# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
import re
from typing import Callable, Sequence, Union, Optional
import pytest

import jax
import jax.numpy as jnp
import numpy as np
from jax.sharding import Mesh, NamedSharding, PartitionSpec
from utils import (
    assert_allclose,
    assert_tree_like_allclose,
    is_devices_enough,
    pytest_parametrize_wrapper,
    use_jax_gemm,
)

from transformer_engine.common import recipe
from transformer_engine.jax.quantize import (
    is_fp8_available,
    ScalingMode,
    get_quantize_config_with_recipe,
)
from transformer_engine.jax import autocast
from transformer_engine.jax.flax import LayerNormMLP
from transformer_engine.jax.layernorm_mlp import layernorm_mlp
from transformer_engine.jax.sharding import (
    HIDDEN_AXES,
    HIDDEN_TP_AXES,
    BATCH_AXES,
    SEQLEN_TP_AXES,
    SEQLEN_AXES,
    W_NO_SHARD_AXES,
    W_FSDP_AXES,
    W_TP_AXES,
    W_JOINED_AXES,
)
from transformer_engine.jax.sharding import MeshResource
from transformer_engine.jax.quantize import (
    QuantizerFactory,
    get_supported_quantization_recipes,
    is_scaling_mode_supported,
)
from transformer_engine.jax.cpp_extensions.misc import get_min_device_compute_capability


is_fp8_supported, reason = is_scaling_mode_supported(ScalingMode.DELAYED_TENSOR_SCALING)
is_mxfp8_supported, reason = is_scaling_mode_supported(ScalingMode.MXFP8_1D_SCALING)
is_nvfp4_supported, reason = is_scaling_mode_supported(ScalingMode.NVFP4_1D_SCALING)

SUPPORTED_RECIPES = get_supported_quantization_recipes()
SUPPORTED_RECIPES = [pytest.param(r, id=r.__class__.__name__) for r in SUPPORTED_RECIPES]

DTYPES = [jnp.bfloat16, jnp.float16]
INPUT_SHAPE = [[4, 128, 256]]  # [batch, seqlen, hidden_in]

LAYERNORM_INPUT_AXES = (BATCH_AXES, SEQLEN_TP_AXES, HIDDEN_AXES)
DOT_1_INPUT_AXES = (BATCH_AXES, SEQLEN_AXES, HIDDEN_AXES)
DOT_2_INPUT_AXES = (BATCH_AXES, SEQLEN_AXES, HIDDEN_TP_AXES)
KERNEL_1_AXES = (W_FSDP_AXES, W_JOINED_AXES, W_TP_AXES)
KERNEL_2_AXES = (W_TP_AXES, W_FSDP_AXES)
LN_SCALE_AXES = (W_NO_SHARD_AXES,)
LN_BIAS_AXES = (W_NO_SHARD_AXES,)
BIAS_1_AXES = (W_JOINED_AXES, W_TP_AXES)
BIAS_2_AXES = (W_NO_SHARD_AXES,)
INTERMEDIATE = 256


# Only test with FSDP and TPSP as DP is not used
def generate_fsdp_and_tpsp_configs():
    configs = []
    if is_devices_enough(4):
        configs.append(
            pytest.param(
                [
                    4,
                    (2, 2),
                    ("fsdp", "tpsp"),
                    MeshResource(fsdp_resource="fsdp", tpsp_resource="tpsp"),
                ],
                id="fsdp2_tpsp2",
            )
        )

    if is_devices_enough(2):
        configs.append(
            pytest.param(
                [
                    2,
                    (1, 2),
                    ("fsdp", "tpsp"),
                    MeshResource(fsdp_resource="fsdp", tpsp_resource="tpsp"),
                ],
                id="fsdp1_tpsp2",
            )
        )
        configs.append(
            pytest.param(
                [
                    2,
                    (2, 1),
                    ("fsdp", "tpsp"),
                    MeshResource(fsdp_resource="fsdp", tpsp_resource="tpsp"),
                ],
                id="fsdp2_tpsp1",
            ),
        )
    return configs


class TestDistributedLayernormMLP:

    def generate_inputs(self, input_shape, activation_type, use_bias, dtype):
        batch, seqlen, hidden_in = input_shape
        hidden_out = hidden_in

        key = jax.random.PRNGKey(0)
        subkeys = jax.random.split(key, 6)

