# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""Dense layer transformation operations for Transformer Engine in JAX.

This module provides optimized dense layer transformation operations for transformer
architectures, including support for quantization and automatic differentiation.
It implements matrix multiplication with optional bias addition and supports
customizable contracting dimensions for flexible tensor operations.
"""

from typing import Tuple, Sequence
from functools import partial
import warnings
import jax
import jax.numpy as jnp

from . import cpp_extensions as tex
from .cpp_extensions.quantization import AmaxScope
from .quantize import (
    ScaledTensorFactory,
    ScalingMode,
    QuantizeLayout,
    QuantizerSet,
    noop_quantizer_set,
    with_sharding_constraint_by_logical_axes,
    is_fp8_gemm_with_all_layouts_supported,
    TensorUsage,
    get_quantize_config,
)


def _all_gather_kernel(kernel, mesh_axis, axis_idx):
    assert mesh_axis is not None
    assert 0 < axis_idx < len(kernel.shape)

    # TODO(Ming Hunag): Add a condition branch for with/without shmap.
    kernel_shape = kernel.shape
    kernel_whole_shape = (*kernel_shape[:axis_idx], -1, *kernel_shape[axis_idx + 1 :])
    global_kernel = jax.lax.all_gather(kernel, mesh_axis, axis=axis_idx)
    global_kernel = global_kernel.reshape(*kernel_whole_shape)
    return global_kernel


def _psum_scatter_kernel(kernel, scattered_kernel_shape, mesh_axis, axis_idx):
    assert mesh_axis is not None
    assert 0 < axis_idx < len(scattered_kernel_shape)

    # TODO(Ming Hunag): Add a condition branch for with/without shmap.
    kernel = kernel.reshape(
        *scattered_kernel_shape[:axis_idx],
        -1,
        scattered_kernel_shape[axis_idx],
        *scattered_kernel_shape[axis_idx + 1 :],
    )
    kernel = jax.lax.psum_scatter(kernel, mesh_axis, scatter_dimension=axis_idx)
    kernel = kernel.reshape(scattered_kernel_shape)
    return kernel


def dense(
    x: jnp.ndarray,
    kernel: jnp.ndarray,
    bias: jnp.ndarray = None,
    contracting_dims: Tuple[Sequence[int], Sequence[int]] = ((1,), (0,)),
    batch_sequence_transpose: bool = False,
    input_axes: Tuple[str, ...] = None,
    kernel_axes: Tuple[str, ...] = None,
    output_axes: Tuple[str, ...] = None,
    collective_op_set: tex.CollectiveOpSet = tex.noop_collective_op_set,
    quantizer_set: QuantizerSet = noop_quantizer_set,
):
    """Perform dense layer transformation with optional quantization.

    This function implements matrix multiplication with optional bias addition,
    supporting quantization and custom contracting dimensions. It's optimized
    for transformer architectures and supports automatic differentiation.

    Args:
        x: Input tensor
        kernel: Weight matrix for the dense layer transformation
        bias: Optional bias tensor to add after the transformation
        contracting_dims: Tuple of sequences specifying which dimensions to contract
        batch_sequence_transpose: Transpose the batch and sequence dimensions of the input tensor.
        input_axes: Logical axes for sharding the activation input
        kernel_axes: Logical axes for sharding the weight matrix
        output_axes: Logical axes for sharding the output
        collective_op_set: A set of CollectiveOp objects for forward and backward passes.
        quantizer_set: QuantizerSet which contains quantizers for different tensor types

    Returns:
        Transformed output tensor
    """
    if batch_sequence_transpose:
        warnings.warn("batch_sequence_transpose is not well tested, use with caution!")

    if not get_quantize_config().is_fp8_enabled():
        input_dtype = x.dtype
        kernel = kernel.astype(input_dtype)

    output = _dense(
        x,
        kernel,
        bias,
        contracting_dims,
        batch_sequence_transpose,
        input_axes,
        kernel_axes,
        output_axes,
        collective_op_set,
        quantizer_set,
    )
    return output


@partial(jax.custom_vjp, nondiff_argnums=(3, 4, 5, 6, 7, 8))
def _dense(
    x,
    kernel,
    bias,
    contracting_dims,
    batch_sequence_transpose,
    input_axes,
    kernel_axes,
    output_axes,
    collective_op_set,
    quantizer_set,  # need to be a diff_arg for DelayedScaling state management
):
    """Internal implementation of dense layer transformation with custom VJP.

