[Feature]: Remove DtoH Copy for lfm2_vl On Default Stream (#32815)

Signed-off-by: Tianshu Yu <tianshuyu.formal@gmail.com>

vllm/model_executor/models/siglip2.py → vllm/model_executor/models/lfm2_siglip2.py

@@ -40,99 +40,111 @@ class Siglip2VisionEmbeddings(nn.Module):
         self.position_embedding_size = int(self.num_patches**0.5)
         self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim)
 
+    def forward(
+        self,
+        pixel_values_packed: torch.FloatTensor,
+        spatial_shapes: torch.LongTensor,
+    ) -> torch.Tensor:
+        """Embed patchified pixel values in packed (unpadded) form.
+
+        Args:
+            pixel_values_packed: (1, total_tokens, patch_dim) or
+                (total_tokens, patch_dim), packed in tile order.
+            spatial_shapes: (num_tiles, 2) on CPU (height, width) per tile.
+
+        Returns:
+            (1, total_tokens, embed_dim) packed embeddings.
+        """
+        assert spatial_shapes.device.type == "cpu", (
+            "Expected `spatial_shapes` on CPU to avoid device-to-host sync in "
+            "variable-length packing."
+        )
+
+        if pixel_values_packed.dim() == 3:
+            assert pixel_values_packed.shape[0] == 1
+            pixel_values_flat = pixel_values_packed[0]
+        else:
+            pixel_values_flat = pixel_values_packed
+
+        lengths = (spatial_shapes[:, 0] * spatial_shapes[:, 1]).to(dtype=torch.int64)
+        lengths_list = lengths.tolist()
+        total_tokens = int(sum(lengths_list))
+        if total_tokens != pixel_values_flat.shape[0]:
+            raise ValueError(
+                "Packed pixel_values token count does not match spatial_shapes: "
+                f"{pixel_values_flat.shape[0]} vs {total_tokens}."
+            )
+
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values_flat.to(dtype=target_dtype))
+
+        positional_embeddings = self.position_embedding.weight.reshape(
+            self.position_embedding_size, self.position_embedding_size, -1
+        )
+        packed_pos_embeds = self.resize_positional_embeddings_packed(
+            positional_embeddings,
+            spatial_shapes,
+            lengths_list=lengths_list,
+        )
+
+        embeddings = patch_embeds + packed_pos_embeds
+        return embeddings.unsqueeze(0)
+
     @staticmethod
-    def resize_positional_embeddings(
+    def resize_positional_embeddings_packed(
         positional_embeddings: torch.Tensor,
         spatial_shapes: torch.LongTensor,
-        max_length: int,
+        lengths_list: list[int],
     ) -> torch.Tensor:
-        """
-        Resize positional embeddings to image-specific size and pad to a fixed size.
+        """Resize positional embeddings per image and return a packed tensor.
 
         Args:
-            positional_embeddings (`torch.Tensor`):
-                Position embeddings of shape (height, width, embed_dim)
-            spatial_shapes (`torch.LongTensor`):
-                Spatial shapes of shape (batch_size, 2) to resize the positional
-                embeddings to
-            max_length (`int`):
-                Maximum length of the positional embeddings to pad resized
-                positional embeddings to
+            positional_embeddings: (height, width, embed_dim) base grid.
+            spatial_shapes: (batch_size, 2) on CPU, (height, width) per image.
+            lengths_list: flattened token length per image (height * width).
 
         Returns:
-            `torch.Tensor`: Embeddings of shape (batch_size, max_length, embed_dim)
+            (total_tokens, embed_dim) packed positional embeddings, concatenated
+            in the same order as `lengths_list`.
         """
-        batch_size = spatial_shapes.shape[0]
+        assert spatial_shapes.device.type == "cpu"
+
         embed_dim = positional_embeddings.shape[-1]
         source_dtype = positional_embeddings.dtype
 
-        resulted_positional_embeddings = torch.empty(
-            (batch_size, max_length, embed_dim),
+        total_tokens = int(sum(lengths_list))
+        packed_pos_embeds = torch.empty(
+            (total_tokens, embed_dim),
             device=positional_embeddings.device,
             dtype=source_dtype,
         )
 
-        # (height, width, embed_dim) -> (1, embed_dim, height, width) for interpolation
-        positional_embeddings = positional_embeddings.permute(2, 0, 1).unsqueeze(0)
+        # (height, width, embed_dim) -> (1, embed_dim, height, width)
+        pos_4d = positional_embeddings.permute(2, 0, 1).unsqueeze(0)
 
         # Upcast to float32 on CPU because antialias is not supported for
-        # bfloat16/float16 on CPU
-        if positional_embeddings.device.type == "cpu":
-            positional_embeddings = positional_embeddings.to(torch.float32)
-
-        for i in range(batch_size):
-            # (1, dim, height, width) -> (1, dim, target_height, target_width)
-            height, width = spatial_shapes[i]
-            resized_embeddings = F.interpolate(
-                positional_embeddings,
+        # bfloat16/float16 on CPU.
+        if pos_4d.device.type == "cpu":
+            pos_4d = pos_4d.to(torch.float32)
+
+        offset = 0
+        for i, length in enumerate(lengths_list):
+            if length <= 0:
+                continue
+            height, width = spatial_shapes[i].tolist()
+            resized = F.interpolate(
+                pos_4d,
                 size=(height, width),
                 mode="bilinear",
                 align_corners=False,
                 antialias=True,
             )
+            resized = resized.reshape(embed_dim, height * width).transpose(0, 1)
+            resized = resized.to(source_dtype)
+            packed_pos_embeds[offset : offset + length] = resized
+            offset += length
 
-            # (1, dim, target_height, target_width) ->
-            # (target_height * target_width, dim)
-            resized_embeddings = resized_embeddings.reshape(
-                embed_dim, height * width
-            ).transpose(0, 1)
-
-            # Cast to original dtype
-            resized_embeddings = resized_embeddings.to(source_dtype)
-
-            resulted_positional_embeddings[i, : height * width] = resized_embeddings
-            resulted_positional_embeddings[i, height * width :] = resized_embeddings[0]
-
-        return resulted_positional_embeddings
-
-    def forward(
-        self, pixel_values: torch.FloatTensor, spatial_shapes: torch.LongTensor
-    ) -> torch.Tensor:
-        """
-        Args:
-            pixel_values (`torch.FloatTensor`):
-                Pixel values of shape (batch_size, max_num_patches,
-                num_channels * patch_size * patch_size)
-            spatial_shapes (`list[tuple[int, int]]`):
-                Spatial shapes of shape (batch_size, 2) to resize the positional
-                embeddings to
-        """
-
-        # Apply patch embeddings to already patchified pixel values
-        target_dtype = self.patch_embedding.weight.dtype
-        patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
-
-        # Get positional resized and padded positional embeddings
-        positional_embeddings = self.position_embedding.weight.reshape(
-            self.position_embedding_size, self.position_embedding_size, -1
-        )
-        resized_positional_embeddings = self.resize_positional_embeddings(
-            positional_embeddings, spatial_shapes, max_length=pixel_values.shape[1]
-        )
-
-        # Add positional embeddings to patch embeddings
-        embeddings = patch_embeds + resized_positional_embeddings
-        return embeddings
+        return packed_pos_embeds
 
 
 class Siglip2Attention(nn.Module):
@@ -402,36 +414,23 @@ class Siglip2VisionTransformer(nn.Module):
 
     def forward(
         self,
-        pixel_values: torch.FloatTensor,
+        pixel_values_packed: torch.FloatTensor,
         spatial_shapes: torch.LongTensor,
-        packed_mask: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        max_seqlen: int | torch.Tensor,
+        max_seqlen: torch.Tensor,
     ) -> torch.Tensor:
         r"""
         spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`):
             Tensor containing the spatial dimensions (height, width)
-            of the input images.
+        of the input images.
         """
-        hidden_states = self.embeddings(pixel_values, spatial_shapes)
-        flat_mask = packed_mask.view(-1)
-        packed_indices = flat_mask.nonzero(as_tuple=True)[0]
-        flat_hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        hidden_states = flat_hidden_states.index_select(0, packed_indices).unsqueeze(0)
+        hidden_states = self.embeddings(pixel_values_packed, spatial_shapes)
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
             cu_seqlens=cu_seqlens,
             max_seqlen=max_seqlen,
         )
-        unpacked = encoder_outputs.new_zeros(
-            packed_mask.numel(), encoder_outputs.shape[-1]
-        )
-        unpacked.index_copy_(0, packed_indices, encoder_outputs.squeeze(0))
-        encoder_outputs = unpacked.view(
-            packed_mask.shape + (encoder_outputs.shape[-1],)
-        )
-        last_hidden_state = self.post_layernorm(encoder_outputs)
-        return last_hidden_state
+        return self.post_layernorm(encoder_outputs)
 
 
 class Siglip2Model(torch.nn.Module):
@@ -453,16 +452,14 @@ class Siglip2Model(torch.nn.Module):
 
     def forward(
         self,
-        pixel_values: torch.FloatTensor,
+        pixel_values_packed: torch.FloatTensor,
         spatial_shapes: torch.LongTensor,
-        packed_mask: torch.Tensor,
         cu_seqlens: torch.Tensor,
-        max_seqlen: int | torch.Tensor,
+        max_seqlen: torch.Tensor,
     ) -> torch.Tensor:
         return self.vision_model(
-            pixel_values=pixel_values,
+            pixel_values_packed=pixel_values_packed,
             spatial_shapes=spatial_shapes,
-            packed_mask=packed_mask,
             cu_seqlens=cu_seqlens,
             max_seqlen=max_seqlen,
         )

vllm/model_executor/models/lfm2_vl.py

@@ -50,7 +50,7 @@ from .interfaces import (
     SupportsMultiModal,
     SupportsPP,
 )
-from .siglip2 import Siglip2Model
+from .lfm2_siglip2 import Siglip2Model
 from .utils import (
     AutoWeightsLoader,
     WeightsMapper,
@@ -450,29 +450,78 @@ class Lfm2VLMultiModalProjector(nn.Module):
             bias=config.projector_bias,
         )
 
-    def forward(self, image_features: torch.Tensor):
-        image_features = self.pixel_unshuffle(image_features)
-        if self.projector_use_layernorm:
-            image_features = self.layer_norm(image_features)
-        hidden_states = self.linear_1(image_features)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.linear_2(hidden_states)
-        return hidden_states
+    def forward(
+        self,
+        vision_features_packed: torch.Tensor,
+        spatial_shapes: torch.Tensor,
+    ) -> torch.Tensor:
+        """Project packed vision features without materializing padded tensors.
 
-    def pixel_unshuffle(self, hidden_states: torch.Tensor):
-        batch_size, width, height, channels = hidden_states.size()
-        hidden_states = hidden_states.reshape(
-            batch_size, width, height // self.factor, channels * self.factor
-        )
-        hidden_states = hidden_states.permute(0, 2, 1, 3)
-        hidden_states = hidden_states.reshape(
-            batch_size,
-            height // self.factor,
-            width // self.factor,
-            channels * self.factor**2,
+        Args:
+            vision_features_packed: (total_tokens, hidden_size) packed in tile order.
+            spatial_shapes: (num_tiles, 2) on CPU (height, width) per tile.
+
+        Returns:
+            projected_packed: (total_projected_tokens, text_hidden_size)
+        """
+        assert spatial_shapes.device.type == "cpu", (
+            "Expected `spatial_shapes` on CPU to avoid device-to-host sync in "
+            "variable-length packing."
         )
-        hidden_states = hidden_states.permute(0, 2, 1, 3)
-        return hidden_states
+        factor = self.factor
+        device = vision_features_packed.device
+        hidden_size = vision_features_packed.shape[-1]
+
+        spatial_shapes_list: list[list[int]] = spatial_shapes.tolist()
+        lengths_list = [h * w for h, w in spatial_shapes_list]
+
+        gather_idx_parts: list[torch.Tensor] = []
+        offset = 0
+
+        dh = torch.arange(factor, dtype=torch.int64)
+        dw = torch.arange(factor, dtype=torch.int64)
+        dh_grid, dw_grid = torch.meshgrid(dh, dw, indexing="ij")
+        dh_flat = dh_grid.reshape(-1)
+        dw_flat = dw_grid.reshape(-1)
+
+        for (height, width), length in zip(spatial_shapes_list, lengths_list):
+            if length <= 0:
+                continue
+            if height % factor != 0 or width % factor != 0:
+                raise ValueError(
+                    "spatial_shapes must be divisible by downsample_factor: "
+                    f"got ({height}, {width}) with factor={factor}."
+                )
+            height_out = height // factor
+            width_out = width // factor
+
+            rows_out = torch.arange(height_out, dtype=torch.int64)
+            cols_out = torch.arange(width_out, dtype=torch.int64)
+            rr, cc = torch.meshgrid(rows_out, cols_out, indexing="ij")
+            rr = rr.reshape(-1)
+            cc = cc.reshape(-1)
+
+            token_idx = (rr[:, None] * factor + dh_flat[None, :]) * width + (
+                cc[:, None] * factor + dw_flat[None, :]
+            )
+            gather_idx_parts.append(token_idx.reshape(-1) + offset)
+            offset += length
+
+        if gather_idx_parts:
+            gather_idx = torch.cat(gather_idx_parts).to(device=device)
+            gathered = vision_features_packed.index_select(0, gather_idx)
+            unshuffled = gathered.reshape(-1, factor * factor * hidden_size)
+        else:
+            unshuffled = vision_features_packed.new_empty(
+                (0, factor * factor * hidden_size)
+            )
+
+        if self.projector_use_layernorm:
+            unshuffled = self.layer_norm(unshuffled)
+        hidden_states = self.linear_1(unshuffled)
+        hidden_states = self.act(hidden_states)
+        projected_packed = self.linear_2(hidden_states)
+        return projected_packed
 
 
 @MULTIMODAL_REGISTRY.register_processor(
@@ -598,61 +647,90 @@ class Lfm2VLForConditionalGeneration(
         pixel_values: torch.FloatTensor,
         spatial_shapes: torch.Tensor,
     ) -> torch.Tensor:
+        assert spatial_shapes.device.type == "cpu", (
+            "Expected `spatial_shapes` on CPU to avoid device-to-host sync in "
+            "variable-length packing."
+        )
+
         pixel_values = pixel_values.to(
             dtype=self.vision_tower.vision_model.embeddings.patch_embedding.weight.dtype
         )  # fp16 compatibility
 
         # LFM2-VL's HF processor pads patch sequences with trailing zeros.
-        # Derive the valid-patch mask from spatial_shapes instead of carrying
-        # pixel_attention_mask through the vLLM multimodal pipeline.
-        max_seq_len = pixel_values.shape[1]
+        # Pack patch tokens upfront so the vision tower runs entirely unpadded.
+        spatial_shapes_list: list[list[int]] = spatial_shapes.tolist()
+        lengths_list = [h * w for h, w in spatial_shapes_list]
+        total_tokens = int(sum(lengths_list))
         lengths_cpu = (spatial_shapes[:, 0] * spatial_shapes[:, 1]).to(
             dtype=torch.int32
         )
         max_seqlen = (
-            lengths_cpu.max().reshape(1).to(device=pixel_values.device)
+            lengths_cpu.max().reshape(1)
             if lengths_cpu.numel()
-            else torch.tensor([0], dtype=torch.int32, device=pixel_values.device)
+            else torch.tensor([0], dtype=torch.int32)
+        )
+
+        if total_tokens == 0:
+            return []
+
+        packed_pixel_values = pixel_values.new_empty(
+            (total_tokens, pixel_values.shape[-1])
         )
-        lengths = lengths_cpu.to(device=pixel_values.device)
-        packed_mask = (
-            torch.arange(max_seq_len, device=pixel_values.device)[None, :]
-            < lengths[:, None]
+        offset = 0
+        for i, length in enumerate(lengths_list):
+            if length <= 0:
+                continue
+            packed_pixel_values[offset : offset + length].copy_(
+                pixel_values[i, :length]
+            )
+            offset += length
+        packed_pixel_values = packed_pixel_values.unsqueeze(0)
+
+        lengths = torch.tensor(
+            lengths_list, dtype=torch.int32, device=pixel_values.device
         )
         cu_seqlens = torch.zeros(
             lengths.shape[0] + 1,
             dtype=torch.int32,
-            device=lengths.device,
+            device=pixel_values.device,
         )
         cu_seqlens[1:] = torch.cumsum(lengths, dim=0)
 
