fix import

modeling_InternLM.py

@@ -2,12 +2,10 @@ import math
 from typing import List, Union
 from typing import Optional, Tuple
 
-import rotary_emb
 import torch
 import torch.utils.checkpoint
 import torch.utils.checkpoint
 from einops import rearrange
-from flash_attn.layers.rotary import ApplyRotaryEmbQKV_ as LegacyApplyRotaryEmbQKV_
 from torch import nn
 from torch.nn import CrossEntropyLoss
 from transformers.activations import ACT2FN
@@ -23,51 +21,70 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "InternLMXComposerConfig"
 
 
-class ApplyRotaryEmbQKV_(torch.autograd.Function):
-    """
-    ApplyRotaryEmbQKV_
-    """
+def rotary_embed(x1, x2, cos, sin, conj):
+    x1, x2 = x1.float(), x2.float()
+    if conj:
+        x1, x2 = x1 * cos + x2 * sin, x1 * sin + x2 * cos
+    else:
+        x1, x2 = x1 * cos - x2 * sin, x1 * sin + x2 * cos
+    return x1, x2
+
+
+class LegacyApplyRotaryEmbQKV_(torch.autograd.Function):
+
     @staticmethod
-    def forward(ctx, qkv, cos, sin, cos_k=None, sin_k=None):
+    def forward(ctx, qkv, cos, sin, cos_k=None, sin_k=None, interleaved=False):
         """
-            qkv: (total, 3, nheads, headdim)
+            qkv: (batch_size, seqlen, 3, nheads, headdim)
             cos, sin: (seqlen, rotary_dim / 2)
             cos_k, sin_k: (seqlen, rotary_dim / 2), optional
+            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 *inplace* to the first rotary_dim of q and k.
         """
-        _, three, _, headdim = qkv.shape
+        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)
-        q1, q2 = qkv[:, 0, :, :rotary_dim].chunk(2, dim=-1)
-        rotary_emb.apply_rotary(q1, q2, rearrange(cos, "s d -> s 1 d"),
-                                rearrange(sin, "s d -> s 1 d"), q1, q2, False)
-        k1, k2 = qkv[:, 1, :, :rotary_dim].chunk(2, dim=-1)
-        rotary_emb.apply_rotary(k1, k2, rearrange(cos_k, "s d -> s 1 d"),
-                                rearrange(sin_k, "s d -> s 1 d"), k1, k2,
-                                False)
+        assert sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2)
+        q_ro = qkv[:, :, 0, :, :rotary_dim]
+        q1, q2 = q_ro.chunk(2, dim=-1) if not interleaved else (q_ro[..., ::2], q_ro[..., 1::2])
+        # rotary_emb.apply_rotary(q1, q2, rearrange(cos[:seqlen], 's d -> s 1 d'),
+        #                         rearrange(sin[:seqlen], 's d -> s 1 d'), q1, q2, False)
+        q1, q2 = rotary_embed(q1, q2, rearrange(cos[:seqlen], 's d -> s 1 d'), rearrange(sin[:seqlen], 's d -> s 1 d'), False)
+        qkv[:, :, 0, :, :rotary_dim] = torch.cat([q1, q2], dim=-1)
+        k_ro = qkv[:, :, 1, :, :rotary_dim]
+        k1, k2 = k_ro.chunk(2, dim=-1) if not interleaved else (k_ro[..., ::2], k_ro[..., 1::2])
+        # rotary_emb.apply_rotary(k1, k2, rearrange(cos_k[:seqlen], 's d -> s 1 d'),
+        #                         rearrange(sin_k[:seqlen], 's d -> s 1 d'), k1, k2, False)
+        k1, k2 = rotary_embed(k1, k2, rearrange(cos_k[:seqlen], 's d -> s 1 d'), rearrange(sin_k[:seqlen], 's d -> s 1 d'), False)
+        qkv[:, :, 1, :, :rotary_dim] = torch.cat([k1, k2], dim=-1)
         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
+        _, seqlen, _, _, headdim = dqkv.shape
         rotary_dim = cos.shape[-1]
         rotary_dim *= 2
-        dq1, dq2 = dqkv[:, 0, :, :rotary_dim].chunk(2, dim=-1)
-        rotary_emb.apply_rotary(dq1, dq2, rearrange(cos, "s d -> s 1 d"),
-                                rearrange(sin, "s d -> s 1 d"), dq1, dq2, True)
-        dk1, dk2 = dqkv[:, 1, :, :rotary_dim].chunk(2, dim=-1)
-        rotary_emb.apply_rotary(dk1, dk2, rearrange(cos_k, "s d -> s 1 d"),
-                                rearrange(sin_k, "s d -> s 1 d"), dk1, dk2,
-                                True)
-        return dqkv, None, None, None, None
+        dq_ro = dqkv[:, :, 0, :, :rotary_dim]
+        dq1, dq2 = (dq_ro.chunk(2, dim=-1) if not ctx.interleaved
+                    else (dq_ro[..., ::2], dq_ro[..., 1::2]))
+        rotary_emb.apply_rotary(dq1, dq2, rearrange(cos[:seqlen], 's d -> s 1 d'),
+                                rearrange(sin[:seqlen], 's d -> s 1 d'), dq1, dq2, True)
+        dk_ro = dqkv[:, :, 1, :, :rotary_dim]
+        dk1, dk2 = (dk_ro.chunk(2, dim=-1) if not ctx.interleaved
+                    else (dk_ro[..., ::2], dk_ro[..., 1::2]))
+        rotary_emb.apply_rotary(dk1, dk2, rearrange(cos_k[:seqlen], 's d -> s 1 d'),
+                                rearrange(sin_k[:seqlen], 's d -> s 1 d'), dk1, dk2, True)
+        return dqkv, None, None, None, None, None
 
