test_numerics.py

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

import math
import os
from typing import Dict, List, Tuple, Optional
import pytest
import random

import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.utils.cpp_extension import IS_HIP_EXTENSION

from transformer_engine.pytorch.quantization import FP8GlobalStateManager
from transformer_engine.pytorch.utils import (
    init_method_normal,
    scaled_init_method_normal,
    attention_mask_func,
)
from transformer_engine.pytorch import (
    autocast,
    quantized_model_init,
    DotProductAttention,
    LayerNormLinear,
    LayerNormMLP,
    Linear,
    GroupedLinear,
    MultiheadAttention,
    RMSNorm,
    TransformerLayer,
    LayerNorm,
    Fp8Padding,
    Fp8Unpadding,
    Float8Quantizer,
    Float8CurrentScalingQuantizer,
    MXFP8Quantizer,
    get_device_compute_capability,
    is_fp8_available,
    is_mxfp8_available,
    is_fp8_block_scaling_available,
    is_bf16_available,
)
from transformer_engine.pytorch import torch_version
from transformer_engine.pytorch import checkpoint as te_checkpoint
from transformer_engine.pytorch.cpp_extensions import general_gemm, general_grouped_gemm
from transformer_engine.pytorch.cpp_extensions.fused_attn import FusedAttnBackend
from transformer_engine.pytorch.module.base import get_multi_stream_cublas_workspace, get_workspace
from transformer_engine.common import recipe
import transformer_engine_torch as tex
from utils import ModelConfig, reset_rng_states, get_available_attention_backends


# Only run FP8 tests on supported devices.
fp8_available, reason_for_no_fp8 = is_fp8_available(return_reason=True)
mxfp8_available, reason_for_no_mxfp8 = is_mxfp8_available(return_reason=True)
fp8_block_scaling_available = is_fp8_block_scaling_available(return_reason=True)

sm_80plus = get_device_compute_capability() >= (8, 0)

seed = 1234
# Reset RNG states.
reset_rng_states()

if torch_version() >= (2, 7, 0):
    torch._dynamo.config.recompile_limit = 16
else:
    torch._dynamo.config.cache_size_limit = 16


model_configs = {
    "small": ModelConfig(1, 128, 8, 16, num_layers=4),
    "126m": ModelConfig(1, 2048, 12, 64, num_layers=12),
}
model_configs_inference = {
    "126m": ModelConfig(1, 256, 12, 64, num_layers=12),
}
backends_inference = ["FlashAttention", "UnfusedAttention", "FusedAttention"]
module_inference = ["TransformerLayer", "MultiheadAttention"]
input_formats_inference = ["sbhd", "bshd"]

param_types = [torch.float32, torch.float16]
if is_bf16_available():  # bf16 requires sm_80 or higher
    param_types.append(torch.bfloat16)

batch_sizes = [1, 2]

all_boolean = [True, False]

all_activations = [
    "gelu",
    "geglu",
    "qgelu",
    "qgeglu",
    "relu",
    "reglu",
    "srelu",
    "sreglu",
    "silu",
    "swiglu",
]

all_normalizations = ["LayerNorm", "RMSNorm"]

mask_types = ["causal", "no_mask"]

NVTE_TEST_NVINSPECT_ENABLED = int(os.environ.get("NVTE_TEST_NVINSPECT_ENABLED", "0"))

if NVTE_TEST_NVINSPECT_ENABLED:
    # The numerics of all the layers should work the same,
    # when debug=True. I fed them with dummy feature
    # to prevent switching off debug, which can happen if
    # no feature is active.
    import nvdlfw_inspect.api as debug_api

    debug_api.initialize(
        os.environ["NVTE_TEST_NVINSPECT_CONFIG_FILE"],
        feature_dirs=os.environ["NVTE_TEST_NVINSPECT_FEATURE_DIRS"],
    )


fp8_recipes = []
if mxfp8_available:
    fp8_recipes.append(recipe.MXFP8BlockScaling())
if fp8_block_scaling_available:
    fp8_recipes.append(recipe.Float8BlockScaling())
if fp8_available:
    fp8_recipes.append(recipe.Float8CurrentScaling())
    fp8_recipes.append(recipe.DelayedScaling())

use_cutlass_grouped_gemm = [False]
# Only enable cutlass grouped gemm on Hopper
if torch.cuda.get_device_capability() == (9, 0):
    use_cutlass_grouped_gemm.append(True)


def is_fused_attn_available(
    config: ModelConfig,
    dtype: torch.dtype,
    qkv_layout="bshd_bshd_bshd",
    is_training=True,
    deterministic=False,
):
    _, _, fused_attn_backends = get_available_attention_backends(
        config,
        qkv_dtype=dtype,
        qkv_layout=qkv_layout,
        is_training=is_training,
        deterministic=deterministic,
    )
    return FusedAttnBackend["F16_arbitrary_seqlen"] in fused_attn_backends


def get_causal_attn_mask(sq: int) -> torch.Tensor:
    return torch.triu(torch.ones(sq, sq, device="cuda"), diagonal=1).bool()


def dtype_tols(dtype: torch.dtype) -> Dict[str, float]:
    """Estimated numerical error for a datatype

    Based on tolerances for torch.testing.assert_close.

    """
    if dtype == torch.float32:
        return dict(rtol=1.3e-6, atol=1e-5)
    if dtype == torch.float16:
        return dict(rtol=1e-3, atol=1e-5)
    if dtype == torch.bfloat16:
        return dict(rtol=1.6e-2, atol=1e-5)
    raise ValueError(f"Unsuppored dtype ({dtype})")


def assert_allclose(
    l1: List[torch.Tensor], l2: List[torch.Tensor], atol: float = None, rtol: float = None
) -> bool:
    """Ensures two lists are equal."""
    assert len(l1) == len(l2), "Unequal number of outputs."
    for i, (t1, t2) in enumerate(zip(l1, l2)):
        tols = dtype_tols(t2.dtype)
        if rtol is not None:
            tols["rtol"] = rtol
        if atol is not None:
            tols["atol"] = atol
        result = torch.allclose(t1, t2, **tols)
        if not result:
            diff = torch.abs(t1 - t2)
            tol = tols["atol"] + (tols["rtol"] * torch.abs(t2))
            exceed_mask = diff > tol
            if exceed_mask.any():
                indices = torch.nonzero(exceed_mask, as_tuple=True)
                max_diff = diff[exceed_mask].max()
                max_idx = (diff[exceed_mask] == max_diff).nonzero(as_tuple=True)[0][0]
                max_location = [idx[max_idx].item() for idx in indices]
                msg = (
                    f"Outputs not close enough in tensor at idx={i}. "
                    f"Maximum difference at location {max_location} "
                    f"with {t1[exceed_mask][max_idx].item()} vs {t2[exceed_mask][max_idx].item()} "
                    f"(diff {max_diff.item()})."
                )
            raise AssertionError(msg)


@pytest.fixture(autouse=True)
def reset_global_fp8_state():
    yield
    FP8GlobalStateManager.reset()


class TorchScaledMaskedSoftmax(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(
        self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
    ) -> torch.Tensor:
        dtype = inp.dtype
        inp = inp.float()

        if scale is not None:
            inp = inp * scale
        mask_output = attention_mask_func(inp, mask) if mask is not None else inp

        probs = torch.nn.Softmax(dim=-1)(mask_output)
        probs = probs.to(dtype)
        return probs


class TorchDotProductAttention(torch.nn.Module):
    def __init__(
        self,
        kv_channels: int,
        attention_dropout: float = 0.0,
    ) -> None:
        super().__init__()

        self.norm_factor = math.sqrt(kv_channels)
        self.scale_mask_softmax = TorchScaledMaskedSoftmax()
        self.attention_dropout = torch.nn.Dropout(attention_dropout)

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]

        # [b, np, sq, sk]
        output_size = (
            query_layer.size(1),
            query_layer.size(2),
            query_layer.size(0),
            key_layer.size(0),
        )

        # [sq, b, np, hn] -> [sq, b * np, hn]
        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
        # [sk, b, np, hn] -> [sk, b * np, hn]
        key_layer = key_layer.reshape(output_size[3], output_size[0] * output_size[1], -1)

        # preallocting result tensor: [b * np, sq, sk]
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
            dtype=query_layer.dtype,
            device=torch.cuda.current_device(),
        )

        # Raw attention scores. [b * np, sq, sk]
        matmul_result = torch.baddbmm(
            matmul_result,
            query_layer.transpose(0, 1),  # [b * np, sq, hn]
            key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
            beta=0.0,
            alpha=(1.0 / self.norm_factor),
        )

        # change view to [b, np, sq, sk]
        attention_scores = matmul_result.view(*output_size)

        # attention scores and attention mask [b, np, sq, sk]
        attention_probs = self.scale_mask_softmax(attention_scores, attention_mask)
        attention_probs = self.attention_dropout(attention_probs)

        # value_layer -> context layer.
        # [sk, b, np, hn] --> [b, np, sq, hn]
        output_size = (
            value_layer.size(1),
            value_layer.size(2),
            query_layer.size(0),
            value_layer.size(3),
        )

