# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""
Tensor quantization classes for TE/JAX.

This module provides classes and utilities for quantizing tensors in JAX.
"""
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from functools import partial
from typing import Union, Optional, Tuple
import warnings

import jax
import jax.numpy as jnp
from jax.tree_util import register_pytree_node_class
from transformer_engine.common import recipe

from .scaling_modes import ScalingMode
from .misc import QuantizeLayout
from .hadamard import apply_rht
from .tensor import (
    ScaledTensor,
    ScaledTensor1x,
    ScaledTensor2x,
    ScaledTensorFactory,
    NoScaleTensor,
)
from .helper import (
    get_global_quantize_recipe,
    get_quantize_config_with_recipe,
    AmaxComputeAlgo,
    TensorSource,
)
from .device_utils import is_fp8_gemm_with_all_layouts_supported
from ..sharding import get_num_devices_in_mesh

__all__ = [
    "Quantizer",
    "QuantizerSet",
    "CurrentScaleQuantizer",
    "DelayedScaleQuantizer",
    "BlockScaleQuantizer",
    "GroupedQuantizer",
    "QuantizerFactory",
    "noop_quantizer_set",
    "compute_scale_from_amax",
]


def compute_scale_from_amax(
    amax: jnp.ndarray, q_dtype: jnp.dtype, margin: float, scale: Optional[jnp.ndarray] = None
) -> jnp.ndarray:
    """Compute scale from amax value.

    Args:
        amax: Maximum absolute value of the tensor
        q_dtype: Quantization data type

    Returns:
        Scale value
    """
    fp8_max = jnp.astype(jnp.finfo(q_dtype).max, jnp.float32)
    if scale is None:
        scale = jnp.ones((1,))
    sf = (fp8_max / amax) / (2**margin)
    sf = jnp.where(amax > 0.0, sf, scale)
    sf = jnp.where(jnp.isfinite(amax), sf, scale)
    assert sf.shape == (1,), f"Expected sf.shape == (1,), but got {sf.shape}"
    return sf


@register_pytree_node_class
@dataclass
class Quantizer(ABC):
    """Base class for quantizers.

    This abstract class defines the interface for tensor quantization, providing
    methods for quantization and scale management.

    Attributes:
        q_dtype: The data type for quantized values
        scaling_mode: The scaling mode to use for quantization
        q_layout: The quantization axis (row-wise, column-wise, or both)
        data_layout: The data layout string (e.g., "NT")
        checkpoint_name: Optional name for checkpointing quantization state
    """

    q_dtype: jnp.dtype
    scaling_mode: ScalingMode
    q_layout: QuantizeLayout
    data_layout: str
    checkpoint_name: Optional[str] = None

    def tree_flatten(self):
        """Flatten the quantizer for JAX tree operations.

        Returns:
            Tuple of (children, aux_data) for tree operations
        """
        children = ()
        aux_data = (
            self.q_dtype,
            self.scaling_mode,
            self.q_layout,
            self.data_layout,
            self.checkpoint_name,
        )
        return (children, aux_data)

    @classmethod
    def tree_unflatten(cls, aux_data, children):
        """Reconstruct a quantizer from its flattened representation.

        Args:
            aux_data: Auxiliary data containing quantizer parameters
            children: Unused children data

        Returns:
            A reconstructed Quantizer instance
        """
        return cls(*aux_data, *children)

    def update(self, *args, **kwargs):
        """Update quantizer state (no-op in base class)."""
        del args, kwargs

    def get_data_layout(self) -> str:
        """Get the data data_layout string.

        Returns:
            Data data_layout in string format

        Raises:
            ValueError: If quantization axis is invalid
        """
        if self.q_layout.is_rowwise_colwise:
            return self.data_layout
        if self.q_layout.is_rowwise_only:
            return self.data_layout[0]
        if self.q_layout.is_colwise_only:
            return self.data_layout[1]
        raise ValueError(f"Invalid q_layout: {self.q_layout}")

    @abstractmethod
    def _quantize_func(self, x, is_colwise=False, dq_dtype=None, flatten_axis=-1) -> ScaledTensor1x:
        """Core quantization function to be implemented by subclasses.

        Args:
            x: Input tensor to quantize
            is_colwise: Whether to use column-wise quantization
            dq_dtype: Data type for dequantized values, default is x.dtype
            flatten_axis: The quantization axis for the tensor

        Returns:
            A ScaledTensor1x containing the quantized data
        """

    def quantize(
        self, x, is_rowwise=None, is_colwise=None, dq_dtype=None, flatten_axis=-1, **kwargs
    ) -> ScaledTensor:
        """Quantize a tensor using the internal _quantize_func().

        Args:
            x: Input tensor to quantize
            is_rowwise: Whether to use row-wise quantization
            is_colwise: Whether to use column-wise quantization
            dq_dtype: Data type for dequantized values
            flatten_axis: The quantization axis for the tensor

        Returns:
            A ScaledTensor1x or ScaledTensor2x containing the quantized data
        """
        del kwargs

        is_rowwise = is_rowwise if is_rowwise is not None else self.q_layout.has_rowwise
        is_colwise = is_colwise if is_colwise is not None else self.q_layout.has_colwise

        if is_rowwise and is_colwise:
            rowwise_tensor = self._quantize_func(x, dq_dtype=dq_dtype, flatten_axis=flatten_axis)
            colwise_tensor = self._quantize_func(
                x, is_colwise=True, dq_dtype=dq_dtype, flatten_axis=flatten_axis
            )
            return ScaledTensor2x(rowwise_tensor, colwise_tensor)

        if is_colwise:
            return self._quantize_func(
                x, is_colwise=True, dq_dtype=dq_dtype, flatten_axis=flatten_axis
            )

        return self._quantize_func(x, dq_dtype=dq_dtype, flatten_axis=flatten_axis)

    def get_scale_shapes(self, data_shape, is_padded=True, flatten_axis=-1, **kwargs):
        """Get shapes for scale tensors.

        Args:
            data_shape: Shape of the input tensor
            is_padded: Whether to use padded shapes

        Returns:
            Tuple of (rowwise_scale_shape, colwise_scale_shape)
        """
        del kwargs
        return self.scaling_mode.get_scale_shape_2x(data_shape, is_padded, flatten_axis)

    def get_scale_dtype(self):
        """Get the data type for scale tensors.