    This function implements the core dense layer transformation logic with support
    for custom vector-Jacobian product (VJP) for automatic differentiation.

    Args:
        x: Input tensor
        kernel: Weight matrix
        bias: Optional bias tensor
        contracting_dims: Contracting dimensions specification
        batch_sequence_transpose: Transpose the batch and sequence dimensions of the input tensor.
        input_axes: Logical axes for sharding the activation input
        output_axes: Logical axes for sharding the output_axes
        kernel_axes: Logical axes for sharding the weight matrix
        collective_op_set: A set of CollectiveOp objects for forward and backward passes.
        quantizer_set: QuantizerSet which contains quantizers for different tensor types

    Returns:
        Transformed output tensor
    """
    output, _ = _dense_fwd_rule(
        x,
        kernel,
        bias,
        contracting_dims,
        batch_sequence_transpose,
        input_axes,
        kernel_axes,
        output_axes,
        collective_op_set,
        quantizer_set,
    )
    return output


def _dense_fwd_rule(
    x,
    kernel,
    bias,
    contracting_dims,
    batch_sequence_transpose,
    input_axes,
    kernel_axes,
    output_axes,
    collective_op_set,
    quantizer_set,
):
    """Forward pass rule for dense layer transformation.

    Returns:
        Tuple of (output, context) for backward pass
    """
    x_contracting_dims, k_contracting_dims = map(
        tex.sanitize_dims, (x.ndim, kernel.ndim), contracting_dims
    )

    # Check supported input layout
    x_is_transposed = x.ndim - 1 not in x_contracting_dims
    k_is_transposed = kernel.ndim - 1 in k_contracting_dims
    assert (
        not x_is_transposed and not k_is_transposed
    ), "Dense layer only supports `NN` layout inputs, i.e. non-transposed X and Kernel."

    flatten_axis_x = -len(x_contracting_dims)
    flatten_axis_k = len(k_contracting_dims) - len(kernel.shape)

    casted_x = tex.quantize(
        x,
        flatten_axis=flatten_axis_x,
        quantizer=quantizer_set.x,
        amax_scope=AmaxScope.TPSP,
        batch_sequence_transpose=batch_sequence_transpose,
    )
    casted_x = with_sharding_constraint_by_logical_axes(casted_x, input_axes)

    casted_kernel = tex.quantize(
        kernel,
        flatten_axis=flatten_axis_k,
        quantizer=quantizer_set.kernel,
        amax_scope=AmaxScope.FSDP,
    )
    casted_kernel = with_sharding_constraint_by_logical_axes(casted_kernel, kernel_axes)

    # GEMM NN
    use_bias = bias is not None
    output = tex.gemm(
        casted_x.get_tensor(usage=TensorUsage.LHS),
        casted_kernel.get_tensor(usage=TensorUsage.RHS),
        contracting_dims=(x_contracting_dims, k_contracting_dims),
        transpose_batch_sequence=batch_sequence_transpose,
        bias=bias if not tex.gemm_uses_jax_dot() else None,
        fuse_bias=use_bias if not tex.gemm_uses_jax_dot() else False,
        collective_op=collective_op_set.forward,
    )
    output = with_sharding_constraint_by_logical_axes(output, output_axes)

    if use_bias and tex.gemm_uses_jax_dot():
        bias_new_shape = (1,) * (output.ndim - bias.ndim) + bias.shape
        output += jnp.reshape(bias, bias_new_shape)

    ctx = (
        casted_x.get_tensor(usage=TensorUsage.LHS_TRANS),
        casted_kernel.get_tensor(usage=TensorUsage.RHS_TRANS),
        x.shape,
        kernel.shape,
        use_bias,
        quantizer_set,
        flatten_axis_k,
    )
    return output, ctx


def _dense_bwd_rule(
    contracting_dims,
    batch_sequence_transpose,
    input_axes,
    kernel_axes,
    output_axes,
    collective_op_set,
    ctx,
    grad,
):
    """Backward pass rule for dense layer transformation.

    Returns:
        Tuple of gradients with respect to inputs
    """
    (
        casted_x_lhs,
        casted_kernel_rhs,
        x_shape,
        kernel_shape,
        use_bias,
        quantizer_set,
        flatten_axis_k,
    ) = ctx
    grad = with_sharding_constraint_by_logical_axes(grad, output_axes)

    fwd_x_contracting_dims, fwd_k_contracting_dims = map(
        tex.sanitize_dims, (casted_x_lhs.ndim, casted_kernel_rhs.ndim), contracting_dims
    )

    casted_grad, dbias = tex.quantize_dbias(
        grad,
        is_dbias=use_bias,
        flatten_axis=flatten_axis_k,
        quantizer=quantizer_set.dgrad,
        amax_scope=AmaxScope.TPSP,
        batch_sequence_transpose=batch_sequence_transpose,
    )

    # GEMM NT
    # k_non_contracting_dims calibrated with the shape difference of grad.ndim vs kernel.ndim
    g_contracting_dim = tuple(
        range(grad.ndim - len(kernel_shape) + len(fwd_k_contracting_dims), grad.ndim)
    )
    # k_non_contracting_dims
    k_contracting_dim = tuple(
        dim for dim in range(len(kernel_shape)) if dim not in fwd_k_contracting_dims
    )

    dgrad = tex.gemm(
        casted_grad.get_tensor(usage=TensorUsage.LHS),
        casted_kernel_rhs,
        contracting_dims=(g_contracting_dim, k_contracting_dim),
        transpose_batch_sequence=batch_sequence_transpose,
        collective_op=collective_op_set.backward,
    )

    # GEMM TN
    # x_non_contracting_dims
    g_contracting_dim = x_contracting_dim = tuple(
        range(0, len(x_shape) - len(fwd_x_contracting_dims))
    )

    wgrad = tex.gemm(
        casted_x_lhs,
        casted_grad.get_tensor(usage=TensorUsage.RHS),
        contracting_dims=(x_contracting_dim, g_contracting_dim),
        transpose_batch_sequence=batch_sequence_transpose,
    )

    dgrad = with_sharding_constraint_by_logical_axes(dgrad, input_axes)
    wgrad = with_sharding_constraint_by_logical_axes(wgrad, kernel_axes)

    return dgrad, wgrad, dbias, quantizer_set


_dense.defvjp(_dense_fwd_rule, _dense_bwd_rule)


def grouped_dense(
    x: jnp.ndarray,
    kernel: jnp.ndarray,
    group_sizes: jnp.ndarray,
    contracting_dims: Tuple[Sequence[int], Sequence[int]] = ((1,), (1,)),
    bias: jnp.ndarray = None,
    kernel_amax: jnp.ndarray = None,
    precision: jax.lax.Precision = jax.lax.Precision.DEFAULT,
    preferred_element_type: jnp.dtype = None,
    group_offset: jnp.array = None,
    quantizer_set: QuantizerSet = noop_quantizer_set,
    kernel_fsdp_info: Tuple[str, int] = (None, -1),
):
    """
    Perform grouped dense (linear) layer transformation with optional quantization.

