test_numerics.py

# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

import math
import os
from typing import List, Optional
import pytest
import copy

import torch
import torch.nn as nn
from torch.nn import Parameter

from transformer_engine.pytorch.fp8 import fp8_autocast, FP8GlobalStateManager, fp8_model_init
from transformer_engine.pytorch.utils import (
    init_method_normal,
    scaled_init_method_normal,
    attention_mask_func,
    is_bf16_compatible,
)
from transformer_engine.pytorch import (
    DotProductAttention, LayerNormLinear, LayerNormMLP, Linear,
    MultiheadAttention, RMSNorm, TransformerLayer, LayerNorm
)
from transformer_engine.pytorch.distributed import checkpoint as te_checkpoint
from transformer_engine.pytorch.distributed import _set_cuda_rng_state, CudaRNGStatesTracker


# Only run FP8 tests on H100.
fp8_available, reason_for_no_fp8 = FP8GlobalStateManager.is_fp8_available()


seed = 1234
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# Record initial RNG state from script run.
_cpu_rng_state = torch.get_rng_state()
_cuda_rng_state = torch.cuda.get_rng_state()


class ModelConfig:
    def __init__(self, hidden_size, eps, num_attention_heads, embed, num_layers, seq_len):
        self.hidden_size = hidden_size
        self.eps = eps
        self.num_attention_heads = num_attention_heads
        self.embed = embed
        self.num_layers = num_layers
        self.seq_len = seq_len


model_configs = {
    "126m": ModelConfig(768, 1e-5, 12, 64, 12, 2048),
}

param_types = [torch.float32, torch.float16]
if is_bf16_compatible():  # bf16 requires sm_80 or higher
    param_types.append(torch.bfloat16)

batch_sizes = [1, 2]

all_boolean = [True, False]

all_activations = ["gelu", "relu", "reglu", "geglu", "swiglu", "qgelu"]

all_normalizations = ["LayerNorm", "RMSNorm"]

mask_types = ["causal", "no_mask"]


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


def assert_all_equal(l1: List[torch.Tensor], l2: List[torch.Tensor]) -> bool:
    """Ensures two lists are equal."""
    assert len(l1) == len(l2), "Unequal number of outputs."
    for t1, t2 in zip(l1, l2):
        assert torch.equal(t1, t2), "Output mismatch."


def assert_allclose(l1: List[torch.Tensor], l2: List[torch.Tensor], atol: float) -> bool:
    """Ensures two lists are equal."""
    assert len(l1) == len(l2), "Unequal number of outputs."
    for t1, t2 in zip(l1, l2):
        result = torch.allclose(t1, t2, atol=atol)
        if not result:
            diff = torch.abs(t1 - t2).flatten()
            m = torch.argmax(diff)
            msg = (f"Outputs not close enough."
                   f"Location of the maximum difference: {m.item()} "
                   f"with {t1.flatten()[m].item()} vs {t2.flatten()[m].item()} "
                   f"(diff {diff[m].item()})."
            )
            raise AssertionError(msg)


def reset_rng_states() -> None:
    """revert back to initial RNG state."""
    torch.set_rng_state(_cpu_rng_state)
    _set_cuda_rng_state(_cuda_rng_state)


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. / 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, bias: bool = True,
                 normalization: str = "LayerNorm",
                 zero_centered_gamma: bool = False):
        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)

    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)

_supported_act = {'geglu'  : nn.GELU(approximate="tanh"),
                  'gelu'  : nn.GELU(approximate="tanh"),
                  'reglu'  : nn.ReLU(),
                  'relu'  : nn.ReLU(),
                  'swiglu' : nn.SiLU(),
                  'qgelu'  : TorchQuickGELU()}


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"):
        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)
        self.fc2 = nn.Linear(ffn_hidden_size, hidden_size)

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


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,
        attn_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        a = self.ln(x)
        b = self.causal_attn(a, attn_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, 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)