        Returns:
            The data type for scale tensors
        """
        return self.scaling_mode.get_scale_dtype()


@register_pytree_node_class
@dataclass
class CurrentScaleQuantizer(Quantizer):
    """Quantizer implementation using current scaling.

    This quantizer uses current scaling mode with float32 scales

    Attributes:
        scaling_mode: Set to NVTE_DELAYED_TENSOR_SCALING
        q_layout: Quantization axis (default: ROWWISE_COLWISE)
        data_layout: Data layout string (default: "NT")
    """

    scaling_mode: ScalingMode = ScalingMode.CURRENT_TENSOR_SCALING
    q_layout: QuantizeLayout = QuantizeLayout.ROWWISE_COLWISE
    data_layout: str = "NT"

    def _quantize_func(
        self,
        x: Union[jnp.ndarray, NoScaleTensor],
        is_colwise=False,
        dq_dtype=None,
        flatten_axis=-1,
    ) -> ScaledTensor1x:
        """Quantize function helper for delayed scaling FP8.

        Args:
            x: Input tensor to quantize
            is_colwise: Whether to use column-wise quantization
            dq_dtype: Data type for dequantized values

        Returns:
            A ScaledTensor1x containing the quantized data
        """
        if isinstance(x, jnp.ndarray):
            x = NoScaleTensor(data=x, amax=None)

        dq_dtype = dq_dtype if dq_dtype is not None else x.data.dtype

        compute_dtype = jnp.float32
        dtype_max = (jnp.finfo(self.q_dtype).max).astype(compute_dtype)
        amax = x.amax or jnp.max(jnp.abs(x.data)).reshape((1,))
        scale = compute_scale_from_amax(amax, self.q_dtype, margin=0.0)
        scaled_x = x.data.astype(compute_dtype) * scale

        clipped_scaled_x = jnp.clip(scaled_x, -dtype_max, dtype_max).astype(self.q_dtype)
        scale_inv = 1.0 / scale
        return ScaledTensorFactory.create_1x(
            data=clipped_scaled_x,
            scale_inv=scale_inv,
            scaling_mode=self.scaling_mode,
            dq_dtype=dq_dtype,
            flatten_axis=flatten_axis,
        )

    def quantize(
        self, x, is_rowwise: bool = None, is_colwise: bool = None, dq_dtype=None, flatten_axis=-1
    ):
        """Quantize a tensor using the internal _quantize_func().

        Args:
            x: Input tensor to quantize
            is_rowwise: Whether to use row-wise quantization
            is_colwise: Whether to use column-wise quantization
            dq_dtype: Data type for dequantized values
            flatten_axis: The quantization axis for the tensor

        Returns:
            A ScaledTensor1x or ScaledTensor2x containing the quantized data
        """
        if isinstance(x, jnp.ndarray):
            x = NoScaleTensor(data=x, amax=None)

        dq_dtype = dq_dtype if dq_dtype is not None else x.data.dtype
        if flatten_axis < 0:
            flatten_axis += x.ndim
        assert 0 < flatten_axis < x.ndim, "flatten_axis is out of bounds!"

        is_rowwise = is_rowwise if is_rowwise is not None else self.q_layout.has_rowwise
        is_colwise = is_colwise if is_colwise is not None else self.q_layout.has_colwise

        rowwise_tensor = self._quantize_func(x, dq_dtype=dq_dtype, flatten_axis=flatten_axis)
        colwise_tensor = None
        if is_colwise:
            colwise_tensor = ScaledTensorFactory.create_1x(
                data=jnp.transpose(
                    rowwise_tensor.data, (*range(flatten_axis, x.ndim), *range(flatten_axis))
                ),
                scale_inv=rowwise_tensor.scale_inv,
                scaling_mode=self.scaling_mode,
                dq_dtype=dq_dtype,
                is_colwise=True,
                data_layout="T",
                flatten_axis=flatten_axis,
            )

        if is_colwise and is_rowwise:
            return ScaledTensor2x(rowwise_tensor, colwise_tensor)
        if is_colwise:
            return colwise_tensor
        return rowwise_tensor


@register_pytree_node_class
@dataclass
class DelayedScaleQuantizer(CurrentScaleQuantizer):
    """Quantizer implementation using delayed scaling.

    This quantizer uses delayed scaling mode with float32 scales and maintains
    a history of maximum absolute values for dynamic scaling.

    Attributes:
        scaling_mode: Set to NVTE_DELAYED_TENSOR_SCALING
        q_layout: Quantization axis (default: ROWWISE_COLWISE)
        data_layout: Data layout string (default: "NT")
        margin: Margin value for scale computation
        amax_compute_algo: Algorithm for computing amax
        scale: Current scaling factor
        amax_history: History of maximum absolute values
    """

    margin: float = 0.0
    amax_compute_algo: AmaxComputeAlgo = AmaxComputeAlgo.MAX

    scale: jnp.ndarray = field(default_factory=lambda: jnp.ones((1,), jnp.float32))
    amax_history: jnp.ndarray = field(default_factory=lambda: jnp.zeros((1024,), jnp.float32))

    def __post_init__(self):
        assert self.margin is not None, "margin must be specified"
        assert self.amax_compute_algo is not None, "amax_compute_algo must be specified"
        assert self.amax_history is not None, "amax_history must be specified"

    def tree_flatten(self):
        """Flatten the quantizer for JAX tree operations.

        Returns:
            Tuple of (children, aux_data) for tree operations
        """
        children = (self.scale, self.amax_history)
        aux_data = (
            self.q_dtype,
            self.scaling_mode,
            self.q_layout,
            self.data_layout,
            self.checkpoint_name,
            self.margin,
            self.amax_compute_algo,
        )
        return (children, aux_data)

    def _quantize_func(
        self, x: jnp.ndarray, is_colwise=False, dq_dtype=None, flatten_axis=-1
    ) -> ScaledTensor1x:
        """Quantize function helper for delayed scaling FP8.

