feat: reimpl rotary embedding for npu

deeplink_ext/ascend_speed/rotary_embedding.py

@@ -4,7 +4,8 @@
 
 platform_type = deeplink_ext_get_platform_type()
 if platform_type == PlatformType.TORCH_NPU:
-    from ._rotary_embedding_npu import RotaryEmbedding
+    # from ._rotary_embedding_npu import RotaryEmbedding
+    from .rotary_embedding_fallback import RotaryEmbeddingTorch as RotaryEmbedding
 elif platform_type == PlatformType.TORCH_DIPU:
     from ._rotary_embedding_dipu import RotaryEmbedding
 else:

deeplink_ext/internevo_ops/_rotary_embedding_npu.py

@@ -7,35 +7,6 @@
 __all__ = ["ApplyRotaryEmb"]
 
 
-def _unsqueeze_to_4d(x: torch.Tensor):
-    while x.dim() < 4:
-        x = x.unsqueeze(0)
-    return x
-
-
-def _apply_rotary(x: torch.Tensor, cos, sin, confj, interleaved):
-    assert interleaved == False, "interleaved not support by torch_npu"
-
-    x_view = _unsqueeze_to_4d(x)
-    cos_view = _unsqueeze_to_4d(cos)
-    sin_view = _unsqueeze_to_4d(sin)
-
-    cos_cat = torch.cat([cos_view, cos_view], -1)
-    sin_cat = torch.cat([sin_view, sin_view], -1)
-
-    if confj:
-        sin_cat.neg_()
-
-    x_view_chunks = x_view.chunk(2, -1)
-    x_view_new = torch.cat([-x_view_chunks[1], x_view_chunks[0]], -1)
-
-    cos_x = torch.mul(cos_cat, x_view)
-    sin_x = torch.mul(sin_cat, x_view_new)
-    out = cos_x + sin_x
-
-    return out
-
-
 # adpated from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py#L35
 class ApplyRotaryEmb(torch.autograd.Function):
     """
@@ -67,45 +38,38 @@ def forward(
         assert seqlen <= rotary_seqlen
         assert sin.shape == (rotary_seqlen, rotary_dim // 2)
 
-        out = _apply_rotary(
-            x[..., :rotary_dim],
-            rearrange(cos[:seqlen], "s d -> s 1 d"),
-            rearrange(sin[:seqlen], "s d -> s 1 d"),
-            False,
-            interleaved,
-        )
+        re_cos = rearrange(cos[:seqlen], "s d -> s 1 d")
+        re_sin = rearrange(sin[:seqlen], "s d -> s 1 d")
+
+        cat_cos = torch.cat([re_cos, re_cos], -1)
+        cat_sin = torch.cat([re_sin, re_sin], -1)
 
-        ctx.save_for_backward(cos, sin)
+        rot = torch_npu.npu_rotary_mul(x[..., :rotary_dim], cat_cos, cat_sin)
+        ctx.save_for_backward(cat_cos, cat_sin)
         ctx.interleaved = interleaved
         ctx.in_place = in_place
-
         if in_place:
-            x[..., :rotary_dim].copy_(out[..., :rotary_dim])
+            x[..., :rotary_dim].copy_(rot)
             return x
         else:
-            if rotary_dim < head_dim:
+            out = x.detach().clone()
+            if rotary_dim < head_dim and not in_place:
                 out[..., rotary_dim:].copy_(x[..., rotary_dim:])
             return out
 
     @staticmethod
     def backward(ctx, do):
-        cos, sin = ctx.saved_tensors
+        cat_cos, cat_sin = ctx.saved_tensors
         *_, seqlen, _, head_dim = do.shape
-        rotary_dim = cos.shape[-1]
-        rotary_dim *= 2
+        rotary_dim = cat_cos.shape[-1]
 
-        out = _apply_rotary(
-            do[..., :rotary_dim],
-            rearrange(cos[:seqlen], "s d -> s 1 d"),
-            rearrange(sin[:seqlen], "s d -> s 1 d"),
-            True,
-            ctx.interleaved,
+        dx_out = torch_npu.npu_rotary_mul(
+            do[..., :rotary_dim], cat_cos, torch.neg(cat_sin)
         )
-
         if ctx.in_place:
-            do[..., :rotary_dim].copy_(out[..., :rotary_dim])
+            do[..., :rotary_dim].copy_(dx_out)
             return do, None, None, None, None
         else:
-            if rotary_dim < head_dim:
-                out[..., rotary_dim:].copy(do[..., rotary_dim:])
-            return out, None, None, None, None
+            dx = do.detach().clone()
+            dx[..., :rotary_dim].copy_(dx_out)
+            return dx, None, None, None, None