        # change view [sk, b * np, hn]
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)

        # change view [b * np, sq, sk]
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)

        # matmul: [b * np, sq, hn]
        context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))

        # change view [b, np, sq, hn]
        context_layer = context_layer.view(*output_size)

        # [b, np, sq, hn] --> [sq, b, np, hn]
        context_layer = context_layer.permute(2, 0, 1, 3).contiguous()

        # [sq, b, np, hn] --> [sq, b, hp]
        context_layer = context_layer.view(seqlen, batch_size, -1)

        return context_layer


class TorchLayerNorm(nn.Module):
    def __init__(self, in_features: int, eps: float, zero_centered_gamma: bool):
        super().__init__()
        self.eps = eps
        self.in_features = in_features
        self.zero_centered_gamma = zero_centered_gamma

        initial_value = torch.ones(in_features) if zero_centered_gamma else torch.zeros(in_features)
        self.weight = nn.Parameter(initial_value)
        self.bias = nn.Parameter(torch.zeros(in_features))
        self.register_parameter("weight", self.weight)
        self.register_parameter("bias", self.bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        w = self.weight if not self.zero_centered_gamma else 1 + self.weight
        w = w.to(torch.float32)
        b = self.bias.to(torch.float32)
        inp = x.to(torch.float32)
        out = torch.nn.functional.layer_norm(
            inp, (self.in_features,), weight=w, bias=b, eps=self.eps
        )
        return out.to(x.dtype)


# Adapted from https://github.com/bzhangGo/rmsnorm/blob/c6691f20ec0af4128c8159c903071f7575404295/rmsnorm_torch.py
class TorchRMSNorm(nn.Module):
    def __init__(self, in_features, zero_centered_gamma, eps=1e-5):
        super().__init__()

        self.eps = eps
        self.in_features = in_features
        self.zero_centered_gamma = zero_centered_gamma

        initial_value = torch.ones(in_features) if zero_centered_gamma else torch.zeros(in_features)
        self.weight = nn.Parameter(initial_value)
        self.register_parameter("weight", self.weight)

    def forward(self, x):
        norm_x2 = torch.sum(x.float() ** 2, dim=-1, keepdim=True)
        d_x = self.in_features

        rms_x2 = norm_x2 / d_x + self.eps
        r_rms_x = rms_x2 ** (-1.0 / 2)
        x_normed = x * r_rms_x

        w = self.weight.float()
        if self.zero_centered_gamma:
            w = 1 + w
        return (w * x_normed).to(x.dtype)


class TorchLayerNormLinear(nn.Module):
    def __init__(
        self,
        in_features: int,
        out_features: int,
        eps: float,
        normalization: str = "LayerNorm",
        zero_centered_gamma: bool = False,
        bias: bool = True,
    ):
        super().__init__()
        if normalization == "LayerNorm":
            self.layernorm = TorchLayerNorm(
                in_features, eps=eps, zero_centered_gamma=zero_centered_gamma
            )
        elif normalization == "RMSNorm":
            self.layernorm = TorchRMSNorm(
                in_features, eps=eps, zero_centered_gamma=zero_centered_gamma
            )
        else:
            raise RuntimeError("Unsupported normalization")

        self.linear = nn.Linear(in_features, out_features, bias=bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.linear(self.layernorm(x))


class TorchMHA(nn.Module):
    def __init__(self, hidden_size: int, num_attention_heads: int):
        super().__init__()
        self.mhsa = nn.MultiheadAttention(
            embed_dim=hidden_size,
            num_heads=num_attention_heads,
            dropout=0.1,
            bias=True,
            batch_first=False,
        )

    def forward(self, x, attention_mask=None):
        output = self.mhsa(x, x, x, attn_mask=attention_mask, need_weights=False)
        if isinstance(output, tuple):
            output = output[0]
        return output


class TorchQuickGELU(nn.Module):
    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return input * torch.sigmoid(1.702 * input)


class TorchSquaredRELU(nn.Module):
    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return (input > 0) * input * input


class TorchGroupedLinearWithPadding(nn.Module):

    def __init__(
        self, num_gemms, in_features, out_features, bias, params_dtype, parallel_mode, fp8
    ) -> None:
        super().__init__()

        self.padding = Fp8Padding(num_gemms)
        self.linear_fn = GroupedLinear(
            num_gemms,
            in_features,
            out_features,
            bias=bias,
            params_dtype=params_dtype,
            parallel_mode=parallel_mode,
            device="cuda",
        )
        self.unpadding = Fp8Unpadding(num_gemms)

        self.fp8 = fp8

    def forward(self, inp: torch.Tensor, m_splits: List[int]) -> torch.Tensor:
        if self.fp8:
            orig_m_splits = m_splits
            inp, m_splits = self.padding(inp, m_splits)

        out = self.linear_fn(inp, m_splits)

        if self.fp8:
            out = self.unpadding(out, orig_m_splits)

        return out


_supported_act = {
    "gelu": nn.GELU(approximate="tanh"),
    "geglu": nn.GELU(approximate="tanh"),
    "qgelu": TorchQuickGELU(),
    "qgeglu": TorchQuickGELU(),
    "relu": nn.ReLU(),
    "reglu": nn.ReLU(),
    "srelu": TorchSquaredRELU(),
    "sreglu": TorchSquaredRELU(),
    "silu": nn.SiLU(),
    "swiglu": nn.SiLU(),
}


class TorchGLU(nn.Module):
    def __init__(self, activation: str):
        super().__init__()
        self.act = _supported_act[activation]

    def forward(self, x):
        shape = x.size(-1)
        a = x[..., : shape // 2]
        b = x[..., (shape // 2) :]
        a = self.act(a)
        return a * b


class TorchLayerNormMLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        ffn_hidden_size: int,
        eps: float = 1e-5,
        activation="gelu",
        normalization: str = "LayerNorm",
        bias: bool = True,
    ):
        super().__init__()
        if normalization == "LayerNorm":
            self.ln = TorchLayerNorm(hidden_size, eps=eps, zero_centered_gamma=False)
        elif normalization == "RMSNorm":
            self.ln = TorchRMSNorm(hidden_size, eps=eps, zero_centered_gamma=False)
        else:
            raise RuntimeError("Unsupported normalization")
        if "glu" in activation:
            fc1_output_features = 2 * ffn_hidden_size
            self.gelu = TorchGLU(activation)
        else:
            fc1_output_features = ffn_hidden_size
            self.gelu = _supported_act[activation]

        self.fc1 = nn.Linear(hidden_size, fc1_output_features, bias=bias)
        self.fc2 = nn.Linear(ffn_hidden_size, hidden_size, bias=bias)

    def forward(self, x):
        t = self.gelu(self.fc1(self.ln(x)))
        return self.fc2(t)


class TorchGPT(nn.Module):
    def __init__(
        self, hidden_size: int, eps: float, num_attention_heads: int, parallel_attention_mlp: bool
    ):
        super().__init__()
        self.ln = nn.LayerNorm(hidden_size, eps=eps)
        self.causal_attn = TorchMHA(hidden_size, num_attention_heads)
        self.ln_mlp = TorchLayerNormMLP(hidden_size, 4 * hidden_size, eps)
        self.parallel_attention_mlp = parallel_attention_mlp

    def forward(
        self,
        x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        a = self.ln(x)
        b = self.causal_attn(a, attention_mask)
        if self.parallel_attention_mlp:
            n = self.ln_mlp(x)
            x = x + nn.functional.dropout(b + n, p=0.1, training=self.training)
        else:
            x = x + nn.functional.dropout(b, p=0.1, training=self.training)
            n = self.ln_mlp(x)
            x = x + nn.functional.dropout(n, p=0.1, training=self.training)
        return x


def _test_e2e_selective_recompute(
    bs, dtype, config, fp8, recipe, fp8_model_params=False, recompute=False
):
    reset_rng_states()
    FP8GlobalStateManager.reset()

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        block = TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
            config.num_heads,
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
            kv_channels=config.kv_channels,
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
            fuse_qkv_params=True,
            device="cuda",
        )

    te_inp_hidden_states = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    te_inp_hidden_states.retain_grad()
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)

    with autocast(enabled=fp8, recipe=recipe):
        te_out = block(
            te_inp_hidden_states,
            attention_mask=te_inp_attn_mask,
            checkpoint_core_attention=recompute,
        )
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()

    outputs = [te_out, te_inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("fp8", all_boolean)
@pytest.mark.parametrize("recipe", fp8_recipes)
@pytest.mark.parametrize("fp8_model_params", all_boolean)
def test_gpt_selective_activation_recompute(dtype, bs, model, fp8, recipe, fp8_model_params):
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)


    config = model_configs[model]

    outputs = _test_e2e_selective_recompute(
        bs, dtype, config, fp8, recipe, fp8_model_params, recompute=False
    )
    outputs_recompute = _test_e2e_selective_recompute(
        bs, dtype, config, fp8, recipe, fp8_model_params, recompute=True
    )

    # Check that results match
    tols = dtype_tols(dtype)
    if dtype in (torch.float16, torch.bfloat16):
        tols["atol"] = 1e-4
    if fp8 or fp8_model_params:
        tols.update(dict(rtol=0.125, atol=0.0675))

    for i, (ref, test) in enumerate(zip(outputs, outputs_recompute)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            **tols,
        )


def _test_e2e_full_recompute(
    bs, dtype, config, fp8, recipe, fp8_model_params=False, recompute=False, use_reentrant=True
):
    reset_rng_states()
    FP8GlobalStateManager.reset()