        Args:
            x: Input tensor to quantize
            is_colwise: Whether to use column-wise quantization
            dq_dtype: Data type for dequantized values
            flatten_axis: The quantization axis for the tensor
        Returns:
            A ScaledTensor1x containing the quantized data
        """
        if isinstance(x, jnp.ndarray):
            x = NoScaleTensor(data=x, amax=None)

        dq_dtype = dq_dtype if dq_dtype is not None else x.data.dtype

        compute_dtype = jnp.float32
        dtype_max = (jnp.finfo(self.q_dtype).max).astype(compute_dtype)
        scaled_x = x.data.astype(compute_dtype) * self.scale

        # quantize() in the old dot.py do this way, leave this code block here for future debugging
        # compute_dtype = x.dtype
        # dtype_max = (jnp.finfo(self.q_dtype).max).astype(compute_dtype)
        # scaled_x = x * self.scale.astype(compute_dtype)

        clipped_scaled_x = jnp.clip(scaled_x, -dtype_max, dtype_max).astype(self.q_dtype)
        scale_inv = 1.0 / self.scale
        amax = x.amax or jnp.max(jnp.abs(x.data)).reshape((1,))
        # Note, this updating of amax here will only be called once because the "quantize" method impl inherited from CurrentScaleQuantizer only calls _quantize_func once then transposes the result for colwise quantization. So we don't have to worry about update being called twice for 2x2x quantization.
        self.update(amax)
        return ScaledTensorFactory.create_1x(
            data=clipped_scaled_x,
            scale_inv=scale_inv,
            scaling_mode=self.scaling_mode,
            dq_dtype=dq_dtype,
            flatten_axis=flatten_axis,
        )

    @staticmethod
    @jax.jit
    def _update_amax_history(amax_history, new_amax):
        """Update AMAX history with new maximum value.

        Args:
            amax_history: Current AMAX history
            new_amax: New maximum value to add

        Returns:
            Updated AMAX history
        """
        amax_history = amax_history.at[0].set(new_amax.reshape((1,))[0])
        return amax_history

    @staticmethod
    @partial(jax.jit, static_argnums=(2, 3, 4))
    def _compute_scale(
        amax_history, scale, q_dtype, amax_compute_algo: AmaxComputeAlgo, margin: float
    ):
        """Compute new scale based on AMAX history.

        Args:
            amax_history: History of maximum absolute values
            scale: Current scale
            q_dtype: Quantization data type

        Returns:
            Updated scale value
        """
        # 2. Calculate the current scale
        if amax_compute_algo is AmaxComputeAlgo.MAX:
            amax = jnp.max(amax_history, axis=-1, keepdims=True)
        else:
            amax = amax_history[0:1]

        return compute_scale_from_amax(amax, q_dtype, margin=margin, scale=scale)

    @staticmethod
    @jax.jit
    def _roll_and_reset_amax_history(amax_history):
        """Roll AMAX history and reset first element.

        Args:
            amax_history: Current AMAX history

        Returns:
            Updated AMAX history
        """
        updated_amax_history = jnp.roll(amax_history, -1, -1)
        amax_history = updated_amax_history.at[0].set(0.0)
        return amax_history

    def update(self, new_amax: jnp.ndarray):
        """Update AMAX history and compute new scale.

        Args:
            new_amax: New maximum absolute value to add to history
        """
        amax_history = self._update_amax_history(self.amax_history, new_amax)
        self.scale = self._compute_scale(
            amax_history, self.scale, self.q_dtype, self.amax_compute_algo, self.margin
        )
        self.amax_history = self._roll_and_reset_amax_history(amax_history)


@register_pytree_node_class
@dataclass
class BlockScaleQuantizer(Quantizer):
    """Quantizer implementation using block-based scaling.

    This quantizer uses block scaling mode with FP8 scales and block-based
    quantization for improved efficiency.

    Attributes:
        scaling_mode: Set to NVTE_MXFP8_1D_SCALING
        q_layout: Quantization axis (default: ROWWISE_COLWISE)
    """

    scaling_mode: ScalingMode = ScalingMode.MXFP8_1D_SCALING
    q_layout: QuantizeLayout = QuantizeLayout.ROWWISE_COLWISE
    data_layout: str = "NN"

    def _quantize_func(self, x, is_colwise=False, dq_dtype=None, flatten_axis=-1) -> ScaledTensor1x:
        """Quantize function helper for block scaling FP8.

        Args:
            x: Input tensor to quantize
            is_colwise: Whether to use column-wise quantization
            dq_dtype: Data type for dequantized values
            flatten_axis: The quantization axis for the tensor

        Returns:
            A ScaledTensor1x containing the quantized data
        """
        if isinstance(x, NoScaleTensor):
            # No need for amax in MXFP8 block scaling, so simply extract the jnp.ndarray data tensor from the NoScaleTensor x.
            x = x.data

        # TODO(Phuong): use quantize_func from JAX
        if flatten_axis < 0:
            flatten_axis = x.ndim + flatten_axis
        assert (
            0 <= flatten_axis < x.ndim
        ), f"Invalid flatten_axis: {flatten_axis} for tensor of shape {x.shape}"

        dq_dtype = dq_dtype if dq_dtype is not None else x.dtype
        x_shape = x.shape
        scale_shape = self.scaling_mode.get_scale_shape(
            x_shape, is_colwise=is_colwise, is_padded=False, flatten_axis=flatten_axis
        )
        scale_dtype = self.scaling_mode.get_scale_dtype()
        x = x.reshape(
            *x_shape[: flatten_axis - 1],
            scale_shape[flatten_axis - 1],
            int(x_shape[flatten_axis - 1] / scale_shape[flatten_axis - 1]),
            *x_shape[flatten_axis:-1],
            scale_shape[-1],
            int(x_shape[-1] / scale_shape[-1]),
        )
        amax = jnp.max(jnp.abs(x), axis=(flatten_axis + 2 - 2, -1), keepdims=True)
        MAX = jnp.finfo(self.q_dtype).max.astype(jnp.float32)
        scales = amax.astype(jnp.float32) / MAX

        scales_q = self._cast_to_e8m0_with_rounding_up(scales)
        scaled_x = x / self._e8m0_to_dtype(scales_q, jnp.float32)

        clipped_x = jnp.clip(scaled_x, -MAX, MAX)
        x_q = clipped_x.astype(self.q_dtype).reshape(x_shape)
        scales_q = scales_q.reshape(scale_shape).view(scale_dtype)

        return ScaledTensorFactory.create_1x(
            x_q,
            scales_q,
            scaling_mode=self.scaling_mode,
            is_colwise=is_colwise,
            dq_dtype=dq_dtype,
            flatten_axis=flatten_axis,
        )

    def _cast_to_e8m0_with_rounding_up(self, scales):
        """Cast scales to E8M0 format with rounding up.

