Fix division-by-zero bug in LoRA triton kernels. (#7785)

python/sglang/srt/lora/triton_ops/gate_up_lora_b.py

@@ -31,28 +31,44 @@ def _gate_up_lora_b_kernel(
     BLOCK_S: tl.constexpr,
     BLOCK_N: tl.constexpr,
     BLOCK_K: tl.constexpr,
-    # For fused output scaling and adding
-    fuse_scaling_add,
+    # For fused output scaling
     scalings,
 ):
-    # This kernel packs 2 sgemms (gate/up) into a single kernel.
-
-    # x: (s, 2 * K), s is the sum of sequence lengths, K equals to lora rank
-    # weights: (num_lora, 2 * output_dim, K)
-    # output: (s, 2 * output_dim)
+    """
+    This kernel packs 2 sgemms (gate/up) into a single kernel. The multiplication
+    results are accumulated into the output tensor.
+
+    When a sequence's rank is 0, the kernel is essentially a no-op, following
+    the convention in pytorch where the product of two matrices of shape (m, 0)
+    and (0, n) is an all-zero matrix of shape (m, n).
+
+    Args:
+        x (Tensor): The input tensor, which is the result of the LoRA A projection.
+            Shape: (s, 2 * K), where s is the sum of all sequence lengths in the
+            batch and K is the maximum LoRA rank.
+        weights (Tensor): The LoRA B weights for all adapters.
+            Shape: (num_lora, 2 * output_dim, K).
+        output (Tensor): The output tensor where the result is stored.
+            Shape: (s, 2 * output_dim).
+    """
     # output_dim >> K
 
     # Current block computes sequence with batch_id,
     # which starts from row seg_start of x with length seg_len.
     # gate_up_id decides which of gate or up (0: gate, 1: up)
     batch_id = tl.program_id(axis=2)
+    w_index = tl.load(weight_indices + batch_id)
+    rank = tl.load(lora_ranks + w_index)
+
+    # If rank is 0, this kernel is a no-op.
+    if rank == 0:
+        return
+
     gate_up_id = tl.program_id(axis=1)
     pid = tl.program_id(axis=0)
     seg_len = tl.load(seg_lens + batch_id)
-    w_index = tl.load(weight_indices + batch_id)
     seg_start = tl.load(seg_indptr + batch_id)
     n_start = gate_up_id * output_dim  # offset on output dim
-    rank = tl.load(lora_ranks + w_index)
     scaling = tl.load(scalings + w_index)
 
     # Adjust K (rank) according to the specific LoRA adapter
@@ -82,14 +98,13 @@ def _gate_up_lora_b_kernel(
     for k in range(0, tl.cdiv(K, BLOCK_K)):
         x_tile = tl.load(
             x_ptrs,
-            mask=(s_offset[:, None] < seg_len)
-            and (k_offset[None, :] < K - k * BLOCK_K),
+            mask=(s_offset[:, None] < seg_len) & (k_offset[None, :] < K - k * BLOCK_K),
             other=0.0,
         )
         w_tile = tl.load(
             w_ptrs,
             mask=(k_offset[:, None] < K - k * BLOCK_K)
-            and (n_offset[None, :] < output_dim),
+            & (n_offset[None, :] < output_dim),
             other=0.0,
         )
         partial_sum += tl.dot(x_tile, w_tile)
@@ -103,9 +118,8 @@ def _gate_up_lora_b_kernel(
     output_ptr = (output + seg_start * output_stride_0 + n_start * output_stride_1) + (
         s_offset[:, None] * output_stride_0 + n_offset[None, :] * output_stride_1
     )
-    output_mask = (s_offset[:, None] < seg_len) and (n_offset[None, :] < output_dim)
-    if fuse_scaling_add:
-        partial_sum += tl.load(output_ptr, mask=output_mask)
+    output_mask = (s_offset[:, None] < seg_len) & (n_offset[None, :] < output_dim)
+    partial_sum += tl.load(output_ptr, mask=output_mask)
     tl.store(output_ptr, partial_sum, mask=output_mask)
 
