[BugFix] Fix precision issue in GQA decode when block_N exceeds seqlen/num_split (#575)

* [CI] Add flash_decoding example to CI

* Add output of ref latency

* format example_gqa_decode.py

* [BugFix] Fix precision issue in GQA decode when block_N exceeds seqlen/num_split

* format example_gqa_decode.py

examples/flash_decoding/example_gqa_decode.py

@@ -41,6 +41,7 @@ def flashattn(batch, heads, groups, seqlen_kv, dim, tune=False):
     def kernel_func(block_N, block_H, num_split, num_stages, threads):
         part_shape = [batch, heads, num_split, dim]
         valid_block_H = min(block_H, kv_group_num)
+        valid_block_N = min(block_N, seqlen_kv // num_split)
 
         @T.macro
         def flash_attn(
@@ -147,15 +148,16 @@ def flashattn(batch, heads, groups, seqlen_kv, dim, tune=False):
                 T.fill(scores_max, -T.infinity(accum_dtype))
 
                 loop_range = T.ceildiv((seqlen_kv // num_split), block_N)
+                T.fill(K_shared, 0)
                 for k in T.Pipelined(loop_range, num_stages=num_stages):
                     T.copy(
                         K[bid, (seqlen_kv // num_split) * sid +
-                          k * block_N:(seqlen_kv // num_split) * sid + (k + 1) * block_N,
-                          cur_kv_head, :], K_shared)
+                          k * valid_block_N:(seqlen_kv // num_split) * sid +
+                          (k + 1) * valid_block_N, cur_kv_head, :], K_shared)
                     T.copy(
                         mask[bid, (seqlen_kv // num_split) * sid +
-                             k * block_N:(seqlen_kv // num_split) * sid + (k + 1) * block_N,
-                             cur_kv_head], mask_local)
+                             k * valid_block_N:(seqlen_kv // num_split) * sid +
+                             (k + 1) * valid_block_N, cur_kv_head], mask_local)
                     T.clear(acc_s)
                     T.gemm(
                         Q_shared,
@@ -164,8 +166,9 @@ def flashattn(batch, heads, groups, seqlen_kv, dim, tune=False):
                         transpose_B=True,
                         policy=T.GemmWarpPolicy.FullRow)
                     for i, j in T.Parallel(block_H, block_N):
-                        acc_s[i, j] = T.if_then_else(mask_local[j] != 0, acc_s[i, j],
-                                                     -T.infinity(accum_dtype))
+                        acc_s[i, j] = T.if_then_else(
+                            (mask_local[j] != 0) & (j < seqlen_kv // num_split), acc_s[i, j],
+                            -T.infinity(accum_dtype))
                     T.copy(scores_max, scores_max_prev)
                     T.fill(scores_max, -T.infinity(accum_dtype))
                     T.reduce_max(acc_s, scores_max, dim=1, clear=False)
@@ -181,16 +184,17 @@ def flashattn(batch, heads, groups, seqlen_kv, dim, tune=False):
                         acc_o[i, j] *= scores_scale[i]
                     T.copy(
                         V[bid, (seqlen_kv // num_split) * sid +
-                          k * block_N:(seqlen_kv // num_split) * sid + (k + 1) * block_N,
-                          cur_kv_head, :], V_shared)
+                          k * valid_block_N:(seqlen_kv // num_split) * sid +
+                          (k + 1) * valid_block_N, cur_kv_head, :], V_shared)
                     T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
                 for i, j in T.Parallel(block_H, dim):
                     acc_o[i, j] /= logsum[i]
                 for i in T.Parallel(block_H):
                     logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale
 
-                T.copy(logsum[:valid_block_H],
-                       glse[bid, hid * valid_block_H:(hid + 1) * valid_block_H, sid])
+                for i in T.Parallel(block_H):
+                    if i < valid_block_H:
+                        glse[bid, hid * valid_block_H + i, sid] = logsum[i]
                 T.copy(acc_o[:valid_block_H, :], O_shared)
                 T.copy(O_shared, Output_partial[bid, hid * valid_block_H:(hid + 1) * valid_block_H,
                                                 sid, :])
@@ -327,7 +331,7 @@ def ref_program(query, key, value, mask, glse, Output_partial):
 
