid_batch_dims(batch_dims)
        assert CastTransposePrimitive.outer_primitive is not None
        assert static_axis_boundary < 0
2944

2945
2946
        x, amax, scale, scale_inv = batched_args
        x_bdim, amax_bdim, *_ = batch_dims
2947

2948
2949
2950
        # Minus batch dim.
        transpose_axis_boundary = _normalize_axis_boundary(transpose_axis_boundary, x.ndim - 1)
        transpose_axis_boundary += 1    # Plus batch dim
2951

2952
2953
2954
2955
2956
2957
2958
2959
2960
        out_bdims = x_bdim, x_bdim, amax_bdim
        return CastTransposePrimitive.outer_primitive.bind(
            x,
            amax,
            scale,
            scale_inv,
            out_dtype=out_dtype,
            static_axis_boundary=x_bdim,
            transpose_axis_boundary=transpose_axis_boundary), out_bdims
2961

2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
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2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
    @staticmethod
    def infer_sharding_from_operands(out_dtype, static_axis_boundary, transpose_axis_boundary, mesh,
                                     arg_infos, result_infos):
        del out_dtype, result_infos
        x_spec = get_padded_spec(arg_infos[0])
        casted_x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, transpose_axis_boundary)
        casted_transposed_x_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
        return (casted_x_sharding, casted_transposed_x_sharding, amax_sharding)

    @staticmethod
    def partition(out_dtype, static_axis_boundary, transpose_axis_boundary, mesh, arg_infos,
                  result_infos):
        del result_infos
        x_spec = get_padded_spec(arg_infos[0])
        casted_x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, transpose_axis_boundary)
        casted_transposed_x_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_shardings = (casted_x_sharding, casted_transposed_x_sharding, amax_sharding)

        def sharded_impl(x, amax, scale, scale_inv):
            local_cx, local_cxt, local_updated_amax = \
                CastTransposePrimitive.impl(x, amax, scale, scale_inv,
                     out_dtype=out_dtype,
                       static_axis_boundary=static_axis_boundary,
                       transpose_axis_boundary=transpose_axis_boundary)
            global_updated_amax = all_reduce_max_along_all_axes_except_PP(local_updated_amax)

            return local_cx, local_cxt, global_updated_amax

        return mesh, sharded_impl, out_shardings, arg_shardings


register_primitive(CastTransposePrimitive)


def cast_transpose(x: jnp.ndarray, amax: jnp.ndarray, scale: jnp.ndarray, scale_inv: jnp.ndarray,
                   out_dtype: jnp.dtype, static_axis_boundary: int,
                   transpose_axis_boundary: int) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
3004
    """
3005
3006
    cast transpose wrapper
    Return two tensors, FP8(inputs) and FP8(inputs.T), which are scaled by `scale`
3007
    """
3008
3009
3010
3011
3012
3013
3014
3015
    return CastTransposePrimitive.outer_primitive.bind(
        x,
        amax,
        scale,
        scale_inv,
        out_dtype=out_dtype,
        static_axis_boundary=static_axis_boundary,
        transpose_axis_boundary=transpose_axis_boundary)
3016
3017


3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
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3089
3090
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3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
class CastFP8Primitive(BasePrimitive):
    """
    Cast Primitive
    """
    name = "te_quantize"
    multiple_results = True
    impl_static_args = (4,)
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def abstract(x_aval, amax_aval, scale_aval, scale_inv_aval, *, out_dtype):
        """
        te_cast abstract
        """
        dtype = dtypes.canonicalize_dtype(x_aval.dtype)
        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32

        casted_x_aval = x_aval.update(shape=x_aval.shape, dtype=out_dtype)
        updated_amax_aval = amax_aval.update(shape=amax_aval.shape, dtype=amax_aval.dtype)

        return casted_x_aval, updated_amax_aval

    @staticmethod
    def lowering(ctx, x, amax, scale, scale_inv, *, out_dtype):
        """
        te_cast lowering rules
        """
        x_aval, amax_aval, scale_aval, scale_inv_aval = ctx.avals_in
        assert x_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
        ir_x_type = ir.RankedTensorType(x.type)
        ir_x_shape = ir_x_type.shape
        ir_out_dtype = jax_dtype_to_ir_dtype(out_dtype)
        ir_amax_type = ir.RankedTensorType(amax.type)
        ir_amax_dtype = ir_amax_type.element_type
        ir_amax_shape = ir_amax_type.shape
        ir_scale_shape = ir_amax_shape
        ir_scale_inv_shape = ir_amax_shape

        out_types = [
            ir.RankedTensorType.get(ir_x_shape, ir_out_dtype),
            ir.RankedTensorType.get(ir_amax_shape, ir_amax_dtype),
        ]
        operands = [x, amax, scale, scale_inv]
        operand_shapes = [ir_x_shape, ir_amax_shape, ir_scale_shape, ir_scale_inv_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

        opaque = transformer_engine_jax.pack_common_descriptor(ir_x_shape,
                                                               jax_dtype_to_te_dtype(x_aval.dtype),
                                                               jax_dtype_to_te_dtype(out_dtype))

        out = custom_caller(CastFP8Primitive.name,
                            args,
                            opaque,
                            False,
                            operand_output_aliases={1: 1})

        return out

    @staticmethod
    def impl(x, amax, scale, scale_inv, out_dtype):
        """
        te_cast implementation
        """
        assert CastFP8Primitive.inner_primitive is not None
        casted_x, updated_amax = \
            CastFP8Primitive.inner_primitive.bind(
                x, amax, scale, scale_inv, out_dtype=out_dtype)
        return casted_x, updated_amax

    @staticmethod
    def batcher(batched_args, batch_dims, *, out_dtype):
        _check_valid_batch_dims(batch_dims)
        assert CastFP8Primitive.outer_primitive is not None

        x, amax, scale, scale_inv = batched_args
        x_bdim, amax_bdim, *_ = batch_dims

3102
        out_bdims = x_bdim, amax_bdim
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
        return CastFP8Primitive.outer_primitive.bind(x, amax, scale, scale_inv,
                                                     out_dtype=out_dtype), out_bdims

    @staticmethod
    def infer_sharding_from_operands(out_dtype, mesh, arg_infos, result_infos):
        del out_dtype, result_infos
        x_spec = get_padded_spec(arg_infos[0])
        casted_x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
        return (casted_x_sharding, amax_sharding)

    @staticmethod
    def partition(out_dtype, mesh, arg_infos, result_infos):
        del result_infos
        x_spec = get_padded_spec(arg_infos[0])
        casted_x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_shardings = (casted_x_sharding, amax_sharding)

        def sharded_impl(x, amax, scale, scale_inv):
            local_cx, local_updated_amax = \
                CastFP8Primitive.impl(x, amax, scale, scale_inv, out_dtype=out_dtype)
            global_updated_amax = all_reduce_max_along_all_axes_except_PP(local_updated_amax)

            return local_cx, global_updated_amax

        return mesh, sharded_impl, out_shardings, arg_shardings


register_primitive(CastFP8Primitive)


def cast_fp8(x: jnp.ndarray, amax: jnp.ndarray, scale: jnp.ndarray, scale_inv: jnp.ndarray,
             out_dtype: TEDType) -> Tuple[jnp.ndarray, jnp.ndarray]:
    """
    Cast wrapper
    Return FP8 tensor
    """
    return CastFP8Primitive.outer_primitive.bind(x, amax, scale, scale_inv, out_dtype=out_dtype)


3145
class TransposePrimitive(BasePrimitive):
3146
    """
3147
    Transpose Primitive
3148
    """
3149
    name = "te_transpose"
3150
    multiple_results = False
3151
3152
3153
    impl_static_args = (1, 2)
    inner_primitive = None
    outer_primitive = None
3154
3155

    @staticmethod
3156
    def abstract(x_aval, *, static_axis_boundary, transpose_axis_boundary):
3157
        """
3158
        _transpose abstract
3159
        """
3160
3161
3162
        transposed_x_shape = _multidim_transpose(x_aval.shape, static_axis_boundary,
                                                 transpose_axis_boundary)
        xt_aval = x_aval.update(shape=transposed_x_shape, dtype=x_aval.dtype)
3163

3164
        return xt_aval
3165
3166

    @staticmethod
3167
    def lowering(ctx, x, *, static_axis_boundary, transpose_axis_boundary):
3168
        """
3169
        _transpose cuda lowering
3170
3171
        """

3172
3173
3174
3175
        x_aval = ctx.avals_in[0]
        assert x_aval.dtype in [
            jnp.float32, jnp.float16, jnp.bfloat16, jnp.float8_e4m3fn, jnp.float8_e5m2
        ]
3176

3177
3178
3179
3180
3181
3182
        ir_x_type = ir.RankedTensorType(x.type)
        ir_x_shape = ir_x_type.shape
        ir_out_dtype = jax_dtype_to_ir_dtype(x_aval.dtype)
        if static_axis_boundary >= 0:
            for i in range(static_axis_boundary + 1):
                assert ir_x_shape[i] == 1
3183