deeplink_ext/internevo_ops/rotary_embedding.py

@@ -4,7 +4,8 @@
 
 platform_type = deeplink_ext_get_platform_type()
 if platform_type == PlatformType.TORCH_NPU:
-    from ._rotary_embedding_npu import ApplyRotaryEmb
+    # from ._rotary_embedding_npu import ApplyRotaryEmb
+    from .rotary_embedding_fallback import ApplyRotaryEmbTorch as ApplyRotaryEmb
 elif platform_type == PlatformType.TORCH_DIPU:
     from ._rotary_embedding_dipu import ApplyRotaryEmb
 else:

deeplink_ext/interntrain_ops/_rotary_embedding_npu.py

@@ -1,5 +1,4 @@
 # Copyright (c) 2024, DeepLink.
-# Copyright (c) 2024, InternEvo.
 
 import torch
 import torch_npu
@@ -8,38 +7,16 @@
 __all__ = ["ApplyRotaryEmb", "ApplyRotaryEmbQKV_"]
 
 
-def _unsqueeze_to_4d(x: torch.Tensor):
-    while x.dim() < 4:
-        x = x.unsqueeze(0)
-    return x
-
-
-def _apply_rotary(x: torch.Tensor, cos, sin, confj, interleaved):
-    assert interleaved == False, "interleaved not support by torch_npu"
-
-    x_view = _unsqueeze_to_4d(x)
-    cos_view = _unsqueeze_to_4d(cos)
-    sin_view = _unsqueeze_to_4d(sin)
-
-    cos_cat = torch.cat([cos_view, cos_view], -1)
-    sin_cat = torch.cat([sin_view, sin_view], -1)
-
-    if confj:
-        sin_cat.neg_()
-
-    x_view_chunks = x_view.chunk(2, -1)
-    x_view_new = torch.cat([-x_view_chunks[1], x_view_chunks[0]], -1)
-
-    cos_x = torch.mul(cos_cat, x_view)
-    sin_x = torch.mul(sin_cat, x_view_new)
-    out = cos_x + sin_x
-
-    return out
-
-
 class ApplyRotaryEmb(torch.autograd.Function):
     """
-    ApplyRotaryEmb
+    Apply rotary positional embedding to input tensor x.
+    Args:
+        x (Tensor): Input tensor x is of shape [seq_length, ... , dim]
+        cos (Tensor): Input tensor cos is of shape [seq_length, ..., dim]
+        sin (Tensor): Input tensor sin is of shape [seq_length, ..., dim]
+
+    Returns:
+        Tensor: The input tensor after applying RoPE
     """
 
     @staticmethod
@@ -59,34 +36,34 @@ def forward(ctx, x, cos, sin, interleaved=False):
         assert seqlen <= rotary_seqlen
         assert sin.shape == (rotary_seqlen, rotary_dim // 2)
         out = torch.empty_like(x)
+
         re_cos = rearrange(cos[:seqlen], "s d -> s 1 d")
         re_sin = rearrange(sin[:seqlen], "s d -> s 1 d")
-        out = _apply_rotary(
-            x[..., :rotary_dim],
-            re_cos,
-            re_sin,
-            False,
-            interleaved,
-        )
+
+        cat_cos = torch.cat([re_cos, re_cos], -1)
+        cat_sin = torch.cat([re_sin, re_sin], -1)
+
+        rot = torch_npu.npu_rotary_mul(x[..., :rotary_dim], cat_cos, cat_sin)
+        out[..., :rotary_dim].copy_(rot)
         if rotary_dim < headdim:
             out[..., rotary_dim:].copy_(x[..., rotary_dim:])
-        ctx.save_for_backward(re_cos, re_sin)
+
+        ctx.save_for_backward(cat_cos, cat_sin)
         ctx.interleaved = interleaved
         return out
 
     @staticmethod
     def backward(ctx, do):
-        re_cos, re_sin = ctx.saved_tensors
+        cat_cos, cat_sin = ctx.saved_tensors
         headdim = do.shape[-1]
-        rotary_dim = re_cos.shape[-1]
-        rotary_dim *= 2
-        dx = _apply_rotary(
-            do[..., :rotary_dim],
-            re_cos,
-            re_sin,
-            True,
-            ctx.interleaved,
+        rotary_dim = cat_cos.shape[-1]
+
+        dx = torch.empty_like(do)
+        dx_rot = torch_npu.npu_rotary_mul(
+            do[..., :rotary_dim], cat_cos, torch.neg(cat_sin)
         )
+        dx.copy_(dx_rot)
+
         if rotary_dim < headdim:
             dx[..., rotary_dim:].copy_(do[..., rotary_dim:])
         return dx, None, None, None
@@ -141,16 +118,10 @@ def forward(ctx, qkv, cos, sin, cos_k=None, sin_k=None, interleaved=False):
             if len(qkv.shape) == 4
             else rearrange(sin[:seqlen], "s d -> s 1 d")
         )
-
-        # qro
-        out = _apply_rotary(
-            q_ro,
-            re_cos,
-            re_sin,
-            False,
-            interleaved,
-        )
-        q_ro.copy_(out)
+        cat_cos = torch.cat([re_cos, re_cos], -1)
+        cat_sin = torch.cat([re_sin, re_sin], -1)
+        q_out = torch_npu.npu_rotary_mul(q_ro, cat_cos, cat_sin)
+        q_ro.copy_(q_out)
 