 
 def flash_split_ref(Q, K, V, mask):
-    num_split = 8
+    num_split = 16
     batch = Q.size(0)
     nheads = Q.size(1)
     groups = K.size(2)
@@ -395,7 +399,7 @@ def flash_split_ref(Q, K, V, mask):
 
 
 def reduce_ref(Q, K, V, mask, glse, Output_partial):
-    num_split = 8
+    num_split = 16
     o = torch.empty_like(Output_partial[:, :, 0, :]).fill_(0)
     lse_logsum = torch.empty_like(glse[:, :, 0]).fill_(0)  # [batch, heads]
     lse_max = glse.max(dim=2, keepdim=False).values
@@ -410,6 +414,37 @@ def reduce_ref(Q, K, V, mask, glse, Output_partial):
     return o.to(torch.float16)
 
 
+def ref_split_program(Q, K, V, mask, glse=None, Output_partial=None):
+    glse_, Output_partial_ = flash_split_ref(Q, K, V, mask)
+    return reduce_ref(Q, K, V, mask, glse_, Output_partial_)
+
+
+def print_red_warning(msg):
+    print(f"\033[91m{msg}\033[0m")
+
+
+def calc_sim(x, y, name="tensor"):
+    x, y = x.data.double(), y.data.double()
+    denominator = (x * x + y * y).sum()
+    if denominator == 0:
+        print_red_warning(f'{name} all zero')
+        return 1
+    sim = 2 * (x * y).sum() / denominator
+    return sim
+
+
+def assert_similar(x, y, eps=1e-2, name="tensor", assert_=False, print_=True):
+    sim = calc_sim(x, y, name)
+    diff = 1. - sim
+    if not (0 <= diff <= eps):
+        print_red_warning(f'{name} Error: {diff}')
+        if assert_:
+            raise AssertionError(f'{name} Error: {diff}')
+    else:
+        if print_:
+            print(f'passed: {name} diff={diff}')
+
+
 def main(batch: int = 1,
          heads: int = 32,
          groups: int = 8,
@@ -435,24 +470,45 @@ def main(batch: int = 1,
                 return {
                     "block_N": 128,
                     "block_H": 64,
-                    "num_split": 8,
+                    "num_split": 16,
                     "num_stages": 0,
                     "threads": 128
-                }
+                }, sm_version
             else:
                 return {
                     "block_N": 128,
                     "block_H": 64,
-                    "num_split": 8,
+                    "num_split": 16,
                     "num_stages": 2,
                     "threads": 128
-                }
+                }, sm_version
 
-        config = get_heuristic_config()
+        config, sm_version = get_heuristic_config()
         program = flashattn(batch, heads, groups, kv_seqlen, dim, tune=tune)(**config)
-        kernel = tilelang.compile(program, out_idx=[6])
+        if sm_version == 90:
+            kernel = tilelang.compile(
+                program, out_idx=[6], pass_configs={"tl.disable_tma_lower": True})
+        else:
+            kernel = tilelang.compile(program, out_idx=[6])
         profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto)
+
+        q = torch.randn(batch, heads, dim, device="cuda", dtype=torch.float16)
+        k = torch.randn(batch, kv_seqlen, groups, dim, device="cuda", dtype=torch.float16)
+        v = torch.randn(batch, kv_seqlen, groups, dim, device="cuda", dtype=torch.float16)
+        mask = torch.randint(0, 2, (batch, kv_seqlen, groups), device="cuda", dtype=torch.uint8)
+        glse = torch.empty(batch, heads, 16, device="cuda", dtype=torch.float16)
+        Output_partial = torch.empty(batch, heads, 16, dim, device="cuda", dtype=torch.float16)
+        o = kernel(q, k, v, mask, glse, Output_partial)
+        o_ref = ref_program(q, k, v, mask, glse, Output_partial)
+        o_ref_split = ref_split_program(q, k, v, mask, glse, Output_partial)
+
+        assert_similar(o, o_ref)
+        assert_similar(o_ref_split, o_ref)
+        torch.testing.assert_close(o, o_ref, rtol=0.01, atol=0.01)
+        torch.testing.assert_close(o_ref_split, o_ref, rtol=0.01, atol=0.01)
+
         profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01)
+        profiler.assert_allclose(ref_split_program, rtol=0.01, atol=0.01)
         print("All checks pass.")
         latency = profiler.do_bench(ref_program, warmup=500)
         print("Ref: {:.2f} ms".format(latency))