[Fix] Address remaining issues of supporting MiniCPMV (sgl-project#2977)

docs/references/supported_models.md

@@ -78,6 +78,7 @@ Another valuable resource is the [vLLM Models Directory](https://github.com/vllm
 To port a model from vLLM to SGLang, you can compare these two files [SGLang Llama Implementation](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/llama.py) and [vLLM Llama Implementation](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llama.py). This comparison will help you understand how to convert a model implementation from vLLM to SGLang. The major difference is the replacement of Attention with RadixAttention. The other parts are almost identical. Specifically,
   - Replace vllm's `Attention` with `RadixAttention`. Note that you need to pass `layer_id` all the way to `RadixAttention`.
   - Replace vllm's `LogitsProcessor` with SGLang's `LogitsProcessor`.
+  - Replace Multi-headed `Attention` of ViT with SGLang's `VisionAttention`.
   - Replace other vLLM layers with SGLang layers (e.g., `RMSNorm`, `SiluAndMul`).
   - Remove `Sample`.
   - Change `forward()` functions, and add `forward_batch`.

python/sglang/srt/layers/attention/triton_ops/prefill_attention.py

@@ -166,6 +166,12 @@ def _fwd_kernel(
 def context_attention_fwd(
     q, k, v, o, b_start_loc, b_seq_len, max_input_len, is_causal=True
 ):
+    """
+    q, k, v: [b * s, head, head_dim]
+    b_start_loc: [b]
+    b_seq_len: [b]
+    out: [b * s, head, head_dim]
+    """
     if is_cuda_available and CUDA_CAPABILITY[0] > 8:
         BLOCK = 128
     else:

python/sglang/srt/layers/attention/vision.py

@@ -4,6 +4,7 @@
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from einops import rearrange, repeat
 
 from sglang.srt.distributed import parallel_state
@@ -63,7 +64,20 @@ def apply_rotary_pos_emb_vision(t: torch.Tensor, freqs: torch.Tensor) -> torch.T
 
 
 class VisionAttention(nn.Module):
-    """Multi-headed attention without any cache, mostly used for ViT."""
+    r"""
+        Multi-headed attention without any cache, mostly used for ViT.
+
+
+    Args:
+        use_qkv_parallel (bool, optional): If True, use QKV-parallel attention.
+        use_context_forward (bool, default to True):
+            if ``True``, a flash_attn style attention will be applied
+            Otherwise, a full-sequence attention will be applied.
+        use_full_precision_softmax (bool, default to False):
+            if ``True``, the softmax will be performed in full-precision
+            Otherwise, it will be performed in half-precision
+
+    """
 
     def __init__(
         self,
@@ -72,25 +86,39 @@ def __init__(
         projection_size: int,
         use_qkv_parallel: bool,
         quant_config: Optional[QuantizationConfig] = None,
+        dropout: float = 0.0,
+        use_context_forward: bool = True,
+        use_full_precision_softmax: bool = False,
+        flatten_batch: bool = False,
         prefix: str = "",
     ):
         super().__init__()
+        self.use_context_forward = use_context_forward
         world_size = parallel_state.get_tensor_model_parallel_world_size()
-
+        self.dropout = dropout
+        self.head_size = embed_dim // num_heads
         self.hidden_size_per_attention_head = dist_utils.divide(
             projection_size, num_heads
         )
         self.num_attention_heads_per_partition = dist_utils.divide(
             num_heads, world_size
         )
-        # self.tp_size = get_tensor_model_parallel_world_size()
-        # num_heads = self.num_heads_per_partition
+
+        if self.use_context_forward:
+            self.qkv_backend = VisionTritonAttention()
+        else:
+            self.qkv_backend = VisionSdpaAttention(
+                head_size=self.head_size,
+                dropout=dropout,
+                flatten_batch=flatten_batch,
+                use_full_precision_softmax=use_full_precision_softmax,
+            )
+
         self.use_qkv_parallel = use_qkv_parallel
         if use_qkv_parallel:
-            self.head_dim = embed_dim // num_heads
             self.qkv_proj = QKVParallelLinear(
                 hidden_size=embed_dim,
-                head_size=self.head_dim,
+                head_size=self.head_size,
                 total_num_heads=num_heads,
                 quant_config=quant_config,
                 prefix=f"{prefix}.qkv_proj",
@@ -114,12 +142,15 @@ def forward(
         x: torch.Tensor,
         cu_seqlens: Optional[torch.Tensor] = None,
         rotary_pos_emb: torch.Tensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
+        r"""
+        Args:
+            x: [b, s, embed_dim]
+            cu_seqlens: [b]
+        Returns:
+             [s, b, num_heads * head]
         """
-        Input shape: [b, s, embed_dim]
-        Output shape: [s, b, num_heads * head_size]
-        """
-
         bsz, s, _ = x.shape
         if self.use_qkv_parallel:
             # [b, s, embed_dim] --> [b, s, embed_dim]
@@ -136,19 +167,19 @@ def forward(
         else:
             # [b, s, embed_dim] --> [s, b, embed_dim]
             x = rearrange(x, "b s ... -> s b ...")
-            # [s, b, embed_dim] --> [s, b, head * 3 * head_dim]
+            # [s, b, embed_dim] --> [s, b, head * 3 * head_size]
             qkv, _ = self.qkv_proj(x)
-            # [s, b, head * 3 * head_dim] --> [s, b, head, 3 * head_dim]
+            # [s, b, head * 3 * head_size] --> [s, b, head, 3 * head_size]
             new_x_shape = qkv.size()[:-1] + (
                 self.num_attention_heads_per_partition,
                 3 * self.hidden_size_per_attention_head,
             )
             qkv = qkv.view(*new_x_shape)
 
-            # [s, b, head, 3 * head_dim] --> 3 [s, b, head, head_dim]
+            # [s, b, head, 3 * head_size] --> 3 [s, b, head, head_size]
             q, k, v = dist_utils.split_tensor_along_last_dim(qkv, 3)
 
-            # [s, b, head, head_dim] --> [b, s, head, head_dim]
+            # [s, b, head, head_size] --> [b, s, head, head_size]
             q, k, v = [
                 rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v)
             ]
@@ -160,45 +191,217 @@ def forward(
         if self.use_qkv_parallel:
             pass
         else:
-            # [b, s, head, head_dim] --> [b * s, head, head_dim]
+            # [b, s, head, head_size] --> [b * s, head, head_size]
             q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
 
-        # [b * s, num_heads, head_size]
-        output = torch.empty_like(q)
-
-        seq_lens = (cu_seqlens[1:] - cu_seqlens[:-1]).cuda()
-        max_seqlen = seq_lens.max().item()
-
-        context_attention_fwd(
-            q,
-            k,
-            v,
-            output,
-            cu_seqlens.cuda(),
-            seq_lens,
-            max_seqlen,
-            is_causal=False,
-        )
+        output = self.qkv_backend.forward(q, k, v, bsz, cu_seqlens, attention_mask)
 
         if self.use_qkv_parallel:
-
-            # [b * s, head, head_dim] --> [b, s, head * head_dim]
+            # [b * s, h, head_size] --> [b, s, h * head_size]
             output = rearrange(output, "(b s) ... h d -> b s ... (h d)", b=bsz)
 
-            # [b, s, head, head_dim] --> [b, s, head, head_dim]
+            # [b, s, h * head_size] --> [b, s, h * head_size]
             output, _ = self.proj(output)
         else:
-            # [b * s, head, head_dim] --> [b, s, head, head_dim]
-            context_layer = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-
-            # [s, b, num_heads * head_size]
+            # [b * s, h, head_size] --> [s, b, h * head_size]
             context_layer = rearrange(
-                context_layer, "b s h d -> s b (h d)"
+                output, "(b s) h d -> s b (h d)", b=bsz, s=s
             ).contiguous()
 
-            # [s, b, num_heads * head_size] --> [s, b, num_heads * head_size]
+            # [s, b, h * head_size] --> [s, b, h * head_size]
             output, _ = self.proj(context_layer)
 
+            # [s, b, h * head_size] --> [b, s, h * head_size]
             output = output.view(bsz, s, -1)
 