         k_ro = (
             qkv[:, 1, :, :rotary_dim]
@@ -167,50 +138,34 @@ def forward(ctx, qkv, cos, sin, cos_k=None, sin_k=None, interleaved=False):
             if len(qkv.shape) == 4
             else rearrange(sin_k[:seqlen], "s d -> s 1 d")
         )
-        out = _apply_rotary(
-            k_ro,
-            re_cos_k,
-            re_sin_k,
-            False,
-            interleaved,
-        )
-        k_ro.copy_(out)
+        cat_cos_k = torch.cat([re_cos_k, re_cos_k], -1)
+        cat_sin_k = torch.cat([re_sin_k, re_sin_k], -1)
+        k_out = torch_npu.npu_rotary_mul(k_ro, cat_cos_k, cat_sin_k)
+        k_ro.copy_(k_out)
 
-        ctx.save_for_backward(re_cos, re_sin, re_cos_k, re_sin_k)
+        ctx.save_for_backward(cat_cos, cat_sin, cat_cos_k, cat_sin_k)
         ctx.interleaved = interleaved
         return qkv
 
     @staticmethod
     def backward(ctx, dqkv):
-        re_cos, re_sin, re_cos_k, re_sin_k = ctx.saved_tensors
-        rotary_dim = re_cos.shape[-1]
-        rotary_dim *= 2
+        cat_cos, cat_sin, cat_cos_k, cat_sin_k = ctx.saved_tensors
+        rotary_dim = cat_cos.shape[-1]
 
         dq_ro = (
             dqkv[:, 0, :, :rotary_dim]
             if len(dqkv.shape) == 4
             else dqkv[:, :, 0, :, :rotary_dim]
         )
-        out = _apply_rotary(
-            dq_ro,
-            re_cos,
-            re_sin,
-            True,
-            ctx.interleaved,
-        )
-        dq_ro.copy_(out)
+        dq_out = torch_npu.npu_rotary_mul(dq_ro, cat_cos, torch.neg(cat_sin))
+        dq_ro.copy_(dq_out)
 
         dk_ro = (
             dqkv[:, 1, :, :rotary_dim]
             if len(dqkv.shape) == 4
             else dqkv[:, :, 1, :, :rotary_dim]
         )
-        out = _apply_rotary(
-            dk_ro,
-            re_cos_k,
-            re_sin_k,
-            True,
-            ctx.interleaved,
-        )
-        dk_ro.copy_(out)
+        dk_out = torch_npu.npu_rotary_mul(dk_ro, cat_cos_k, torch.neg(cat_sin_k))
+        dk_ro.copy_(dk_out)
+
         return dqkv, None, None, None, None, None

deeplink_ext/interntrain_ops/rms_norm.py

@@ -4,7 +4,8 @@
 
 platform_type = deeplink_ext_get_platform_type()
 if platform_type == PlatformType.TORCH_NPU:
-    from ._mixed_rms_norm_npu import MixedFusedRMSNorm
+    # from ._mixed_rms_norm_npu import MixedFusedRMSNorm
+    from .rms_norm_fallback import MixedRMSNormTorch as MixedFusedRMSNorm
 elif platform_type == PlatformType.TORCH_DIPU:
     from ._mixed_rms_norm_dipu import MixedFusedRMSNorm
 else:

deeplink_ext/interntrain_ops/rotary_embedding.py

@@ -4,7 +4,9 @@
 
 platform_type = deeplink_ext_get_platform_type()
 if platform_type == PlatformType.TORCH_NPU:
-    from ._rotary_embedding_npu import ApplyRotaryEmb, ApplyRotaryEmbQKV_
+    # from ._rotary_embedding_npu import ApplyRotaryEmb, ApplyRotaryEmbQKV_
+    from .rotary_embedding_fallback import ApplyRotaryEmbTorch as ApplyRotaryEmb
+    from .rotary_embedding_fallback import ApplyRotaryEmbQKV_Torch as ApplyRotaryEmbQKV_
 elif platform_type == PlatformType.TORCH_DIPU:
     from ._rotary_embedding_dipu import ApplyRotaryEmb, ApplyRotaryEmbQKV_
 else: