[Performance, Triton] Optimize over mask compute to tl.load in fused_moe_kernel (#1980)

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

@@ -507,6 +507,12 @@ def _decode_grouped_att_m_fwd(
 
     num_warps = 4
 
+    extra_kargs = {}
+    if is_hip():
+        # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
+        # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
+        extra_kargs = {"waves_per_eu": 4, "matrix_instr_nonkdim": 16, "kpack": 2}
+
     _fwd_grouped_kernel_stage1[grid](
         q,
         k_buffer,
@@ -532,6 +538,7 @@ def _decode_grouped_att_m_fwd(
         num_warps=num_warps,
         num_stages=1,
         Lk=Lk,
+        **extra_kargs,
     )
 
 

python/sglang/srt/layers/fused_moe/fused_moe.py

@@ -54,6 +54,7 @@ def fused_moe_kernel(
     top_k: tl.constexpr,
     compute_type: tl.constexpr,
     use_fp8: tl.constexpr,
+    even_Ks: tl.constexpr,
 ):
     """
     Implements the fused computation for a Mixture of Experts (MOE) using
@@ -130,16 +131,24 @@ def fused_moe_kernel(
     # of fp32 values for higher accuracy.
     # `accumulator` will be converted back to fp16 after the loop.
     accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-
     for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
         # Load the next block of A and B, generate a mask by checking the
         # K dimension.
-        a = tl.load(
-            a_ptrs,
-            mask=token_mask[:, None] & (offs_k[None, :] < K - k * BLOCK_SIZE_K),
-            other=0.0,
-        )
-        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
+        if even_Ks:
+            a = tl.load(
+                a_ptrs,
+                mask=token_mask[:, None],
+                other=0.0,
+            )
+            b = tl.load(b_ptrs)
+        else:
+            a = tl.load(
+                a_ptrs,
+                mask=token_mask[:, None] & (offs_k[None, :] < K - k * BLOCK_SIZE_K),
+                other=0.0,
+            )
+            b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
+
         # We accumulate along the K dimension.
         if use_fp8:
             accumulator = tl.dot(a, b, acc=accumulator)
@@ -253,6 +262,12 @@ def invoke_fused_moe_kernel(
         * triton.cdiv(B.shape[1], META["BLOCK_SIZE_N"]),
     )
 
+    K = B.shape[2] - padding_size
+    if K % config["BLOCK_SIZE_K"] == 0:
+        even_ks = True
+    else:
+        even_ks = False
+
     fused_moe_kernel[grid](
         A,
         B,
@@ -278,6 +293,7 @@ def invoke_fused_moe_kernel(
         top_k=top_k,
         compute_type=compute_type,
         use_fp8=use_fp8,
+        even_Ks=even_ks,
         **config,
     )