 
@@ -143,11 +157,9 @@ def gate_up_lora_b_fwd(
     )
 
     if base_output is None:
-        output = torch.empty((s, 2 * output_dim), device=x.device, dtype=x.dtype)
-        fuse_scaling_add = False
+        output = torch.zeros((s, 2 * output_dim), device=x.device, dtype=x.dtype)
     else:
         output = base_output
-        fuse_scaling_add = True
 
     _gate_up_lora_b_kernel[grid_b](
         x,
@@ -169,7 +181,6 @@ def gate_up_lora_b_fwd(
         BLOCK_S,
         BLOCK_OUT,
         BLOCK_R,
-        fuse_scaling_add,
         batch_info.scalings,
     )
 

python/sglang/srt/lora/triton_ops/qkv_lora_b.py

@@ -33,29 +33,45 @@ def _qkv_lora_b_kernel(
     BLOCK_S: tl.constexpr,
     BLOCK_N: tl.constexpr,
     BLOCK_K: tl.constexpr,
-    # For fused output scaling and adding
-    fuse_scaling_add,
+    # For fused output scaling
     scalings,
 ):
-    # This kernel packs 3 sgemms (q/k/v) into a single kernel.
-
-    # x: (s, 3 * K), s is the sum of sequence lengths, K equals to lora rank
-    # weights: (num_lora, N_Q + 2 * N_KV, K)
-    # output: (s, N_Q + 2 * N_KV)
-    # N_Q >> K, N_KV >> K
+    """
+    This kernel packs 3 sgemms (q/k/v) into a single kernel. The multiplication
+    results are accumulated into the output tensor.
+
+    When a sequence's rank is 0, the kernel is essentially a no-op, following
+    the convention in pytorch where the product of two matrices of shape (m, 0)
+    and (0, n) is an all-zero matrix of shape (m, n).
+
+    Args:
+        x (Tensor): The input tensor, which is the result of the LoRA A projection.
+            Shape: (s, 3 * K), where s is the sum of all sequence lengths in the
+            batch and K is the maximum LoRA rank. The second dimension is partitioned
+            for Q, K, and V.
+        weights (Tensor): The LoRA B weights for all adapters.
+            Shape: (num_lora, N_Q + 2 * N_KV, K).
+        output (Tensor): The output tensor where the result is stored.
+            Shape: (s, N_Q + 2 * N_KV).
+    """
 
     # Current block computes sequence with batch_id,
     # which starts from row seg_start of x with length seg_len.
     # qkv_id decides which of q,k,v to compute (0: q, 1: k, 2: v)
     batch_id = tl.program_id(axis=2)
+    w_index = tl.load(weight_indices + batch_id)
+    rank = tl.load(lora_ranks + w_index)
+
+    # If rank is 0, this kernel is a no-op.
+    if rank == 0:
+        return
+
     qkv_id = tl.program_id(axis=1)
     pid = tl.program_id(axis=0)
     seg_len = tl.load(seg_lens + batch_id)
-    w_index = tl.load(weight_indices + batch_id)
     seg_start = tl.load(seg_indptr + batch_id)
     n_start = tl.load(n_offs + qkv_id)
     n_size = tl.load(n_offs + qkv_id + 1) - n_start
-    rank = tl.load(lora_ranks + w_index)
     scaling = tl.load(scalings + w_index)
     # Adjust K (rank) according to the specific LoRA adapter
     K = tl.minimum(K, rank)
@@ -84,13 +100,12 @@ def _qkv_lora_b_kernel(
     for k in range(0, tl.cdiv(K, BLOCK_K)):
         x_tile = tl.load(
             x_ptrs,
-            mask=(s_offset[:, None] < seg_len)
-            and (k_offset[None, :] < K - k * BLOCK_K),
+            mask=(s_offset[:, None] < seg_len) & (k_offset[None, :] < K - k * BLOCK_K),
             other=0.0,
         )
         w_tile = tl.load(
             w_ptrs,
-            mask=(k_offset[:, None] < K - k * BLOCK_K) and (n_offset[None, :] < n_size),
+            mask=(k_offset[:, None] < K - k * BLOCK_K) & (n_offset[None, :] < n_size),
             other=0.0,
         )
         partial_sum += tl.dot(x_tile, w_tile)
@@ -105,8 +120,7 @@ def _qkv_lora_b_kernel(
         s_offset[:, None] * output_stride_0 + n_offset[None, :] * output_stride_1
     )
     output_mask = (s_offset[:, None] < seg_len) and (n_offset[None, :] < n_size)
-    if fuse_scaling_add:
-        partial_sum += tl.load(output_ptr, mask=output_mask)
+    partial_sum += tl.load(output_ptr, mask=output_mask)
     tl.store(output_ptr, partial_sum, mask=output_mask)
 