 
 class ConvertedInternLMRotaryEmbedding(torch.nn.Module):
@@ -120,23 +137,6 @@ class ConvertedInternLMRotaryEmbedding(torch.nn.Module):
                 self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
                 self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
 
-    def forward(self,
-                qkv: torch.Tensor,
-                indexes=0) -> Tuple[torch.Tensor, torch.Tensor]:
-        self._update_cos_sin_cache(qkv, indexes)
-        if self.scale is None:
-            return apply_rotary_emb_qkv_(qkv, self._cos_cached[indexes],
-                                         self._sin_cached[indexes]).to(
-                                             qkv.dtype)
-        else:
-            return apply_rotary_emb_qkv_(
-                qkv,
-                self._cos_cached[indexes],
-                self._sin_cached[indexes],
-                self._cos_k_cached[indexes],
-                self._sin_k_cached[indexes],
-            ).to(qkv.dtype)
-
     def eval_forward(self, qkv, seqlen_offset=0):
         """
         seqlen_offset: can be used in generation where the qkv being passed in is only the last
@@ -157,7 +157,6 @@ class ConvertedInternLMRotaryEmbedding(torch.nn.Module):
             )
 
 
-apply_rotary_emb_qkv_ = ApplyRotaryEmbQKV_.apply
 legacy_apply_rotary_embed_qkv = LegacyApplyRotaryEmbQKV_.apply
 
 
@@ -487,7 +486,6 @@ class InternLMPreTrainedModel(PreTrainedModel):
 class InternLMModel(InternLMPreTrainedModel):
     """
     Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLMDecoderLayer`]
-
     Args:
         config: InternLMXComposerConfig
     """
@@ -631,7 +629,7 @@ class InternLMModel(InternLMPreTrainedModel):
             past_key_value = past_key_values[
                 idx] if past_key_values is not None else None
 
-            if self.gradient_checkpointing and self.training and idx % 2 == 0:
+            if self.gradient_checkpointing and self.training:
 
                 def create_custom_forward(module):
                     def custom_forward(*inputs):
@@ -696,7 +694,6 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
             setattr(config, 'kqvo_bias', config.kqvo_bias)
         else:
             setattr(config, 'kqvo_bias', False)
-
         self.model = InternLMModel(config)
 
         self.lm_head = nn.Linear(config.hidden_size,
@@ -762,20 +759,14 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
                 Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                 config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                 (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
         Returns:
-
         Example:
-
         ```python
         >>> from transformers import AutoTokenizer, InternLMForCausalLM
-
         >>> model = InternLMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
         >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
         >>> prompt = "Hey, are you consciours? Can you talk to me?"
         >>> inputs = tokenizer(prompt, return_tensors="pt")
-
         >>> # Generate
         >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
         >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]