        Args:
            scales: Input scales to convert

        Returns:
            Scales in E8M0 format
        """
        temp = scales.astype(jnp.float32).view(jnp.uint32)
        exp = temp >> 23
        mant = temp & 0x7FFFFF
        is_ru = jnp.logical_and(
            jnp.logical_and((mant > 0), (exp != 0xFE)),
            ~jnp.logical_and((exp == 0), (mant <= 0x400000)),
        )
        exp = jnp.where(is_ru, exp + 1, exp)
        new_scales = exp.astype(jnp.uint8)
        return new_scales

    def _e8m0_to_dtype(self, x, dtype):
        """Convert E8M0 format to specified data type.

        Args:
            x: Input in E8M0 format
            dtype: Target data type

        Returns:
            Converted values in target data type
        """
        temp = x.astype(jnp.uint32)
        exp = temp << 23
        new_x = exp.view(jnp.float32)
        near_zero_value = 2**-15 if dtype == jnp.float16 else 2**-127
        new_x = jnp.where(new_x == 0, jnp.array(near_zero_value, jnp.float32), new_x)
        return new_x.astype(dtype)


@register_pytree_node_class
@dataclass
class NVFP4Quantizer(Quantizer):
    """Quantizer implementation using current scaling.

    This quantizer uses current scaling mode with float32 scales

    Attributes:
        scaling_mode: Set to NVFP4_1D_SCALING or NVFP4_2D_SCALING
        q_layout: Quantization axis
        data_layout: Data layout string (default: "NT")
        stochastic_rounding_rng_state: RNG state for stochastic rounding, must be of shape (4,) and dtype uint32. If None, stochastic rounding is disabled.
        use_rht: Whether to apply Randomized Hadamard Transform (RHT) before quantization.
    """

    scaling_mode: ScalingMode = ScalingMode.NVFP4_1D_SCALING
    q_layout: QuantizeLayout = QuantizeLayout.ROWWISE_COLWISE
    data_layout: str = "NT"
    use_rht: bool = False
    stochastic_rounding_rng_state: Optional[jnp.ndarray] = None

    def __post_init__(self):
        assert (
            self.q_dtype == jnp.float4_e2m1fn
        ), "NVFP4 quantization must use a q_dtype of float4_e2m1fn"
        assert self.scaling_mode.is_nvfp4_scaling, "NVFP4Quantizer must use NVFP4 scaling modes"

    def tree_flatten(self):
        """Flatten the quantizer for JAX tree operations.

        Returns:
            Tuple of (children, aux_data) for tree operations
        """
        children = (self.stochastic_rounding_rng_state,)
        aux_data = (
            self.q_dtype,
            self.scaling_mode,
            self.q_layout,
            self.data_layout,
            self.checkpoint_name,
            self.use_rht,
        )
        return (children, aux_data)

    @classmethod
    def tree_unflatten(cls, aux_data, children):
        """Reconstruct a quantizer from its flattened representation.

        Args:
            aux_data: Auxiliary data containing quantizer parameters
            children: Unused children data

        Returns:
            A reconstructed Quantizer instance
        """
        stochastic_rounding_rng_state = children[0]
        return cls(*aux_data, stochastic_rounding_rng_state=stochastic_rounding_rng_state)

    def _apply_stochastic_rounding(self, x):
        assert (
            self.stochastic_rounding_rng_state is not None
        ), "Stochastic rounding RNG state is not initialized"
        expected_sr_rng_state_shape = (get_num_devices_in_mesh(), 4)
        assert self.stochastic_rounding_rng_state.shape == expected_sr_rng_state_shape, (
            "Stochastic rounding RNG state must be of shape (num_devices_in_mesh, 4). Expected"
            f" {expected_sr_rng_state_shape}, but got {self.stochastic_rounding_rng_state.shape}"
        )
        assert (
            self.stochastic_rounding_rng_state.dtype == jnp.uint32
        ), "Stochastic rounding RNG state must be of dtype uint32"

        # Default RNG state in JAX expects 2x 32-bit integers, use first 2 uint32s for initial state and fold in the other 2 uint32s
        key_bits = jnp.array(
            [
                # only take the first device's RNG state as the pure-JAX stochastic rounding impl only uses a single-device
                self.stochastic_rounding_rng_state[0][0],
                self.stochastic_rounding_rng_state[0][1],
            ],
            dtype=jnp.uint32,
        )
        key = jax.random.wrap_key_data(key_bits)
        key = jax.jit(jax.random.fold_in)(key, self.stochastic_rounding_rng_state[0][2])
        key = jax.jit(jax.random.fold_in)(key, self.stochastic_rounding_rng_state[0][3])

        abs_x = jnp.abs(x)
        sign_x = jnp.sign(x)

        floor = (
            (abs_x >= 0.5) * 0.5
            + (abs_x >= 1) * 0.5
            + (abs_x >= 2)
            + (abs_x >= 3)
            + (abs_x >= 4)
            + (abs_x >= 6) * 2
        )
        ceil = (
            0.5
            + (abs_x > 0.5) * 0.5
            + (abs_x > 1) * 1
            + (abs_x > 2)
            + (abs_x > 3)
            + (abs_x > 4) * 2
        )
        frac = (abs_x - floor) / (ceil - floor)

        rand = jax.random.uniform(key, abs_x.shape)
        return sign_x * jnp.where(frac >= rand, ceil, floor)

    def _quantize_func(self, x, is_colwise=False, dq_dtype=None, flatten_axis=-1) -> ScaledTensor1x:
        """Quantize function helper for block scaling FP8.

