refactor!(internlm): migrate to internevo

README.md

@@ -16,7 +16,9 @@ pip install -e .
 
 ## Usage
 
-### InternLM
+### InternEvo
+
+适配版本 https://github.com/DeepLink-org/InternEvo/tree/deeplinkext
 
 ```python
 import deeplink_ext.patch_internlm

deeplink_ext/internlm_ops/mha/mha.py

@@ -78,12 +78,12 @@ def forward(
         q,
         kv,
         causal=None,
-        cu_seqlens_q=None,
-        max_seqlen_q=None,
+        cu_seqlens=None,
+        max_seqlen=None,
         cu_seqlens_k=None,
         max_seqlen_k=None,
     ):
-        if cu_seqlens_q is None:
+        if cu_seqlens is None:
             # padded
             return DeepLinkMultiHeadAttentionKVPackedFunc.apply(
                 q,
@@ -98,9 +98,9 @@ def forward(
             return DeepLinkMultiHeadAttentionVarLenKVPackedFunc.apply(
                 q,
                 kv,
-                cu_seqlens_q,
+                cu_seqlens,
                 cu_seqlens_k,
-                max_seqlen_q,
+                max_seqlen,
                 max_seqlen_k,
                 self.dropout_p if self.training else 0.0,
                 self.softmax_scale,

deeplink_ext/internlm_ops/mha/mha_varlen_kvpacked_func.py

@@ -25,8 +25,8 @@ def forward(
             softmax_scale = q.shape[-1] ** (-0.5)
         out, softmax_lse, rng, S_dmask = ext.mha_varlen_fwd(
             q,
-            kv[:, :, 0],
-            kv[:, :, 1],
+            kv[:, 0],
+            kv[:, 1],
             cu_seqlens_q,
             cu_seqlens_k,
             max_seqlen_q,
@@ -40,7 +40,8 @@ def forward(
             q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng.get_state()
         )
         ctx.dropout_p = dropout_p
-        ctx.max_seqlen = max_seqlen_q
+        ctx.max_seqlen_q = max_seqlen_q
+        ctx.max_seqlen_k = max_seqlen_k
         ctx.softmax_scale = softmax_scale
         ctx.causal = causal
         return out if not return_softmax else (out, softmax_lse, S_dmask)
@@ -63,20 +64,20 @@ def backward(ctx, dout):
         ext.mha_varlen_bwd(
             dout,
             q,
-            kv[:, :, 0],
-            kv[:, :, 1],
+            kv[:, 0],
+            kv[:, 1],
             out,
             softmax_lse,
             cu_seqlens_q,
             cu_seqlens_k,
-            ctx.max_seqlen,
-            ctx.max_seqlen,
+            ctx.max_seqlen_q,
+            ctx.max_seqlen_k,
             ctx.dropout_p,
             ctx.causal,
             rng,
             ctx.softmax_scale,
             dq,
-            dkv[:, :, 0],
-            dkv[:, :, 1],
+            dkv[:, 0],
+            dkv[:, 1],
         )
         return dq, dkv, None, None, None, None, None, None, None, None

deeplink_ext/internlm_ops/rotary/__init__.py

@@ -1,16 +1,13 @@
 # Copyright (c) 2024, DeepLink.
 
 try:
-    from .deeplink import DeepLinkApplyRotaryEmbQKV_, DeepLinkApplyRotaryEmb
+    from .deeplink import apply_rotary
 except:
     print(
         "[deeplink_ext] rotary is not implemented in diopi. Falling back to the slower implementation.\n",
         end="",
     )
-    from .fallback import (
-        ApplyRotaryEmbQKV_ as DeepLinkApplyRotaryEmbQKV_,
-        ApplyRotaryEmb as DeepLinkApplyRotaryEmb,
-    )
+    from .fallback import apply_rotary
 from . import fallback
 
-__all__ = ["DeepLinkApplyRotaryEmbQKV_", "DeepLinkApplyRotaryEmb", "fallback"]
+__all__ = ["apply_rotary", "fallback"]

deeplink_ext/internlm_ops/rotary/deeplink.py