 
@@ -153,11 +167,9 @@ def qkv_lora_b_fwd(
     )
 
     if base_output is None:
-        output = torch.empty((s, output_dim), device=x.device, dtype=x.dtype)
-        fuse_scaling_add = False
+        output = torch.zeros((s, output_dim), device=x.device, dtype=x.dtype)
     else:
         output = base_output
-        fuse_scaling_add = True
 
     _qkv_lora_b_kernel[grid_b](
         x,
@@ -180,7 +192,6 @@ def qkv_lora_b_fwd(
         BLOCK_S,
         BLOCK_OUT,
         BLOCK_R,
-        fuse_scaling_add,
         batch_info.scalings,
     )
 

python/sglang/srt/lora/triton_ops/sgemm_lora_a.py

@@ -33,19 +33,36 @@ def _sgemm_lora_a_kernel(
     BLOCK_N: tl.constexpr,
     BLOCK_K: tl.constexpr,
 ):
-
-    # x: (s, K), s is the sum of sequence lengths
-    # weights: (num_lora, N, K)
-    # output: (s, N)
+    """
+    Computes a segmented batched matrix multiplication for the LoRA A matrix.
+
+    The kernel ensures that output[seg_start:seg_start + seg_len, :rank * stack_num]
+    stores the product of the input `x` and the LoRA weights for the corresponding
+    sequence. This implies that when rank is 0, the kernel is essentially a no-op,
+    as output[seg_start:seg_start + seg_len, :0] is trivially correct (empty).
+
+    Args:
+        x (torch.Tensor): The input activations tensor of shape `(s, K)`, where `s`
+            is the sum of all sequence lengths in the batch.
+        weights (torch.Tensor): The LoRA 'A' weights for all available adapters,
+            with shape `(num_lora, N, K)`.
+        output (torch.Tensor): The output tensor of shape `(s, N)`.
+    """
 
     # Current block computes sequence with batch_id,
     # which starts from row seg_start of x with length seg_len
     batch_id = tl.program_id(axis=1)
-    pid = tl.program_id(axis=0)
-    seg_len = tl.load(seg_lens + batch_id)
     w_index = tl.load(weight_indices + batch_id)
-    seg_start = tl.load(seg_indptr + batch_id)
     rank = tl.load(lora_ranks + w_index)
+
+    # If rank is 0, this kernel becomes a no-op as the output is always trivially correct.
+    if rank == 0:
+        return
+
+    pid = tl.program_id(axis=0)
+    seg_start = tl.load(seg_indptr + batch_id)
+    seg_len = tl.load(seg_lens + batch_id)
+
     # Adjust N (stack_num * max_rank) according to the specific LoRA adapter
     N = tl.minimum(N, rank * stack_num)
 
@@ -72,13 +89,12 @@ def _sgemm_lora_a_kernel(
     for k in range(0, tl.cdiv(K, BLOCK_K)):
         x_tile = tl.load(
             x_ptrs,
-            mask=(s_offset[:, None] < seg_len)
-            and (k_offset[None, :] < K - k * BLOCK_K),
+            mask=(s_offset[:, None] < seg_len) & (k_offset[None, :] < K - k * BLOCK_K),
             other=0.0,
         )
         w_tile = tl.load(
             w_ptrs,
-            mask=(k_offset[:, None] < K - k * BLOCK_K) and (n_offset[None, :] < N),
+            mask=(k_offset[:, None] < K - k * BLOCK_K) & (n_offset[None, :] < N),
             other=0.0,
         )
         partial_sum += tl.dot(x_tile, w_tile)
@@ -91,7 +107,7 @@ def _sgemm_lora_a_kernel(
     output_ptr = (output + seg_start * output_stride_0) + (
         s_offset[:, None] * output_stride_0 + n_offset[None, :] * output_stride_1
     )
-    output_mask = (s_offset[:, None] < seg_len) and (n_offset[None, :] < N)
+    output_mask = (s_offset[:, None] < seg_len) & (n_offset[None, :] < N)
     tl.store(output_ptr, partial_sum, mask=output_mask)
 