    Args:
        x: Input tensor of shape (M, K)
        kernel: Weight matrix of shape (G, K, N)
        group_sizes: 1D array of shape (G,) specifying the size of each group
        contracting_dims: Tuple of sequences specifying which dimensions to contract
                          (currently only supports ((1,), (1,)))
        bias: Bias tensor of shape (G, N)
        kernel_amax: The amax values of weight matrix of shape (G,)
        precision: JAX precision for the GEMM operation
        preferred_element_type: Preferred data type for the output tensor
        group_offset: 1D array containing offsets for each group (not yet implemented)
        quantizer_set: Set of quantizers for FP8 quantization of the input and output
        kernel_fsdp_info: A tuple containing FSDP-related information for a weight matrix
                          represented in the format (str, int). The first element is the
                          FSDP mesh axis, and the second element is the dimension along
                          which the weight is sharded.

    Returns:
        A jnp.ndarray containing the result of the grouped linear operation
    """
    output = _grouped_dense(
        x,
        kernel,
        group_sizes,
        contracting_dims,
        bias,
        kernel_amax,
        precision,
        preferred_element_type,
        group_offset,
        quantizer_set,
        kernel_fsdp_info,
    )
    return output


@partial(jax.custom_vjp, nondiff_argnums=(3, 6, 7, 8, 10))
def _grouped_dense(
    x,
    kernel,
    group_sizes,
    contracting_dims,
    bias,
    kernel_amax,
    precision,
    preferred_element_type,
    group_offset,
    quantizer_set,
    kernel_fsdp_info,
):
    output, _ = _grouped_dense_fwd_rule(
        x,
        kernel,
        group_sizes,
        contracting_dims,
        bias,
        kernel_amax,
        precision,
        preferred_element_type,
        group_offset,
        quantizer_set,
        kernel_fsdp_info,
    )
    return output


def _grouped_dense_fwd_rule(
    x,
    kernel,
    group_sizes,
    contracting_dims,
    bias,
    kernel_amax,
    precision,
    preferred_element_type,
    group_offset,
    quantizer_set,
    kernel_fsdp_info,
):
    use_bias = bias is not None
    is_noop_quantizer_set = quantizer_set == noop_quantizer_set

    kernel_fsdp_mesh_axis, kernel_fsdp_axis_idx = kernel_fsdp_info
    kernel_fsdp_enabled = kernel_fsdp_mesh_axis is not None

    if is_noop_quantizer_set:
        grouped_gemm_x = x
        grouped_gemm_kernel = kernel
        ctx_x = x
        ctx_kernel = kernel
        flatten_axis_k = None

        if kernel_fsdp_enabled:
            kernel = _all_gather_kernel(kernel, kernel_fsdp_mesh_axis, kernel_fsdp_axis_idx)
    else:
        original_quantizer_set_kernel_q_layout = quantizer_set.kernel.q_layout

        x_contracting_dims, k_contracting_dims = contracting_dims
        flatten_axis_x = -len(x_contracting_dims)
        flatten_axis_k = len(k_contracting_dims) - len(kernel.shape) + 1  # +1 for G axis

        assert x.ndim == 2, "Grouped dense expects a 2D input tensor of shape (M, K)"
        assert kernel.ndim == 3, "Grouped dense expects a 3D kernel tensor of shape (G, K, N)"
        # Expected k_contracting_dims == (1,), need to tweak it for grouped_gemm FP8 extra transpose
        # TODO(Hua): Do we have a better way for this? What if is_gemm_with_all_layouts_supported()?
        assert x_contracting_dims == (1,) and k_contracting_dims == (1,), (
            "grouped_dense for FP8 can only handle x_contracting_dims=(1,) "
            "and k_contracting_dims=(1,) for now, "
            f"got {x_contracting_dims=} and {k_contracting_dims=}"
        )

        casted_x = tex.grouped_quantize(
            x,
            quantizer_set.x,
            group_sizes,
            flatten_axis=flatten_axis_x,
        )