        x = jax.random.normal(subkeys[0], (batch, seqlen, hidden_in), dtype)
        gamma = jax.random.normal(subkeys[5], (hidden_in,), dtype=dtype)
        k1 = jax.random.normal(
            subkeys[1], (hidden_in, len(activation_type), INTERMEDIATE), dtype
        ) / jnp.sqrt(hidden_in)
        k2 = jax.random.normal(subkeys[2], (INTERMEDIATE, hidden_out), dtype) / jnp.sqrt(
            INTERMEDIATE
        )
        if use_bias:
            b1 = jax.random.normal(subkeys[3], (len(activation_type), INTERMEDIATE), dtype)
            b2 = jax.random.normal(subkeys[4], (hidden_out,), dtype)
        else:
            b1 = None
            b2 = None

        return (x, gamma, k1, k2, b1, b2)

    def layernorm_fp8_mlp_prim_func(
        self,
        x: jnp.ndarray,
        ln_scale: jnp.ndarray,
        kernel_1: jnp.ndarray,
        kernel_2: jnp.ndarray,
        bias_1: Optional[jnp.ndarray],
        bias_2: Optional[jnp.ndarray],
        layernorm_type: str = "rmsnorm",
        activation_type: Sequence[Union[str, Callable]] = ("gelu",),
        multi_gpus: bool = False,
        quantization_recipe: recipe.Recipe = None,
    ) -> jnp.ndarray:

        if multi_gpus:
            layernorm_input_axes = LAYERNORM_INPUT_AXES
            dot_1_input_axes = DOT_1_INPUT_AXES
            dot_2_input_axes = DOT_2_INPUT_AXES
            kernel_1_axes = KERNEL_1_AXES
            kernel_2_axes = KERNEL_2_AXES
        else:
            layernorm_input_axes = None
            dot_1_input_axes = dot_2_input_axes = None
            kernel_1_axes = kernel_2_axes = None

        quantizer_sets = QuantizerFactory.create_set(
            n_quantizer_sets=2, fp8_recipe=quantization_recipe
        )

        # out = ((x * kernel_1) + bias_1) * kernel_2 + bias_2
        return jnp.mean(
            layernorm_mlp(
                x,
                ln_scale,
                None,
                [kernel_1, kernel_2],
                [bias_1, bias_2],
                layernorm_type,
                norm_input_axes=layernorm_input_axes,
                dot_1_input_axes=dot_1_input_axes,
                dot_2_input_axes=dot_2_input_axes,
                kernel_1_axes=kernel_1_axes,
                kernel_2_axes=kernel_2_axes,
                activation_type=activation_type,
                quantizer_sets=quantizer_sets,
            )
        )

    def _test_layernorm_mlp_grad(
        self,
        mesh_config,
        activation_type,
        use_bias,
        input_shape,
        dtype,
        quantization_recipe,
        use_shardy,
        with_jax_gemm,
    ):
        jax.config.update("jax_use_shardy_partitioner", use_shardy)
        device_count, mesh_shape, mesh_axes, mesh_resource = mesh_config
        layernorm_type = "rmsnorm"

        inputs = [x, gamma, k1, k2, b1, b2] = self.generate_inputs(
            input_shape, activation_type, use_bias, dtype
        )
        static_inputs = [layernorm_type, activation_type]

        with use_jax_gemm(enabled=with_jax_gemm):
            value_and_grad_func = jax.value_and_grad(
                self.layernorm_fp8_mlp_prim_func, argnums=range(len(inputs))
            )

            # Single GPU
            with autocast(
                enabled=quantization_recipe is not None,
                recipe=quantization_recipe,
                mesh_resource=MeshResource(),
            ):
                single_jitter = jax.jit(
                    value_and_grad_func,
                    static_argnums=range(len(inputs), len(static_inputs) + len(inputs)),
                )
                single_fwd, single_grads = single_jitter(*inputs, *static_inputs)

            # Multi GPUs
            devices = np.asarray(jax.devices()[:device_count]).reshape(*mesh_shape)
            mesh = Mesh(devices, mesh_axes)
            with mesh, autocast(
                enabled=quantization_recipe is not None,
                recipe=quantization_recipe,
                mesh_resource=mesh_resource,
            ):
                k1_sharding = NamedSharding(mesh, PartitionSpec("fsdp", None, "tpsp"))
                k2_sharding = NamedSharding(mesh, PartitionSpec("tpsp", "fsdp"))
                k1_ = jax.device_put(k1, k1_sharding)
                k2_ = jax.device_put(k2, k2_sharding)
                if use_bias:
                    b1_sharding = NamedSharding(mesh, PartitionSpec(None, "tpsp"))
                    b1_ = jax.device_put(b1, b1_sharding)
                else:
                    b1_sharding = b1_ = None
                multi_inputs = [*inputs[:2], k1_, k2_, b1_, *inputs[5:]]