        Args:
            x: Input tensor to quantize
            is_colwise: Whether to use column-wise quantization
            dq_dtype: Data type for dequantized values
            flatten_axis: The quantization axis for the tensor

        Returns:
            A ScaledTensor1x containing the quantized data
        """
        # TODO(Phuong): use quantize_func from JAX
        if flatten_axis < 0:
            flatten_axis = x.ndim + flatten_axis
        assert (
            0 <= flatten_axis < x.ndim
        ), f"Invalid flatten_axis: {flatten_axis} for tensor of shape {x.shape}"

        should_apply_rht = self.scaling_mode == ScalingMode.NVFP4_1D_SCALING and is_colwise

        global_amax = None
        if isinstance(x, NoScaleTensor):
            global_amax = (
                x.amax if not should_apply_rht else None
            )  # RHT changes the amax so don't use precalculated amax for colwise 1D nvfp4 quantization with RHT
            x = x.data

        # Transpose if required
        rowwise_flatten_axis = flatten_axis
        data_layout = self.data_layout[0]
        if is_colwise:
            x = jnp.transpose(x, (*range(flatten_axis, x.ndim), *range(flatten_axis)))
            data_layout = self.data_layout[1]
            # convert flatten_axis from N layout to T layout
            flatten_axis = x.ndim - flatten_axis
        x_shape = x.shape

        # We currently only have a single flag 'use_rht' on the quantizer. To avoid an unused rowwise flag, we assume RHT is only used for colwise quantization for now.
        use_rht = self.use_rht and is_colwise and self.scaling_mode == ScalingMode.NVFP4_1D_SCALING
        if use_rht:
            x = apply_rht(x)

        dq_dtype = dq_dtype if dq_dtype is not None else x.dtype
        scale_shape = self.scaling_mode.get_scale_shape(
            x_shape,
            data_layout=data_layout,
            is_colwise=is_colwise,
            is_padded=False,
            flatten_axis=rowwise_flatten_axis,
        )
        scale_dtype = self.scaling_mode.get_scale_dtype()
        x = x.reshape(
            *x_shape[: flatten_axis - 1],
            scale_shape[flatten_axis - 1],
            int(x_shape[flatten_axis - 1] / scale_shape[flatten_axis - 1]),
            *x_shape[flatten_axis:-1],
            scale_shape[-1],
            int(x_shape[-1] / scale_shape[-1]),
        )

        # Dtype max constants
        DATA_DTYPE_MAX = jnp.finfo(self.q_dtype).max.astype(jnp.float32)
        SCALE_DTYPE_MAX = jnp.finfo(scale_dtype).max.astype(jnp.float32)

        # Level 1: Current Tensor Scaling
        global_amax = (
            global_amax
            if global_amax is not None
            else jnp.max(jnp.abs(x)).reshape((1,)).astype(jnp.float32)
        )
        tensor_scale = DATA_DTYPE_MAX * SCALE_DTYPE_MAX / global_amax
        tensor_scale = jnp.minimum(
            tensor_scale, jnp.array(jnp.finfo(jnp.float32).max, dtype=jnp.float32)
        )
        tensor_scale = jnp.where(
            tensor_scale == jnp.array(0.0, dtype=jnp.float32),
            jnp.array(1.0, dtype=jnp.float32),
            tensor_scale,
        )
        tensor_scale_inv = 1.0 / tensor_scale

        # Level 2: Block Scaling
        block_amax = jnp.max(jnp.abs(x), axis=(flatten_axis + 2 - 2, -1), keepdims=True).astype(
            jnp.float32
        )
        block_scale_inv = jnp.divide(block_amax, DATA_DTYPE_MAX)
        block_scale_inv = block_scale_inv * tensor_scale
        block_scale_inv = jnp.minimum(
            block_scale_inv, jnp.array(jnp.finfo(jnp.float32).max, dtype=jnp.float32)
        )
        block_scale_inv = jnp.clip(block_scale_inv, -SCALE_DTYPE_MAX, SCALE_DTYPE_MAX)
        # We cast block_scale_inv to scale_dtype here to account for any rounding during the cast. This will ensure the quantized data incorporates the rounded scale value into its computation so dequantization is accurate.
        block_scale_inv = block_scale_inv.astype(scale_dtype)
        # Note, with JIT jax removes this intermediate cast leading to slightly incorrect results during DQ and worse convergence to the original tensor during many samples of Q+SR->DQ. So we use reduce_precision to simulate the cast to scale_dtype.
        assert scale_dtype == jnp.float8_e4m3fn, "Only float8_e4m3fn is supported for scale_dtype"
        block_scale_inv = jax.lax.reduce_precision(block_scale_inv, 4, 3)
        block_scale = jnp.minimum(
            jnp.divide(1.0, block_scale_inv.astype(jnp.float32) * tensor_scale_inv),
            jnp.array(jnp.finfo(jnp.float32).max, dtype=jnp.float32),
        )

        # Apply scaling
        scaled_x = x.astype(jnp.float32) * block_scale
        if self.stochastic_rounding_rng_state is not None:
            scaled_x = self._apply_stochastic_rounding(scaled_x)
        clipped_x = jnp.clip(scaled_x, -DATA_DTYPE_MAX, DATA_DTYPE_MAX)

        # Cast to the right dtype
        quantized_data = clipped_x.reshape(x_shape).astype(self.q_dtype)
        block_scale_inv = block_scale_inv.reshape(scale_shape).astype(scale_dtype)

        # In the 2D scaling mode, the scale shape is 2D but it needs to be broadcasted to 1D for GEMM.
        # TODO(Phuong): expose this broadcast_2d_scale_shape_to_1d option to the
        # quantizer.quantize() API
        broadcasted_1d_scale_shape = self.scaling_mode.get_scale_shape(
            x_shape,
            data_layout=data_layout,
            is_colwise=is_colwise,
            is_padded=False,
            flatten_axis=rowwise_flatten_axis,
            broadcast_2d_scale_shape_to_1d=True,
        )

        # Broadcast and tile x to match the target shape
        def repeat_to_shape(x, target_shape):
            x_shape = x.shape
            reps = [int(t // s) for s, t in zip(x_shape, target_shape)]
            return jnp.tile(x, reps)

        block_scale_inv = repeat_to_shape(block_scale_inv, broadcasted_1d_scale_shape)

        return ScaledTensorFactory.create_1x(
            data=quantized_data,
            data_layout=data_layout,
            is_colwise=is_colwise,
            scale_inv=block_scale_inv,
            amax=global_amax,
            scaling_mode=self.scaling_mode,
            dq_dtype=dq_dtype,
            flatten_axis=rowwise_flatten_axis,
            has_rht_applied=use_rht,
        )


@register_pytree_node_class
@dataclass
class QuantizerSet:
    """Set of quantizers for different tensor types.

    This class manages quantizers for input tensors, kernel tensors, and
    gradient tensors.

    Attributes:
        x: Quantizer for input tensors
        kernel: Quantizer for kernel tensors
        dgrad: Quantizer for gradient tensors
    """

    x: Optional[Quantizer]
    kernel: Optional[Quantizer]
    dgrad: Optional[Quantizer]

    def tree_flatten(self):
        """Flatten the quantizer set for JAX tree operations.

        Returns:
            Tuple of (children, aux_data) for tree operations
        """
        children = (self.x, self.kernel, self.dgrad)
        aux_data = ()
        return (children, aux_data)

    @classmethod
    def tree_unflatten(cls, aux_data, children):
        """Reconstruct a quantizer set from its flattened representation.

        Args:
            aux_data: Unused auxiliary data
            children: Tuple of quantizers

        Returns:
            A reconstructed QuantizerSet instance
        """
        return cls(*aux_data, *children)


@register_pytree_node_class
@dataclass
class GroupedQuantizer(Quantizer):
    """Quantizer for grouped arrays.

    This class extends Quantizer to support quantization of arrays in grouped manner,
    where elements are grouped along a specified axis then quantized separately.

    Attributes:
        data_layout: The data layout specification
        n_groups: Number of groups for quantization
        quantizers: Tuple of quantizers for each group
    """

    data_layout: str = None
    n_groups: int = 1
    quantizers: Tuple[Quantizer] = field(default_factory=lambda: (None,))

    def tree_flatten(self):
        """Flatten the quantizer for JAX tree operations.

        Returns:
            Tuple of (children, aux_data) for tree operations
        """
        children = (self.quantizers,)
        aux_data = (
            self.q_dtype,
            self.scaling_mode,
            self.q_layout,
            self.data_layout,
            self.checkpoint_name,
            self.n_groups,
        )
        return (children, aux_data)

    def __post_init__(self):
        if self.quantizers[0] is None:
            quantizers = QuantizerFactory.create(
                self.n_groups, self.scaling_mode, self.q_dtype, self.q_layout
            )
            self.quantizers = (quantizers,) if not isinstance(quantizers, tuple) else quantizers
        self.data_layout = self.quantizers[0].data_layout

    def _create_grouped_tensor_from_tensor_list(
        self, tensor_list, group_sizes, original_shape, group_axis, mode
    ):
        # mode 0 = concate, mode 1 = add
        # TODO(Ming Huang): Consider to apply Enum for mode.
        assert mode in [0, 1]
        grouped_data = (
            [] if mode == 0 else jnp.zeros(tensor_list[0].data.shape, tensor_list[0].data.dtype)
        )
        grouped_scale_inv = []

        for tensor in tensor_list:
            if mode == 0:
                grouped_data.append(tensor.data.flatten())
            else:
                grouped_data += tensor.data
            grouped_scale_inv.append(tensor.scale_inv.flatten())

        grouped_data = jnp.concatenate(grouped_data) if mode == 0 else grouped_data.flatten()
        grouped_scale_inv = jnp.concatenate(grouped_scale_inv)

        return ScaledTensorFactory.create_1x(
            grouped_data,
            grouped_scale_inv,
            scaling_mode=self.scaling_mode,
            dq_dtype=tensor_list[0].dq_dtype,
            is_colwise=tensor_list[0].is_colwise,
            data_layout=tensor_list[0].data_layout,
            flatten_axis=tensor_list[0].flatten_axis,
            group_sizes=group_sizes,
            original_shape=original_shape,
            group_axis=group_axis,
        )

    def _quantize_func(self, *args, **kwargs):
        pass

    def quantize(
        self,
        x,
        is_rowwise: bool = None,
        is_colwise: bool = None,
        dq_dtype=None,
        flatten_axis=-1,
        group_sizes=None,
        group_axis=0,
    ):
        """Quantize a tensor in grouped manner.

        Expected input shape: [M, K] or [G, K, N]
        Split to x.shape[group_axis] number of groups if group_sizes is not given

        Args:
            x: Input tensor to quantize
            is_rowwise: Whether to use row-wise quantization
            is_colwise: Whether to use column-wise quantization
            dq_dtype: Data type for dequantized values
            flatten_axis: The axis along which the tensor could be flattened to 2D (default: -1)
            group_sizes: Array of ints containing the size of each group (default: None)
            group_axis: The axis along which grouping is performed (default: 0)

        Returns:
            A ScaledTensor1x or ScaledTensor2x containing the quantized data
        """
        assert group_axis == 0, "Only group_axis == 0 is supported now!"

        dq_dtype = dq_dtype if dq_dtype is not None else x.dtype
        if flatten_axis < 0:
            flatten_axis += x.ndim
        assert 0 < flatten_axis < x.ndim, "flatten_axis is out of bounds!"

        is_rowwise = is_rowwise if is_rowwise is not None else self.q_layout.has_rowwise
        is_colwise = is_colwise if is_colwise is not None else self.q_layout.has_colwise
        assert is_rowwise or is_colwise, "No quantization layout is specified"

        original_shape = x.shape

        if group_sizes is not None:
            assert not is_colwise, "Not yet implememted!"
            assert group_sizes.ndim == 1, (
                "GroupedQuantizer only support 1D group_sizes, got group_sizes.ndim ="
                f" {group_sizes.ndim}"
            )

