Update modeling_custom_seq2seq_llm.py

modeling_custom_seq2seq_llm.py

@@ -3,13 +3,1033 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from transformers.modeling_outputs import Seq2SeqLMOutput
 from transformers.activations import ACT2FN
-from flash_atten import MHA  # Import the MHA class from the provided implementation
 from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
 from liger_kernel.transformers.rms_norm import LigerRMSNorm
 from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 from transformers import PreTrainedModel, PretrainedConfig
 
 
+# Copyright (c) 2023, Tri Dao.
+
+import math
+import os
+from functools import partial
+
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+
+from flash_attn.utils.distributed import get_dim_for_local_rank
+
+try:
+    from flash_attn import (
+        flash_attn_kvpacked_func,
+        flash_attn_qkvpacked_func,
+        flash_attn_varlen_kvpacked_func,
+        flash_attn_varlen_qkvpacked_func,
+        flash_attn_with_kvcache,
+    )
+except ImportError:
+    flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func = None, None
+    flash_attn_qkvpacked_func, flash_attn_kvpacked_func = None, None
+    flash_attn_with_kvcache = None
+
+try:
+    from flash_attn.ops.fused_dense import ColumnParallelLinear, FusedDense, RowParallelLinear
+except ImportError:
+    FusedDense, ColumnParallelLinear, RowParallelLinear = None, None, None
+
+try:
+    from flash_attn.layers.rotary import RotaryEmbedding
+except ImportError:
+    RotaryEmbedding = None
+
+
+# From https://github.com/ofirpress/attention_with_linear_biases/blob/4b92f28a005ead2567abe2359f633e73e08f3833/fairseq/models/transformer.py#L742
+def get_alibi_slopes(nheads):
+    def get_slopes_power_of_2(nheads):
+        start = 2 ** (-(2 ** -(math.log2(nheads) - 3)))
+        ratio = start
+        return [start * ratio**i for i in range(nheads)]
+
+    if math.log2(nheads).is_integer():
+        return get_slopes_power_of_2(nheads)
+    else:
+        closest_power_of_2 = 2 ** math.floor(math.log2(nheads))
+        return (
+            get_slopes_power_of_2(closest_power_of_2)
+            + get_alibi_slopes(2 * closest_power_of_2)[0::2][: nheads - closest_power_of_2]
+        )
+
+
+class FlashSelfAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(
+        self,
+        causal=False,
+        softmax_scale=None,
+        attention_dropout=0.0,
+        window_size=(-1, -1),
+        alibi_slopes=None,
+        deterministic=False,
+    ):
+        super().__init__()
+        assert flash_attn_varlen_qkvpacked_func is not None, "FlashAttention is not installed"
+        assert flash_attn_qkvpacked_func is not None, "FlashAttention is not installed"
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+        self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
+        self.window_size = window_size
+        self.deterministic = deterministic
+
+    def forward(self, qkv, causal=None, cu_seqlens=None, max_seqlen=None):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value.
+                If cu_seqlens is None and max_seqlen is None, then qkv has shape (B, S, 3, H, D).
+                If cu_seqlens is not None and max_seqlen is not None, then qkv has shape
+                (total, 3, H, D), where total is the sum of the sequence lengths in the batch.
+            causal: if passed, will override self.causal
+            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into qkv.
+            max_seqlen: int. Maximum sequence length in the batch.
+        Returns:
+        --------
+            out: (total, H, D) if cu_seqlens is not None and max_seqlen is not None,
+                else (B, S, H, D).
+        """
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+        causal = self.causal if causal is None else causal
+        unpadded = cu_seqlens is not None
+        if self.alibi_slopes is not None:
+            self.alibi_slopes = self.alibi_slopes.to(torch.float32)
+        if unpadded:
+            assert cu_seqlens.dtype == torch.int32
+            assert max_seqlen is not None
+            assert isinstance(max_seqlen, int)
+            return flash_attn_varlen_qkvpacked_func(
+                qkv,
+                cu_seqlens,
+                max_seqlen,
+                self.drop.p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.window_size,
+                deterministic=self.deterministic,
+            )
+        else:
+            return flash_attn_qkvpacked_func(
+                qkv,
+                self.drop.p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.window_size,
+                deterministic=self.deterministic,
+            )
+
+
+class FlashCrossAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(
+        self,
+        causal=False,
+        softmax_scale=None,
+        attention_dropout=0.0,
+        alibi_slopes=None,
+        window_size=(-1, -1),
+        deterministic=False,
+    ):
+        super().__init__()
+        assert flash_attn_varlen_kvpacked_func is not None, "FlashAttention is not installed"
+        assert flash_attn_kvpacked_func is not None, "FlashAttention is not installed"
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+        self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
+        self.window_size = window_size
+        self.deterministic = deterministic
+
+    def forward(
+        self,
+        q,
+        kv,
+        causal=None,
+        cu_seqlens=None,
+        max_seqlen=None,
+        cu_seqlens_k=None,
+        max_seqlen_k=None,
+    ):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q: The tensor containing the query. (B, Sq, H, D)
+            kv: The tensor containing the key and value. (B, Sk, 2, H_k, D)
+            causal: if passed, will override self.causal
+            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into q.