                # Position ref for sharding pspec lists
                #   x, gamma, k1, k2, b1,
                #   b2
                in_shardings = (
                    None,
                    None,
                    k1_sharding,
                    k2_sharding,
                    b1_sharding,
                    None,
                )
                out_shardings = (
                    None,
                    (None, None, k1_sharding, k2_sharding, b1_sharding, None),
                )

                multi_jitter = jax.jit(
                    value_and_grad_func,
                    in_shardings=in_shardings,
                    out_shardings=out_shardings,
                    static_argnums=range(
                        len(multi_inputs), len(static_inputs) + len(multi_inputs) + 1
                    ),
                )  # +1 for multi_gpus

                multi_fwd, multi_grads = multi_jitter(*multi_inputs, *static_inputs, True)

        fwd_test_type = bwd_test_type = dtype
        if quantization_recipe is not None:
            quantize_config = get_quantize_config_with_recipe(quantization_recipe)
            fwd_test_type = quantize_config.FWD_DTYPE
            bwd_test_type = quantize_config.BWD_DTYPE

        if fwd_test_type == jnp.float16 and use_bias:
            assert_allclose(multi_fwd, single_fwd, atol=0.04, rtol=1.5)
        else:
            assert_allclose(multi_fwd, single_fwd, dtype=fwd_test_type)

        for i in range(len(inputs)):
            if multi_grads[i] is not None:
                if isinstance(multi_grads[i], list):
                    assert isinstance(single_grads[i], list)
                    for m_grad, s_grad in zip(multi_grads[i], single_grads[i]):
                        assert_allclose(
                            m_grad,
                            s_grad,
                            dtype=bwd_test_type,
                            err_msg=f"multi_grads[{i}] is not close",
                        )
                else:
                    assert_allclose(
                        multi_grads[i],
                        single_grads[i],
                        dtype=bwd_test_type,
                        err_msg=f"multi_grads[{i}] is not close",
                    )

    @pytest_parametrize_wrapper("mesh_config", generate_fsdp_and_tpsp_configs())
    @pytest_parametrize_wrapper("input_shape", INPUT_SHAPE)
    @pytest_parametrize_wrapper("activation_type", [("gelu",), ("gelu", "linear")])
    @pytest_parametrize_wrapper("dtype", DTYPES)
    @pytest_parametrize_wrapper("use_bias", [True, False])
    @pytest_parametrize_wrapper("quantization_recipe", [None] + SUPPORTED_RECIPES)
    @pytest_parametrize_wrapper("with_jax_gemm", [False, True])
    def test_layernorm_mlp_grad(
        self,
        mesh_config,
        activation_type,
        use_bias,
        input_shape,
        dtype,
        quantization_recipe,
        with_jax_gemm,
    ):
        if dtype == jnp.float16 and quantization_recipe is not None and quantization_recipe.nvfp4():
            pytest.skip("NVFP4 GEMM + Float16 output is unsupported!")
        self._test_layernorm_mlp_grad(
            mesh_config,
            activation_type,
            use_bias,
            input_shape,
            dtype,
            quantization_recipe,
            use_shardy=False,
            with_jax_gemm=with_jax_gemm,
        )

    @pytest_parametrize_wrapper("mesh_config", generate_fsdp_and_tpsp_configs())
    @pytest_parametrize_wrapper("input_shape", INPUT_SHAPE)
    @pytest_parametrize_wrapper("activation_type", [("gelu",), ("gelu", "linear")])
    @pytest_parametrize_wrapper("dtype", DTYPES)
    @pytest_parametrize_wrapper("use_bias", [True, False])
    @pytest_parametrize_wrapper("quantization_recipe", [None] + SUPPORTED_RECIPES)
    @pytest_parametrize_wrapper("with_jax_gemm", [False, True])
    def test_layernorm_mlp_grad_shardy(
        self,
        mesh_config,
        activation_type,
        use_bias,
        input_shape,
        dtype,
        quantization_recipe,
        with_jax_gemm,
    ):
        if dtype == jnp.float16 and quantization_recipe is not None and quantization_recipe.nvfp4():
            pytest.skip("NVFP4 GEMM + Float16 output is unsupported!")
        self._test_layernorm_mlp_grad(
            mesh_config,
            activation_type,
            use_bias,
            input_shape,
            dtype,
            quantization_recipe=quantization_recipe,
            use_shardy=True,
            with_jax_gemm=with_jax_gemm,
        )