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        block = TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
            config.num_heads,
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
            kv_channels=config.kv_channels,
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
            fuse_qkv_params=True,
            device="cuda",
        )

    te_inp_hidden_states = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=use_reentrant,
    )
    if use_reentrant:
        te_inp_hidden_states.retain_grad()
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)

    with autocast(enabled=fp8, recipe=recipe):
        if recompute:
            te_out = te_checkpoint(
                block,
                te_inp_hidden_states,
                attention_mask=te_inp_attn_mask,
                checkpoint_core_attention=False,
                distribute_saved_activations=False,
                tp_group=None,
                use_reentrant=use_reentrant,
            )
        else:
            te_out = block(
                te_inp_hidden_states,
                attention_mask=te_inp_attn_mask,
                checkpoint_core_attention=False,
            )
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()

    outputs = [te_out]
    names = ["output"]
    if use_reentrant:
        outputs.append(te_inp_hidden_states.grad)
        names.append("input")
    for name, p in block.named_parameters():
        if p.requires_grad:
            outputs.append(p.grad)
            names.append(name)

    return outputs, names


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("fp8", all_boolean)
@pytest.mark.parametrize("recipe", fp8_recipes)
@pytest.mark.parametrize("fp8_model_params", all_boolean)
@pytest.mark.parametrize("use_reentrant", all_boolean)
def test_gpt_full_activation_recompute(
    dtype, bs, model, fp8, recipe, fp8_model_params, use_reentrant
):
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)


    config = model_configs[model]

    if not use_reentrant:
        # Non-reentrant checkpoint becomes non-deterministic with bias+GELU fusion
        os.environ["NVTE_BIAS_GELU_NVFUSION"] = "0"

    outputs, names = _test_e2e_full_recompute(
        bs,
        dtype,
        config,
        fp8,
        recipe,
        fp8_model_params,
        recompute=False,
        use_reentrant=use_reentrant,
    )
    outputs_recompute, _ = _test_e2e_full_recompute(
        bs,
        dtype,
        config,
        fp8,
        recipe,
        fp8_model_params,
        recompute=True,
        use_reentrant=use_reentrant,
    )

    if not use_reentrant:
        # Reset bias+GELU fusion flag to avoid contaminating other tests
        del os.environ["NVTE_BIAS_GELU_NVFUSION"]

    # Check that results match
    tols = dtype_tols(dtype)
    if dtype in (torch.float16, torch.bfloat16):
        tols["atol"] = 1e-3
    if fp8 or fp8_model_params:
        tols.update(dict(rtol=0.125, atol=0.0675))
    for i, (ref, test) in enumerate(zip(outputs, outputs_recompute)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            **tols,
        )


def _test_e2e_checkpointing_get_model(config, dtype):
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

    return TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
        config.num_heads,
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        params_dtype=dtype,
        device="cuda",
    )


def _test_e2e_checkpointing(bs, dtype, config, checkpoint=False, steps=10, path="checkpoint.pt"):
    reset_rng_states()

    te_inp_hidden_states = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    te_inp_hidden_states.retain_grad()

    block = _test_e2e_checkpointing_get_model(config, dtype)

    for _ in range(steps // 2):
        te_out = block(
            te_inp_hidden_states,
            None,
        )
        loss = te_out.sum()
        loss.backward()

    if checkpoint:
        # This process is necessary so that we can start afresh with
        # a new model while erasing all internal state to ensure that
        # loading from a checkpoint gives bitwise identical results.
        # Since gradients are being accumulated, it is important to
        # restore them post loading the checkpoint.
        torch.save(block.state_dict(), path)

        param_grads = []
        for p in block.parameters():
            if p.requires_grad:
                param_grads.append(p.grad.clone())

        _cpu_rng_state = torch.get_rng_state()
        _cuda_rng_state = torch.cuda.get_rng_state()

        del block
        block = _test_e2e_checkpointing_get_model(config, dtype)
        block.load_state_dict(torch.load(path, weights_only=False))
        torch.set_rng_state(_cpu_rng_state)
        torch.cuda.set_rng_state(_cuda_rng_state)

        for p in block.parameters():
            if p.requires_grad:
                p.grad = param_grads.pop(0)

        assert not param_grads, "Oops!"

    for _ in range(steps // 2):
        te_out = block(
            te_inp_hidden_states,
            None,
        )
        loss = te_out.sum()
        loss.backward()

    torch.cuda.synchronize()

    if os.path.exists(path):
        os.remove(path)

    outputs = [te_out, te_inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
def test_gpt_checkpointing(dtype, bs, model):
    config = model_configs[model]
    if not is_fused_attn_available(config, dtype, deterministic=True):
        pytest.skip("No attention backend available.")
    outputs = _test_e2e_checkpointing(bs, dtype, config, checkpoint=False)
    outputs_checkpoint = _test_e2e_checkpointing(bs, dtype, config, checkpoint=True)

    # Check that results match
    tols = dtype_tols(dtype)
    if dtype in (torch.float16, torch.bfloat16):
        tols.update(dict(rtol=2e-2, atol=2e-3))
    for i, (ref, test) in enumerate(zip(outputs, outputs_checkpoint)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            **tols,
        )


def _test_e2e_gpt_accuracy(block, bs, dtype, config):
    reset_rng_states()

    inp_hidden_states = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()
    inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)

    out = block(inp_hidden_states, attention_mask=inp_attn_mask)
    loss = out.sum()
    loss.backward()

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("parallel_attention_mlp", all_boolean)
def test_gpt_accuracy(dtype, bs, model, parallel_attention_mlp):
    config = model_configs[model]
    if not is_fused_attn_available(
        config, dtype, qkv_layout="sb3hd", is_training=True, deterministic=True
    ):
        pytest.skip("No attention backend available.")

    te_gpt = TransformerLayer(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
        num_attention_heads=config.num_heads,
        layernorm_epsilon=config.eps,
        attention_dropout=0.1,
        hidden_dropout=0.1,
        params_dtype=dtype,
        fuse_qkv_params=True,
        qkv_weight_interleaved=False,
        parallel_attention_mlp=parallel_attention_mlp,
        device="cuda",
    ).eval()

    torch_gpt = (
        TorchGPT(
            config.hidden_size,
            config.eps,
            config.num_heads,
            parallel_attention_mlp=parallel_attention_mlp,
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
        torch_gpt.ln.weight = Parameter(
            te_gpt.self_attention.layernorm_qkv.layer_norm_weight.clone()
        )
        torch_gpt.ln.bias = Parameter(te_gpt.self_attention.layernorm_qkv.layer_norm_bias.clone())
        torch_gpt.causal_attn.mhsa.in_proj_weight = Parameter(
            te_gpt.self_attention.layernorm_qkv.weight.clone()
        )
        torch_gpt.causal_attn.mhsa.in_proj_bias = Parameter(
            te_gpt.self_attention.layernorm_qkv.bias.clone()
        )
        torch_gpt.causal_attn.mhsa.out_proj.weight = Parameter(
            te_gpt.self_attention.proj.weight.clone()
        )
        torch_gpt.causal_attn.mhsa.out_proj.bias = Parameter(
            te_gpt.self_attention.proj.bias.clone()
        )
        torch_gpt.ln_mlp.ln.weight = Parameter(te_gpt.layernorm_mlp.layer_norm_weight.clone())
        torch_gpt.ln_mlp.ln.bias = Parameter(te_gpt.layernorm_mlp.layer_norm_bias.clone())
        torch_gpt.ln_mlp.fc1.weight = Parameter(te_gpt.layernorm_mlp.fc1_weight.clone())
        torch_gpt.ln_mlp.fc1.bias = Parameter(te_gpt.layernorm_mlp.fc1_bias.clone())
        torch_gpt.ln_mlp.fc2.weight = Parameter(te_gpt.layernorm_mlp.fc2_weight.clone())
        torch_gpt.ln_mlp.fc2.bias = Parameter(te_gpt.layernorm_mlp.fc2_bias.clone())

    te_outputs = _test_e2e_gpt_accuracy(te_gpt, bs, dtype, config)
    torch_outputs = _test_e2e_gpt_accuracy(torch_gpt, bs, dtype, config)

    atol = {
        torch.float32: 5e-3,
        torch.half: 5e-2,
        torch.bfloat16: 1e-1,
    }

    # Check output.
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])

    # Check gradients, only for small model
    if model == "small":
        atol[torch.float32] = 5e-2
        rtol = {
            torch.float32: 1e-2,
            torch.half: 1e-2,
            torch.bfloat16: 1e-2,
        }
        for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
            assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])


def _test_mha_accuracy(block, bs, dtype, config, mask_type, te=True):
    reset_rng_states()

    inp_hidden_states = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()
    inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q) if mask_type == "causal" else None

    forward_kwargs = {}
    if te:
        forward_kwargs["attn_mask_type"] = mask_type
    forward_kwargs["attention_mask"] = inp_attn_mask

    out = block(inp_hidden_states, **forward_kwargs)
    loss = out.sum()
    loss.backward()

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("mask_type", mask_types)
def test_mha_accuracy(dtype, bs, model, mask_type):
    config = model_configs[model]
    if not is_fused_attn_available(
        config, dtype, qkv_layout="sb3hd", is_training=True, deterministic=True
    ):
        pytest.skip("No attention backend available.")