+            max_seqlen: int. Maximum sequence length in the batch of q.
+            cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into kv.
+            max_seqlen_k: int. Maximum sequence length in the batch of k and v.
+        """
+        assert q.dtype in [torch.float16, torch.bfloat16]
+        assert q.is_cuda and kv.is_cuda
+        causal = self.causal if causal is None else causal
+        unpadded = cu_seqlens is not None
+        if self.alibi_slopes is not None:
+            self.alibi_slopes = self.alibi_slopes.to(torch.float32)
+        if unpadded:
+            assert cu_seqlens.dtype == torch.int32
+            assert max_seqlen is not None
+            assert isinstance(max_seqlen, int)
+            assert cu_seqlens_k is not None
+            assert cu_seqlens_k.dtype == torch.int32
+            assert max_seqlen_k is not None
+            assert isinstance(max_seqlen_k, int)
+            return flash_attn_varlen_kvpacked_func(
+                q,
+                kv,
+                cu_seqlens,
+                cu_seqlens_k,
+                max_seqlen,
+                max_seqlen_k,
+                self.drop.p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.window_size,
+                deterministic=self.deterministic,
+            )
+        else:
+            batch_size, seqlen_q = q.shape[0], q.shape[1]
+            seqlen_k = kv.shape[1]
+            assert kv.shape[0] == batch_size and kv.shape[4] == q.shape[3]
+            return flash_attn_kvpacked_func(
+                q,
+                kv,
+                self.drop.p if self.training else 0.0,
+                causal=causal,
+                softmax_scale=self.softmax_scale,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.window_size,
+                deterministic=self.deterministic,
+            )
+
+
+class SelfAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
+        super().__init__()
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+
+    def forward(self, qkv, causal=None, key_padding_mask=None):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
+            causal: if passed, will override self.causal
+            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
+                False means to mask out. (B, S)
+        """
+        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
+        causal = self.causal if causal is None else causal
+        q, k, v = qkv.unbind(dim=2)
+        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
+        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
+        if key_padding_mask is not None:
+            padding_mask = torch.full(
+                (batch_size, seqlen), -10000.0, dtype=scores.dtype, device=scores.device
+            )
+            padding_mask.masked_fill_(key_padding_mask, 0.0)
+            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
+        if causal:
+            # "triu_tril_cuda_template" not implemented for 'BFloat16'
+            # So we have to construct the mask in float
+            causal_mask = torch.triu(
+                torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1
+            )
+            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+            scores = scores + causal_mask.to(dtype=scores.dtype)
+        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
+        attention_drop = self.drop(attention)
+        output = torch.einsum("bhts,bshd->bthd", attention_drop, v)
+        return output
+
+
+class CrossAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
+        super().__init__()
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+
+    def forward(self, q, kv, causal=None, key_padding_mask=None):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q: The tensor containing the query. (B, Sq, H, D)
+            kv: The tensor containing the key and value. (B, Sk, 2, H_k, D)
+            causal: if passed, will override self.causal
+            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
+                False means to mask out. (B, Sk)
+        """
+        batch_size, seqlen_q = q.shape[0], q.shape[1]
+        causal = self.causal if causal is None else causal
+        seqlen_k = kv.shape[1]
+        assert kv.shape[0] == batch_size and kv.shape[4] == q.shape[3]
+        if kv.shape[3] != q.shape[2]:  # MQA/GQA
+            kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
+        k, v = kv.unbind(dim=2)
+        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
+        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
+        if key_padding_mask is not None:
+            padding_mask = torch.full(
+                (batch_size, seqlen_k), -10000.0, dtype=scores.dtype, device=scores.device
+            )
+            padding_mask.masked_fill_(key_padding_mask, 0.0)
+            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
+        if causal:
+            # causal mask needs to take into account the difference between seqlen_q and seqlen_k
+            row_idx = rearrange(
+                torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1"
+            )
+            col_idx = torch.arange(seqlen_k, device=kv.device, dtype=torch.long)
+            sk = (
+                seqlen_k
+                if key_padding_mask is None
+                else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
+            )
+            causal_mask = col_idx > row_idx + sk - seqlen_q
+            scores = scores.masked_fill(causal_mask, -10000.0)
+        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
+        attention_drop = self.drop(attention)
+        output = torch.einsum("bhts,bshd->bthd", attention_drop, v)
+        return output
+
+
+class LinearResidual(nn.Linear):
+    """Wrap nn.Linear to return the residual as well. For compatibility with FusedDense."""
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return super().forward(input), input
+
+
+def _update_kv_cache(kv, inference_params, layer_idx):
+    """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
+    # Pre-allocate memory for key-values for inference.