        ctx_kernel_usage = TensorUsage.RHS_TRANS
        if kernel_fsdp_enabled:
            assert quantizer_set.kernel.scaling_mode in [
                ScalingMode.CURRENT_TENSOR_SCALING,
                ScalingMode.DELAYED_TENSOR_SCALING,
            ]
            # Perform `cast` only
            ctx_kernel_usage = TensorUsage.LHS
            quantizer_set.kernel.q_layout = QuantizeLayout.ROWWISE

        casted_kernel = tex.grouped_quantize(
            kernel, quantizer_set.kernel, amax=kernel_amax, flatten_axis=flatten_axis_k
        )
        contracting_dims = (x_contracting_dims, k_contracting_dims)

        # For x_contracting_dims == (1,) and k_contracting_dims == (1,), we should have
        # rowwise_casted_x.original_shape == (M, K)
        # colwise_casted_kernel.original_shape == (G, N, K)
        grouped_gemm_x = casted_x.get_tensor(usage=TensorUsage.LHS)
        ctx_x = casted_x.get_tensor(usage=TensorUsage.LHS_TRANS)
        ctx_kernel = casted_kernel.get_tensor(usage=ctx_kernel_usage)

        if kernel_fsdp_enabled:
            ctx_kernel_in_original_shape = ctx_kernel.data.reshape(ctx_kernel.original_shape)
            global_ctx_kernel_data = _all_gather_kernel(
                ctx_kernel_in_original_shape, kernel_fsdp_mesh_axis, kernel_fsdp_axis_idx
            )
            kernel_shape = global_ctx_kernel_data.shape

            ctx_kernel = ScaledTensorFactory.create_1x(
                global_ctx_kernel_data.reshape(-1),
                ctx_kernel.scale_inv,
                scaling_mode=ctx_kernel.scaling_mode,
                dq_dtype=ctx_kernel.dq_dtype,
                is_colwise=False,
                data_layout="N",
                flatten_axis=ctx_kernel.flatten_axis,
                group_sizes=ctx_kernel.group_sizes,
                original_shape=kernel_shape,
                group_axis=ctx_kernel.group_axis,
            )

            if is_fp8_gemm_with_all_layouts_supported():
                grouped_gemm_kernel = ctx_kernel
            else:
                grouped_gemm_kernel_data = global_ctx_kernel_data.transpose(0, 2, 1)
                grouped_gemm_kernel = ScaledTensorFactory.create_1x(
                    grouped_gemm_kernel_data.reshape(-1),
                    ctx_kernel.scale_inv,
                    scaling_mode=ctx_kernel.scaling_mode,
                    dq_dtype=ctx_kernel.dq_dtype,
                    is_colwise=True,
                    data_layout="T",
                    flatten_axis=ctx_kernel.flatten_axis,
                    group_sizes=ctx_kernel.group_sizes,
                    original_shape=kernel_shape,
                    group_axis=ctx_kernel.group_axis,
                )
        else:
            grouped_gemm_kernel = casted_kernel.get_tensor(usage=TensorUsage.RHS)

        # Reset quantizer_set.kernel.q_layout to align the PyTree as the given one.
        # This is needed especially when kernel_fsdp_enabled == True AND FP8 enabled.
        quantizer_set.kernel.q_layout = original_quantizer_set_kernel_q_layout

    output = tex.grouped_gemm(
        grouped_gemm_x,
        grouped_gemm_kernel,
        group_sizes,
        contracting_dims,
        bias,
        precision,
        preferred_element_type,
        group_offset,
    )

    ctx = (
        group_sizes,
        ctx_x,
        ctx_kernel,
        x.shape,
        kernel.shape,
        use_bias,
        is_noop_quantizer_set,
        quantizer_set,
        flatten_axis_k,
    )
    return output, ctx


def _grouped_dense_bwd_rule(
    contracting_dims, precision, preferred_element_type, group_offset, kernel_fsdp_info, ctx, grad
):
    fwd_x_contracting_dims, fwd_k_contracting_dims = contracting_dims