    te_mha = MultiheadAttention(
        config.hidden_size,
        config.num_heads,
        fuse_qkv_params=True,
        params_dtype=dtype,
        qkv_weight_interleaved=False,
        input_layernorm=False,
        device="cuda",
    ).eval()

    torch_mha = (
        TorchMHA(
            config.hidden_size,
            config.num_heads,
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
        torch_mha.mhsa.in_proj_weight = Parameter(te_mha.qkv.weight.clone())
        torch_mha.mhsa.in_proj_bias = Parameter(te_mha.qkv.bias.clone())
        torch_mha.mhsa.out_proj.weight = Parameter(te_mha.proj.weight.clone())
        torch_mha.mhsa.out_proj.bias = Parameter(te_mha.proj.bias.clone())

    te_outputs = _test_mha_accuracy(te_mha, bs, dtype, config, mask_type, te=True)
    torch_outputs = _test_mha_accuracy(torch_mha, bs, dtype, config, mask_type, te=False)

    # Check output.
    if dtype == torch.float32:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-3)
    else:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-2)

    # Check gradients, only for small model
    if model == "small":
        atol = {
            torch.float32: 5e-2,
            torch.half: 5e-2,
            torch.bfloat16: 5e-2,
        }
        rtol = {
            torch.float32: 1e-2,
            torch.half: 1e-2,
            torch.bfloat16: 1e-2,
        }
        for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
            assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])


def _test_granular_accuracy(block, bs, dtype, config, delay_wgrad_compute=False, recipe=None):
    reset_rng_states()
    fp8 = recipe is not None
    if fp8:
        FP8GlobalStateManager.reset()

    inp_hidden_states = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()

    with autocast(enabled=fp8, recipe=recipe):
        out = block(inp_hidden_states)
        if isinstance(out, (List, Tuple)):
            out = out[0]
    loss = out.sum()
    loss.backward()
    if delay_wgrad_compute:
        block.backward_dw()

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            if getattr(p, "main_grad", None) is not None:
                outputs.append(p.main_grad)
                assert p.grad is None  # grad should be None if fuse_wgrad_accumulation is True
            else:
                outputs.append(p.grad)
    return outputs


def _test_dpa_accuracy(block, bs, dtype, config):
    reset_rng_states()

    mask = torch.triu(
        torch.ones(config.max_seqlen_q, config.max_seqlen_kv, dtype=torch.bool, device="cuda"),
        diagonal=1,
    )
    query, key, value = [
        torch.randn(
            (config.max_seqlen_q, bs, config.num_heads, config.kv_channels),
            dtype=dtype,
            device="cuda",
            requires_grad=True,
        )
        for _ in range(3)
    ]

    query.retain_grad()
    key.retain_grad()
    value.retain_grad()

    out = block(query, key, value, attention_mask=mask)
    loss = out.sum()
    loss.backward()

    torch.cuda.synchronize()

    return [out, query.grad, key.grad, value.grad]


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
def test_dpa_accuracy(dtype, bs, model):
    config = model_configs[model]

    te_dpa = (
        DotProductAttention(
            config.num_heads,
            config.kv_channels,
            attention_dropout=0.0,  # disable dropout, FU uses rng differently
        )
        .to(dtype=dtype)
        .cuda()
    )

    torch_dpa = (
        TorchDotProductAttention(
            config.kv_channels,
            0.0,  # dropout
        )
        .to(dtype=dtype)
        .cuda()
    )

    te_outputs = _test_dpa_accuracy(te_dpa, bs, dtype, config)
    torch_outputs = _test_dpa_accuracy(torch_dpa, bs, dtype, config)

    # Check output.
    if dtype == torch.float32:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-3)
    else:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-2)

    for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
        assert_allclose(te_output, torch_output, atol=5e-2, rtol=1e-2)


class TestReturnBiasModule(nn.Module):
    def __init__(self, mod, **kwargs):
        super().__init__()
        self.te_module = mod(**kwargs)
        self.return_bias = kwargs["return_bias"]
        self.bias = kwargs["bias"]

    def forward(self, x):
        if self.return_bias:
            out, bias = self.te_module(x)
            if self.bias:
                out = out + bias
            return out
        return self.te_module(x)


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("return_bias", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
def test_linear_accuracy(dtype, bs, model, return_bias, bias):
    config = model_configs[model]

    te_linear = TestReturnBiasModule(
        Linear,
        in_features=config.hidden_size,
        out_features=4 * config.hidden_size,
        params_dtype=dtype,
        return_bias=return_bias,
        bias=bias,
        device="cuda",
    )

    torch_linear = torch.nn.Linear(
        config.hidden_size,
        4 * config.hidden_size,
        bias=bias,
        device="cuda",
        dtype=dtype,
    )

    # Share params
    with torch.no_grad():
        torch_linear.weight = Parameter(te_linear.te_module.weight.clone())
        if bias:
            torch_linear.bias = Parameter(te_linear.te_module.bias.clone())

    te_outputs = _test_granular_accuracy(te_linear, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_linear, bs, dtype, config)

    # Check output.
    if model == "small":
        tolerance = 5e-3 if dtype == torch.float32 else 5e-2
        rtol = {
            torch.float32: 1.3e-6,
            torch.half: 1e-2,
            torch.bfloat16: 2e-2,
        }
        for te_output, torch_output in zip(te_outputs, torch_outputs):
            assert_allclose(te_output, torch_output, tolerance, rtol[dtype])


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
def test_linear_accuracy_delay_wgrad_compute(dtype, bs, model, bias, fuse_wgrad_accumulation):
    config = model_configs[model]

    te_linear_ref = Linear(
        config.hidden_size,
        4 * config.hidden_size,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        delay_wgrad_compute=False,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    te_linear = Linear(
        config.hidden_size,
        4 * config.hidden_size,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        delay_wgrad_compute=True,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    # Share params
    with torch.no_grad():
        te_linear_ref.weight = Parameter(te_linear.weight.clone())
        if bias:
            te_linear_ref.bias = Parameter(te_linear.bias.clone())
        if fuse_wgrad_accumulation:
            weight = getattr(te_linear, f"weight")
            weight.main_grad = torch.rand_like(weight, dtype=torch.float32)
            te_linear_ref.weight.main_grad = weight.main_grad.clone()

    te_outputs = _test_granular_accuracy(te_linear, bs, dtype, config, delay_wgrad_compute=True)
    te_outputs_ref = _test_granular_accuracy(
        te_linear_ref, bs, dtype, config, delay_wgrad_compute=False
    )

    # Should be bit-wise match
    for _, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("recipe", fp8_recipes + [None])
def test_linear_accuracy_save_original_input(dtype, model, recipe):
    bs = 1
    fuse_wgrad_accumulation = True
    fp8_model_params = False
    fp8 = recipe is not None
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if fp8 and recipe.delayed():
        pytest.skip("DelayedScaling recipe is not supported with save_original_input")
    if fp8 and recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)

    config = model_configs[model]
    if config.max_seqlen_q % 16 != 0 and fp8:
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        te_linear_ref = Linear(
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            device="cuda",
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
            save_original_input=False,
        ).eval()

        te_linear = Linear(
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            device="cuda",
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
            save_original_input=True,
        ).eval()

    # Share params
    with torch.no_grad():
        te_linear_ref.weight = Parameter(te_linear.weight.clone())
        if fuse_wgrad_accumulation:
            weight = getattr(te_linear, f"weight")
            weight.main_grad = torch.rand_like(weight, dtype=torch.float32)
            te_linear_ref.weight.main_grad = weight.main_grad.clone()

    te_outputs = _test_granular_accuracy(te_linear, bs, dtype, config, recipe=recipe)
    te_outputs_ref = _test_granular_accuracy(te_linear_ref, bs, dtype, config, recipe=recipe)

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("eps", [1e-1, 1e-3, 1e-5, 1e-7])
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
def test_rmsnorm_accuracy(dtype, bs, model, eps, zero_centered_gamma):
    config = model_configs[model]

    te_rmsnorm = RMSNorm(
        config.hidden_size,
        eps=eps,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
    ).eval()

    torch_rmsnorm = (
        TorchRMSNorm(config.hidden_size, eps=eps, zero_centered_gamma=zero_centered_gamma)
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
        torch_rmsnorm.weight = Parameter(te_rmsnorm.weight.clone())

    te_outputs = _test_granular_accuracy(te_rmsnorm, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_rmsnorm, bs, dtype, config)

    atol = {
        torch.float32: 1e-7,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
    }

    # Check output.
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])

    atol[torch.float32] = 2e-3
    rtol = {
        torch.float32: 1.3e-6,
        torch.half: 1e-3,
        torch.bfloat16: 1.6e-2,
    }
    # Check gradients
    for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
        assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("eps", [1e-1, 1e-3, 1e-5, 1e-7])
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
def test_layernorm_accuracy(dtype, bs, model, eps, zero_centered_gamma):
    config = model_configs[model]

    te_layernorm = LayerNorm(
        config.hidden_size,
        eps=eps,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
    ).eval()

    torch_layernorm = (
        TorchLayerNorm(config.hidden_size, eps=eps, zero_centered_gamma=zero_centered_gamma)
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
        torch_layernorm.weight = Parameter(te_layernorm.weight.clone())
        torch_layernorm.bias = Parameter(te_layernorm.bias.clone())

    te_outputs = _test_granular_accuracy(te_layernorm, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_layernorm, bs, dtype, config)

    atol = {
        torch.float32: 1e-7,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
    }