 

python/sglang/srt/lora/triton_ops/sgemm_lora_b.py

@@ -31,22 +31,39 @@ def _sgemm_lora_b_kernel(
     BLOCK_S: tl.constexpr,
     BLOCK_N: tl.constexpr,
     BLOCK_K: tl.constexpr,
-    # For fused output scaling and adding
-    fuse_scaling_add,
+    # For fused output scaling
     scalings,
 ):
-    # x: (s, K), s is the sum of sequence lengths
-    # weights: (num_lora, N, K)
-    # output: (s, N)
+    """
+    Computes a segmented batched matrix multiplication for the LoRA B matrix
+    and adds the result to the output in-place.
+
+    When a sequence's rank is 0, the kernel is essentially a no-op, following
+    the convention in pytorch where the product of two matrices of shape (m, 0)
+    and (0, n) is an all-zero matrix of shape (m, n).
+
+    Args:
+        x (torch.Tensor): The intermediate tensor from the LoRA 'A' multiplication,
+            of shape `(s, K)`, where `s` is the total number of tokens.
+        weights (torch.Tensor): The LoRA 'B' weights for all available adapters,
+            with shape `(num_lora, N, K)`.
+        output (torch.Tensor): The output tensor of shape `(s, N)`. This can be
+            the base model's output for a fused add operation.
+    """
 
     # Current block computes sequence with batch_id,
     # which starts from row seg_start of x with length seg_len
     batch_id = tl.program_id(axis=1)
+    w_index = tl.load(weight_indices + batch_id)
+    rank = tl.load(lora_ranks + w_index)
+
+    # If rank is 0, this kernel is a no-op.
+    if rank == 0:
+        return
+
     pid = tl.program_id(axis=0)
     seg_len = tl.load(seg_lens + batch_id)
-    w_index = tl.load(weight_indices + batch_id)
     seg_start = tl.load(seg_indptr + batch_id)
-    rank = tl.load(lora_ranks + w_index)
     scaling = tl.load(scalings + w_index)
     # Adjust K (rank) according to the specific LoRA adapter
     K = tl.minimum(K, rank)
@@ -74,8 +91,7 @@ def _sgemm_lora_b_kernel(
     for k in range(0, tl.cdiv(K, BLOCK_K)):
         x_tile = tl.load(
             x_ptrs,
-            mask=(s_offset[:, None] < seg_len)
-            and (k_offset[None, :] < K - k * BLOCK_K),
+            mask=(s_offset[:, None] < seg_len) & (k_offset[None, :] < K - k * BLOCK_K),
             other=0.0,
         )
         w_tile = tl.load(
@@ -95,8 +111,7 @@ def _sgemm_lora_b_kernel(
         s_offset[:, None] * output_stride_0 + n_offset[None, :] * output_stride_1
     )
     output_mask = s_offset[:, None] < seg_len
-    if fuse_scaling_add:
-        partial_sum += tl.load(output_ptr, mask=output_mask)
+    partial_sum += tl.load(output_ptr, mask=output_mask)
     tl.store(output_ptr, partial_sum, mask=output_mask)
 
 
@@ -132,11 +147,9 @@ def sgemm_lora_b_fwd(
     )
 
     if base_output is None:
-        output = torch.empty((S, N), device=x.device, dtype=x.dtype)
-        fuse_scaling_add = False
+        output = torch.zeros((S, N), device=x.device, dtype=x.dtype)
     else:
         output = base_output
-        fuse_scaling_add = True
 
     _sgemm_lora_b_kernel[grid](
         x,
@@ -158,7 +171,6 @@ def sgemm_lora_b_fwd(
         BLOCK_S,
         BLOCK_N,
         BLOCK_R,
-        fuse_scaling_add,
         batch_info.scalings,
     )
     return output