3184
3185
3186
3187
3188
3189
        transposed_x_shape = _multidim_transpose(ir_x_shape, static_axis_boundary,
                                                 transpose_axis_boundary)

        out_types = [ir.RankedTensorType.get(transposed_x_shape, ir_out_dtype)]
        operands = [x]
        operand_shapes = [ir_x_shape]
3190
3191
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

3192
3193
3194
3195
3196
        te_dtype = jax_dtype_to_te_dtype(x_aval.dtype)
        contracted_x_shape = (reduce(operator.mul, ir_x_shape[:transpose_axis_boundary]),
                              reduce(operator.mul, ir_x_shape[transpose_axis_boundary:]))
        opaque = transformer_engine_jax.pack_common_descriptor(contracted_x_shape, te_dtype,
                                                               te_dtype)
3197

3198
        out = custom_caller(TransposePrimitive.name, args, opaque, False)
3199
3200
3201

        return [out]

3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
    @staticmethod
    def impl(x, static_axis_boundary, transpose_axis_boundary):
        """
        tcast_transpose implementation
        """
        assert TransposePrimitive.inner_primitive is not None
        transposed_x = \
            TransposePrimitive.inner_primitive.bind(x,
                                                    static_axis_boundary=static_axis_boundary,
                                                    transpose_axis_boundary=transpose_axis_boundary)
        return transposed_x
3213

3214
3215
3216
3217
3218
    @staticmethod
    def batcher(batched_args, batch_dims, *, static_axis_boundary, transpose_axis_boundary):
        _check_valid_batch_dims(batch_dims)
        assert TransposePrimitive.outer_primitive is not None
        assert static_axis_boundary < 0
3219

3220
3221
        x, = batched_args
        x_bdim, = batch_dims
3222

3223
3224
3225
        # Minus batch dim.
        transpose_axis_boundary = _normalize_axis_boundary(transpose_axis_boundary, x.ndim - 1)
        transpose_axis_boundary += 1    # Plus batch dim
3226

3227
3228
3229
3230
        out_bdims = x_bdim
        return TransposePrimitive.outer_primitive.bind(
            x, static_axis_boundary=x_bdim,
            transpose_axis_boundary=transpose_axis_boundary), out_bdims
3231
3232

    @staticmethod
3233
3234
3235
3236
3237
3238
3239
    def infer_sharding_from_operands(static_axis_boundary, transpose_axis_boundary, mesh, arg_infos,
                                     result_infos):
        del result_infos
        x_spec = get_padded_spec(arg_infos[0])
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, transpose_axis_boundary)
        transposed_x_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))
        return transposed_x_sharding
3240
3241

    @staticmethod
3242
3243
3244
3245
3246
3247
3248
    def partition(static_axis_boundary, transpose_axis_boundary, mesh, arg_infos, result_infos):
        del result_infos
        x_spec = get_padded_spec(arg_infos[0])
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, transpose_axis_boundary)
        transposed_x_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_shardings = transposed_x_sharding
3249

3250
3251
3252
        impl = partial(TransposePrimitive.impl,
                       static_axis_boundary=static_axis_boundary,
                       transpose_axis_boundary=transpose_axis_boundary)
3253

3254
        return mesh, impl, out_shardings, arg_shardings
3255
3256


3257
register_primitive(TransposePrimitive)
3258
3259


3260
3261
def transpose(x: jnp.ndarray, static_axis_boundary: int,
              transpose_axis_boundary: int) -> jnp.ndarray:
3262
    """
3263
    transpose wrapper
3264
    """
3265
3266
3267
    return TransposePrimitive.outer_primitive.bind(x,
                                                   static_axis_boundary=static_axis_boundary,
                                                   transpose_axis_boundary=transpose_axis_boundary)
3268
3269


3270
class LayerNormFwdFp8Primitive(BasePrimitive):
3271
    """
3272
    Layer Normalization Forward FP8 Primitive
3273
    """
3274
3275
3276
3277
3278
    name = "te_layernorm_forward_fp8"
    multiple_results = True
    impl_static_args = (6, 7, 8)    # out_type, zero_centered_gamma, epsilon
    inner_primitive = None
    outer_primitive = None
3279
3280

    @staticmethod
3281
3282
    def abstract(x_aval, gamma_aval, beta_aval, amax_aval, scale_aval, scale_inv_aval, *, out_dtype,
                 zero_centered_gamma, epsilon):
3283
        """
3284
        LayerNorm fwd (fp8 out) inner primitive abstract
3285
        """
3286
        x_dtype = dtypes.canonicalize_dtype(x_aval.dtype)
3287

3288
3289
3290
3291
        assert x_dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
3292

3293
3294
3295
3296
        mu_rsigama_dtype = jnp.float32

        assert gamma_aval.size == beta_aval.size

3297
        wkspace_info, barrier_info = transformer_engine_jax.get_layernorm_fwd_workspace_sizes(
3298
3299
3300
3301
            x_aval.size // gamma_aval.size,    # batch size
            gamma_aval.size,    # hidden size
            jax_dtype_to_te_dtype(x_aval.dtype),    # in type
            jax_dtype_to_te_dtype(gamma_aval.dtype),    # weight type
3302
            jax_dtype_to_te_dtype(out_dtype),
3303
3304
3305
            True,
            zero_centered_gamma,
            epsilon)
3306

3307
3308
3309
        out_aval = x_aval.update(shape=x_aval.shape, dtype=out_dtype)
        mu_aval = rsigma_aval = out_aval.update(shape=out_aval.shape[:-1], dtype=mu_rsigama_dtype)
        updated_amax_aval = amax_aval.update(shape=amax_aval.shape, dtype=amax_aval.dtype)
3310
3311
3312
3313
3314
3315
        wkspace_aval = x_aval.update(shape=wkspace_info[0],
                                     dtype=te_dtype_to_jax_dtype(wkspace_info[1]))
        barrier_aval = x_aval.update(shape=barrier_info[0],
                                     dtype=te_dtype_to_jax_dtype(barrier_info[1]))

        return out_aval, mu_aval, rsigma_aval, updated_amax_aval, wkspace_aval, barrier_aval
3316

3317
3318
3319
3320
3321
3322
3323
    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
        LayerNorm fwd (fp8 out) outer primitive abstract
        """
        out_aval, mu_aval, rsigma_aval, updated_amax_aval, _, _ = \
            LayerNormFwdFp8Primitive.abstract(*args, **kwargs)
3324
        return out_aval, mu_aval, rsigma_aval, updated_amax_aval
3325
3326

    @staticmethod
3327
3328
    def lowering(ctx, x, gamma, beta, amax, scale, scale_inv, *, out_dtype, zero_centered_gamma,
                 epsilon):
3329
        """
3330
        LayerNorm fwd (fp8 out) lowering rules
3331
        """
3332
        x_aval, gamma_aval, beta_aval, amax_aval, scale_aval, scale_inv_aval = ctx.avals_in
3333

3334
3335
        # Currently only support casting to E4M3 only in C side.
        assert out_dtype == jnp.float8_e4m3fn
3336

3337
3338
3339
3340
3341
        assert x_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert gamma_aval.dtype == beta_aval.dtype
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
3342

3343
3344
3345
3346
3347
3348
        x_type = ir.RankedTensorType(x.type)
        x_shape = x_type.shape
        g_type = ir.RankedTensorType(gamma.type)
        g_shape = g_type.shape
        b_type = ir.RankedTensorType(beta.type)
        b_shape = b_type.shape
3349

3350
3351
        assert g_type == b_type
        assert g_shape == b_shape
3352

3353
3354
3355
3356
3357
3358
3359
3360
        ir_out_dtype = jax_dtype_to_ir_dtype(out_dtype)
        ir_mu_dtype = ir.F32Type.get()
        ir_rsigma_dtype = ir.F32Type.get()
        ir_amax_type = ir.RankedTensorType(amax.type)
        ir_amax_dtype = ir_amax_type.element_type
        ir_amax_shape = ir_amax_type.shape
        ir_scale_shape = ir_amax_shape
        ir_scale_inv_shape = ir_amax_shape
3361

3362
3363
3364
3365
        out_shape = x_shape
        hidden_size = reduce(operator.mul, g_shape)
        batch_shape = out_shape[:-1]
        batch_size = reduce(operator.mul, x_shape) // hidden_size
3366

3367
3368
        wkspace_aval, barrier_aval = ctx.avals_out[-2:]

