[None][feat] reuse triton slicing kernel for GDN prefill transpose

Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com>

Author: nv-guomingz

tensorrt_llm/_torch/modules/mamba/fuse_elementwise_ops.py

@@ -51,6 +51,36 @@ def _extract_transpose_prefill_kernel(
     tl.store(dst_ptr + dst_offsets, tl.trans(data), mask=conv_mask[:, None] & seq_mask[None, :])
 
 
+def extract_transpose_prefill_slice(
+    src: torch.Tensor,
+    num_prefill_tokens: int,
+    start_col: int,
+    width: int,
+) -> torch.Tensor:
+    """
+    Extract and transpose a contiguous prefill slice for causal_conv1d_fn.
+
+    Input:  src[num_tokens, num_cols]
+    Output: [width, num_prefill_tokens]
+    """
+    out = torch.empty(width, num_prefill_tokens, dtype=src.dtype, device=src.device)
+
+    BLOCK_SEQ, BLOCK_CONV = 32, 128
+    grid = (triton.cdiv(num_prefill_tokens, BLOCK_SEQ), triton.cdiv(width, BLOCK_CONV))
+
+    _extract_transpose_prefill_kernel[grid](
+        src,
+        out,
+        num_prefill_tokens,
+        src.shape[1],
+        start_col,
+        width,
+        BLOCK_SEQ,
+        BLOCK_CONV,
+    )
+    return out
+
+
 def extract_transpose_xbc_prefill(
     zxbcdt: torch.Tensor,
     num_prefill_tokens: int,
@@ -63,22 +93,12 @@ def extract_transpose_xbc_prefill(
     Input:  zxbcdt[num_tokens, d_in_proj]
     Output: [conv_dim, num_prefill_tokens]
     """
-    out = torch.empty(conv_dim, num_prefill_tokens, dtype=zxbcdt.dtype, device=zxbcdt.device)
-
-    BLOCK_SEQ, BLOCK_CONV = 32, 128
-    grid = (triton.cdiv(num_prefill_tokens, BLOCK_SEQ), triton.cdiv(conv_dim, BLOCK_CONV))
-
-    _extract_transpose_prefill_kernel[grid](
+    return extract_transpose_prefill_slice(
         zxbcdt,
-        out,
         num_prefill_tokens,
-        zxbcdt.shape[1],
         d_inner,
         conv_dim,
-        BLOCK_SEQ,
-        BLOCK_CONV,
     )
-    return out
 
 
 @triton.jit

tensorrt_llm/_torch/modules/mamba/gdn_mixer.py

@@ -28,6 +28,7 @@
 from ..multi_stream_utils import maybe_execute_in_parallel
 from .causal_conv1d import causal_conv1d_fn, causal_conv1d_update
 from .causal_conv1d_triton import causal_conv1d_update as causal_conv1d_update_triton
+from .fuse_elementwise_ops import extract_transpose_prefill_slice
 from .layernorm_gated import RMSNorm as RMSNormGated
 from .mamba2_metadata import Mamba2Metadata
 
@@ -544,16 +545,22 @@ def forward_extend(
             query_start_loc_p = query_start_loc[: num_prefill + 1]
             has_initial_states_p = has_initial_states[:num_prefill]
 
-            mixed_qkv_p = causal_conv1d_fn(
-                mixed_qkv_p.transpose(0, 1),
+            mixed_qkv_p_t = extract_transpose_prefill_slice(
+                mixed_qkv_p,
+                mixed_qkv_p.shape[0],
+                0,
+                mixed_qkv_p.shape[1],
+            )
+            mixed_qkv_p_t = causal_conv1d_fn(
+                mixed_qkv_p_t,
                 self.conv1d.weight,
                 self.conv1d.bias,
                 activation=self.activation,
                 conv_states=conv_states_to_use,
                 has_initial_state=has_initial_states_p,
                 cache_indices=state_indices_p,
                 query_start_loc=query_start_loc_p,
-            ).transpose(0, 1)
+            )
 
             if is_target_verify:
                 draft_token_num = spec_metadata.max_draft_len + 1
@@ -588,18 +595,25 @@ def forward_extend(
                     activation=self.activation,
                     conv_state_indices=state_indices_d,
                 )
+            mixed_qkv_p.copy_(mixed_qkv_p_t.transpose(0, 1))
             mixed_qkv = torch.cat((mixed_qkv_p, mixed_qkv_d), dim=0)
         else:
+            mixed_qkv_t = extract_transpose_prefill_slice(
+                mixed_qkv,
+                mixed_qkv.shape[0],
+                0,
+                mixed_qkv.shape[1],
+            )
             mixed_qkv = causal_conv1d_fn(
-                mixed_qkv.transpose(0, 1),
+                mixed_qkv_t,
                 self.conv1d.weight,
                 self.conv1d.bias,
                 activation=self.activation,
                 conv_states=conv_states_to_use,
                 has_initial_state=has_initial_states,
                 cache_indices=cache_indices,
                 query_start_loc=query_start_loc,
-            ).transpose(0, 1)
+            )
 
         key_split_dim = self.key_dim // self.attn_tp_size
         value_split_dim = self.value_dim // self.attn_tp_size