    # Check output.
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])

    rtol = {
        torch.float32: 1.3e-6,
        torch.half: 1e-3,
        torch.bfloat16: 1.6e-2,
    }
    atol[torch.float32] = 1e-4
    # Check gradients
    for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
        assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("normalization", all_normalizations)
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
@pytest.mark.parametrize("return_bias", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
def test_layernorm_linear_accuracy(
    dtype, bs, model, normalization, zero_centered_gamma, return_bias, bias
):
    config = model_configs[model]

    te_ln_linear = TestReturnBiasModule(
        LayerNormLinear,
        in_features=config.hidden_size,
        out_features=4 * config.hidden_size,
        eps=config.eps,
        normalization=normalization,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        return_bias=return_bias,
        bias=bias,
        device="cuda",
    )

    torch_ln_linear = (
        TorchLayerNormLinear(
            config.hidden_size,
            4 * config.hidden_size,
            config.eps,
            normalization=normalization,
            zero_centered_gamma=zero_centered_gamma,
            bias=bias,
        )
        .to(dtype=dtype)
        .cuda()
    )

    # Share params
    with torch.no_grad():
        torch_ln_linear.layernorm.weight = Parameter(
            te_ln_linear.te_module.layer_norm_weight.clone()
        )
        if normalization != "RMSNorm":
            torch_ln_linear.layernorm.bias = Parameter(
                te_ln_linear.te_module.layer_norm_bias.clone()
            )
        torch_ln_linear.linear.weight = Parameter(te_ln_linear.te_module.weight.clone())
        if bias:
            torch_ln_linear.linear.bias = Parameter(te_ln_linear.te_module.bias.clone())

    te_outputs = _test_granular_accuracy(te_ln_linear, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_ln_linear, bs, dtype, config)

    atol = {
        torch.float32: 2.5e-4,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
    }
    rtol = {
        torch.float32: 1e-3,
        torch.half: 4e-2,
        torch.bfloat16: 4e-2,
    }

    # Check output.
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype], rtol[dtype])

    if model == "small":
        atol = {
            torch.float32: 1e-3,
            torch.half: 5e-2,
            torch.bfloat16: 5e-2,
        }
        rtol = {
            torch.float32: 1e-3,
            torch.half: 4e-2,
            torch.bfloat16: 4e-2,
        }
        # Check gradients
        for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
            assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("normalization", all_normalizations)
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
def test_layernorm_linear_accuracy_delay_wgrad_compute(
    dtype, bs, model, normalization, zero_centered_gamma, bias, fuse_wgrad_accumulation
):
    config = model_configs[model]

    ln_linear_ref = LayerNormLinear(
        config.hidden_size,
        4 * config.hidden_size,
        config.eps,
        bias=bias,
        normalization=normalization,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
        delay_wgrad_compute=False,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    ln_linear = LayerNormLinear(
        config.hidden_size,
        4 * config.hidden_size,
        config.eps,
        bias=bias,
        normalization=normalization,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
        delay_wgrad_compute=True,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    # Share params
    with torch.no_grad():
        ln_linear_ref.layer_norm_weight = Parameter(ln_linear.layer_norm_weight.clone())
        if normalization != "RMSNorm":
            ln_linear_ref.layer_norm_bias = Parameter(ln_linear.layer_norm_bias.clone())
        ln_linear_ref.weight = Parameter(ln_linear.weight.clone())
        if bias:
            ln_linear_ref.bias = Parameter(ln_linear.bias.clone())
        if fuse_wgrad_accumulation:
            weight = getattr(ln_linear, f"weight")
            weight.main_grad = torch.rand_like(weight, dtype=torch.float32)
            ln_linear_ref.weight.main_grad = weight.main_grad.clone()

    te_outputs = _test_granular_accuracy(ln_linear, bs, dtype, config, delay_wgrad_compute=True)
    te_outputs_ref = _test_granular_accuracy(
        ln_linear_ref, bs, dtype, config, delay_wgrad_compute=False
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("activation", all_activations)
@pytest.mark.parametrize("normalization", all_normalizations)
@pytest.mark.parametrize("return_bias", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
def test_layernorm_mlp_accuracy(dtype, bs, model, activation, normalization, return_bias, bias):
    config = model_configs[model]

    te_ln_mlp = TestReturnBiasModule(
        LayerNormMLP,
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
        activation=activation,
        normalization=normalization,
        params_dtype=dtype,
        return_bias=return_bias,
        bias=bias,
        device="cuda",
    )

    torch_ln_mlp = (
        TorchLayerNormMLP(
            config.hidden_size,
            4 * config.hidden_size,
            activation=activation,
            normalization=normalization,
            bias=bias,
        )
        .to(dtype=dtype)
        .cuda()
    )

    # Share params
    with torch.no_grad():
        torch_ln_mlp.ln.weight = Parameter(te_ln_mlp.te_module.layer_norm_weight.clone())
        if normalization != "RMSNorm":
            torch_ln_mlp.ln.bias = Parameter(te_ln_mlp.te_module.layer_norm_bias.clone())
        torch_ln_mlp.fc1.weight = Parameter(te_ln_mlp.te_module.fc1_weight.clone())
        torch_ln_mlp.fc2.weight = Parameter(te_ln_mlp.te_module.fc2_weight.clone())
        if bias:
            torch_ln_mlp.fc1.bias = Parameter(te_ln_mlp.te_module.fc1_bias.clone())
            torch_ln_mlp.fc2.bias = Parameter(te_ln_mlp.te_module.fc2_bias.clone())

    te_outputs = _test_granular_accuracy(te_ln_mlp, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_ln_mlp, bs, dtype, config)

    atol = {
        torch.float32: 2e-2,
        torch.half: 5e-2,
        torch.bfloat16: 5e-2,
    }

    rtol = {
        torch.float32: 1e-3,
        torch.half: 4e-2,
        torch.bfloat16: 4e-2,
    }

    # Check output.
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype], rtol[dtype])

    # Check gradients, only for small model
    rtol = {
        torch.float32: 1e-3,
        torch.half: 1e-2,
        torch.bfloat16: 4e-2,
    }
    atol[torch.half] = 2e-1
    atol[torch.bfloat16] = 2e-1
    if model == "small":
        for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
            assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", [2])
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
def test_layernorm_mlp_accuracy_delay_wgrad_compute(
    dtype, bs, model, bias, fuse_wgrad_accumulation
):
    config = model_configs[model]

    ln_mlp = LayerNormMLP(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
        eps=config.eps,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        delay_wgrad_compute=True,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    ln_mlp_ref = LayerNormMLP(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
        eps=config.eps,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        delay_wgrad_compute=False,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    # Share params
    with torch.no_grad():
        ln_mlp_ref.layer_norm_weight = Parameter(ln_mlp.layer_norm_weight.clone())
        ln_mlp_ref.layer_norm_bias = Parameter(ln_mlp.layer_norm_bias.clone())
        ln_mlp_ref.fc1_weight = Parameter(ln_mlp.fc1_weight.clone())
        ln_mlp_ref.fc2_weight = Parameter(ln_mlp.fc2_weight.clone())
        if bias:
            ln_mlp_ref.fc1_bias = Parameter(ln_mlp.fc1_bias.clone())
            ln_mlp_ref.fc2_bias = Parameter(ln_mlp.fc2_bias.clone())
        if fuse_wgrad_accumulation:
            ln_mlp.fc1_weight.main_grad = torch.rand_like(ln_mlp.fc1_weight, dtype=torch.float32)
            ln_mlp_ref.fc1_weight.main_grad = ln_mlp.fc1_weight.main_grad.clone()
            ln_mlp.fc2_weight.main_grad = torch.rand_like(ln_mlp.fc2_weight, dtype=torch.float32)
            ln_mlp_ref.fc2_weight.main_grad = ln_mlp.fc2_weight.main_grad.clone()

    te_outputs = _test_granular_accuracy(ln_mlp, bs, dtype, config, delay_wgrad_compute=True)
    te_outputs_ref = _test_granular_accuracy(
        ln_mlp_ref, bs, dtype, config, delay_wgrad_compute=False
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


def _test_grouped_linear_accuracy(
    block,
    num_gemms,
    bs,
    dtype,
    config,
    recipe,
    fp8,
    fuse_wgrad_accumulation,
    delay_wgrad_compute=False,
):
    reset_rng_states()
    if fp8:
        FP8GlobalStateManager.reset()

    inp_hidden_states = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()

    if num_gemms > 1:
        split_size = 1
        if fp8:
            split_size = 16
            if recipe.mxfp8():
                split_size = 128
        m = config.max_seqlen_q // split_size
        dist = torch.sort(torch.randint(0, m, (num_gemms - 2,))).values.tolist()
        dist.append(dist[-1])  # Manually add a zero
        m_splits = torch.tensor(dist + [m]) - torch.tensor([0] + dist)
        m_splits = m_splits * split_size
        assert m_splits.sum() == config.max_seqlen_q and len(m_splits) == num_gemms
    else:
        m_splits = torch.tensor([config.max_seqlen_q])

    with autocast(enabled=fp8, recipe=recipe):
        if isinstance(block, GroupedLinear):
            m_splits = m_splits * bs
            out = block(inp_hidden_states, m_splits.tolist())
        else:
            out = torch.cat(
                [
                    block[i](inp)
                    for i, inp in enumerate(torch.split(inp_hidden_states, m_splits.tolist()))
                ]
            )
    loss = out.sum()
    loss.backward()
    if delay_wgrad_compute:
        if isinstance(block, GroupedLinear):
            block.backward_dw()
        else:
            for i in range(num_gemms):
                block[i].backward_dw()