3369
3370
3371
3372
3373
        out_types = [
            ir.RankedTensorType.get(out_shape, ir_out_dtype),
            ir.RankedTensorType.get(batch_shape, ir_mu_dtype),
            ir.RankedTensorType.get(batch_shape, ir_rsigma_dtype),
            ir.RankedTensorType.get(ir_amax_shape, ir_amax_dtype),
3374
3375
            ir.RankedTensorType.get(wkspace_aval.shape, jax_dtype_to_ir_dtype(wkspace_aval.dtype)),
            ir.RankedTensorType.get(barrier_aval.shape, jax_dtype_to_ir_dtype(barrier_aval.dtype))
3376
3377
3378
3379
3380
3381
        ]
        operands = [x, gamma, beta, amax, scale, scale_inv]
        operand_shapes = [
            x_shape, g_shape, b_shape, ir_amax_shape, ir_scale_shape, ir_scale_inv_shape
        ]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)
3382

3383
3384
        sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0"))

3385
3386
3387
        opaque = transformer_engine_jax.pack_norm_descriptor(
            batch_size,
            hidden_size,
3388
3389
            wkspace_aval.size,
            barrier_aval.size,
3390
3391
            (0,),    # no dgamma_part in FWD pass
            (0,),    # no dbeta_part in BWD pass
3392
3393
            jax_dtype_to_te_dtype(x_aval.dtype),
            jax_dtype_to_te_dtype(gamma_aval.dtype),
3394
3395
            jax_dtype_to_te_dtype(wkspace_aval.dtype),
            jax_dtype_to_te_dtype(barrier_aval.dtype),
3396
3397
            TEDType.kByte,    # dummy dgamma_part te_dtype
            TEDType.kByte,    # dummy dbeta_part te_dtype
3398
3399
            zero_centered_gamma,
            epsilon,
3400
            sm_margin,
3401
        )
3402

3403
3404
3405
3406
3407
        out = custom_caller(LayerNormFwdFp8Primitive.name,
                            args,
                            opaque,
                            False,
                            operand_output_aliases={3: 3})
3408

3409
        return out
3410
3411

    @staticmethod
3412
    def impl(x, gamma, beta, amax, scale, scale_inv, out_dtype, zero_centered_gamma, epsilon):
3413
        """
3414
        to describe implementation
3415
        """
3416
        assert LayerNormFwdFp8Primitive.inner_primitive is not None
3417
        out, mu, rsigma, updated_amax, _, _ = LayerNormFwdFp8Primitive.inner_primitive.bind(
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
            x,
            gamma,
            beta,
            amax,
            scale,
            scale_inv,
            out_dtype=out_dtype,
            zero_centered_gamma=zero_centered_gamma,
            epsilon=epsilon)
        return out, mu, rsigma, updated_amax
3428
3429

    @staticmethod
3430
    def batcher(batched_args, batch_dims, *, out_dtype, zero_centered_gamma, epsilon):
3431
        """
3432
        to describe batch rules for vmap
3433
        """
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
        _check_valid_batch_dims(batch_dims)
        assert LayerNormFwdFp8Primitive.outer_primitive is not None
        x, gamma, beta, amax, scale, scale_inv = batched_args
        x_bdim, _, _, amax_bdim, _, _ = batch_dims

        out_bdims = x_bdim, x_bdim, x_bdim, amax_bdim
        return LayerNormFwdFp8Primitive.outer_primitive.bind(
            x,
            gamma,
            beta,
            amax,
            scale,
            scale_inv,
            out_dtype=out_dtype,
            zero_centered_gamma=zero_centered_gamma,
            epsilon=epsilon), out_bdims

    @staticmethod
    def infer_sharding_from_operands(out_dtype, zero_centered_gamma, epsilon, mesh, arg_infos,
                                     result_infos):
        del out_dtype, zero_centered_gamma, epsilon, result_infos
        x_spec = get_padded_spec(arg_infos[0])
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {LayerNormFwdPrimitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance.")

        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
        mu_sharding = rsigma_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1]))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[3])))
        return (out_sharding, mu_sharding, rsigma_sharding, amax_sharding)

    @staticmethod
    def partition(out_dtype, zero_centered_gamma, epsilon, mesh, arg_infos, result_infos):
        del result_infos
        x_spec = get_padded_spec(arg_infos[0])
3471
3472
        g_spec = get_padded_spec(arg_infos[1])
        b_spec = get_padded_spec(arg_infos[2])
3473
3474
        if x_spec[-1] is not None:
            warnings.warn(
3475
                f"Does not support to shard hidden dim in {LayerNormFwdFp8Primitive.name}! " \
3476
3477
3478
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
        if g_spec[-1] is not None:
            warnings.warn(
                f"{LayerNormFwdFp8Primitive.name} does not support sharding of parameter gamma " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )
        if b_spec[-1] is not None:
            warnings.warn(
                f"{LayerNormFwdFp8Primitive.name} does not support sharding of parameter beta " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )
3489
        x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
3490
3491
        g_sharding = NamedSharding(mesh, PartitionSpec(None))
        b_sharding = NamedSharding(mesh, PartitionSpec(None))
3492
3493
3494
3495
3496
3497
3498
        out_sharding = x_sharding
        mu_sharding = rsigma_sharding = NamedSharding(
            mesh, PartitionSpec(*get_padded_spec(arg_infos[0])[:-1]))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[3])))
        fp8_meta_sharding = amax_sharding
        arg_shardings = (x_sharding, g_sharding, b_sharding) + (fp8_meta_sharding,) * 3
        out_shardings = (out_sharding, mu_sharding, rsigma_sharding, amax_sharding)
3499

3500
3501
3502
3503
3504
3505
3506
        def sharded_impl(x, gamma, beta, amax, scale, scale_inv):
            local_x, local_mu, local_rsigma, local_amax = \
                LayerNormFwdFp8Primitive.impl(x, gamma, beta, amax, scale, scale_inv,
                                            out_dtype=out_dtype,
                                            zero_centered_gamma=zero_centered_gamma,
                                            epsilon=epsilon)
            global_updated_amax = all_reduce_max_along_all_axes_except_PP(local_amax)
3507

3508
            return local_x, local_mu, local_rsigma, global_updated_amax
3509

3510
        return mesh, sharded_impl, out_shardings, arg_shardings
3511

3512
3513
3514
3515
3516
3517
3518

register_primitive(LayerNormFwdFp8Primitive)


def layernorm_fwd_fp8(x: jnp.ndarray, gamma: jnp.ndarray, beta: jnp.ndarray, amax: jnp.ndarray,
                      scale: jnp.ndarray, scale_inv: jnp.ndarray, out_dtype: jnp.dtype,
                      zero_centered_gamma: bool, epsilon: float):
3519
    """
3520
    Wrapper for TE layernorm fwd (fp8 out)
3521
    """
3522
3523
3524
3525
3526
3527
3528
3529
3530
    return LayerNormFwdFp8Primitive.outer_primitive.bind(x,
                                                         gamma,
                                                         beta,
                                                         amax,
                                                         scale,
                                                         scale_inv,
                                                         out_dtype=out_dtype,
                                                         zero_centered_gamma=zero_centered_gamma,
                                                         epsilon=epsilon)
3531
3532


3533
class RmsNormFwdFp8Primitive(BasePrimitive):
3534
    """
3535
    RMS Normalization Forward FP8 Primitive
3536
    """
3537
3538
3539
3540
3541
    name = "te_rmsnorm_forward_fp8"
    multiple_results = True
    impl_static_args = (5, 6)    # out_dtype, epsilon
    inner_primitive = None
    outer_primitive = None
3542

3543
3544
    @staticmethod
    def abstract(x_aval, gamma_aval, amax_aval, scale_aval, scale_inv_aval, out_dtype, epsilon):
3545
        """
3546
        RMSNorm fwd (fp8 out) inner primitive abstract
3547
        """
3548
        x_dtype = dtypes.canonicalize_dtype(x_aval.dtype)
3549

3550
3551
3552
3553
        assert x_dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
3554

3555
3556
        hidden_size = gamma_aval.size
        assert x_aval.size % hidden_size == 0
3557

3558
        rsigama_dtype = jnp.float32
3559

3560
        wkspace_info, barrier_info = transformer_engine_jax.get_layernorm_fwd_workspace_sizes(
3561
            x_aval.size // hidden_size,    # batch_size
3562
            hidden_size,
3563
3564
3565
3566
3567
3568
            jax_dtype_to_te_dtype(x_aval.dtype),    # in te_dtype
            jax_dtype_to_te_dtype(gamma_aval.dtype),    # weight te_dtype
            jax_dtype_to_te_dtype(out_dtype),    # out te_dtype
            False,
            False,
            epsilon)
3569

3570
3571
3572
        out_aval = x_aval.update(shape=x_aval.shape, dtype=out_dtype)
        rsigma_aval = out_aval.update(shape=out_aval.shape[:-1], dtype=rsigama_dtype)
        amax_aval = out_aval.update(shape=amax_aval.shape, dtype=amax_aval.dtype)
3573
3574
3575
3576
3577
3578
        wkspace_aval = x_aval.update(shape=wkspace_info[0],
                                     dtype=te_dtype_to_jax_dtype(wkspace_info[1]))
        barrier_aval = x_aval.update(shape=barrier_info[0],
                                     dtype=te_dtype_to_jax_dtype(barrier_info[1]))

        return out_aval, rsigma_aval, amax_aval, wkspace_aval, barrier_aval
3579

3580
3581
3582
3583
3584
3585
    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
        RMSNorm fwd (fp8 out) outer primitive abstract
        """
        out_aval, rsigma_aval, amax_aval, _, _ = RmsNormFwdFp8Primitive.abstract(*args, **kwargs)
3586
        return out_aval, rsigma_aval, amax_aval
3587
3588