         with set_forward_context(None, self.vllm_config):
             vision_outputs = self.vision_tower(
-                pixel_values=pixel_values,
+                pixel_values_packed=packed_pixel_values,
                 spatial_shapes=spatial_shapes,
-                packed_mask=packed_mask,
                 cu_seqlens=cu_seqlens,
                 max_seqlen=max_seqlen,
             )
-        image_outputs = getattr(vision_outputs, "last_hidden_state", vision_outputs)
-
-        image_features = []
-
-        # spatial_shapes is on CPU (keep_on_cpu=True), so .tolist() is instant
-        spatial_shapes_list = spatial_shapes.tolist()
-        for img_idx, (feature_org_h, feature_org_w) in enumerate(spatial_shapes_list):
-            feature_len = feature_org_h * feature_org_w
-            feature = image_outputs[img_idx, :feature_len]
-
-            # reshape to original height and width
-            feature = feature.reshape(1, feature_org_h, feature_org_w, -1)
+        image_outputs_packed = getattr(
+            vision_outputs, "last_hidden_state", vision_outputs
+        )
+        vision_features_packed = image_outputs_packed[0]
+
+        factor = self.multi_modal_projector.factor
+        projected_lengths_list: list[int] = []
+        for (height, width), length in zip(spatial_shapes_list, lengths_list):
+            if length <= 0:
+                projected_lengths_list.append(0)
+                continue
+            if height % factor != 0 or width % factor != 0:
+                raise ValueError(
+                    "spatial_shapes must be divisible by downsample_factor: "
+                    f"got ({height}, {width}) with factor={factor}."
+                )
+            projected_lengths_list.append((height // factor) * (width // factor))
 
-            # project the image representation
-            img_embedding = self.multi_modal_projector(feature)
+        projected_packed = self.multi_modal_projector(
+            vision_features_packed=vision_features_packed,
+            spatial_shapes=spatial_shapes,
+        )
 
-            # flatten here to handle variable length in naflex
-            img_embedding = img_embedding.reshape(-1, img_embedding.size(-1))
-            image_features.append(img_embedding)
+        image_features: list[torch.Tensor] = []
+        offset = 0
+        for out_len in projected_lengths_list:
+            image_features.append(projected_packed[offset : offset + out_len])
+            offset += out_len
 
         return image_features
 

vllm/v1/attention/backends/gdn_attn.py

@@ -155,9 +155,11 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
         m = common_attn_metadata
 
         query_start_loc = m.query_start_loc
+        query_start_loc_cpu = m.query_start_loc_cpu
         context_lens_tensor = m.compute_num_computed_tokens()
         nums_dict, batch_ptr, token_chunk_offset_ptr = None, None, None
 
+        spec_sequence_masks_cpu: torch.Tensor | None = None
         if (
             not self.use_spec_decode
             or num_decode_draft_tokens_cpu is None
@@ -169,12 +171,13 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
             spec_sequence_masks = None
             num_spec_decodes = 0
         else:
-            spec_sequence_masks = num_decode_draft_tokens_cpu >= 0
-            num_spec_decodes = spec_sequence_masks.sum().item()
+            spec_sequence_masks_cpu = num_decode_draft_tokens_cpu >= 0
+            num_spec_decodes = spec_sequence_masks_cpu.sum().item()
             if num_spec_decodes == 0:
                 spec_sequence_masks = None
+                spec_sequence_masks_cpu = None
             else:
-                spec_sequence_masks = spec_sequence_masks.to(
+                spec_sequence_masks = spec_sequence_masks_cpu.to(
                     query_start_loc.device, non_blocking=True
                 )
 
@@ -189,9 +192,12 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
             non_spec_state_indices_tensor = m.block_table_tensor[:, 0]
             spec_query_start_loc = None
             non_spec_query_start_loc = query_start_loc
+            non_spec_query_start_loc_cpu = query_start_loc_cpu
             num_accepted_tokens = None
         else:
             query_lens = query_start_loc[1:] - query_start_loc[:-1]
+            assert spec_sequence_masks_cpu is not None
+            query_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
 
             non_spec_query_lens = query_lens[~spec_sequence_masks]
             num_decodes = (non_spec_query_lens == 1).sum().item()
@@ -219,6 +225,7 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
                 non_spec_state_indices_tensor = None
                 spec_query_start_loc = query_start_loc
                 non_spec_query_start_loc = None
+                non_spec_query_start_loc_cpu = None
             else:
                 spec_token_masks = torch.repeat_interleave(
                     spec_sequence_masks, query_lens
@@ -253,6 +260,15 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
                     dim=0,
                     out=non_spec_query_start_loc[1:],
                 )
+                non_spec_query_start_loc_cpu = torch.zeros(
+                    query_lens_cpu.size(0) - num_spec_decodes + 1,
+                    dtype=torch.int32,
+                )
+                torch.cumsum(
+                    query_lens_cpu[~spec_sequence_masks_cpu],
+                    dim=0,
+                    out=non_spec_query_start_loc_cpu[1:],
+                )
 
             assert num_accepted_tokens is not None
             num_accepted_tokens = num_accepted_tokens[spec_sequence_masks]
@@ -261,8 +277,12 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
             has_initial_state = context_lens_tensor > 0
             if spec_sequence_masks is not None:
                 has_initial_state = has_initial_state[~spec_sequence_masks]
+                assert non_spec_query_start_loc_cpu is not None
             nums_dict, batch_ptr, token_chunk_offset_ptr = (
-                compute_causal_conv1d_metadata(non_spec_query_start_loc)
+                compute_causal_conv1d_metadata(
+                    non_spec_query_start_loc_cpu,
+                    device=query_start_loc.device,
+                )
             )
         else:
             has_initial_state = None

vllm/v1/attention/backends/mamba_attn.py

@@ -219,21 +219,24 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
         if num_prefills > 0:
             if num_computed_tokens is None:
                 num_computed_tokens = common_attn_metadata.compute_num_computed_tokens()
-            num_computed_tokens_cpu = num_computed_tokens.cpu()
 
+            query_start_loc_p_cpu = (
+                common_attn_metadata.query_start_loc_cpu[-num_prefills - 1 :]
+                - num_decode_tokens
+            )
             query_start_loc_p = (
                 common_attn_metadata.query_start_loc[-num_prefills - 1 :]
                 - num_decode_tokens
             )
-            has_initial_states_cpu = (
-                num_computed_tokens_cpu[num_reqs - num_prefills : num_reqs] > 0
-            )
-            has_initial_states_p = has_initial_states_cpu.to(
-                common_attn_metadata.query_start_loc.device
+            has_initial_states_p = (
+                num_computed_tokens[num_reqs - num_prefills : num_reqs] > 0
             )
 
             nums_dict, batch_ptr, token_chunk_offset_ptr = (
-                compute_causal_conv1d_metadata(query_start_loc_p)
+                compute_causal_conv1d_metadata(
+                    query_start_loc_p_cpu,
+                    device=common_attn_metadata.query_start_loc.device,
+                )
             )
 
             if self.vllm_config.cache_config.enable_prefix_caching:

vllm/v1/attention/backends/utils.py

@@ -732,13 +732,17 @@ def create_fast_prefill_custom_backend(
     return attn_backend
 
 
-def compute_causal_conv1d_metadata(query_start_loc_p: torch.Tensor):
-    # Needed for causal_conv1d
-    seqlens = query_start_loc_p.diff().to("cpu")
+def compute_causal_conv1d_metadata(
+    query_start_loc_p_cpu: torch.Tensor,
+    *,
+    device: torch.device,
+):
+    # Needed for causal_conv1d. Use the CPU query_start_loc to avoid DtoH sync.
+    assert query_start_loc_p_cpu.device.type == "cpu"
+    seqlens = query_start_loc_p_cpu.diff()
     nums_dict = {}  # type: ignore
     batch_ptr = None
     token_chunk_offset_ptr = None
-    device = query_start_loc_p.device
     for BLOCK_M in [8]:  # cover all BLOCK_M values
         nums = -(-seqlens // BLOCK_M)
         nums_dict[BLOCK_M] = {}