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            if getattr(p, "main_grad", None) is not None:
                outputs.append(p.main_grad)
                assert p.grad is None  # grad should be None if fuse_wgrad_accumulation is True
            else:
                outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types, ids=str)
@pytest.mark.parametrize("num_gemms", [3, 6])
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("recipe", fp8_recipes + [None])
@pytest.mark.parametrize("fp8_model_params", all_boolean)
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("delay_wgrad_compute", all_boolean)
def test_grouped_linear_accuracy(
    dtype,
    num_gemms,
    bs,
    model,
    recipe,
    fp8_model_params,
    fuse_wgrad_accumulation,
    bias,
    delay_wgrad_compute,
    parallel_mode=None,
    use_cutlass=False,
):
    fp8 = recipe is not None
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if fp8 and recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
    if fp8 and fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")

    config = model_configs[model]
    if config.max_seqlen_q % 16 != 0 and fp8:
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=bias,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            device="cuda",
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
            delay_wgrad_compute=delay_wgrad_compute,
            save_original_input=False,
        ).eval()
        sequential_linear = torch.nn.ModuleList(
            [
                Linear(
                    config.hidden_size,
                    4 * config.hidden_size,
                    bias=bias,
                    params_dtype=dtype,
                    parallel_mode=parallel_mode,
                    device="cuda",
                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
                ).eval()
                for _ in range(num_gemms)
            ]
        )

    # Share params
    with torch.no_grad():
        for i in range(num_gemms):
            sequential_linear[i].weight = Parameter(getattr(grouped_linear, f"weight{i}").clone())
            if bias:
                sequential_linear[i].bias = Parameter(getattr(grouped_linear, f"bias{i}").clone())
            if fuse_wgrad_accumulation:
                weight_i = getattr(grouped_linear, f"weight{i}")
                weight_i.main_grad = torch.rand_like(weight_i, dtype=torch.float32)
                sequential_linear[i].weight.main_grad = weight_i.main_grad.clone()
    if IS_HIP_EXTENSION:
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "1"
    outputs_ref = _test_grouped_linear_accuracy(
        sequential_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
    )
    outputs = _test_grouped_linear_accuracy(
        grouped_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
    )
    if IS_HIP_EXTENSION:
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "0"
    for o, o_ref in zip(outputs, outputs_ref):
        if use_cutlass:
            torch.testing.assert_close(o, o_ref, rtol=1e-3, atol=1e-3)
        else:
            # cuBLAS implementation should be bit-wise match
            torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


@pytest.mark.skipif(
    torch.cuda.get_device_capability() != (9, 0),
    reason="Only enable CUTLASS grouped gemm on Hopper",
)
@pytest.mark.parametrize("dtype", param_types, ids=str)
@pytest.mark.parametrize("num_gemms", [3, 6])
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
@pytest.mark.parametrize("delay_wgrad_compute", all_boolean)
def test_grouped_linear_accuracy_cutlass(
    dtype,
    num_gemms,
    bs,
    model,
    fuse_wgrad_accumulation,
    delay_wgrad_compute,
):
    os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = "1"
    test_grouped_linear_accuracy(
        dtype,
        num_gemms,
        bs,
        model,
        None,
        False,
        fuse_wgrad_accumulation,
        False,
        delay_wgrad_compute,
        None,
        use_cutlass=True,
    )
    os.environ.pop("NVTE_USE_CUTLASS_GROUPED_GEMM", None)


@pytest.mark.parametrize("dtype", param_types, ids=str)
@pytest.mark.parametrize("num_gemms", [3])
@pytest.mark.parametrize("bs", [1])
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("recipe", fp8_recipes + [None])
@pytest.mark.parametrize("fp8_model_params", [False])
@pytest.mark.parametrize("fuse_wgrad_accumulation", [True])
@pytest.mark.parametrize("bias", [False])
@pytest.mark.parametrize("delay_wgrad_compute", [True])
def test_grouped_linear_accuracy_save_original_input(
    dtype,
    num_gemms,
    bs,
    model,
    recipe,
    fp8_model_params,
    fuse_wgrad_accumulation,
    bias,
    delay_wgrad_compute,
    parallel_mode=None,
):
    fp8 = recipe is not None
    if fp8 and fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
    if fp8 and recipe.delayed():
        pytest.skip("DelayedScaling recipe is not supported with save_original_input")
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if fp8 and recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)

    config = model_configs[model]
    if config.max_seqlen_q % 16 != 0 and fp8:
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=bias,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            device="cuda",
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
            delay_wgrad_compute=delay_wgrad_compute,
            save_original_input=True,
        ).eval()
        sequential_linear = torch.nn.ModuleList(
            [
                Linear(
                    config.hidden_size,
                    4 * config.hidden_size,
                    bias=bias,
                    params_dtype=dtype,
                    parallel_mode=parallel_mode,
                    device="cuda",
                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
                ).eval()
                for _ in range(num_gemms)
            ]
        )

    # Share params
    with torch.no_grad():
        for i in range(num_gemms):
            sequential_linear[i].weight = Parameter(getattr(grouped_linear, f"weight{i}").clone())
            if bias:
                sequential_linear[i].bias = Parameter(getattr(grouped_linear, f"bias{i}").clone())
            if fuse_wgrad_accumulation:
                weight_i = getattr(grouped_linear, f"weight{i}")
                weight_i.main_grad = torch.rand_like(weight_i, dtype=torch.float32)
                sequential_linear[i].weight.main_grad = weight_i.main_grad.clone()

    # Force the sequential_linear and grouped_linear to use hipblaslt rather than hipblas
    if IS_HIP_EXTENSION:
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "1"
    
    outputs_ref = _test_grouped_linear_accuracy(
        sequential_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
    )
    outputs = _test_grouped_linear_accuracy(
        grouped_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
    )
    if IS_HIP_EXTENSION:
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "0"

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(outputs, outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


@pytest.mark.parametrize("recipe", fp8_recipes + [None])
def test_grouped_linear_accuracy_single_gemm(recipe):
    """Split the tests to save CI time"""
    test_grouped_linear_accuracy(
        dtype=torch.float32,
        num_gemms=1,
        bs=2,
        model="126m",
        recipe=recipe,
        fp8_model_params=True,
        fuse_wgrad_accumulation=True,
        bias=True,
        delay_wgrad_compute=False,
    )


def _test_padding_grouped_linear_accuracy(block, num_gemms, bs, dtype, config, recipe, fp8=False):

    def _pad_tensor_for_fp8(hidden_states, tokens_per_expert):
        align_size = 16
        if recipe.mxfp8():
            align_size = 32
        padded_tokens_per_expert = [
            (num_tokens + align_size - 1) // align_size * align_size
            for num_tokens in tokens_per_expert
        ]
        hidden_states = torch.split(hidden_states, tokens_per_expert)
        padded_hidden_states = []
        for hidden_state, actual_num_tokens, padded_num_tokens in zip(
            hidden_states, tokens_per_expert, padded_tokens_per_expert
        ):
            padded_hidden_states.append(hidden_state)
            if padded_num_tokens > actual_num_tokens:
                pad_tensor = torch.zeros(
                    padded_num_tokens - actual_num_tokens,
                    hidden_state.shape[1],
                    dtype=hidden_state.dtype,
                    device=hidden_state.device,
                )
                padded_hidden_states.append(pad_tensor)
        padded_hidden_states = torch.cat(padded_hidden_states, dim=0)
        return padded_hidden_states, padded_tokens_per_expert

    def _unpad_tensor_for_fp8(padded_hidden_states, actual_tokens_per_expert, tokens_per_expert):
        inputmats = torch.split(
            padded_hidden_states.view(-1, padded_hidden_states.shape[-1]), tokens_per_expert
        )
        hidden_states = torch.cat(
            [
                grad_output_mat[: actual_tokens_per_expert[i]]
                for i, grad_output_mat in enumerate(inputmats)
            ],
            dim=0,
        )

        return hidden_states

    def _generate_random_numbers(n, total_sum):
        if n <= 0:
            return []

        # reset seed
        random.seed(seed)

        breaks = sorted(random.sample(range(1, total_sum), n - 1))
        random_numbers = (
            [breaks[0]]
            + [breaks[i] - breaks[i - 1] for i in range(1, n - 1)]
            + [total_sum - breaks[-1]]
        )

        return random_numbers

    reset_rng_states()
    if fp8:
        FP8GlobalStateManager.reset()

    inp_hidden_states = torch.randn(
        (config.max_seqlen_q * bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()

    m_splits = _generate_random_numbers(num_gemms, config.max_seqlen_q * bs)

    with autocast(enabled=fp8, recipe=recipe):
        if isinstance(block, TorchGroupedLinearWithPadding):
            out = block(inp_hidden_states, m_splits)
        else:
            if fp8:
                padded_inp_hidden_states, padding_m_splits = _pad_tensor_for_fp8(
                    inp_hidden_states, m_splits
                )
                padded_inp_hidden_states = block(padded_inp_hidden_states, padding_m_splits)
                out = _unpad_tensor_for_fp8(padded_inp_hidden_states, m_splits, padding_m_splits)
            else:
                out = block(inp_hidden_states, m_splits)

    loss = out.sum()
    loss.backward()