    @staticmethod
3589
    def lowering(ctx, x, gamma, amax, scale, scale_inv, *, out_dtype, epsilon):
3590
        """
3591
        RMSNorm fwd (fp8 out) lowering rules
3592
3593
        """

3594
3595
        # Currently only support casting to E4M3 only in C side.
        assert out_dtype == jnp.float8_e4m3fn
3596

3597
        x_aval, gamma_aval, amax_aval, scale_aval, scale_inv_aval = ctx.avals_in
3598

3599
3600
3601
3602
        assert x_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
3603

3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
        x_type = ir.RankedTensorType(x.type)
        x_shape = x_type.shape
        g_type = ir.RankedTensorType(gamma.type)
        g_shape = g_type.shape

        ir_out_dtype = jax_dtype_to_ir_dtype(out_dtype)
        ir_rsigma_dtype = ir.F32Type.get()
        ir_amax_type = ir.RankedTensorType(amax.type)
        ir_amax_dtype = ir_amax_type.element_type
        ir_amax_shape = ir_amax_type.shape
        ir_scale_shape = ir_amax_shape
        ir_scale_inv_shape = ir_amax_shape

        out_shape = x_shape
        hidden_size = reduce(operator.mul, g_shape)
        batch_shape = out_shape[:-1]
        batch_size = reduce(operator.mul, x_shape) // hidden_size
3621

3622
3623
        wkspace_aval, barrier_aval = ctx.avals_out[-2:]

3624
3625
3626
3627
        out_types = [
            ir.RankedTensorType.get(out_shape, ir_out_dtype),
            ir.RankedTensorType.get(batch_shape, ir_rsigma_dtype),
            ir.RankedTensorType.get(ir_amax_shape, ir_amax_dtype),
3628
3629
            ir.RankedTensorType.get(wkspace_aval.shape, jax_dtype_to_ir_dtype(wkspace_aval.dtype)),
            ir.RankedTensorType.get(barrier_aval.shape, jax_dtype_to_ir_dtype(barrier_aval.dtype))
3630
3631
3632
3633
3634
        ]
        operands = [x, gamma, amax, scale, scale_inv]
        operand_shapes = [x_shape, g_shape, ir_amax_shape, ir_scale_shape, ir_scale_inv_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

3635
3636
        sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0"))

3637
3638
3639
        opaque = transformer_engine_jax.pack_norm_descriptor(
            batch_size,
            hidden_size,
3640
3641
            wkspace_aval.size,
            barrier_aval.size,
3642
3643
            (0,),    # no dgamma_part in FWD pass
            (0,),    # no dbeta_part in BWD pass
3644
3645
            jax_dtype_to_te_dtype(x_aval.dtype),
            jax_dtype_to_te_dtype(gamma_aval.dtype),
3646
3647
            jax_dtype_to_te_dtype(wkspace_aval.dtype),
            jax_dtype_to_te_dtype(barrier_aval.dtype),
3648
3649
            TEDType.kByte,    # dummy dgamma_part te_dtype
            TEDType.kByte,    # dummy dbeta_part te_dtype
3650
3651
            False,    # RMSNorm doesn't support zero_centered_gamma
            epsilon,
3652
            sm_margin,
3653
3654
        )

3655
3656
3657
3658
3659
3660
3661
3662
        out = custom_caller(RmsNormFwdFp8Primitive.name,
                            args,
                            opaque,
                            False,
                            operand_output_aliases={2: 2})

        return out

3663
    @staticmethod
3664
    def impl(x, gamma, amax, scale, scale_inv, out_dtype, epsilon):
3665
        """
3666
        to describe implementation
3667
        """
3668
        assert RmsNormFwdFp8Primitive.inner_primitive is not None
3669
3670
3671
3672
3673
3674
3675
        out, rsigma, amax, _, _ = RmsNormFwdFp8Primitive.inner_primitive.bind(x,
                                                                              gamma,
                                                                              amax,
                                                                              scale,
                                                                              scale_inv,
                                                                              out_dtype=out_dtype,
                                                                              epsilon=epsilon)
3676
        return out, rsigma, amax
3677

3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
    @staticmethod
    def batcher(batched_args, batch_dims, *, out_dtype, epsilon):
        """
        to describe batch rules for vmap
        """
        _check_valid_batch_dims(batch_dims)
        assert RmsNormFwdFp8Primitive.outer_primitive is not None
        x, gamma, amax, scale, scale_inv = batched_args
        x_bdim, _, amax_bdim, _, _ = batch_dims
        out_bdims = x_bdim, x_bdim, amax_bdim
        return RmsNormFwdFp8Primitive.outer_primitive.bind(x,
                                                           gamma,
                                                           amax,
                                                           scale,
                                                           scale_inv,
                                                           out_dtype=out_dtype,
                                                           epsilon=epsilon), out_bdims
3695

3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
    @staticmethod
    def infer_sharding_from_operands(out_dtype, epsilon, mesh, arg_infos, result_infos):
        del out_dtype, epsilon, result_infos
        x_spec = get_padded_spec(arg_infos[0])
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {RmsNormFwdFp8Primitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
        rsigma_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1]))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[2])))
        return (out_sharding, rsigma_sharding, amax_sharding)
3710

3711
3712
3713
3714
    @staticmethod
    def partition(out_dtype, epsilon, mesh, arg_infos, result_infos):
        del result_infos
        x_spec = get_padded_spec(arg_infos[0])
3715
        g_spec = get_padded_spec(arg_infos[1])
3716
3717
3718
3719
3720
3721
        if x_spec[-1] is not None:
            warnings.warn(
                f"Does not support to shard hidden dim in {RmsNormFwdFp8Primitive.name}! " \
                f"Force to not shard the hidden dim, which might introduce extra collective ops, " \
                f"and hurt performance."
            )
3722
3723
3724
3725
3726
        if g_spec[-1] is not None:
            warnings.warn(
                f"{RmsNormFwdFp8Primitive.name} does not support sharding of parameter gamma " \
                f"Enforcing no sharding of parameters hidden dim! " \
            )
3727
        x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-1], None))
3728
        g_sharding = NamedSharding(mesh, PartitionSpec(None))
3729
3730
3731
3732
3733
3734
        out_sharding = x_sharding
        rsigma_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[0])[:-1]))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[2])))
        fp8_meta_sharding = amax_sharding
        arg_shardings = (x_sharding, g_sharding) + (fp8_meta_sharding,) * 3
        out_shardings = (out_sharding, rsigma_sharding, amax_sharding)
3735

3736
3737
3738
3739
3740
        def sharded_impl(x, gamma, amax, scale, scale_inv):
            local_x, local_rsigma, local_amax= \
                RmsNormFwdFp8Primitive.impl(x, gamma, amax, scale, scale_inv,
                                            out_dtype=out_dtype, epsilon=epsilon)
            global_updated_amax = all_reduce_max_along_all_axes_except_PP(local_amax)
3741

3742
            return local_x, local_rsigma, global_updated_amax
3743

3744
        return mesh, sharded_impl, out_shardings, arg_shardings
3745
3746


3747
register_primitive(RmsNormFwdFp8Primitive)
3748

3749
3750
3751

def rmsnorm_fwd_fp8(x: jnp.ndarray, gamma: jnp.ndarray, amax: jnp.ndarray, scale: jnp.ndarray,
                    scale_inv: jnp.ndarray, out_dtype: jnp.dtype, epsilon: float):
3752
    """
3753
    Wrapper for TE rmsnorm fwd (fp8 out)
3754
    """
3755
3756
3757
3758
3759
3760
3761
    return RmsNormFwdFp8Primitive.outer_primitive.bind(x,
                                                       gamma,
                                                       amax,
                                                       scale,
                                                       scale_inv,
                                                       out_dtype=out_dtype,
                                                       epsilon=epsilon)
3762
3763


3764
class ActLuFp8Primitive(BasePrimitive):
3765
    """
3766
    ActLu FP8 Primitive
3767
    """
3768
    name = "te_act_lu_fp8"
3769
    multiple_results = True
3770
    impl_static_args = (4, 5)    #out_dtype, act_enum
3771
3772
3773
3774
    inner_primitive = None
    outer_primitive = None

    @staticmethod
3775
3776
    def abstract(x_aval, amax_aval, scale_aval, scale_inv_aval, *, out_dtype,
                 act_enum):  # pylint: disable=unused-argument
3777
        """
3778
        te_act_lu_p abstract
3779
3780
3781
3782
3783
3784
3785
3786
3787
        """
        dtype = dtypes.canonicalize_dtype(x_aval.dtype)
        # Currently only support casting to E4M3 only in C side.
        assert out_dtype == jnp.float8_e4m3fn
        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32

3788
3789
3790
3791
3792
        assert (x_aval.shape[-2] == 1 or x_aval.shape[-2] == 2)
        hidden_size = x_aval.shape[-1]
        batch_shape = x_aval.shape[:-2]
        out_shape = (batch_shape) + (hidden_size,)
        out_aval = x_aval.update(shape=out_shape, dtype=out_dtype)
3793
3794
3795
3796
3797
        updated_amax_aval = amax_aval.update(shape=amax_aval.shape, dtype=amax_aval.dtype)

        return out_aval, updated_amax_aval

    @staticmethod
3798
    def lowering(ctx, x, amax, scale, scale_inv, *, out_dtype, act_enum):
3799
        """
3800
        te_gated_act_lu_p lowering rules
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
        """
        x_aval, amax_aval, scale_aval, scale_inv_aval = ctx.avals_in
        assert x_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
        ir_x_type = ir.RankedTensorType(x.type)
        ir_x_shape = ir_x_type.shape
        ir_out_dtype = jax_dtype_to_ir_dtype(out_dtype)
        ir_amax_type = ir.RankedTensorType(amax.type)
        ir_amax_dtype = ir_amax_type.element_type
        ir_amax_shape = ir_amax_type.shape
        ir_scale_shape = ir_amax_shape
        ir_scale_inv_shape = ir_amax_shape

        hidden_size = ir_x_shape[-1]
3817
3818
3819
        batch_shape = ir_x_shape[:-2]
        batch_size = reduce(operator.mul, batch_shape)
        out_shape = batch_shape + [hidden_size]
3820
3821
3822
3823
3824
3825
3826
3827
        out_types = [
            ir.RankedTensorType.get(out_shape, ir_out_dtype),
            ir.RankedTensorType.get(ir_amax_shape, ir_amax_dtype),
        ]
        operands = [x, amax, scale, scale_inv]
        operand_shapes = [ir_x_shape, ir_amax_shape, ir_scale_shape, ir_scale_inv_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)

3828
3829
3830
3831
3832
        opaque = transformer_engine_jax.pack_common_descriptor((
            batch_size, hidden_size),
            jax_dtype_to_te_dtype(x_aval.dtype),
            jax_dtype_to_te_dtype(out_dtype),
            act_enum)
3833

3834
        out = custom_caller(ActLuFp8Primitive.name,
3835
3836
3837
3838
3839
3840
3841
3842
                            args,
                            opaque,
                            False,
                            operand_output_aliases={1: 1})

        return out

    @staticmethod
3843
    def impl(x, amax, scale, scale_inv, out_dtype, act_enum):
3844
3845
3846
        """
        to describe implementation
        """
3847
3848
3849
3850
3851
3852
3853
        assert ActLuFp8Primitive.inner_primitive is not None
        out, updated_amax = ActLuFp8Primitive.inner_primitive.bind(x,
                                                                   amax,
                                                                   scale,
                                                                   scale_inv,
                                                                   out_dtype=out_dtype,
                                                                   act_enum=act_enum)
3854
3855
3856
        return out, updated_amax

    @staticmethod
3857
    def batcher(batched_args, batch_dims, *, out_dtype, act_enum):
3858
3859
3860
3861
        """
        to describe batch rules for vmap
        """
        _check_valid_batch_dims(batch_dims)
3862
        assert ActLuFp8Primitive.outer_primitive is not None
3863
3864
3865
3866
        x, amax, scale, scale_inv = batched_args
        x_bdim, amax_bdim, _, _ = batch_dims

        out_bdims = x_bdim, amax_bdim
3867
3868
3869
        return ActLuFp8Primitive.outer_primitive.bind(x, amax, scale, scale_inv,
                                                      out_dtype=out_dtype,
                                                      act_enum=act_enum), out_bdims
3870
3871

    @staticmethod
3872
3873
    def infer_sharding_from_operands(out_dtype, act_enum, mesh, arg_infos, result_infos):
        del out_dtype, result_infos, act_enum
3874
        x_spec = get_padded_spec(arg_infos[0])
3875
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-2], x_spec[-1]))
3876
3877
3878
3879
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
        return (out_sharding, amax_sharding)

    @staticmethod
3880
    def partition(out_dtype, act_enum, mesh, arg_infos, result_infos):
3881
3882
        del result_infos
        x_spec = get_padded_spec(arg_infos[0])
3883
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec[:-2], x_spec[-1]))
3884
3885
3886
3887
3888
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_shardings = (out_sharding, amax_sharding)

        def sharded_impl(x, amax, scale, scale_inv):
3889
3890
3891
3892
3893
3894
            local_x, local_amax = ActLuFp8Primitive.impl(x,
                                                         amax,
                                                         scale,
                                                         scale_inv,
                                                         out_dtype=out_dtype,
                                                         act_enum=act_enum)
3895
3896
3897
3898
3899
3900
3901
            global_updated_amax = all_reduce_max_along_all_axes_except_PP(local_amax)

            return local_x, global_updated_amax

        return mesh, sharded_impl, out_shardings, arg_shardings


3902
register_primitive(ActLuFp8Primitive)
3903
3904


3905
3906
3907
def act_lu_fp8(x: jnp.ndarray, amax: jnp.ndarray, scale: jnp.ndarray, scale_inv: jnp.ndarray,
             out_dtype: jnp.dtype, activation_type: Sequence[Union[str, Callable]]
               ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
3908
    """
3909
3910
3911
3912
    act wrapper
    Return FP8(act_lu(x))
    Input shape: (N, 1, H) for non-gated activations
                 (N, 2, H) for gated activations
3913
    """
3914
3915
3916
    act_type_id = ActivationEnum[activation_type]
    return ActLuFp8Primitive.outer_primitive.bind(x, amax, scale, scale_inv, out_dtype=out_dtype,
                                                  act_enum = act_type_id)
3917
3918


3919
class DActLuDBiasCastTransposePrimitive(BasePrimitive):
3920
    """
3921
    DActLu DBias Cast Transpose Primitive
3922
    """
3923
    name = "te_dact_lu_dbias_cast_transpose"
3924
    multiple_results = True
3925
3926
    # out_dtype, static_axis_boundary, transpose_axis_boundary, act_enum
    impl_static_args = (5, 6, 7, 8)
3927
3928
3929
3930
3931
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def abstract(dz_aval, x_aval, amax_aval, scale_aval, scale_inv_aval, *, out_dtype,
3932
3933
                 static_axis_boundary, transpose_axis_boundary,
                 act_enum):  # pylint: disable=unused-argument
3934
        """
3935
        te_dact_lu_dbais_cast_transpose_p abstract
3936
3937
3938
3939
3940
3941
3942
3943
3944
3945
        """
        dtype = dtypes.canonicalize_dtype(dz_aval.dtype)
        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert x_aval.dtype == dtype
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
        ir_hidden_szie = dz_aval.shape[-1]
        gi_hidden_size = x_aval.shape[-1]
        assert ir_hidden_szie == gi_hidden_size
3946
3947
        t_shape = _multidim_transpose(x_aval.shape,
                                      static_axis_boundary, transpose_axis_boundary)
3948
3949
3950
3951
3952
3953
3954
3955
        out = dz_aval.update(shape=x_aval.shape, dtype=out_dtype)
        t_out = dz_aval.update(shape=t_shape, dtype=out_dtype)

        dbias_shape = (*x_aval.shape[:static_axis_boundary + 1], gi_hidden_size)
        dbias = dz_aval.update(shape=dbias_shape, dtype=dtype)

        updated_amax_aval = amax_aval.update(shape=amax_aval.shape, dtype=amax_aval.dtype)

3956
        wkspace_info, = transformer_engine_jax.get_dact_dbias_ct_workspace_sizes(
3957
3958
3959
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
            x_aval.size // gi_hidden_size,
            gi_hidden_size,
            jax_dtype_to_te_dtype(x_aval.dtype),
            jax_dtype_to_te_dtype(out_dtype),
        )
        wkspace_aval = x_aval.update(shape=wkspace_info[0],
                                     dtype=te_dtype_to_jax_dtype(wkspace_info[1]))

        return out, t_out, dbias, updated_amax_aval, wkspace_aval

    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
3970
        te_dact_lu_dbais_cast_transpose_p outer abstract
3971
3972
3973
        """

        out, t_out, dbias, updated_amax_aval, _ = \
3974
            DActLuDBiasCastTransposePrimitive.abstract(*args, **kwargs)
3975
3976
3977
3978
        return out, t_out, dbias, updated_amax_aval