+    num_heads, head_dim = kv.shape[-2:]
+    if layer_idx not in inference_params.key_value_memory_dict:
+        kv_cache = torch.empty(
+            inference_params.max_batch_size,
+            inference_params.max_seqlen,
+            2,
+            num_heads,
+            head_dim,
+            dtype=kv.dtype,
+            device=kv.device,
+        )
+        inference_params.key_value_memory_dict[layer_idx] = kv_cache
+    else:
+        kv_cache = inference_params.key_value_memory_dict[layer_idx]
+    # Adjust key and value for inference
+    batch_start = inference_params.batch_size_offset
+    batch_end = batch_start + kv.shape[0]
+    sequence_start = inference_params.seqlen_offset
+    sequence_end = sequence_start + kv.shape[1]
+    assert batch_end <= kv_cache.shape[0]
+    assert sequence_end <= kv_cache.shape[1]
+    assert kv_cache is not None
+    kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
+    return kv_cache[batch_start:batch_end, :sequence_end, ...]
+
+
+class MHA(nn.Module):
+    """Multi-head self-attention and cross-attention"""
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        num_heads_kv=None,
+        cross_attn=False,
+        qkv_proj_bias=True,
+        out_proj_bias=True,
+        dropout=0.0,
+        softmax_scale=None,
+        causal=False,
+        layer_idx=None,
+        dwconv=False,
+        rotary_emb_dim=0,
+        rotary_emb_base=10000.0,
+        rotary_emb_scale_base=None,
+        rotary_emb_interleaved=False,
+        use_alibi=False,
+        window_size=(-1, -1),
+        fused_bias_fc=False,
+        use_flash_attn=False,
+        return_residual=False,
+        checkpointing=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        """
+        num_heads_kv: can be used to toggle MQA / GQA. If None, use num_heads.
+        return_residual: whether to return the input x along with the output. This is for
+            performance reason: for post-norm architecture, returning the input allows us
+            to fuse the backward of nn.Linear with the residual connection.
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.cross_attn = cross_attn
+        self.causal = causal
+        self.layer_idx = layer_idx
+        self.dwconv = dwconv
+        self.rotary_emb_dim = rotary_emb_dim
+        self.use_flash_attn = use_flash_attn
+        self.return_residual = return_residual
+        self.checkpointing = checkpointing
+        if use_alibi:
+            assert use_flash_attn, "ALiBi code path requires flash_attn"
+            alibi_slopes = torch.tensor(get_alibi_slopes(num_heads), device=device)
+        else:
+            alibi_slopes = None
+        if window_size != (-1, -1):
+            assert use_flash_attn, "Local (sliding window) attention code path requires flash_attn"
+
+        self.num_heads = num_heads
+        self.num_heads_kv = num_heads_kv if num_heads_kv is not None else num_heads
+        assert (
+            self.num_heads % self.num_heads_kv == 0
+        ), "num_heads must be divisible by num_heads_kv"
+        assert self.embed_dim % num_heads == 0, "embed_dim must be divisible by num_heads"
+        self.head_dim = self.embed_dim // num_heads
+        qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv)
+        kv_dim = 2 * self.head_dim * self.num_heads_kv
+
+        if self.rotary_emb_dim > 0:
+            assert not cross_attn, "MHA with rotary embedding does not support cross-attention yet"
+            assert RotaryEmbedding is not None, "rotary_emb is not installed"
+            self.rotary_emb = RotaryEmbedding(
+                self.rotary_emb_dim,
+                base=rotary_emb_base,
+                scale_base=rotary_emb_scale_base,
+                interleaved=rotary_emb_interleaved,
+                device=device,
+            )
+
+        if fused_bias_fc and FusedDense is None:
+            raise ImportError("fused_dense is not installed")
+        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
+        linear_resid_cls = (
+            LinearResidual if not fused_bias_fc else partial(FusedDense, return_residual=True)
+        )
+        wqkv_cls = linear_cls if not self.return_residual else linear_resid_cls
+        inner_attn_cls = (
+            partial(FlashSelfAttention, alibi_slopes=alibi_slopes, window_size=window_size)
+            if use_flash_attn
+            else SelfAttention
+        )
+        inner_cross_attn_cls = (
+            partial(FlashCrossAttention, alibi_slopes=alibi_slopes, window_size=window_size)
+            if use_flash_attn
+            else CrossAttention
+        )
+        if not self.cross_attn:
+            self.Wqkv = wqkv_cls(embed_dim, qkv_dim, bias=qkv_proj_bias, **factory_kwargs)
+        else:
+            self.Wq = linear_cls(embed_dim, embed_dim, bias=qkv_proj_bias, **factory_kwargs)
+            self.Wkv = wqkv_cls(embed_dim, kv_dim, bias=qkv_proj_bias, **factory_kwargs)
+        if self.dwconv:
+            if self.num_heads_kv == self.num_heads:
+                self.dwconv_qkv = nn.Conv1d(
+                    qkv_dim, qkv_dim, kernel_size=3, padding=2, groups=qkv_dim