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("num_gemms", [3, 6])
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("fp8", [True])
@pytest.mark.parametrize("recipe", fp8_recipes)
@pytest.mark.parametrize("fp8_model_params", all_boolean)
def test_padding_grouped_linear_accuracy(
    dtype,
    num_gemms,
    bs,
    model,
    fp8,
    recipe,
    fp8_model_params,
    parallel_mode=None,
):
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)

    config = model_configs[model]
    if config.max_seqlen_q % 16 != 0 and fp8:
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        grouped_linear = TorchGroupedLinearWithPadding(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            fp8=fp8,
        ).eval()

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        ref_grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            device="cuda",
            save_original_input=False,
        ).eval()

    # Share params
    with torch.no_grad():
        inner_grouped_linear = grouped_linear.linear_fn
        for i in range(num_gemms):
            setattr(
                ref_grouped_linear,
                f"weight{i}",
                Parameter(getattr(inner_grouped_linear, f"weight{i}").clone()),
            )

    outputs = _test_padding_grouped_linear_accuracy(
        grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
    )
    outputs_ref = _test_padding_grouped_linear_accuracy(
        ref_grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(outputs, outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("num_gemms", [3])
@pytest.mark.parametrize("bs", [1])
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("fp8", [True])
@pytest.mark.parametrize("recipe", fp8_recipes)
@pytest.mark.parametrize("fp8_model_params", [False])
def test_padding_grouped_linear_accuracy_save_original_input(
    dtype,
    num_gemms,
    bs,
    model,
    fp8,
    recipe,
    fp8_model_params,
    parallel_mode=None,
):
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
    if fp8 and recipe.delayed():
        pytest.skip("DelayedScaling recipe is not supported with save_original_input")
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)

    config = model_configs[model]
    if config.max_seqlen_q % 16 != 0 and fp8:
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        grouped_linear = TorchGroupedLinearWithPadding(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            fp8=fp8,
        ).eval()

    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        ref_grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            device="cuda",
            save_original_input=True,
        ).eval()

    # Share params
    with torch.no_grad():
        inner_grouped_linear = grouped_linear.linear_fn
        for i in range(num_gemms):
            setattr(
                ref_grouped_linear,
                f"weight{i}",
                Parameter(getattr(inner_grouped_linear, f"weight{i}").clone()),
            )

    outputs = _test_padding_grouped_linear_accuracy(
        grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
    )
    outputs_ref = _test_padding_grouped_linear_accuracy(
        ref_grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(outputs, outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


def _test_gpt_e2e_cuda_graph(block, bs, dtype, config, graph):
    reset_rng_states()

    # Initialize loss function and optimizer.
    loss_fn = torch.nn.MSELoss()
    optimizer = torch.optim.SGD(block.parameters(), lr=0.1)

    # Placeholders used for graph capture.
    static_input = torch.randn(
        config.max_seqlen_q, bs, config.hidden_size, device="cuda", dtype=dtype, requires_grad=True
    )
    static_target = torch.randn(
        config.max_seqlen_q, bs, config.hidden_size, device="cuda", dtype=dtype
    )

    real_input = torch.rand_like(static_input)
    real_target = torch.rand_like(static_target)

    # Basic training loop.
    def train_step():
        optimizer.zero_grad(set_to_none=False)
        out = block(static_input)
        loss = loss_fn(out, static_target)
        loss.backward()
        optimizer.step()
        return out

    # Warmup steps in a separate stream.
    s = torch.cuda.Stream()
    s.wait_stream(torch.cuda.current_stream())
    with torch.cuda.stream(s):
        for _ in range(3):
            train_step()
    torch.cuda.current_stream().wait_stream(s)

    # Capture graph.
    g = None
    static_output = None
    if graph:
        g = torch.cuda.CUDAGraph()
        with torch.cuda.graph(g):
            static_output = train_step()

    # Run with new data.
    with torch.no_grad():
        static_input.copy_(real_input)
        static_target.copy_(real_target)
    if graph:
        g.replay()
    else:
        static_output = train_step()

    grads = [static_input.grad]
    for p in block.parameters():
        if p.requires_grad:
            grads.append(p.grad)

    with torch.no_grad():
        output = static_output.clone()
    return output, grads


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
def test_gpt_cuda_graph(dtype, bs, model):
    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("Cuda Graphs are not supported in debug mode.")
    config = model_configs[model]

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

    block_args = (
        config.hidden_size,
        4 * config.hidden_size,
        config.num_heads,
    )
    block_kwargs = dict(
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
    )
    block = TransformerLayer(*block_args, **block_kwargs)
    graphed_block = TransformerLayer(*block_args, **block_kwargs)
    with torch.no_grad():
        for param1, param2 in zip(block.parameters(), graphed_block.parameters()):
            param2.copy_(param1)

    out, grads = _test_gpt_e2e_cuda_graph(block, bs, dtype, config, False)
    graphed_out, graphed_grads = _test_gpt_e2e_cuda_graph(graphed_block, bs, dtype, config, True)
    params = list(block.parameters())
    graphed_params = list(graphed_block.parameters())

    # Check that results match
    assert_allclose(out, graphed_out, 1e-3)
    assert_allclose(params, graphed_params, 1e-3)
    assert_allclose(grads, graphed_grads, 1e-3)


def _test_gpt_fp8_parameters(bs, dtype, config, fp8_model_params, recipe):
    reset_rng_states()
    FP8GlobalStateManager.reset()

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

    with quantized_model_init(enabled=fp8_model_params, recipe=recipe):
        block = TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
            config.num_heads,
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
            kv_channels=config.kv_channels,
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
            fuse_qkv_params=True,
            device="cuda",
        )

    te_inp_hidden_states = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    te_inp_hidden_states.retain_grad()
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)

    with autocast(enabled=True, recipe=recipe):
        te_out = block(te_inp_hidden_states, attention_mask=te_inp_attn_mask)
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()

    outputs = [te_out, te_inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("recipe", fp8_recipes)
def test_gpt_fp8_parameters(dtype, bs, model, recipe):
    if not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)

    config = model_configs[model]

    outputs = _test_gpt_fp8_parameters(bs, dtype, config, False, recipe)
    outputs_fp8_params = _test_gpt_fp8_parameters(bs, dtype, config, True, recipe)

    # Check that results match
    tols = dict(rtol=0.125, atol=0.0675)
    for i, (ref, test) in enumerate(zip(outputs, outputs_fp8_params)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            rtol=0.125,
            atol=0.0675,
        )


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
def test_transformer_layer_hidden_states_format(dtype, bs, model):
    config = model_configs[model]

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

    # Set `torch.manual_seed` to make sure the weights are identical to the
    # other layer. Set `*dropout` values to 0 to make sure the forward pass
    # is identical to the other layer.
    torch.manual_seed(0)
    block_sbhd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
        config.num_heads,
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
        attn_input_format="sbhd",
    )

    # Set `torch.manual_seed` to make sure the weights are identical to the
    # other layer. Set `*dropout` values to 0 to make sure the forward pass
    # is identical to the other layer.
    torch.manual_seed(0)
    block_bshd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
        config.num_heads,
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
        attn_input_format="bshd",
    )

    torch.manual_seed(0)
    block_thd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
        config.num_heads,
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
        attn_input_format="thd",
        self_attn_mask_type="padding_causal",
    )

    for (n1, p1), (n2, p2), (n3, p3) in zip(
        block_bshd.named_parameters(), block_sbhd.named_parameters(), block_thd.named_parameters()
    ):
        assert torch.all(torch.eq(p1, p2) & torch.eq(p1, p3)), f"{n1}, {n2} and {n3} not identical"

    x_sbhd = torch.randn(
        (config.max_seqlen_q, bs, config.hidden_size),
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )

    x_bshd = x_sbhd.transpose(0, 1).contiguous()
    x_thd = x_bshd.reshape(bs * config.max_seqlen_q, config.hidden_size).contiguous()
    x_thd_cumsum = torch.arange(bs + 1, device="cuda", dtype=torch.int32) * config.max_seqlen_q

    # To make sure forward is also identical (just in case some module decides
    # to act fancy)
    torch.manual_seed(0)
    y_sbhd = block_sbhd(x_sbhd)

    # To make sure forward is also identical (just in case some module decides
    # to act fancy)
    torch.manual_seed(0)
    y_bshd = block_bshd(x_bshd)

    # Check that results match
    torch.testing.assert_close(
        y_bshd,
        y_sbhd.transpose(0, 1).contiguous(),
    )

    # THD is not supported in float32 and on GPUs older than Ampere, skip the test here
    if dtype != torch.float32 and sm_80plus:
        # To make sure forward is also identical (just in case some module decides
        # to act fancy)
        torch.manual_seed(0)
        y_thd = block_thd(
            x_thd,
            cu_seqlens_q=x_thd_cumsum,
            cu_seqlens_kv=x_thd_cumsum,
            max_seqlen_q=config.max_seqlen_q,
            max_seqlen_kv=config.max_seqlen_kv,
        )

        torch.testing.assert_close(
            y_bshd,
            y_thd.reshape(bs, config.max_seqlen_q, config.hidden_size).contiguous(),
        )


@pytest.mark.parametrize(
    "shape",
    [
        (1, 127, 128, 512),
        (8, 15, 128, 512),
        (8, 1027, 128, 512),
        (16, 10027, 128, 512),
    ],
)
@pytest.mark.parametrize("dtype", param_types, ids=str)
@pytest.mark.parametrize("layout", ["TN", "NN", "NT"])
@pytest.mark.parametrize("accumulate", [False, True])
@pytest.mark.parametrize("use_cutlass", use_cutlass_grouped_gemm)
def test_grouped_gemm(shape, dtype, layout, accumulate, use_cutlass):
    torch.manual_seed(0)
    z, m, k, n = shape

    dist = torch.sort(torch.randint(0, m, (z - 1,))).values.tolist()
    m_splits = torch.tensor(dist + [m]) - torch.tensor([0] + dist)
    assert m_splits.sum() == m and len(m_splits) == z
    m_splits = m_splits.tolist()

    if layout == "TN":
        A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
        B = list(torch.split(torch.randn(m, k, dtype=dtype, device="cuda"), m_splits))  # input
        out = [torch.randn(m, n, dtype=dtype, device="cuda")]  # output
        out_ref = [o.clone() for o in torch.split(out[0], m_splits)]
        grad = False
        single_output = True
    elif layout == "NN":
        A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
        B = list(
            torch.split(torch.randn(m, n, dtype=dtype, device="cuda"), m_splits)
        )  # grad_output
        out = [torch.randn(m, k, dtype=dtype, device="cuda")]  # dgrad
        out_ref = [o.clone() for o in torch.split(out[0], m_splits)]
        grad = True
        single_output = True
    else:  # layout == "NT"
        A = list(torch.split(torch.randn(m, k, dtype=dtype, device="cuda"), m_splits))  # input
        B = list(
            torch.split(torch.randn(m, n, dtype=dtype, device="cuda"), m_splits)
        )  # grad_output
        out = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # wgrad
        out_ref = [o.clone() for o in out]
        grad = True
        single_output = False

    if use_cutlass:
        os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = "1"

    # Force the sequential_linear and grouped_linear to use hipblaslt rather than hipblas
    if IS_HIP_EXTENSION:
        ori_force_rocm_gemm = os.environ.get("NVTE_FORCE_ROCM_GEMM", None)
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "1"

    for i in range(z):
        general_gemm(
            A[i],
            B[i],
            get_workspace(),
            dtype,
            grad=grad,
            accumulate=accumulate,
            layout=layout,
            out=out_ref[i],
        )
    if single_output:
        out_ref = [torch.cat(out_ref)]

    general_grouped_gemm(
        A,
        B,
        out,
        dtype,
        get_multi_stream_cublas_workspace(),
        m_splits=m_splits,
        grad=grad,
        accumulate=accumulate,
        layout=layout,
        single_output=single_output,
    )
    if IS_HIP_EXTENSION:
        if ori_force_rocm_gemm is not None:
            os.environ["NVTE_FORCE_ROCM_GEMM"] = ori_force_rocm_gemm
        else:
            del os.environ["NVTE_FORCE_ROCM_GEMM"]

    for o, o_ref in zip(out, out_ref):
        if not use_cutlass:
            # cublas implementation should be bit-wise match
            torch.testing.assert_close(o, o_ref, rtol=0, atol=0)
        else:
            torch.testing.assert_close(o, o_ref, rtol=1.5e-2, atol=1.5e-2)

    if use_cutlass:
        os.environ.pop("NVTE_USE_CUTLASS_GROUPED_GEMM", None)


@pytest.mark.parametrize("N", [32])
@pytest.mark.parametrize("datatype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize(
    "input_quantizer",
    [
        Float8CurrentScalingQuantizer(fp8_dtype=tex.DType.kFloat8E4M3, device="cuda"),
        MXFP8Quantizer(fp8_dtype=tex.DType.kFloat8E4M3),
    ],
)
@pytest.mark.parametrize(
    "out_quantizer",
    [
        Float8CurrentScalingQuantizer(fp8_dtype=tex.DType.kFloat8E4M3, device="cuda"),
        MXFP8Quantizer(fp8_dtype=tex.DType.kFloat8E4M3),
        Float8Quantizer(
            torch.ones(1).cuda().squeeze(), torch.ones(1).cuda().squeeze(), tex.DType.kFloat8E4M3
        ),
    ],
)
def test_fp8gemm_with_unfused_quantization(N, datatype, input_quantizer, out_quantizer):
    # For MXFP8 and CurrentScaling, below unfused quantization should happen
    # FP8 input --> cublas GEMM --> BF16 output --> Quantize to FP8 --> fp8 Output
    # Skip invalid configurations
    is_mxfp8_needed = isinstance(input_quantizer, MXFP8Quantizer) or isinstance(
        out_quantizer, MXFP8Quantizer
    )
    if not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if is_mxfp8_needed and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    inp_fp8 = input_quantizer(torch.randn(N, N, device="cuda", dtype=datatype))
    weight_fp8 = input_quantizer(torch.randn(N, N, device="cuda", dtype=datatype))
    outp_type = torch.float32
    quantized_out, *_ = general_gemm(
        weight_fp8,
        inp_fp8,
        get_workspace(),
        outp_type,
        quantization_params=out_quantizer,
        bias=None,
        use_split_accumulator=False,
    )

    out, *_ = general_gemm(
        weight_fp8,
        inp_fp8,
        get_workspace(),
        outp_type,
        quantization_params=None,
        bias=None,
        use_split_accumulator=False,
    )
    expected_quantized_out = out_quantizer(out)

    # Match results again Pytorch GEMM and allow for quantization tolerance
    pytorch_out = torch.matmul(
        inp_fp8.dequantize().to(torch.float64),
        torch.transpose(weight_fp8.dequantize().to(torch.float64), 0, 1),
    )
    fp8_tols = dict(rtol=0.125, atol=0.0675)
    torch.testing.assert_close(
        pytorch_out.to(outp_type), expected_quantized_out.dequantize(), **fp8_tols
    )
    # Match results between quantization happening inside vs outside general_gemm
    torch.testing.assert_close(expected_quantized_out.dequantize(), quantized_out.dequantize())


@pytest.mark.parametrize(
    "shape",
    [
        (1, 128, 128, 512),
        (8, 1024, 128, 512),
        (16, 4096, 128, 512),
    ],
)
@pytest.mark.parametrize("accumulate", [False, True])
def test_fp8_grouped_gemm(shape, accumulate):
    if not fp8_available:
        pytest.skip(reason_for_no_fp8)

    z, m, k, n = shape
    m_splits = [m // z] * z

    dtype = torch.bfloat16
    A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
    B = torch.split(torch.randn(m, k, dtype=dtype, device="cuda"), m_splits)  # input
    out = torch.split(torch.randn(m, n, dtype=dtype, device="cuda"), m_splits)  # output
    out_ref = [o.clone() for o in out]

    # fp8 should be robust enough to this fake scale
    scale = 1 + torch.rand(1, dtype=torch.float32, device="cuda").squeeze()
    amax = torch.zeros(1, 1, dtype=torch.float32, device="cuda")

    a_quantizers = [
        Float8Quantizer(
            scale.clone(),
            amax.clone(),
            tex.DType.kFloat8E4M3,
        )
        for _ in range(z)
    ]
    b_quantizers = [
        Float8Quantizer(
            scale.clone(),
            amax.clone(),
            tex.DType.kFloat8E4M3,
        )
        for _ in range(z)
    ]

    A_fp8 = []
    B_fp8 = []

    for i in range(z):
        A_fp8.append(a_quantizers[i](A[i]))
        B_fp8.append(b_quantizers[i](B[i]))

    # baseline
    for i in range(z):
        general_gemm(
            A_fp8[i],
            B_fp8[i],
            get_workspace(),
            dtype,
            out=out_ref[i],
            accumulate=accumulate,
        )
    general_grouped_gemm(
        A_fp8,
        B_fp8,
        out,
        dtype,
        get_multi_stream_cublas_workspace(),
        m_splits=m_splits,
        accumulate=accumulate,
    )

    # should be bit-wise match
    for o, o_ref in zip(out, out_ref):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


def test_noncontiguous():
    def _create2modules(m, params):
        mod1 = m(*params)
        mod2 = m(*params)
        for p1, p2 in zip(mod1.parameters(), mod2.parameters()):
            p2.data = p1.data.clone()

        return mod1, mod2

    def _run_module(m, inp):
        out = m(inp)
        out.sum().backward()
        ret = [out]
        if inp.grad is not None:
            ret.append(inp.grad)

        for p in m.parameters():
            if p.requires_grad:
                ret.append(p.grad)
        return ret

    a = torch.randn((128, 256), device="cuda", requires_grad=True)
    a = a.T
    assert not a.is_contiguous(), "The test is supposed to test noncontiguous input."

    b = a.contiguous()

    # LayerNorm
    ln1, ln2 = _create2modules(LayerNorm, [128])
    outT = _run_module(ln1, a)
    out = _run_module(ln2, b)

    assert_allclose(out, outT, 1e-7)

    # RMSNorm
    ln1, ln2 = _create2modules(RMSNorm, [128])
    outT = _run_module(ln1, a)
    out = _run_module(ln2, b)

    assert_allclose(out, outT, 1e-7)

    # GEMM
    g1, g2 = _create2modules(Linear, [128, 128])
    outT = _run_module(g1, a)
    out = _run_module(g2, b)

    assert_allclose(out, outT, 1e-7)