    @staticmethod
    def lowering(ctx, dz, x, amax, scale, scale_inv, *, out_dtype, static_axis_boundary,
3979
                 transpose_axis_boundary, act_enum):
3980
        """
3981
        te_dgated_act_lu_cast_transpose_p lowering rules
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
        """
        dz_aval, x_aval, amax_aval, scale_aval, scale_inv_aval = ctx.avals_in
        assert dz_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert x_aval.dtype == dz_aval.dtype
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
        ir_dz_type = ir.RankedTensorType(dz.type)
        ir_dz_shape = ir_dz_type.shape
        x_type = ir.RankedTensorType(x.type)
        x_shape = x_type.shape
3993
3994
3995
        dz_batch_szie = reduce(operator.mul, ir_dz_shape[:-1])
        x_batch_size = reduce(operator.mul, x_shape[:-2])
        assert dz_batch_szie == x_batch_size
3996
        ir_hidden_szie = ir_dz_shape[-1]
3997
        contracted_x_shape = (x_batch_size, ir_hidden_szie)
3998
3999
4000
4001
4002
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
4021
4022

        ir_out_dtype = jax_dtype_to_ir_dtype(out_dtype)
        ir_amax_type = ir.RankedTensorType(amax.type)
        ir_amax_dtype = ir_amax_type.element_type
        ir_amax_shape = ir_amax_type.shape
        ir_scale_shape = ir_amax_shape
        ir_scale_inv_shape = ir_amax_shape
        transposed_x_shape = _multidim_transpose(x_shape, static_axis_boundary,
                                                 transpose_axis_boundary)
        dbias_shape = (*x_shape[:static_axis_boundary + 1], ir_hidden_szie)

        wkspace_aval = ctx.avals_out[-1]

        out_types = [
            ir.RankedTensorType.get(x_shape, ir_out_dtype),
            ir.RankedTensorType.get(transposed_x_shape, ir_out_dtype),
            ir.RankedTensorType.get(dbias_shape, ir_dz_type.element_type),
            ir.RankedTensorType.get(ir_amax_shape, ir_amax_dtype),
            ir.RankedTensorType.get(wkspace_aval.shape, jax_dtype_to_ir_dtype(wkspace_aval.dtype)),
        ]
        operands = [dz, x, amax, scale, scale_inv]
        operand_shapes = [ir_dz_shape, x_shape, ir_amax_shape, ir_scale_shape, ir_scale_inv_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)
        opaque = transformer_engine_jax.pack_common_wk_descriptor(
            contracted_x_shape, wkspace_aval.shape, jax_dtype_to_te_dtype(dz_aval.dtype),
4023
4024
            jax_dtype_to_te_dtype(out_dtype), jax_dtype_to_te_dtype(wkspace_aval.dtype),
            act_enum)
4025

4026
        out = custom_caller(DActLuDBiasCastTransposePrimitive.name,
4027
4028
4029
4030
4031
4032
4033
4034
4035
                            args,
                            opaque,
                            False,
                            operand_output_aliases={2: 3})

        return out

    @staticmethod
    def impl(dz, x, amax, scale, scale_inv, out_dtype, static_axis_boundary,
4036
             transpose_axis_boundary, act_enum):
4037
4038
4039
        """
        to describe implementation
        """
4040
4041
        assert DActLuDBiasCastTransposePrimitive.inner_primitive is not None
        out, t_out, dbias, updated_amax, _ = DActLuDBiasCastTransposePrimitive.inner_primitive.bind(
4042
4043
4044
4045
4046
4047
4048
            dz,
            x,
            amax,
            scale,
            scale_inv,
            out_dtype=out_dtype,
            static_axis_boundary=static_axis_boundary,
4049
4050
            transpose_axis_boundary=transpose_axis_boundary,
            act_enum=act_enum)
4051
4052
4053
4054
        return out, t_out, dbias, updated_amax

    @staticmethod
    def batcher(batched_args, batch_dims, *, out_dtype, static_axis_boundary,
4055
                transpose_axis_boundary, act_enum):
4056
4057
4058
4059
4060
        """
        to describe batch rules for vmap
        """
        del static_axis_boundary
        _check_valid_batch_dims(batch_dims)
4061
        assert DActLuDBiasCastTransposePrimitive.outer_primitive is not None
4062
4063
4064
4065
4066
4067
4068
4069
        dz, x, amax, scale, scale_inv = batched_args
        x_bdim, _, amax_bdim, _, _ = batch_dims

        # Minus batch dim.
        transpose_axis_boundary = _normalize_axis_boundary(transpose_axis_boundary, x.ndim - 1)
        transpose_axis_boundary += 1    # Plus batch dim

        out_bdims = x_bdim, x_bdim, x_bdim, amax_bdim
4070
        return DActLuDBiasCastTransposePrimitive.outer_primitive.bind(
4071
4072
4073
4074
4075
4076
4077
            dz,
            x,
            amax,
            scale,
            scale_inv,
            out_dtype=out_dtype,
            static_axis_boundary=x_bdim,
4078
4079
            transpose_axis_boundary=transpose_axis_boundary,
            act_enum=act_enum), out_bdims
4080
4081

    @staticmethod
4082
4083
4084
    def infer_sharding_from_operands(out_dtype, static_axis_boundary, transpose_axis_boundary,
                                     act_enum, mesh, arg_infos, result_infos):
        del out_dtype, result_infos, act_enum
4085
4086
4087
4088
4089
4090
4091
4092
4093
4094
        x_spec = get_padded_spec(arg_infos[1])
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, transpose_axis_boundary)
        tranposed_out_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))
        dbias_shaprding = NamedSharding(
            mesh, PartitionSpec(*x_spec[:static_axis_boundary + 1], x_spec[-1]))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[2])))
        return (out_sharding, tranposed_out_sharding, dbias_shaprding, amax_sharding)

    @staticmethod
4095
4096
    def partition(out_dtype, static_axis_boundary, transpose_axis_boundary,
                  act_enum, mesh, arg_infos, result_infos):
4097
4098
4099
4100
4101
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
        del result_infos
        x_spec = get_padded_spec(arg_infos[1])
        casted_x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, transpose_axis_boundary)
        casted_transposed_x_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))

        dbias_shaprding = NamedSharding(
            mesh, PartitionSpec(*x_spec[:static_axis_boundary + 1], x_spec[-1]))

        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[2])))
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_shardings = (casted_x_sharding, casted_transposed_x_sharding, dbias_shaprding,
                         amax_sharding)

        def sharded_impl(dz, x, amax, scale, scale_inv):
4112
4113
            local_out, local_t_out, local_dbias, local_amax =\
            DActLuDBiasCastTransposePrimitive.impl(
4114
4115
4116
4117
4118
4119
4120
                dz,
                x,
                amax,
                scale,
                scale_inv,
                out_dtype=out_dtype,
                static_axis_boundary=static_axis_boundary,
4121
4122
                transpose_axis_boundary=transpose_axis_boundary,
                act_enum=act_enum)
4123
4124
4125
4126
4127
4128
4129
            global_dbias = all_reduce_sum_along_dp_fsdp(local_dbias)
            global_updated_amax = all_reduce_max_along_all_axes_except_PP(local_amax)
            return local_out, local_t_out, global_dbias, global_updated_amax

        return mesh, sharded_impl, out_shardings, arg_shardings


4130
register_primitive(DActLuDBiasCastTransposePrimitive)
4131
4132


4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
def dact_lu_dbias_cast_transpose(
    dz: jnp.ndarray,
    x: jnp.ndarray,
    amax: jnp.ndarray,
    scale: jnp.ndarray,
    scale_inv: jnp.ndarray,
    out_dtype: TEDType,
    static_axis_boundary: int,
    transpose_axis_boundary: int = -1,
    activation_type: Sequence[Union[str, Callable]] = ('gelu',)
    ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
4144
    """
4145
4146
4147
    cast transpose dact_lu and dbias fusion wrapper
    Return FP8(dact_lu(inputs)), dbias
    ONLY support non-gated activation type
4148
4149
4150
4151
    """
    if static_axis_boundary < 0:
        static_axis_boundary = -1    # means no static axes

4152
4153
    act_type_id = ActivationEnum[activation_type]
    return DActLuDBiasCastTransposePrimitive.outer_primitive.bind(
4154
4155
4156
4157
4158
4159
4160
        dz,
        x,
        amax,
        scale,
        scale_inv,
        out_dtype=out_dtype,
        static_axis_boundary=static_axis_boundary,
4161
4162
        transpose_axis_boundary=transpose_axis_boundary,
        act_enum=act_type_id)
4163
4164


4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
class DBiasCastTransposePrimitive(BasePrimitive):
    """
    DBias Cast Transpose Primitive
    """
    name = "te_dbias_cast_transpose"
    multiple_results = True
    # out_dtype, static_axis_boundary, transpose_axis_boundary
    impl_static_args = (4, 5, 6)
    inner_primitive = None
    outer_primitive = None