         return output
+
+
+class VisionSdpaAttention(nn.Module):
+    r"""
+    Scaled Dot Product Attention inner product
+
+    """
+
+    # TODO: Should it be released after used?
+    _mask_cache = {}
+
+    def __init__(
+        self,
+        head_size: int,
+        dropout: float = 0.0,
+        flatten_batch: bool = False,
+        use_full_precision_softmax: bool = False,
+    ):
+        super().__init__()
+        self.head_size = head_size
+        self.flatten_batch = flatten_batch
+        self.use_full_precision_softmax = use_full_precision_softmax
+        self.dropout = dropout
+
+    def generate_patch_attention_mask(
+        self,
+        s: int,
+        bsz: int,
+        device,
+        cu_seqlens: Optional[torch.Tensor],
+        flatten_batch: bool = False,
+        dtype=torch.bfloat16,
+    ) -> torch.Tensor:
+        r"""
+        Creates a non-causal 4D mask of shape `(b, 1, s, s)` or `(1, 1, s, s)`.
+
+        When `flatten_batch` is True:
+            - All sequences in the batch are flattened into a single dimension
+            - `s` represents the total number of tokens across all sequences in the batch
+            - Returns a unified mask of shape `(1, 1, s, s)`
+
+        When `flatten_batch` is False:
+            - Each sequence has its own attention mask
+            - `s` represents the maximum sequence length in the batch
+            - Returns separate masks of shape `(b, 1, s, s)`
+
+        Args:
+            flatten_batch: (bool):
+                If True, treats all sequences in the batch as a single flattened sequence
+                If False, generates separate masks for each sequence
+
+        Returns:
+            Tensor of shape `(b, 1, s, s)` or `(1, 1, s, s)`.
+        """
+
+        cache_key = (s, bsz, flatten_batch, tuple(cu_seqlens.cpu().tolist()))
+
+        if cache_key in VisionSdpaAttention._mask_cache:
+            cached_mask = VisionSdpaAttention._mask_cache[cache_key]
+            # print(f"cache hit for key: {cache_key}")
+            return cached_mask.to(device=device, dtype=dtype)
+
+        if cu_seqlens is None:
+            raise ValueError("Internal Error: cu_seqlens cannot be None")
+
+        if flatten_batch:
+            mask = torch.zeros([1, s, s], device=device, dtype=torch.bool)
+            for i in range(1, len(cu_seqlens)):
+                start = cu_seqlens[i - 1]
+                end = cu_seqlens[i]
+                mask[
+                    ...,
+                    start:end,
+                    start:end,
+                ] = True
+        else:
+            # [1, 1, 1, s]
+            row_indices = torch.arange(s, device=device).view(1, 1, 1, s)
+            # [1, 1, s, 1]
+            col_indices = torch.arange(s, device=device).view(1, 1, s, 1)
+            # [b, 1, 1, 1]
+            seq_lens = (
+                (cu_seqlens[1:] - cu_seqlens[:-1]).to(device=device).view(-1, 1, 1, 1)
+            )
+
+            mask = (row_indices < seq_lens) & (col_indices < seq_lens)
+
+        # Convert to attention mask format (False -> 0, True -> -inf)
+        mask = (~mask).to(dtype) * torch.finfo(dtype).min
+
+        VisionSdpaAttention._mask_cache[cache_key] = mask
+
+        return mask
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        bsz: int,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            cu_seqlens: [b]
+        Returns:
+             [b * s, h, head_size]
+        """
+
+        s = q.shape[0] // bsz
+
+        # [b, 1, s, s]
+        if attention_mask is None:
+            attention_mask = self.generate_patch_attention_mask(
+                s, bsz, q.device, cu_seqlens, self.flatten_batch, q.dtype
+            )
+        q, k, v = [rearrange(x, "(b s) h d -> b h s d", b=bsz) for x in [q, k, v]]
+        # [b, 1, s]
+        if self.use_full_precision_softmax:
+            scale = self.head_size**-0.5
+            k_transposed = rearrange(k, "b h s d -> b h d s")
+            attn_weights = torch.matmul(q, k_transposed) * scale
+            del k, k_transposed
+            attn_weights = attn_weights + attention_mask
+            del attention_mask
+            # full-precision
+            attn_weights = nn.functional.softmax(
+                attn_weights, dim=-1, dtype=torch.float32
+            ).to(q.dtype)
+            attn_weights = nn.functional.dropout(
+                attn_weights, p=self.dropout, training=False
+            )
+            output = torch.matmul(attn_weights, v)
+            del attn_weights, v
+        else:
+            # SDPA
+            # [b, h, s, head_size]
+            output = F.scaled_dot_product_attention(
+                q, k, v, attention_mask, dropout_p=self.dropout
+            )
+
+        # [b, h, s, head_size] --> [b * s, h, head_size]
+        output = rearrange(output, "b h s d -> (b s) h d")
+
+        return output
+
+
+class VisionTritonAttention(nn.Module):
+    """
+    Triton-implemented attention without a causal mask
+    """
+
+    def __init__(
+        self,
+    ):
+        super().__init__()
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        _bsz: int,
+        cu_seqlens: Optional[torch.Tensor],
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            cu_seqlens: [b]
+        Returns:
+             [b * s, h, head_size]
+        """
+
+        # [b * s, head, head_size]
+        output = torch.empty_like(q)
+        seq_lens = cu_seqlens[1:] - cu_seqlens[:-1]
+        max_seqlen = seq_lens.max().item()
+        context_attention_fwd(
+            q,
+            k,
+            v,
+            output,
+            cu_seqlens.cuda(),
+            seq_lens.cuda(),
+            max_seqlen,
+            is_causal=False,
+        )
+
+        return output