            _zeros = partial(jax.lax.full_like, fill_value=0)

            x_iota = jax.lax.broadcasted_iota(group_sizes.dtype, x.shape, 0)
            group_ends = jnp.cumulative_sum(group_sizes)
            group_starts = jax.lax.concatenate(
                [_zeros(group_sizes)[:1], group_ends[:-1]],
                dimension=0,
            )
            x_zero = _zeros(x)

            tensor_list = []
            for i in range(len(group_sizes)):
                mask = jax.lax.bitwise_and(group_starts[i] <= x_iota, x_iota < group_ends[i])
                x_selected = jax.lax.select(mask, x, x_zero)
                tensor = self.quantizers[i].quantize(
                    x_selected, is_rowwise, is_colwise, dq_dtype, flatten_axis
                )
                tensor_list.append(tensor)
            combine_mode = 1  # Add
        else:
            group_sizes = jnp.ones(x.shape[group_axis], dtype=jnp.int32)
            x = jnp.split(x, x.shape[group_axis], axis=group_axis)

            tensor_list = []
            for i in range(len(group_sizes)):
                tensor = self.quantizers[i].quantize(
                    x[i], is_rowwise, is_colwise, dq_dtype, flatten_axis
                )
                tensor_list.append(tensor)
            combine_mode = 0  # Concate

        grouped_rowwise_tensor = grouped_colwise_tensor = None
        if is_rowwise:
            rowwise_tensor_list = [tensor.get_rowwise_tensor() for tensor in tensor_list]
            grouped_rowwise_tensor = self._create_grouped_tensor_from_tensor_list(
                rowwise_tensor_list, group_sizes, original_shape, group_axis, combine_mode
            )
        if is_colwise:
            colwise_tensor_list = [tensor.get_colwise_tensor() for tensor in tensor_list]
            grouped_colwise_tensor = self._create_grouped_tensor_from_tensor_list(
                colwise_tensor_list, group_sizes, original_shape, group_axis, combine_mode
            )

        if is_colwise and is_rowwise:
            return ScaledTensor2x(grouped_rowwise_tensor, grouped_colwise_tensor)
        if is_colwise:
            return grouped_colwise_tensor
        return grouped_rowwise_tensor

    def get_scale_shapes(self, data_shape, is_padded=True, flatten_axis=-1, group_sizes=None):
        assert group_sizes, "Empty group_sizes was given!"
        return self.scaling_mode.get_grouped_scale_shape_2x(
            data_shape, group_sizes, is_padded, flatten_axis
        )


@dataclass
class QuantizerFactory:
    """Factory class for creating quantizers.

    This class provides static methods to create individual quantizers and
    sets of quantizers with various configurations.
    """

    quantizer_type_map = {
        ScalingMode.DELAYED_TENSOR_SCALING: DelayedScaleQuantizer,
        ScalingMode.CURRENT_TENSOR_SCALING: CurrentScaleQuantizer,
        ScalingMode.MXFP8_1D_SCALING: BlockScaleQuantizer,
        ScalingMode.NVFP4_1D_SCALING: NVFP4Quantizer,
        ScalingMode.NVFP4_2D_SCALING: NVFP4Quantizer,
    }

    @staticmethod
    def create(
        n_quantizers: int = 1,
        scaling_mode: ScalingMode = None,
        q_dtype: jnp.dtype = None,
        q_layout: QuantizeLayout = None,
        n_groups: int = None,
        checkpoint_name: Optional[str] = None,
        **kwargs,
    ) -> Quantizer:
        """Create one or more quantizers with specified parameters.

        Args:
            n_quantizers: Number of quantizers to create
            scaling_mode: Scaling mode to use
            q_dtype: Quantization data type
            q_layout: Quantization axis
            flatten_axis: The quantization axis for the tensor
            n_groups: Number of quantizers if GroupedQuantizer
            checkpoint_name: Optional name for checkpointing quantizations
            **kwargs: Additional arguments for quantizer initialization

        Returns:
            A single quantizer or tuple of quantizers
        """
        assert isinstance(scaling_mode, ScalingMode), "Invalid scaling_mode type"
        if n_groups:
            if n_quantizers != 1:
                warnings.warn(
                    "Using more than one GroupedQuantizer for a grouped input is not recommended"
                )
            quantizer_type = GroupedQuantizer
            kwargs["n_groups"] = n_groups
        else:
            quantizer_type = QuantizerFactory.quantizer_type_map.get(scaling_mode)

        if scaling_mode == ScalingMode.NO_SCALING:
            quantizers = [None] * n_quantizers
        else:
            quantizers = []
            for _ in range(n_quantizers):
                quantizers.append(
                    quantizer_type(
                        q_dtype=q_dtype,
                        scaling_mode=scaling_mode,
                        q_layout=q_layout,
                        checkpoint_name=checkpoint_name,
                        **kwargs,
                    )
                )
        return quantizers[0] if len(quantizers) == 1 else tuple(quantizers)

    @staticmethod
    def _create_set(
        x_scaling_mode,
        kernel_scaling_mode,
        grad_scaling_mode,
        fwd_dtype,
        bwd_dtype,
        is_2x2x,
        n_groups,
        is_inference_mode=False,
        checkpoint_name: Optional[str] = None,
        **kwargs,
    ) -> QuantizerSet:
        """Create a set of quantizers for forward and backward passes.

        Args:
            x_scaling_mode: Scaling mode to use for input tensor 'x'
            kernel_scaling_mode: Scaling mode to use for kernel tensor
            grad_scaling_mode: Scaling mode to use for gradient tensor
            fwd_dtype: Data type for forward pass
            bwd_dtype: Data type for backward pass
            is_2x2x: Whether to use 2x2x quantization
            n_groups
            is_inference_mode: Whether to create quantizers for inference mode. This option is not fully supported yet
            checkpoint_name: Optional name for checkpointing quantizations
            **kwargs: Additional arguments for quantizer initialization

        Returns:
            A QuantizerSet instance
        """
        if is_2x2x:
            q_layout_x = q_layout_kernel = q_layout_dgrad = QuantizeLayout.ROWWISE_COLWISE
        else:
            q_layout_x = q_layout_kernel = q_layout_dgrad = QuantizeLayout.ROWWISE
            if kernel_scaling_mode.is_1d_block_scaling():
                q_layout_kernel = QuantizeLayout.COLWISE
            if is_inference_mode:
                q_layout_dgrad = None

        if "quantize_meta_set" in kwargs:
            quantize_meta_set = kwargs.get("quantize_meta_set")
            args_x = quantize_meta_set.x.get_kwargs_dictionary()
            args_kernel = quantize_meta_set.kernel.get_kwargs_dictionary()
            args_grad = quantize_meta_set.grad.get_kwargs_dictionary()
        else:
            args_x = args_kernel = args_grad = {}

        q_x = QuantizerFactory.create(
            1,
            x_scaling_mode,
            fwd_dtype,
            q_layout_x,
            n_groups,
            checkpoint_name=checkpoint_name,
            **args_x,
        )
        q_kernel = QuantizerFactory.create(
            1,
            kernel_scaling_mode,
            fwd_dtype,
            q_layout_kernel,
            n_groups,
            checkpoint_name=checkpoint_name,
            **args_kernel,
        )
        q_dgrad = QuantizerFactory.create(
            1,
            grad_scaling_mode,
            bwd_dtype,
            q_layout_dgrad,
            n_groups,
            checkpoint_name=checkpoint_name,
            **args_grad,
        )
        return QuantizerSet(x=q_x, kernel=q_kernel, dgrad=q_dgrad)

    @staticmethod
    def create_set(
        n_quantizer_sets: int = 1,
        scaling_mode: Optional[ScalingMode] = None,
        fwd_dtype: jnp.dtype = None,
        bwd_dtype: jnp.dtype = None,
        is_2x2x: bool = None,
        n_groups: int = None,
        checkpoint_name: Optional[str] = None,
        # TODO(jberchtold): rename fp8_recipe to quantization_recipe
        fp8_recipe: Optional[recipe.Recipe] = None,
        **kwargs,
    ) -> tuple[Union[tuple[Quantizer], None]]:
        """Create one or more sets of quantizers.

        Args:
            n_quantizer_sets: Number of quantizer sets to create
            scaling_mode: Scaling mode to use, default is get the scaling mode from the specified or global recipe
            fwd_dtype: Data type for forward pass, default is get the fwd dtype from the specified or global recipe
            bwd_dtype: Data type for backward pass, default is get the bwd dtype from the specified or global recipe
            is_2x2x: Whether to use 2x2x quantization, default is determined based on the specified or global recipe
            n_groups:
            checkpoint_name: Optional name for checkpointing quantizations
            fp8_recipe: Recipe to use for quantization. Scaling mode can be specified directly via the scaling_mode parameter or indirectly via recipe. Recipe is preferred as it will support additional recipes in future where scaling mode differs between x, kernel, and grad in the quantizer set.
            **kwargs: Additional arguments for quantizer initialization

        Returns:
            A single quantizer set or tuple of quantizer sets
        """

        assert scaling_mode is None or fp8_recipe is None, (
            "Cannot specify both scaling_mode and fp8_recipe when creating a quantizer set. Scaling"
            " mode can be specified directly via the scaling_mode parameter or indirectly via"
            " recipe. Recipe is preferred as it will support additional recipes in future where"
            " scaling mode differs between x, kernel, and grad in the quantizer set."
        )

        # TODO(jberchtold): Currently this is a limitation because we only support automatically populating quantizer fields based on a given recipe when using Flax. In the generic quantizer logic, we cannot assume Flax is being used, so we require the user to provide the quantize_meta_set created by quantize_config.get_quantize_flax_meta() or the same data created by themselves if they are passing a recipe here directly.
        assert (
            fp8_recipe is None or "quantize_meta_set" in kwargs
        ), "When fp8_recipe is specified, quantize_meta_set must be provided in kwargs."

        if fp8_recipe is None:
            fp8_recipe = get_global_quantize_recipe()

        if fp8_recipe is not None:
            assert scaling_mode is None, (
                "scaling_mode should not be specified when fp8_recipe is provided either directly"
                " or through an autocast context."
            )
            assert fwd_dtype is None, (
                "fwd_dtype should not be specified when fp8_recipe is provided either directly or"
                " through an autocast context."
            )
            assert bwd_dtype is None, (
                "bwd_dtype should not be specified when fp8_recipe is provided either directly or"
                " through an autocast context."
            )
            quantize_config = get_quantize_config_with_recipe(fp8_recipe)
            x_scaling_mode = quantize_config.get_scaling_mode(TensorSource.X)
            kernel_scaling_mode = quantize_config.get_scaling_mode(TensorSource.KERNEL)
            grad_scaling_mode = quantize_config.get_scaling_mode(TensorSource.DGRAD)
            fwd_dtype = quantize_config.FWD_DTYPE
            bwd_dtype = quantize_config.BWD_DTYPE
            is_inference_mode = quantize_config.INFERENCE_MODE
        else:
            if scaling_mode is not None:
                x_scaling_mode = scaling_mode
                kernel_scaling_mode = scaling_mode
                grad_scaling_mode = scaling_mode
            else:
                # TODO(jberchtold): make a way to explicitly pass a no scaling recipe here if we need other quantization config attributes in the future since NoOpQuantizeConfig already exists, we just can't use it here with direct recipe passing because we cannot differentiate between fp8_recipe=None meaning no recipe specified vs explicitly no quantization desired.
                x_scaling_mode = ScalingMode.NO_SCALING
                kernel_scaling_mode = ScalingMode.NO_SCALING
                grad_scaling_mode = ScalingMode.NO_SCALING
            is_inference_mode = False

        if is_2x2x is None:
            # TODO(Jeremy): check x, kernel, grad separately for 2x
            if x_scaling_mode.is_1d_block_scaling():
                is_2x2x = True
            elif x_scaling_mode.is_tensor_scaling():
                is_2x2x = not is_fp8_gemm_with_all_layouts_supported()
            else:  # NO_SCALING ignores is_2x2x for now
                is_2x2x = False
        assert not is_inference_mode, "Inference mode is not supported yet!"

        q_set = []
        for _ in range(n_quantizer_sets):
            q_set.append(
                QuantizerFactory._create_set(
                    x_scaling_mode=x_scaling_mode,
                    kernel_scaling_mode=kernel_scaling_mode,
                    grad_scaling_mode=grad_scaling_mode,
                    fwd_dtype=fwd_dtype,
                    bwd_dtype=bwd_dtype,
                    is_2x2x=is_2x2x,
                    n_groups=n_groups,
                    is_inference_mode=is_inference_mode,
                    checkpoint_name=checkpoint_name,
                    **kwargs,
                )
            )

        return q_set[0] if len(q_set) == 1 else tuple(q_set)


noop_quantizer_set = QuantizerFactory.create_set(scaling_mode=ScalingMode.NO_SCALING, is_2x2x=False)