@@ -1,131 +1,65 @@
 # Copyright (c) 2024, DeepLink.
 
+from typing import Optional, Union
 import torch
 from einops import rearrange
 import deeplink_ext.cpp_extensions as ext
 
 assert hasattr(ext, "apply_rotary")
 
+__all__ = ["apply_rotary"]
 
-class DeepLinkApplyRotaryEmbQKV_(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, qkv, cos, sin, cos_k=None, sin_k=None, interleaved=False):
-        batch, seqlen, three, nheads, headdim = qkv.shape
-        assert three == 3
-        rotary_seqlen, rotary_dim = cos.shape
-        rotary_dim *= 2
-        assert rotary_dim <= headdim
-        assert seqlen <= rotary_seqlen
-        cos_k = cos if cos_k is None else cos_k
-        sin_k = sin if sin_k is None else sin_k
-        assert (
-            sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2)
-        )
-        q_ro = qkv[:, :, 0, :, :rotary_dim]
-        ext.apply_rotary(
-            q_ro,
-            q_ro,
-            rearrange(cos[:seqlen], "s d -> s 1 d"),
-            rearrange(sin[:seqlen], "s d -> s 1 d"),
-            False,
-            interleaved,
-        )
-        k_ro = qkv[:, :, 1, :, :rotary_dim]
-        ext.apply_rotary(
-            k_ro,
-            k_ro,
-            rearrange(cos[:seqlen], "s d -> s 1 d"),
-            rearrange(sin[:seqlen], "s d -> s 1 d"),
-            False,
-            interleaved,
-        )
-        ctx.save_for_backward(cos, sin, cos_k, sin_k)
-        ctx.interleaved = interleaved
-        return qkv
-
-    @staticmethod
-    def backward(ctx, dqkv):
-        cos, sin, cos_k, sin_k = ctx.saved_tensors
-        interleaved = ctx.interleaved
-        _, seqlen, _, _, headdim = dqkv.shape
-        rotary_dim = cos.shape[-1]
-        rotary_dim *= 2
-        dq_ro = dqkv[:, :, 0, :, :rotary_dim]
-        ext.apply_rotary(
-            dq_ro,
-            dq_ro,
-            rearrange(cos[:seqlen], "s d -> s 1 d"),
-            rearrange(sin[:seqlen], "s d -> s 1 d"),
-            True,
-            interleaved,
-        )
-        dk_ro = dqkv[:, :, 1, :, :rotary_dim]
-        ext.apply_rotary(
-            dk_ro,
-            dk_ro,
-            rearrange(cos[:seqlen], "s d -> s 1 d"),
-            rearrange(sin[:seqlen], "s d -> s 1 d"),
-            True,
-            interleaved,
-        )
-        return dqkv, None, None, None, None, None
-
-
-class DeepLinkApplyRotaryEmb(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x, cos, sin, interleaved=False, inplace=False):
-        """
-            x: (batch_size, seqlen, nheads, headdim)
-            cos, sin: (seqlen, rotary_dim / 2)
-            interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
-                of 1st half and 2nd half (GPT-NeoX style).
-        rotary_dim must be <= headdim
-        Apply rotary embedding to the first rotary_dim of x.
-        """
-        batch, seqlen, nheads, headdim = x.shape
-        rotary_seqlen, rotary_dim = cos.shape
-        rotary_dim *= 2
-        assert rotary_dim <= headdim
-        assert seqlen <= rotary_seqlen
-        assert sin.shape == (rotary_seqlen, rotary_dim // 2)
-        x_ro = x[..., :rotary_dim]
-        out = torch.empty_like(x) if not inplace else x
-        out_ro = out[..., :rotary_dim]
 
-        ext.apply_rotary(
-            out_ro,
-            x_ro,
-            rearrange(cos[:seqlen], "s d -> s 1 d"),
-            rearrange(sin[:seqlen], "s d -> s 1 d"),
-            False,
-            interleaved,
+def apply_rotary(
+    x: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    seqlen_offsets: Union[int, torch.Tensor] = 0,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[int] = None,
+    interleaved=False,
+    inplace=False,
+    conjugate=False,
+) -> torch.Tensor:
+    """
+    Arguments:
+        x: (batch, seqlen, nheads, headdim) if cu_seqlens is None
+            else (total_seqlen, nheads, headdim).