    (
        group_sizes,
        ctx_x,
        ctx_kernel,
        x_shape,
        kernel_shape,
        use_bias,
        is_noop_quantizer_set,
        quantizer_set,
        flatten_axis_k,
    ) = ctx

    if is_noop_quantizer_set:
        # The 1 in range is for excluding the group dimension (shall we use the hardcoded results below?)
        # g_contracting_dim = (1, )
        # k_contracting_dim = (2, )
        g_contracting_dim = tuple(
            range(1 + grad.ndim - len(kernel_shape) + len(fwd_k_contracting_dims), grad.ndim)
        )
        k_contracting_dim = tuple(
            dim for dim in range(1, len(kernel_shape)) if dim not in fwd_k_contracting_dims
        )
        dgrad_contracting_dims = (g_contracting_dim, k_contracting_dim)
        dgrad_grad = grad
        dgrad_kernel_T = ctx_kernel

        # g_contracting_dim = (0, )
        # x_contracting_dim = (0, )
        g_contracting_dim = x_contracting_dim = tuple(
            range(0, len(x_shape) - len(fwd_x_contracting_dims))
        )
        wgrad_contracting_dims = (x_contracting_dim, g_contracting_dim)
        wgrad_x_T = ctx_x
        wgrad_grad = grad
    else:
        casted_grad = tex.grouped_quantize(
            grad, quantizer_set.dgrad, group_sizes, flatten_axis=flatten_axis_k
        )

        # For x_contracting_dims == (1,) and k_contracting_dims == (1,), we need to use
        # g_contracting_dim = (1,) and k_contracting_dim = (2,) to make it work after the
        # extra transpose for FP8 in grouped_gemm
        # TODO(Hua): Do we have a better way for this? What if is_gemm_with_all_layouts_supported()?
        g_contracting_dim = (1,)
        k_contracting_dim = (2,)
        dgrad_contracting_dims = (g_contracting_dim, k_contracting_dim)
        dgrad_grad = casted_grad.get_tensor(usage=TensorUsage.LHS)
        dgrad_kernel_T = ctx_kernel

        # We need to use g_contracting_dim = (0,) and x_contracting_dim = (0,) to make it work
        # after the extra transpose for FP8 in grouped_gemm
        # TODO(Hua): Do we have a better way for this? What if is_gemm_with_all_layouts_supported()?
        g_contracting_dim = (0,)
        x_contracting_dim = (0,)
        wgrad_contracting_dims = (x_contracting_dim, g_contracting_dim)
        wgrad_x_T = ctx_x
        wgrad_grad = casted_grad.get_tensor(usage=TensorUsage.RHS)

    dgrad = tex.grouped_gemm(
        dgrad_grad,
        dgrad_kernel_T,
        group_sizes,
        dgrad_contracting_dims,
        precision=precision,
        preferred_element_type=preferred_element_type,
        group_offset=group_offset,
    )

    wgrad = tex.grouped_gemm(
        wgrad_x_T,
        wgrad_grad,
        group_sizes,
        wgrad_contracting_dims,
        precision=precision,
        preferred_element_type=preferred_element_type,
        group_offset=group_offset,
    )
    kernel_fsdp_mesh_axis, kernel_fsdp_axis_idx = kernel_fsdp_info
    if kernel_fsdp_mesh_axis is not None:
        wgrad = _psum_scatter_kernel(
            wgrad, kernel_shape, kernel_fsdp_mesh_axis, kernel_fsdp_axis_idx
        )

    group_sizes_grad = None
    dbias = tex.grouped_dbias(grad, group_sizes) if use_bias else None
    dkernel_amax = None

    return dgrad, wgrad, group_sizes_grad, dbias, dkernel_amax, quantizer_set


_grouped_dense.defvjp(_grouped_dense_fwd_rule, _grouped_dense_bwd_rule)