    _DUMMY_CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()
    _DUMMY_CUDA_RNG_STATE_TRACKER.add("model-parallel-rng", seed)

    def get_dummy_cuda_rng_tracker():
        """Get cuda rng tracker."""
        return _DUMMY_CUDA_RNG_STATE_TRACKER

    with fp8_model_init(enabled=fp8 and fp8_model_params):
        block = (
            TransformerLayer(
                config.hidden_size,
                4 * config.hidden_size,
                config.num_attention_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.embed,
                apply_residual_connection_post_layernorm=False,
                output_layernorm=False,
                get_rng_state_tracker=get_dummy_cuda_rng_tracker,
                params_dtype=dtype,
                fuse_qkv_params=True,
            )
            .cuda()
        )

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

    with fp8_autocast(enabled=fp8):
        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", model_configs.keys())
@pytest.mark.parametrize("fp8", all_boolean)
@pytest.mark.parametrize("fp8_model_params", all_boolean)
def test_gpt_selective_activation_recompute(dtype, bs, model, fp8, fp8_model_params):
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)

    config = model_configs[model]

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


def _test_e2e_full_recompute(bs, dtype, config, fp8, 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)

    _DUMMY_CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()
    _DUMMY_CUDA_RNG_STATE_TRACKER.add("model-parallel-rng", seed)

    def get_dummy_cuda_rng_tracker():
        """Get cuda rng tracker."""
        return _DUMMY_CUDA_RNG_STATE_TRACKER

    with fp8_model_init(enabled=fp8 and fp8_model_params):
        block = (
        TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
            config.num_attention_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.embed,
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            get_rng_state_tracker=get_dummy_cuda_rng_tracker,
            params_dtype=dtype,
            fuse_qkv_params=True,
        )
        .cuda()
        )

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

    with fp8_autocast(enabled=fp8):
        if recompute:
            te_out = te_checkpoint(
                block,
                False,  # distribute_saved_activations
                get_dummy_cuda_rng_tracker,
                None,  # tp_group
                te_inp_hidden_states,
                attention_mask=te_inp_attn_mask,
                checkpoint_core_attention=False,
            )
        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, 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", model_configs.keys())
@pytest.mark.parametrize("fp8", all_boolean)
@pytest.mark.parametrize("fp8_model_params", all_boolean)
def test_gpt_full_activation_recompute(dtype, bs, model, fp8, fp8_model_params):
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)

    config = model_configs[model]

    outputs = _test_e2e_full_recompute(bs, dtype, config, fp8, fp8_model_params, recompute=False)
    outputs_recompute = _test_e2e_full_recompute(bs, dtype, config, fp8, fp8_model_params, recompute=True)
    assert_all_equal(outputs, outputs_recompute)


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_attention_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.embed,
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
        )
        .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.seq_len, bs, config.hidden_size, dtype=dtype, requires_grad=True
    ).cuda()
    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())

        global _cpu_rng_state, _cuda_rng_state
        _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))
        reset_rng_states()

        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", model_configs.keys())
def test_gpt_checkpointing(dtype, bs, model):
    config = model_configs[model]
    outputs = _test_e2e_checkpointing(bs, dtype, config, checkpoint=False)
    outputs_checkpoint = _test_e2e_checkpointing(bs, dtype, config, checkpoint=True)
    assert_all_equal(outputs, outputs_checkpoint)


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

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

    out = block(inp_hidden_states, 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", model_configs.keys())
@pytest.mark.parametrize("parallel_attention_mlp", all_boolean)
def test_gpt_accuracy(dtype, bs, model, parallel_attention_mlp):
    config = model_configs[model]

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

    torch_gpt = (
        TorchGPT(
            config.hidden_size,
            config.eps,
            config.num_attention_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)

    # 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)


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

    inp_hidden_states = torch.randn(
        config.seq_len, bs, config.hidden_size, dtype=dtype, requires_grad=True
    ).cuda()
    inp_hidden_states.retain_grad()
    inp_attn_mask = get_causal_attn_mask(config.seq_len) 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", model_configs.keys())
@pytest.mark.parametrize("mask_type", mask_types)
def test_mha_accuracy(dtype, bs, model, mask_type):
    config = model_configs[model]

    te_mha = (
        MultiheadAttention(
            config.hidden_size,
            config.num_attention_heads,
            fuse_qkv_params=True,
            qkv_weight_interleaved=False,
            input_layernorm=False,
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    torch_mha = (
        TorchMHA(
            config.hidden_size,
            config.num_attention_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)