+        cos: (seqlen_ro, rotary_dim / 2)
+        sin: (seqlen_ro, rotary_dim / 2)
+        seqlen_offsets: integer or integer tensor of size (batch,)
+        cu_seqlens: (batch + 1,) or None
+        max_seqlen: int
+    Returns:
+        y: (batch, seqlen, nheads, headdim)
+    """
+    if seqlen_offsets != 0 and cu_seqlens is None and max_seqlen is None:
+        raise NotImplementedError(
+            "apply_rotary: seqlen_offsets, cu_seqlens and max_seqlen are not supported yet"
         )
+    batch, seqlen, nheads, headdim = x.shape
+    seqlen_ro, rotary_dim = cos.shape
+    assert sin.shape == cos.shape
+    rotary_dim *= 2
+    assert rotary_dim <= headdim, "rotary_dim must be <= headdim"
+    assert headdim <= 256, "Only support headdim <= 256"
+    assert seqlen_ro >= seqlen, "seqlen_ro must be >= seqlen"
+    assert (
+        cos.dtype == sin.dtype
+    ), f"cos and sin must have the same dtype, got {cos.dtype} and {sin.dtype}"
+    assert (
+        x.dtype == cos.dtype
+    ), f"Input and cos/sin must have the same dtype, got {x.dtype} and {cos.dtype}"
 
-        if not inplace and rotary_dim < headdim:
-            out[..., rotary_dim:].copy_(x[..., rotary_dim:])
-        ctx.save_for_backward(cos, sin)
-        ctx.interleaved = interleaved
-        ctx.inplace = inplace
-        return out if not inplace else x
-
-    @staticmethod
-    def backward(ctx, do):
-        cos, sin = ctx.saved_tensors
-        _, seqlen, _, headdim = do.shape
-        rotary_dim = cos.shape[-1]
-        rotary_dim *= 2
-        inplace = ctx.inplace
-        do_ro = do[..., :rotary_dim]
-        dx = torch.empty_like(do) if not inplace else do
-        dx_ro = dx[..., :rotary_dim]
-        ext.apply_rotary(
-            dx_ro,
-            do_ro,
-            rearrange(cos[:seqlen], "s d -> s 1 d"),
-            rearrange(sin[:seqlen], "s d -> s 1 d"),
-            True,
-            ctx.interleaved,
-        )
-        if not inplace and rotary_dim < headdim:
-            dx[..., rotary_dim:].copy_(do[..., rotary_dim:])
-        return dx, None, None, None, None
+    output = torch.empty_like(x) if not inplace else x
+    if rotary_dim < headdim and not inplace:
+        output[..., rotary_dim:].copy_(x[..., rotary_dim:])
+    ext.apply_rotary(
+        output,
+        x,
+        rearrange(cos[:seqlen], "s d -> s 1 d"),
+        rearrange(sin[:seqlen], "s d -> s 1 d"),
+        conjugate,
+        interleaved,
+    )
+    return output

deeplink_ext/internlm_ops/rotary/fallback.py

@@ -0,0 +1,98 @@
+# Copyright (c) 2024, DeepLink.
+
+from typing import Optional, Union
+import torch
+from einops import rearrange, repeat
+
+__all__ = ["apply_rotary"]
+
+
+def _rotate_half(x: torch.Tensor, interleaved=False):
+    if not interleaved:
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    else:
+        x1, x2 = x[..., ::2], x[..., 1::2]
+        return rearrange(
+            torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
+        )
+
+
+def _apply_rotary_emb_torch(
+    x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved=False
+):
+    """
+    x: (batch_size, seqlen, nheads, headdim)
+    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
+    """
+
+    data_type = x.dtype
+    x = x.to(torch.float32)
+    cos = cos.to(torch.float32)
+    sin = sin.to(torch.float32)
+
+    ro_dim = cos.shape[-1] * 2
+    assert ro_dim <= x.shape[-1]
+    cos = repeat(
+        cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    )
+    sin = repeat(
+        sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    )
+    return torch.cat(
+        [
+            x[..., :ro_dim] * cos + _rotate_half(x[..., :ro_dim], interleaved) * sin,
+            x[..., ro_dim:],
+        ],
+        dim=-1,
+    ).to(data_type)
+
+
+def apply_rotary(
+    x: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    seqlen_offsets: Union[int, torch.Tensor] = 0,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[int] = None,
+    interleaved=False,
+    inplace=False,
+    conjugate=False,
+) -> torch.Tensor:
+    """
+    Arguments:
+        x: (batch, seqlen, nheads, headdim) if cu_seqlens is None
+            else (total_seqlen, nheads, headdim).
+        cos: (seqlen_ro, rotary_dim / 2)
+        sin: (seqlen_ro, rotary_dim / 2)
+        seqlen_offsets: integer or integer tensor of size (batch,)
+        cu_seqlens: (batch + 1,) or None
+        max_seqlen: int
+    Returns:
+        y: (batch, seqlen, nheads, headdim)
+    """
+    if seqlen_offsets != 0 and cu_seqlens is None and max_seqlen is None:
+        raise NotImplementedError(
+            "apply_rotary: seqlen_offsets, cu_seqlens and max_seqlen are not supported yet"
+        )
+    batch, seqlen, nheads, headdim = x.shape
+    seqlen_ro, rotary_dim = cos.shape
+    assert sin.shape == cos.shape
+    rotary_dim *= 2
+    assert rotary_dim <= headdim, "rotary_dim must be <= headdim"
+    assert headdim <= 256, "Only support headdim <= 256"
+    assert seqlen_ro >= seqlen, "seqlen_ro must be >= seqlen"
+    assert (
+        cos.dtype == sin.dtype
+    ), f"cos and sin must have the same dtype, got {cos.dtype} and {sin.dtype}"
+    assert (
+        x.dtype == cos.dtype
+    ), f"Input and cos/sin must have the same dtype, got {x.dtype} and {cos.dtype}"
+
+    if conjugate:
+        sin = -sin
+    out = _apply_rotary_emb_torch(x, cos[:seqlen], sin[:seqlen], interleaved)
+    if inplace:
+        x.copy_(out)
+        out = x
+    return out