+                )
+            else:
+                self.dwconv_q = nn.Conv1d(
+                    embed_dim, embed_dim, kernel_size=3, padding=2, groups=embed_dim
+                )
+                self.dwconv_kv = nn.Conv1d(kv_dim, kv_dim, kernel_size=3, padding=2, groups=kv_dim)
+        self.inner_attn = inner_attn_cls(
+            causal=causal,
+            softmax_scale=softmax_scale,
+            attention_dropout=dropout,
+        )
+        self.inner_cross_attn = inner_cross_attn_cls(
+            causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
+        )
+        self.out_proj = linear_cls(embed_dim, embed_dim, bias=out_proj_bias, **factory_kwargs)
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        dtype = self.out_proj.weight.dtype if dtype is None else dtype
+        device = self.out_proj.weight.device
+        return torch.empty(
+            batch_size,
+            max_seqlen,
+            2,
+            self.num_heads_kv,
+            self.head_dim,
+            dtype=dtype,
+            device=device,
+        )
+
+    def _update_kv_cache(self, kv, inference_params):
+        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
+        assert not self.dwconv, "Generation does not support dwconv yet"
+        assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
+        return _update_kv_cache(kv, inference_params, self.layer_idx)
+
+    def _apply_rotary_update_kvcache_attention(self, q, kv, inference_params):
+        """
+        Fast path that combine 3 steps: apply rotary to Q and K, update kv cache, and apply attention.
+        q: (batch_size, seqlen_q, nheads, head_dim)
+        kv: (batch_size, seqlen_k, 2, nheads_kv, head_dim)
+        """
+        assert inference_params is not None and inference_params.seqlen_offset > 0
+        assert self.use_flash_attn
+        if self.rotary_emb_dim > 0:
+            assert self.rotary_emb.scale is None, "This code path does not support xPos"
+            self.rotary_emb._update_cos_sin_cache(
+                inference_params.max_seqlen, device=q.device, dtype=q.dtype
+            )
+            rotary_cos, rotary_sin = self.rotary_emb._cos_cached, self.rotary_emb._sin_cached
+        else:
+            rotary_cos, rotary_sin = None, None
+        batch = q.shape[0]
+        kv_cache = inference_params.key_value_memory_dict[self.layer_idx][:batch]
+        cache_seqlens = (
+            inference_params.lengths_per_sample[:batch]
+            if inference_params.lengths_per_sample is not None
+            else inference_params.seqlen_offset
+        )
+        alibi_slopes = getattr(self.inner_cross_attn, "alibi_slopes", None)
+        context = flash_attn_with_kvcache(
+            q,
+            kv_cache[:, :, 0],
+            kv_cache[:, :, 1],
+            kv[:, :, 0],
+            kv[:, :, 1],
+            rotary_cos=rotary_cos,
+            rotary_sin=rotary_sin,
+            cache_seqlens=cache_seqlens,
+            softmax_scale=self.inner_cross_attn.softmax_scale,
+            causal=self.inner_cross_attn.causal,
+            rotary_interleaved=self.rotary_emb.interleaved if self.rotary_emb_dim > 0 else False,
+            alibi_slopes=alibi_slopes,
+        )
+        return context
+
+    def _update_kvcache_attention(self, q, kv, inference_params):
+        """Write kv to inference_params, then do attention"""
+        if (
+            inference_params.seqlen_offset == 0
+            or flash_attn_with_kvcache is None
+            or not self.use_flash_attn
+        ):
+            # TODO: this only uses seqlen_offset and not lengths_per_sample.
+            kv = self._update_kv_cache(kv, inference_params)
+            return self.inner_cross_attn(q, kv)
+        else:
+            batch = q.shape[0]
+            kv_cache = inference_params.key_value_memory_dict[self.layer_idx][:batch]
+            cache_seqlens = (
+                inference_params.lengths_per_sample[:batch]
+                if inference_params.lengths_per_sample is not None
+                else inference_params.seqlen_offset
+            )
+            alibi_slopes = getattr(self.inner_cross_attn, "alibi_slopes", None)
+            return flash_attn_with_kvcache(
+                q,
+                kv_cache[:, :, 0],
+                kv_cache[:, :, 1],
+                kv[:, :, 0],
+                kv[:, :, 1],
+                cache_seqlens=cache_seqlens,
+                softmax_scale=self.inner_cross_attn.softmax_scale,
+                causal=self.inner_cross_attn.causal,
+                alibi_slopes=alibi_slopes,
+            )
+
+    def forward(
+        self,
+        x,
+        x_kv=None,
+        key_padding_mask=None,
+        cu_seqlens=None,
+        max_seqlen=None,
+        mixer_subset=None,
+        inference_params=None,
+        **kwargs,
+    ):
+        """
+        Arguments:
+            x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
+                cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
+                is the is the sum of the sequence lengths in the batch.