    def _test_layernorm_mlp(
        self,
        mesh_config,
        activation_type,
        use_bias,
        input_shape,
        dtype,
        use_fp8,
        quantization_recipe,
        use_shardy,
        with_jax_gemm,
    ):
        jax.config.update("jax_use_shardy_partitioner", use_shardy)
        batch, seqlen, hidden_in = input_shape
        layernorm_type = "rmsnorm"

        rng = jax.random.PRNGKey(0)
        subkeys = jax.random.split(rng, 3)

        x = jax.random.normal(subkeys[0], (batch, seqlen, hidden_in), dtype)
        init_rngs = {"params": subkeys[1], "sr_rng": subkeys[2]}

        with use_jax_gemm(enabled=with_jax_gemm):
            # Single GPUs
            with autocast(
                enabled=use_fp8, recipe=quantization_recipe, mesh_resource=MeshResource()
            ):
                ln_mlp_single = LayerNormMLP(
                    layernorm_type=layernorm_type,
                    intermediate_dim=INTERMEDIATE,
                    activations=activation_type,
                    use_bias=use_bias,
                    return_layernorm_output=True,
                )
                params_single = ln_mlp_single.init(init_rngs, x, deterministic=True)
                mlp_out_single, ln_out_single = ln_mlp_single.apply(
                    params_single, x, deterministic=True, rngs={"sr_rng": subkeys[2]}
                )

            # Multi GPUs
            device_count, mesh_shape, mesh_axes, mesh_resource = mesh_config
            devices = np.asarray(jax.devices()[:device_count]).reshape(*mesh_shape)
            mesh = Mesh(devices, mesh_axes)
            with mesh, autocast(
                enabled=use_fp8, recipe=quantization_recipe, mesh_resource=mesh_resource
            ):
                ln_mlp_sharded = LayerNormMLP(
                    layernorm_type=layernorm_type,
                    intermediate_dim=INTERMEDIATE,
                    activations=activation_type,
                    scale_axes=LN_SCALE_AXES,
                    ln_bias_axes=LN_BIAS_AXES,
                    kernel_axes_1=KERNEL_1_AXES,
                    kernel_axes_2=KERNEL_2_AXES,
                    use_bias=use_bias,
                    bias_axes_1=BIAS_1_AXES,
                    bias_axes_2=BIAS_2_AXES,
                    layernorm_input_axes=LAYERNORM_INPUT_AXES,
                    dot_1_input_axes=DOT_1_INPUT_AXES,
                    dot_2_input_axes=DOT_2_INPUT_AXES,
                    name="mlp",
                    return_layernorm_output=True,
                )
                params_sharded = ln_mlp_sharded.init(init_rngs, x, deterministic=True)
                mlp_out_sharded, ln_out_sharded = ln_mlp_sharded.apply(
                    params_sharded, x, deterministic=True, rngs={"sr_rng": subkeys[2]}
                )

        # Make sure params values are the same
        assert_tree_like_allclose(params_sharded["params"], params_single["params"])
        assert_allclose(ln_out_sharded, ln_out_single, dtype=dtype)

        # TODO(Phuong): check if these tols updates are still needed
        atol = None
        rtol = None
        l40_tolerance_update = (
            get_min_device_compute_capability() == 89
            and use_fp8
            and quantization_recipe.delayed()
            and dtype == jnp.float16
            and activation_type == ("gelu",)
        )
        if l40_tolerance_update:
            atol = 0.04
            rtol = 11

        # JAX's FP8 GEMM, jax.lax.dot_general, now uses the
        # Triton backend by default. The error of
        # the Triton FP8 gemm has been verified to be less than or equal
        # to the error of the cuDNN FP8 gemm w.r.t a float32 ground truth.
        # However, Triton can auto-tune a different kernel for the single GPU
        # and multi-GPU run in this test, meaning the diff between single GPU
        # and multi-GPU can be larger in some cases, even though both are
        # within tolerance to the float32 ground truth.
        jax_triton_gemm_precision_tolerance_update = (
            with_jax_gemm
            and quantization_recipe is not None
            and (quantization_recipe.delayed() or quantization_recipe.float8_current_scaling())
            and dtype in (jnp.bfloat16, jnp.float16)
            and activation_type == ("gelu", "linear"),
        )
        if jax_triton_gemm_precision_tolerance_update:
            atol = 0.08
            rtol = 15

        assert_allclose(mlp_out_sharded, mlp_out_single, dtype=dtype, atol=atol, rtol=rtol)

    @pytest_parametrize_wrapper("input_shape", INPUT_SHAPE)
    @pytest_parametrize_wrapper("mesh_config", generate_fsdp_and_tpsp_configs())
    @pytest_parametrize_wrapper("activation_type", [("gelu",), ("silu", "linear")])
    @pytest_parametrize_wrapper("dtype", DTYPES)
    @pytest_parametrize_wrapper("use_bias", [True, False])
    @pytest_parametrize_wrapper("with_jax_gemm", [False, True])
    def test_layernorm_mlp_layer(
        self, mesh_config, activation_type, use_bias, input_shape, dtype, with_jax_gemm
    ):
        self._test_layernorm_mlp(
            mesh_config,
            activation_type,
            use_bias,
            input_shape,
            dtype,
            use_fp8=False,
            quantization_recipe=None,
            use_shardy=False,
            with_jax_gemm=with_jax_gemm,
        )

    @pytest_parametrize_wrapper("mesh_config", generate_fsdp_and_tpsp_configs())
    @pytest_parametrize_wrapper("activation_type", [("gelu",), ("gelu", "linear")])
    @pytest_parametrize_wrapper("use_bias", [True, False])
    @pytest_parametrize_wrapper("input_shape", INPUT_SHAPE)
    @pytest_parametrize_wrapper("dtype", DTYPES)
    @pytest_parametrize_wrapper("quantization_recipe", SUPPORTED_RECIPES)
    @pytest_parametrize_wrapper("with_jax_gemm", [False, True])
    def test_layernorm_mlp_layer_fp8(
        self,
        mesh_config,
        activation_type,
        use_bias,
        input_shape,
        dtype,
        quantization_recipe,
        with_jax_gemm,
    ):
        if dtype == jnp.float16 and quantization_recipe is not None and quantization_recipe.nvfp4():
            pytest.skip("NVFP4 GEMM + Float16 output is unsupported!")
        self._test_layernorm_mlp(
            mesh_config,
            activation_type,
            use_bias,
            input_shape,
            dtype,
            use_fp8=True,
            quantization_recipe=quantization_recipe,
            use_shardy=False,
            with_jax_gemm=with_jax_gemm,
        )

    @pytest_parametrize_wrapper("input_shape", INPUT_SHAPE)
    @pytest_parametrize_wrapper("mesh_config", generate_fsdp_and_tpsp_configs())
    @pytest_parametrize_wrapper("activation_type", [("gelu",), ("silu", "linear")])
    @pytest_parametrize_wrapper("dtype", DTYPES)
    @pytest_parametrize_wrapper("use_bias", [True, False])
    @pytest_parametrize_wrapper("with_jax_gemm", [False, True])
    def test_layernorm_mlp_layer_shardy(
        self, mesh_config, activation_type, use_bias, input_shape, dtype, with_jax_gemm
    ):
        self._test_layernorm_mlp(
            mesh_config,
            activation_type,
            use_bias,
            input_shape,
            dtype,
            use_fp8=False,
            quantization_recipe=None,
            use_shardy=True,
            with_jax_gemm=with_jax_gemm,
        )

    @pytest_parametrize_wrapper("mesh_config", generate_fsdp_and_tpsp_configs())
    @pytest_parametrize_wrapper("activation_type", [("gelu",), ("gelu", "linear")])
    @pytest_parametrize_wrapper("use_bias", [True, False])
    @pytest_parametrize_wrapper("input_shape", INPUT_SHAPE)
    @pytest_parametrize_wrapper("dtype", DTYPES)
    @pytest_parametrize_wrapper("quantization_recipe", SUPPORTED_RECIPES)
    @pytest_parametrize_wrapper("with_jax_gemm", [False, True])
    def test_layernorm_mlp_layer_fp8_shardy(
        self,
        mesh_config,
        activation_type,
        use_bias,
        input_shape,
        dtype,
        quantization_recipe,
        with_jax_gemm,
    ):
        if dtype == jnp.float16 and quantization_recipe is not None and quantization_recipe.nvfp4():
            pytest.skip("NVFP4 GEMM + Float16 output is unsupported!")
        self._test_layernorm_mlp(
            mesh_config,
            activation_type,
            use_bias,
            input_shape,
            dtype,
            use_fp8=True,
            quantization_recipe=quantization_recipe,
            use_shardy=True,
            with_jax_gemm=with_jax_gemm,
        )