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

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

    out = block(inp_hidden_states)
    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


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

    mask = torch.triu(torch.ones(config.seq_len, config.seq_len, device="cuda"), diagonal=1).bool()
    query, key, value = [
        torch.randn(config.seq_len, bs, config.num_attention_heads,
        config.embed, dtype=dtype, requires_grad=True).cuda() 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", model_configs.keys())
def test_dpa_accuracy(dtype, bs, model):
    config = model_configs[model]

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

    torch_dpa = (
        TorchDotProductAttention(
            config.embed,
            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)


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

    te_linear = (
        Linear(
            config.hidden_size,
            4 * config.hidden_size,
            bias=True,
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    torch_linear = (
        torch.nn.Linear(
            config.hidden_size,
            4 * config.hidden_size,
            bias=True,
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
        torch_linear.weight = Parameter(te_linear.weight.clone())
        torch_linear.bias = Parameter(te_linear.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 dtype == torch.float32:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-3)
    else:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-2)


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", model_configs.keys())
@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,
            zero_centered_gamma=zero_centered_gamma
        )
        .to(dtype=dtype)
        .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)

    # Check output.
    atol = {torch.float32 : 1e-7,
            torch.half    : 2e-3,
            torch.bfloat16: 2e-2,
    }
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])

@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", model_configs.keys())
@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,
            zero_centered_gamma=zero_centered_gamma
        )
        .to(dtype=dtype)
        .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)

    # Check output.
    atol = {torch.float32 : 1e-7,
            torch.half    : 2e-3,
            torch.bfloat16: 2e-2,
    }
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])


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

    te_ln_linear = (
        LayerNormLinear(
            config.hidden_size,
            4 * config.hidden_size,
            config.eps,
            bias=True,
            normalization=normalization,
            zero_centered_gamma=zero_centered_gamma,
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

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

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

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

    # Check output.
    atol = {torch.float32 : 2e-4,
            torch.half    : 2e-3,
            torch.bfloat16: 2e-2,
    }
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])


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

    te_ln_mlp = (
        LayerNormMLP(
            config.hidden_size,
            4 * config.hidden_size,
            activation=activation,
            normalization=normalization,
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

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

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

    # 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)


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.seq_len, bs, config.hidden_size, device='cuda', dtype=dtype, requires_grad=True)
    static_target = torch.randn(config.seq_len, 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", model_configs.keys())
def test_gpt_cuda_graph(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)

    block = (
        TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
            config.num_attention_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.embed,
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
        )
        .to(dtype=dtype)
        .cuda()
    )
    graphed_block = copy.deepcopy(block)

    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):
    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)

    _DUMMY_CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()
    _DUMMY_CUDA_RNG_STATE_TRACKER.add("model-parallel-rng", seed)

    def get_dummy_cuda_rng_tracker():
        """Get cuda rng tracker."""
        return _DUMMY_CUDA_RNG_STATE_TRACKER

    with fp8_model_init(enabled=fp8_model_params):
        block = (
            TransformerLayer(
                config.hidden_size,
                4 * config.hidden_size,
                config.num_attention_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.embed,
                apply_residual_connection_post_layernorm=False,
                output_layernorm=False,
                get_rng_state_tracker=get_dummy_cuda_rng_tracker,
                params_dtype=dtype,
                fuse_qkv_params=True,
            )
            .cuda()
        )

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

    with fp8_autocast(enabled=True):
        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", model_configs.keys())
def test_gpt_fp8_parameters(dtype, bs, model):
    if not fp8_available:
        pytest.skip(reason_for_no_fp8)

    config = model_configs[model]

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

@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", model_configs.keys())
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_attention_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.embed,
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            attn_input_format="sbhd"
        )
        .to(dtype=dtype)
        .cuda()
    )

    # 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_attention_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.embed,
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            attn_input_format="bshd"
        )
        .to(dtype=dtype)
        .cuda()
    )

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

    x_sbhd = torch.randn(
        config.seq_len, bs, config.hidden_size, dtype=dtype, requires_grad=True
    ).to(dtype).cuda()

    x_bshd = x_sbhd.transpose(0,1).contiguous()

    # 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)

    assert_all_equal([y_bshd], [y_sbhd.transpose(0,1).contiguous()])