    @staticmethod
    def abstract(dz_aval, amax_aval, scale_aval, scale_inv_aval, *, out_dtype,
                 static_axis_boundary, transpose_axis_boundary):
        """
        te_dbias_cast_transpose_p abstract
        """
        dtype = dtypes.canonicalize_dtype(dz_aval.dtype)
        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
4187
        gi_hidden_size = reduce(operator.mul, dz_aval.shape[transpose_axis_boundary:])
4188
4189
4190
4191
4192
4193
4194
4195
4196
4197
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        t_shape = _multidim_transpose(dz_aval.shape, static_axis_boundary, transpose_axis_boundary)
        out = dz_aval.update(shape=dz_aval.shape, dtype=out_dtype)
        t_out = dz_aval.update(shape=t_shape, dtype=out_dtype)

        dbias_shape = (*dz_aval.shape[:static_axis_boundary + 1], gi_hidden_size)
        dbias = dz_aval.update(shape=dbias_shape, dtype=dtype)

        updated_amax_aval = amax_aval.update(shape=amax_aval.shape, dtype=amax_aval.dtype)
        wkspace_info, = transformer_engine_jax.get_dbias_ct_workspace_sizes(
            dz_aval.size // gi_hidden_size,
            gi_hidden_size,
            jax_dtype_to_te_dtype(dz_aval.dtype),
            jax_dtype_to_te_dtype(out_dtype)
        )
        wkspace_aval = dz_aval.update(shape=wkspace_info[0],
                                     dtype=te_dtype_to_jax_dtype(wkspace_info[1]))

        return out, t_out, dbias, updated_amax_aval, wkspace_aval

    @staticmethod
    def outer_abstract(*args, **kwargs):
        """
        te_dbias_cast_transpose_p outer abstract
        """

        out, t_out, dbias, updated_amax_aval, _ = \
        DBiasCastTransposePrimitive.abstract(*args, **kwargs)
        return out, t_out, dbias, updated_amax_aval

    @staticmethod
    def lowering(ctx, dz, amax, scale, scale_inv, *, out_dtype, static_axis_boundary,
                 transpose_axis_boundary):
        """
        te_dbias_cast_transpose_p lowering rules
        """
        dz_aval, amax_aval, scale_aval, scale_inv_aval = ctx.avals_in
        assert dz_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
        ir_dz_type = ir.RankedTensorType(dz.type)
        ir_dz_shape = ir_dz_type.shape
4230
4231
4232
        batch_size = reduce(operator.mul, ir_dz_shape[:transpose_axis_boundary])
        ir_hidden_size = reduce(operator.mul, ir_dz_shape[transpose_axis_boundary:])
        contracted_dz_shape = (batch_size, ir_hidden_size)
4233
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4236
4237
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4240
        ir_out_dtype = jax_dtype_to_ir_dtype(out_dtype)
        ir_amax_type = ir.RankedTensorType(amax.type)
        ir_amax_dtype = ir_amax_type.element_type
        ir_amax_shape = ir_amax_type.shape
        ir_scale_shape = ir_amax_shape
        ir_scale_inv_shape = ir_amax_shape
        transposed_dz_shape = _multidim_transpose(ir_dz_shape, static_axis_boundary,
                                                 transpose_axis_boundary)
4241
        dbias_shape = (*ir_dz_shape[:static_axis_boundary + 1], ir_hidden_size)
4242
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4310
4311
4312
4313

        wkspace_aval = ctx.avals_out[-1]

        out_types = [
            ir.RankedTensorType.get(ir_dz_shape, ir_out_dtype),
            ir.RankedTensorType.get(transposed_dz_shape, ir_out_dtype),
            ir.RankedTensorType.get(dbias_shape, ir_dz_type.element_type),
            ir.RankedTensorType.get(ir_amax_shape, ir_amax_dtype),
            ir.RankedTensorType.get(wkspace_aval.shape, jax_dtype_to_ir_dtype(wkspace_aval.dtype)),
        ]
        operands = [dz, amax, scale, scale_inv]
        operand_shapes = [ir_dz_shape, ir_amax_shape, ir_scale_shape, ir_scale_inv_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)
        opaque = transformer_engine_jax.pack_common_wk_descriptor(
            contracted_dz_shape, wkspace_aval.shape, jax_dtype_to_te_dtype(dz_aval.dtype),
            jax_dtype_to_te_dtype(out_dtype), jax_dtype_to_te_dtype(wkspace_aval.dtype))

        out = custom_caller(DBiasCastTransposePrimitive.name,
                            args,
                            opaque,
                            False,
                            operand_output_aliases={1: 3})

        return out

    @staticmethod
    def impl(dz, amax, scale, scale_inv, out_dtype, static_axis_boundary,
             transpose_axis_boundary):
        """
        to describe implementation
        """
        assert DBiasCastTransposePrimitive.inner_primitive is not None
        out, t_out, dbias, updated_amax, _ = DBiasCastTransposePrimitive.inner_primitive.bind(
            dz,
            amax,
            scale,
            scale_inv,
            out_dtype=out_dtype,
            static_axis_boundary=static_axis_boundary,
            transpose_axis_boundary=transpose_axis_boundary)
        return out, t_out, dbias, updated_amax

    @staticmethod
    def batcher(batched_args, batch_dims, *, out_dtype, static_axis_boundary,
                transpose_axis_boundary):
        """
        to describe batch rules for vmap
        """
        del static_axis_boundary
        _check_valid_batch_dims(batch_dims)
        assert DBiasCastTransposePrimitive.outer_primitive is not None
        dz, amax, scale, scale_inv = batched_args
        dz_bdim, _, amax_bdim, _, _ = batch_dims

        # Minus batch dim.
        transpose_axis_boundary = _normalize_axis_boundary(transpose_axis_boundary, dz.ndim - 1)
        transpose_axis_boundary += 1    # Plus batch dim

        out_bdims = dz_bdim, dz_bdim, dz_bdim, amax_bdim
        return DBiasCastTransposePrimitive.outer_primitive.bind(
            dz,
            amax,
            scale,
            scale_inv,
            out_dtype=out_dtype,
            static_axis_boundary=dz_bdim,
            transpose_axis_boundary=transpose_axis_boundary), out_bdims

    @staticmethod
    def infer_sharding_from_operands(out_dtype, static_axis_boundary, transpose_axis_boundary, mesh,
                                     arg_infos, result_infos):
        del out_dtype, result_infos
4314
        x_spec = get_padded_spec(arg_infos[0])
4315
4316
4317
4318
4319
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, transpose_axis_boundary)
        tranposed_out_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))
        dbias_shaprding = NamedSharding(
            mesh, PartitionSpec(*x_spec[:static_axis_boundary + 1], x_spec[-1]))
4320
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
4321
4322
4323
4324
4325
4326
        return (out_sharding, tranposed_out_sharding, dbias_shaprding, amax_sharding)

    @staticmethod
    def partition(out_dtype, static_axis_boundary, transpose_axis_boundary, mesh, arg_infos,
                  result_infos):
        del result_infos
4327
        x_spec = get_padded_spec(arg_infos[0])
4328
4329
4330
4331
4332
4333
4334
        casted_x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, transpose_axis_boundary)
        casted_transposed_x_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))

        dbias_shaprding = NamedSharding(
            mesh, PartitionSpec(*x_spec[:static_axis_boundary + 1], x_spec[-1]))

4335
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[1])))
4336
4337
4338
4339
4340
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4380
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4382
4383
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_shardings = (casted_x_sharding, casted_transposed_x_sharding, dbias_shaprding,
                         amax_sharding)

        def sharded_impl(dz, amax, scale, scale_inv):
            local_out, local_t_out, local_dbias, local_amax = DBiasCastTransposePrimitive.impl(
                dz,
                amax,
                scale,
                scale_inv,
                out_dtype=out_dtype,
                static_axis_boundary=static_axis_boundary,
                transpose_axis_boundary=transpose_axis_boundary)
            global_dbias = all_reduce_sum_along_dp_fsdp(local_dbias)
            global_updated_amax = all_reduce_max_along_all_axes_except_PP(local_amax)
            return local_out, local_t_out, global_dbias, global_updated_amax

        return mesh, sharded_impl, out_shardings, arg_shardings


register_primitive(DBiasCastTransposePrimitive)


def dbias_cast_transpose(
    dz: jnp.ndarray,
    amax: jnp.ndarray,
    scale: jnp.ndarray,
    scale_inv: jnp.ndarray,
    out_dtype: TEDType,
    static_axis_boundary: int,
    transpose_axis_boundary: int = -1) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
    """
    cast transpose dbias partial fusion wrapper
    Return FP8(inputs), dbias
    """
    if static_axis_boundary < 0:
        static_axis_boundary = -1    # means no static axes

    return DBiasCastTransposePrimitive.outer_primitive.bind(
        dz,
        amax,
        scale,
        scale_inv,
        out_dtype=out_dtype,
        static_axis_boundary=static_axis_boundary,
        transpose_axis_boundary=transpose_axis_boundary)


4384
class DgatedActLuCastTransposePrimitive(BasePrimitive):
4385
    """
4386
    Dgated ActLu Cast Transpose Primitive
4387
    """
4388
    name = "te_dgated_act_lu_cast_transpose"
4389
    multiple_results = True
4390
    impl_static_args = (5, 6, 7)    # out_dtype, static_axis_boundary, act_enum
4391
4392
    inner_primitive = None
    outer_primitive = None
4393
4394

    @staticmethod
4395
4396
    def abstract(dz_aval, x_aval, amax_aval, scale_aval, scale_inv_aval, *, out_dtype,
                 static_axis_boundary, act_enum):  # pylint: disable=unused-argument
4397
        """
4398
        te_dgated_act_lu_cast_transpose_p abstract
4399
        """
4400
        dtype = dtypes.canonicalize_dtype(dz_aval.dtype)
4401
        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
4402
4403
        assert x_aval.dtype == dtype
        assert x_aval.shape[-2] == 2    # Linear + GeLU
4404
4405
4406
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
4407
4408
4409
4410
4411
4412
        ir_hidden_szie = dz_aval.shape[-1]
        gi_hidden_size = x_aval.shape[-1]
        assert ir_hidden_szie == gi_hidden_size
        t_shape = _multidim_transpose(x_aval.shape, static_axis_boundary, -2)
        out = dz_aval.update(shape=x_aval.shape, dtype=out_dtype)
        t_out = dz_aval.update(shape=t_shape, dtype=out_dtype)
4413
        updated_amax_aval = amax_aval.update(shape=amax_aval.shape, dtype=amax_aval.dtype)
4414
        return out, t_out, updated_amax_aval
4415
4416

    @staticmethod
4417
    def lowering(ctx, dz, x, amax, scale, scale_inv, *, out_dtype, static_axis_boundary, act_enum):
4418
        """
4419
        te_dgated_act_lu_cast_transpose_p lowering rules
4420
        """
4421
4422
4423
        dz_aval, x_aval, amax_aval, scale_aval, scale_inv_aval = ctx.avals_in
        assert dz_aval.dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
        assert x_aval.dtype == dz_aval.dtype
4424
4425
4426
        assert amax_aval.dtype == jnp.float32
        assert scale_aval.dtype == jnp.float32
        assert scale_inv_aval.dtype == jnp.float32
4427
4428
4429
4430
4431
4432
4433
4434
4435
4436
4437
        ir_dz_type = ir.RankedTensorType(dz.type)
        ir_dz_shape = ir_dz_type.shape
        x_type = ir.RankedTensorType(x.type)
        x_shape = x_type.shape
        dz_batch_szie = reduce(operator.mul, ir_dz_shape[:-1])
        x_batch_size = reduce(operator.mul, x_shape[:-2])
        assert dz_batch_szie == x_batch_size
        assert x_shape[-2] == 2    # Linear + GeLU
        ir_hidden_szie = ir_dz_shape[-1]
        gi_hidden_size = x_shape[-1]
        assert ir_hidden_szie == gi_hidden_size
4438
4439
4440
4441
4442
4443
4444
4445
4446
4447
4448
4449
4450
4451
4452
4453
        ir_out_dtype = jax_dtype_to_ir_dtype(out_dtype)
        ir_amax_type = ir.RankedTensorType(amax.type)
        ir_amax_dtype = ir_amax_type.element_type
        ir_amax_shape = ir_amax_type.shape
        ir_scale_shape = ir_amax_shape
        ir_scale_inv_shape = ir_amax_shape
        transposed_x_shape = _multidim_transpose(x_shape, static_axis_boundary, -2)
        out_types = [
            ir.RankedTensorType.get(x_shape, ir_out_dtype),
            ir.RankedTensorType.get(transposed_x_shape, ir_out_dtype),
            ir.RankedTensorType.get(ir_amax_shape, ir_amax_dtype),
        ]
        operands = [dz, x, amax, scale, scale_inv]
        operand_shapes = [ir_dz_shape, x_shape, ir_amax_shape, ir_scale_shape, ir_scale_inv_shape]
        args = CustomCallArgsWrapper(out_types, operands, operand_shapes)
        contracted_x_shape = (x_batch_size, x_shape[-1])
4454
4455
4456
4457
4458
        opaque = transformer_engine_jax.pack_common_descriptor(
            contracted_x_shape,
            jax_dtype_to_te_dtype(dz_aval.dtype),
            jax_dtype_to_te_dtype(out_dtype),
            act_enum)
4459

4460
        out = custom_caller(DgatedActLuCastTransposePrimitive.name,
4461
4462
4463
4464
4465
4466
4467
4468
                            args,
                            opaque,
                            False,
                            operand_output_aliases={2: 2})

        return out

    @staticmethod
4469
    def impl(dz, x, amax, scale, scale_inv, out_dtype, static_axis_boundary, act_enum):
4470
4471
4472
        """
        to describe implementation
        """
4473
4474
        assert DgatedActLuCastTransposePrimitive.inner_primitive is not None
        out, t_out, updated_amax = DgatedActLuCastTransposePrimitive.inner_primitive.bind(
4475
4476
4477
4478
4479
4480
            dz,
            x,
            amax,
            scale,
            scale_inv,
            out_dtype=out_dtype,
4481
4482
            static_axis_boundary=static_axis_boundary,
            act_enum=act_enum)
4483
4484
4485
        return out, t_out, updated_amax

    @staticmethod
4486
    def batcher(batched_args, batch_dims, *, out_dtype, static_axis_boundary, act_enum):
4487
4488
4489
4490
4491
        """
        to describe batch rules for vmap
        """
        del static_axis_boundary
        _check_valid_batch_dims(batch_dims)
4492
        assert DgatedActLuCastTransposePrimitive.outer_primitive is not None
4493
4494
4495
4496
        dz, x, amax, scale, scale_inv = batched_args
        x_bdim, _, amax_bdim, _, _ = batch_dims

        out_bdims = x_bdim, x_bdim, amax_bdim
4497
        return DgatedActLuCastTransposePrimitive.outer_primitive.bind(
4498
            dz, x, amax, scale, scale_inv, out_dtype=out_dtype,
4499
4500
            static_axis_boundary=x_bdim,
            act_enum=act_enum), out_bdims
4501
4502

    @staticmethod
4503
4504
4505
    def infer_sharding_from_operands(out_dtype, static_axis_boundary, act_enum,
                                     mesh, arg_infos, result_infos):
        del out_dtype, result_infos, act_enum
4506
4507
4508
4509
4510
4511
4512
4513
        x_spec = get_padded_spec(arg_infos[1])
        out_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, -2)
        tranposed_out_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))
        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[2])))
        return (out_sharding, tranposed_out_sharding, amax_sharding)

    @staticmethod
4514
4515
    def partition(out_dtype, static_axis_boundary, act_enum,
                  mesh, arg_infos, result_infos):
4516
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
        del result_infos
        x_spec = get_padded_spec(arg_infos[1])
        casted_x_sharding = NamedSharding(mesh, PartitionSpec(*x_spec))
        xt_spec = _multidim_transpose(x_spec, static_axis_boundary, -2)
        casted_transposed_x_sharding = NamedSharding(mesh, PartitionSpec(*xt_spec))

        amax_sharding = NamedSharding(mesh, PartitionSpec(*get_padded_spec(arg_infos[2])))
        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
        out_shardings = (casted_x_sharding, casted_transposed_x_sharding, amax_sharding)

        def sharded_impl(dz, x, amax, scale, scale_inv):
4527
            local_out, local_t_out, local_amax = DgatedActLuCastTransposePrimitive.impl(
4528
4529
4530
4531
4532
4533
                dz,
                x,
                amax,
                scale,
                scale_inv,
                out_dtype=out_dtype,
4534
4535
                static_axis_boundary=static_axis_boundary,
                act_enum=act_enum)
4536
4537
4538
4539
4540
4541
            global_updated_amax = all_reduce_max_along_all_axes_except_PP(local_amax)
            return local_out, local_t_out, global_updated_amax

        return mesh, sharded_impl, out_shardings, arg_shardings


4542
register_primitive(DgatedActLuCastTransposePrimitive)
4543
4544


4545
4546
4547
4548
4549
4550
def dgated_act_lu_cast_transpose(
    dz: jnp.ndarray, x: jnp.ndarray, amax: jnp.ndarray, scale: jnp.ndarray,
    scale_inv: jnp.ndarray, out_dtype: TEDType,
    static_axis_boundary: int,
    activation_type: Sequence[Union[str, Callable]]
    ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
4551
    """
4552
4553
    cast transpose d_gated_act_lu fusion wrapper
    Return FP8(dgated_act_lu(inputs))
4554
    """
4555
4556
    act_type_id = ActivationEnum[activation_type]
    return DgatedActLuCastTransposePrimitive.outer_primitive.bind(
4557
4558
4559
4560
4561
4562
        dz,
        x,
        amax,
        scale,
        scale_inv,
        out_dtype=out_dtype,
4563
4564
        static_axis_boundary=static_axis_boundary,
        act_enum=act_type_id)