Update modeling_intern_vit.py

modeling_intern_vit.py

@@ -12,24 +12,124 @@ from einops import rearrange
 from timm.models.layers import DropPath
 from torch import nn
 from transformers.activations import ACT2FN
-from transformers.modeling_outputs import (BaseModelOutput,
-                                           BaseModelOutputWithPooling)
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import logging
 
 from .configuration_intern_vit import InternVisionConfig
 
+
 try:
-    from .flash_attention import FlashAttention
+    from triton_flash_atn import _attention
+
+    from triton_bert_pading import pad_input, unpad_input
+
     has_flash_attn = True
 except:
-    print('FlashAttention is not installed.')
+    print("FlashAttention is not installed.")
     has_flash_attn = False
 
-
 logger = logging.get_logger(__name__)
 
 
+class FlashAttention(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, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None
+    ):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(
+        self,
+        qkv,
+        key_padding_mask=None,
+        causal=False,
+        cu_seqlens=None,
+        max_s=None,
+        need_weights=False,
+    ):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+                if unpadded: (nnz, 3, h, d)
+            key_padding_mask: a bool tensor of shape (B, S)
+        """
+        assert not need_weights
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        if cu_seqlens is None:
+            batch_size = qkv.shape[0]
+            seqlen = qkv.shape[1]
+            if key_padding_mask is None:
+                qkv = rearrange(qkv, "b s ... -> (b s) ...")
+                max_s = seqlen
+                cu_seqlens = torch.arange(
+                    0,
+                    (batch_size + 1) * seqlen,
+                    step=seqlen,
+                    dtype=torch.int32,
+                    device=qkv.device,
+                )
+                output = _attention.apply(
+                    qkv,
+                    cu_seqlens,
+                    max_s,
+                    self.dropout_p if self.training else 0.0,
+                    sm_scale=self.softmax_scale,
+                    causal=causal,
+                )
+                output = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
+            else:
+                nheads = qkv.shape[-2]
+                x = rearrange(qkv, "b s three h d -> b s (three h d)")
+                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+                x_unpad = rearrange(
+                    x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
+                )
+                output_unpad = _attention.apply(
+                    x_unpad,
+                    cu_seqlens,
+                    max_s,
+                    self.dropout_p if self.training else 0.0,
+                    sm_scale=self.softmax_scale,
+                    causal=causal,
+                )
+                output = rearrange(
+                    pad_input(
+                        rearrange(output_unpad, "nnz h d -> nnz (h d)"),
+                        indices,
+                        batch_size,
+                        seqlen,
+                    ),
+                    "b s (h d) -> b s h d",
+                    h=nheads,
+                )
+        else:
+            assert max_s is not None
+            output = _attention.apply(
+                qkv,
+                cu_seqlens,
+                max_s,
+                self.dropout_p if self.training else 0.0,
+                sm_scale=self.softmax_scale,
+                causal=causal,
+            )
+
+        return output, None
+
 class InternRMSNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
         super().__init__()