+            x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
+            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into x. Only applicable when using
+                FlashAttention.
+            max_seqlen: int. Maximum sequence length in the batch.
+            key_padding_mask: boolean mask, True means to keep, False means to mask out.
+                (batch, seqlen). Only applicable when not using FlashAttention.
+            mixer_subset: for cross-attention only. If not None, will take a subset of x
+                before applying the query projection. Useful for e.g., ViT where we only care
+                about the CLS token in the last layer.
+            inference_params: for generation. Adapted from Megatron-LM (and Apex)
+            https://github.com/NVIDIA/apex/blob/3ff1a10f72ec07067c4e44759442329804ac5162/apex/transformer/testing/standalone_transformer_lm.py#L470
+        """
+        if cu_seqlens is not None:
+            assert max_seqlen is not None
+            assert key_padding_mask is None
+            assert self.use_flash_attn
+            assert not self.dwconv
+            assert self.rotary_emb_dim == 0
+        if key_padding_mask is not None:
+            assert cu_seqlens is None
+            assert max_seqlen is None
+            assert not self.use_flash_attn
+        if inference_params is not None:
+            assert key_padding_mask is None
+            assert cu_seqlens is None and max_seqlen is None
+            assert not self.dwconv
+
+        kwargs = (
+            {"cu_seqlens": cu_seqlens, "max_seqlen": max_seqlen, **kwargs}
+            if self.use_flash_attn
+            else {"key_padding_mask": key_padding_mask, **kwargs}
+        )
+        seqlen_offset = (
+            0
+            if inference_params is None
+            else (
+                inference_params.lengths_per_sample
+                if inference_params.lengths_per_sample is not None
+                else inference_params.seqlen_offset
+            )
+        )
+        rotary_max_seqlen = inference_params.max_seqlen if inference_params is not None else None
+        batch, seqlen = x.shape[:2]
+        if not self.cross_attn and self.num_heads_kv == self.num_heads:
+            assert x_kv is None and mixer_subset is None
+            if not self.return_residual:
+                qkv = self.Wqkv(x)
+            else:
+                qkv, x = self.Wqkv(x)
+            if self.dwconv:
+                qkv = rearrange(
+                    self.dwconv_qkv(rearrange(qkv, "b s d -> b d s"))[..., :-2], "b d s -> b s d"
+                ).contiguous()
+            qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
+            if (
+                inference_params is None
+                or inference_params.seqlen_offset == 0
+                or (self.rotary_emb_dim == 0 or self.rotary_emb_dim % 16 != 0)
+                or not self.use_flash_attn
+            ):
+                if self.rotary_emb_dim > 0:
+                    qkv = self.rotary_emb(
+                        qkv, seqlen_offset=seqlen_offset, max_seqlen=rotary_max_seqlen
+                    )
+                if inference_params is None:
+                    if not self.checkpointing:
+                        context = self.inner_attn(qkv, **kwargs)
+                    else:
+                        context = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, **kwargs)
+                else:
+                    context = self._update_kvcache_attention(
+                        qkv[:, :, 0], qkv[:, :, 1:], inference_params
+                    )
+            else:
+                context = self._apply_rotary_update_kvcache_attention(
+                    qkv[:, :, 0], qkv[:, :, 1:], inference_params
+                )
+        else:
+            if self.cross_attn:
+                if not self.return_residual:
+                    q = self.Wq(x if mixer_subset is None else x[:, mixer_subset])
+                    kv = self.Wkv(x_kv if x_kv is not None else x)
+                else:
+                    if x_kv is not None:
+                        kv, x_kv = self.Wkv(x_kv)
+                    else:
+                        kv, x = self.Wkv(x)
+                    q = self.Wq(x if mixer_subset is None else x[:, mixer_subset])
+            else:
+                assert self.num_heads_kv != self.num_heads
+                if not self.return_residual:
+                    qkv = self.Wqkv(x)
+                else:
+                    qkv, x = self.Wqkv(x)
+                q = qkv[..., : self.num_heads * self.head_dim]
+                kv = qkv[..., self.num_heads * self.head_dim :]
+            q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
+            kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
+            if self.dwconv:
+                q = rearrange(
+                    self.dwconv_q(rearrange(q, "b s d -> b d s"))[..., :-2], "b d s -> b s d"
+                ).contiguous()
+                kv = rearrange(
+                    self.dwconv_kv(rearrange(kv, "b s d -> b d s"))[..., :-2], "b d s -> b s d"
+                ).contiguous()
+            if (
+                inference_params is None
+                or inference_params.seqlen_offset == 0
+                or (self.rotary_emb_dim == 0 or self.rotary_emb_dim % 16 != 0)
+                or not self.use_flash_attn
+            ):
+                if self.rotary_emb_dim > 0:
+                    q, kv = self.rotary_emb(
+                        q, kv, seqlen_offset=seqlen_offset, max_seqlen=rotary_max_seqlen
+                    )
+                if inference_params is None:
+                    if not self.checkpointing:
+                        context = self.inner_cross_attn(q, kv, **kwargs)
+                    else:
+                        context = torch.utils.checkpoint.checkpoint(
+                            self.inner_cross_attn, q, kv, **kwargs
+                        )
+                else:
+                    context = self._update_kvcache_attention(q, kv, inference_params)
+            else:
+                context = self._apply_rotary_update_kvcache_attention(q, kv, inference_params)
+        out = self.out_proj(rearrange(context, "... h d -> ... (h d)"))
+        return out if not self.return_residual else (out, x)
+
+
+class ParallelMHA(nn.Module):
+    """Multi-head self-attention and cross-attention"""
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        process_group,
+        num_heads_kv=None,
+        qkv_proj_bias=True,
+        out_proj_bias=True,
+        dropout=0.0,
+        softmax_scale=None,
+        causal=False,
+        layer_idx=None,
+        rotary_emb_dim=0,
+        rotary_emb_base=10000.0,
+        rotary_emb_scale_base=None,
+        rotary_emb_interleaved=False,
+        use_alibi=False,
+        window_size=(-1, -1),
+        use_flash_attn=False,
+        checkpointing=False,
+        sequence_parallel=True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.causal = causal
+        self.layer_idx = layer_idx
+        self.rotary_emb_dim = rotary_emb_dim
+        self.use_flash_attn = use_flash_attn
+        self.checkpointing = checkpointing
+        self.process_group = process_group
+        self.world_size = process_group.size()
+        self.local_rank = torch.distributed.get_rank(process_group)
+
+        self.num_heads = num_heads
+        assert self.embed_dim % self.num_heads == 0, "embed_dim must be divisible by num_heads"
+
+        self.num_heads_kv = num_heads_kv if num_heads_kv is not None else num_heads
+        assert (
+            self.num_heads % self.num_heads_kv == 0
+        ), "num_heads must be divisible by num_heads_kv"
+
+        self.num_heads_per_rank = get_dim_for_local_rank(
+            self.num_heads, self.world_size, self.local_rank
+        )
+        self.num_heads_kv_per_rank = get_dim_for_local_rank(
+            self.num_heads_kv, self.world_size, self.local_rank
+        )
+        self.head_dim = self.embed_dim // num_heads
+        qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv)
+
+        if use_alibi:
+            assert use_flash_attn, "ALiBi code path requires flash_attn"
+            num_heads_local = math.ceil(self.num_heads / self.world_size)
+            alibi_slopes = torch.tensor(
+                get_alibi_slopes(num_heads)[
+                    self.local_rank * num_heads_local : (self.local_rank + 1) * num_heads_local
+                ],
+                device=device,
+            )
+        else:
+            alibi_slopes = None
+        if window_size != (-1, -1):
+            assert use_flash_attn, "Local (sliding window) attention code path requires flash_attn"
+
+        if self.rotary_emb_dim > 0:
+            assert RotaryEmbedding is not None, "rotary_emb is not installed"
+            self.rotary_emb = RotaryEmbedding(
+                self.rotary_emb_dim,
+                base=rotary_emb_base,
+                scale_base=rotary_emb_scale_base,
+                interleaved=rotary_emb_interleaved,
+                device=device,
+            )
+
+        if ColumnParallelLinear is None or RowParallelLinear is None:
+            raise ImportError("fused_dense is not installed")
+        self.Wqkv = ColumnParallelLinear(
+            embed_dim,
+            qkv_dim,
+            process_group,
+            bias=qkv_proj_bias,
+            sequence_parallel=sequence_parallel,
+            multiple_of=self.head_dim * (self.num_heads // self.num_heads_kv + 2),
+            **factory_kwargs,
+        )
+        inner_attn_cls = (
+            partial(FlashSelfAttention, alibi_slopes=alibi_slopes, window_size=window_size)
+            if use_flash_attn
+            else SelfAttention
+        )
+        inner_cross_attn_cls = (
+            partial(FlashCrossAttention, alibi_slopes=alibi_slopes, window_size=window_size)
+            if use_flash_attn
+            else CrossAttention
+        )
+        self.inner_attn = inner_attn_cls(
+            causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
+        )
+        self.inner_cross_attn = inner_cross_attn_cls(
+            causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
+        )
+        self.out_proj = RowParallelLinear(
+            embed_dim,
+            embed_dim,
+            process_group,
+            bias=out_proj_bias,
+            sequence_parallel=sequence_parallel,
+            multiple_of=self.head_dim,
+            **factory_kwargs,
+        )
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        dtype = self.out_proj.weight.dtype if dtype is None else dtype
+        device = self.out_proj.weight.device
+        return torch.empty(
+            batch_size,
+            max_seqlen,
+            2,
+            self.num_heads_kv_per_rank,
+            self.head_dim,
+            dtype=dtype,
+            device=device,
+        )
+
+    def _update_kv_cache(self, kv, inference_params):
+        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
+        assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
+        return _update_kv_cache(kv, inference_params, self.layer_idx)
+
+    def _apply_rotary_update_kvcache_attention(self, q, kv, inference_params):
+        """
+        Fast path that combine 3 steps: apply rotary to Q and K, update kv cache, and apply attention.
+        q: (batch_size, seqlen_q, nheads, head_dim)
+        kv: (batch_size, seqlen_k, 2, nheads_kv, head_dim)
+        """
+        assert inference_params is not None and inference_params.seqlen_offset > 0
+        assert self.use_flash_attn
+        if self.rotary_emb_dim > 0:
+            assert self.rotary_emb.scale is None, "This code path does not support xPos"
+            self.rotary_emb._update_cos_sin_cache(
+                inference_params.max_seqlen, device=q.device, dtype=q.dtype
+            )
+            rotary_cos, rotary_sin = self.rotary_emb._cos_cached, self.rotary_emb._sin_cached
+        else:
+            rotary_cos, rotary_sin = None, None
+        batch = q.shape[0]
+        kv_cache = inference_params.key_value_memory_dict[self.layer_idx][:batch]
+        cache_seqlens = (
+            inference_params.lengths_per_sample[:batch]
+            if inference_params.lengths_per_sample is not None
+            else inference_params.seqlen_offset
+        )
+        alibi_slopes = getattr(self.inner_cross_attn, "alibi_slopes", None)
+        context = flash_attn_with_kvcache(
+            q,
+            kv_cache[:, :, 0],
+            kv_cache[:, :, 1],
+            kv[:, :, 0],
+            kv[:, :, 1],
+            rotary_cos=rotary_cos,
+            rotary_sin=rotary_sin,
+            cache_seqlens=cache_seqlens,
+            softmax_scale=self.inner_cross_attn.softmax_scale,
+            causal=self.inner_cross_attn.causal,
+            rotary_interleaved=self.rotary_emb.interleaved if self.rotary_emb_dim > 0 else False,
+            alibi_slopes=alibi_slopes,
+        )
+        return context
+
+    def _update_kvcache_attention(self, q, kv, inference_params):
+        """Write kv to inference_params, then do attention"""
+        if inference_params.seqlen_offset == 0 or not self.use_flash_attn:
+            # TODO: this only uses seqlen_offset and not lengths_per_sample.
+            kv = self._update_kv_cache(kv, inference_params)
+            return self.inner_cross_attn(q, kv)
+        else:
+            batch = q.shape[0]
+            kv_cache = inference_params.key_value_memory_dict[self.layer_idx][:batch]
+            cache_seqlens = (
+                inference_params.lengths_per_sample[:batch]
+                if inference_params.lengths_per_sample is not None
+                else inference_params.seqlen_offset
+            )
+            alibi_slopes = getattr(self.inner_cross_attn, "alibi_slopes", None)
+            context = flash_attn_with_kvcache(
+                q,
+                kv_cache[:, :, 0],
+                kv_cache[:, :, 1],
+                kv[:, :, 0],
+                kv[:, :, 1],
+                cache_seqlens=cache_seqlens,
+                softmax_scale=self.inner_cross_attn.softmax_scale,
+                causal=self.inner_cross_attn.causal,
+                alibi_slopes=alibi_slopes,
+            )
+            return context
+
+    def forward(self, x, seqlen=None, inference_params=None, **kwargs):
+        """
+        Arguments:
+            x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if seqlen=None.
+                If seqlen is not None, x is (batch * seqlen, hidden_dim). This is so that when we
+                split x during sequence parallel, we split the batch * seqlen dimension
+                (in case batch is small).
+        """
+        qkv = self.Wqkv(x)
+        if seqlen is not None:
+            qkv = rearrange(qkv, "(b s) ... -> b s ...", s=seqlen)
+        seqlen_offset = (
+            0
+            if inference_params is None
+            else (
+                inference_params.lengths_per_sample
+                if inference_params.lengths_per_sample is not None
+                else inference_params.seqlen_offset
+            )
+        )
+        rotary_max_seqlen = inference_params.max_seqlen if inference_params is not None else None
+        if self.num_heads_kv == self.num_heads:
+            qkv = rearrange(qkv, "b s (three h d) -> b s three h d", three=3, d=self.head_dim)
+            if (
+                inference_params is None
+                or inference_params.seqlen_offset == 0
+                or (self.rotary_emb_dim == 0 or self.rotary_emb_dim % 16 != 0)
+                or not self.use_flash_attn
+            ):
+                if self.rotary_emb_dim > 0:
+                    qkv = self.rotary_emb(
+                        qkv, seqlen_offset=seqlen_offset, max_seqlen=rotary_max_seqlen
+                    )
+                if inference_params is None:
+                    if not self.checkpointing:
+                        context = self.inner_attn(qkv, **kwargs)
+                    else:
+                        context = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, **kwargs)
+                else:
+                    context = self._update_kvcache_attention(
+                        qkv[:, :, 0], qkv[:, :, 1:], inference_params
+                    )
+            else:
+                context = self._apply_rotary_update_kvcache_attention(
+                    qkv[:, :, 0], qkv[:, :, 1:], inference_params
+                )
+        else:
+            q = rearrange(
+                qkv[..., : self.num_heads_per_rank * self.head_dim],
+                "... (h d) -> ... h d",
+                d=self.head_dim,
+            )
+            kv = rearrange(
+                qkv[..., self.num_heads_per_rank * self.head_dim :],
+                "... (two hkv d) -> ... two hkv d",
+                two=2,
+                d=self.head_dim,
+            )
+            if (
+                inference_params is None
+                or inference_params.seqlen_offset == 0
+                or (self.rotary_emb_dim == 0 or self.rotary_emb_dim % 16 != 0)
+                or not self.use_flash_attn
+            ):
+                if self.rotary_emb_dim > 0:
+                    q, kv = self.rotary_emb(
+                        q, kv, seqlen_offset=seqlen_offset, max_seqlen=rotary_max_seqlen
+                    )
+                if inference_params is None:
+                    if not self.checkpointing:
+                        context = self.inner_cross_attn(q, kv, **kwargs)
+                    else:
+                        context = torch.utils.checkpoint.checkpoint(
+                            self.inner_cross_attn, q, kv, **kwargs
+                        )
+                else:
+                    context = self._update_kvcache_attention(q, kv, inference_params)
+            else:
+                context = self._apply_rotary_update_kvcache_attention(q, kv, inference_params)
+        context = rearrange(context, "b s h d -> b s (h d)")
+        if seqlen is not None:
+            context = rearrange(context, "b s d -> (b s) d")
+        out = self.out_proj(context)
+        return out
 
 
 class RMSNorm(nn.Module):
@@ -106,6 +1126,24 @@ class CustomSeq2SeqLLM(PreTrainedModel):
 
     def get_output_embeddings(self):
         return self.lm_head
+    
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
+    ):
+        # Cut decoder_input_ids if past is used
+        if past is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {
+            "decoder_input_ids": input_ids,
+            "past_key_values": past,
+            "encoder_outputs": encoder_outputs,
+            "attention_mask": attention_mask,
+            "use_cache": use_cache,
+        }
 
     def forward(
         self,
@@ -166,6 +1204,57 @@ class CustomSeq2SeqLLM(PreTrainedModel):
         shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
         shifted_input_ids[..., 0] = self.config.pad_token_id
         return shifted_input_ids
+    
+    def save_pretrained(self, save_directory, safe_serialization=True):
+        # Save the config
+        self.config.save_pretrained(save_directory)
+
+        # Prepare state dict
+        state_dict = self.state_dict()
+
+        # Handle shared weights
+        if self.config.tie_word_embeddings:
+            state_dict["lm_head.weight"] = state_dict["shared.weight"]
+
+        # Convert state_dict to CPU tensors
+        cpu_state_dict = {k: v.cpu() for k, v in state_dict.items()}
+
+        if safe_serialization:
+            # Save using safetensors
+            safe_filepath = os.path.join(save_directory, "model.safetensors")
+            save_file(cpu_state_dict, safe_filepath)
+        else:
+            # Save using PyTorch
+            torch_filepath = os.path.join(save_directory, "pytorch_model.bin")
+            torch.save(cpu_state_dict, torch_filepath)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        config = kwargs.pop("config", None)
+        state_dict = kwargs.pop("state_dict", None)
+
+        if config is None:
+            config = cls.config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        model = cls(config)
+
+        if state_dict is None:
+            # Try loading safetensors first
+            safe_filepath = os.path.join(pretrained_model_name_or_path, "model.safetensors")
+            if os.path.exists(safe_filepath):
+                from safetensors.torch import load_file
+                state_dict = load_file(safe_filepath)
+            else:
+                # Fall back to PyTorch format
+                torch_filepath = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin")
+                state_dict = torch.load(torch_filepath, map_location="cpu")
+
+        # Handle shared weights
+        if config.tie_word_embeddings and "lm_head.weight" not in state_dict:
+            state_dict["lm_head.weight"] = state_dict["shared.weight"]
+
+        model.load_state_dict(state_dict)
+        return model
 
 class CustomEncoder(nn.Module):
     def __init__(self, config):
@@ -260,18 +1349,3 @@ class DecoderLayer(nn.Module):
         
         return hidden_states
 
-class FeedForward(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.act = ACT2FN[config.hidden_act]
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.dropout(x)
-        x = self.fc2(x)
-        x = self.dropout(x)
-        return x