Add big tensor tests for fused kernels

tests/ops/test_fused_big_tensor.py

@@ -0,0 +1,80 @@
+import paddle
+import paddle.incubate.nn.functional as F
+import FusedQuantOps as FQO
+import numpy as np
+paddle.seed(0)
+
+
+def test_fused_spaq(height, width):
+    print(f'test_fused_spaq', (height, width))
+
+    x = paddle.randn([height, width], dtype='bfloat16').clip_(min=-50, max=50)
+    prob = paddle.randn([height, 1]).astype("float32")
+
+    out, scale = FQO.fused_spaq(x, prob, using_pow2_scaling=False)
+    paddle.base.core.eager._for_test_check_cuda_error()
+
+    out_golden = F.swiglu(x) * prob
+    out_dequant = (
+        out.astype('float32') *
+        scale.repeat_interleave(128, axis=1)[:, :out.shape[-1]]
+    )
+    paddle.base.core.eager._for_test_check_cuda_error()
+
+    np.testing.assert_allclose(out_dequant, out_golden, atol=1, rtol=1e-2)
+
+
+def test_fused_act_dequant(height, width):
+    print(f"test_fused_act_dequant height:{height}, width:{width}")
+
+    x_fp8 = paddle.ones([height, width], dtype="float8_e4m3fn")
+    scale = paddle.randn([height, width // 128], dtype="float32")
+
+    x_dequant = FQO.fused_act_dequant(x_fp8, scale)
+    paddle.base.core.eager._for_test_check_cuda_error()
+
+    x_gold = scale.repeat_interleave(128, axis=1)
+    x_dequant = x_dequant.astype('float32')
+    np.testing.assert_allclose(x_gold, x_dequant, atol=1e-2, rtol=1e-2)
+
+
+def test_fused_swiglu_probs_bwd(topk, seq_len, moe_intermediate_size):
+    print(f'test_fused_swiglu_probs_bwd topk:{topk} seq_len:{seq_len} moe_intermediate_size:{moe_intermediate_size}')
+
+    o1 = paddle.rand([topk, seq_len, moe_intermediate_size * 2], dtype="bfloat16")
+    unzipped_probs = paddle.rand([topk, seq_len, 1], dtype="float32")
+    do2_s = paddle.rand([topk, seq_len , moe_intermediate_size], dtype="bfloat16")
+
+    do1, pg, o2_s = FQO.fused_swiglu_probs_bwd(o1, do2_s, unzipped_probs)
+    paddle.base.core.eager._for_test_check_cuda_error()
+
+    def fn_gold():
+        o2 = F.swiglu(o1)
+        o2_s = (o2 * unzipped_probs)
+        do2 = (do2_s.cast(paddle.float32) * unzipped_probs)
+        do2 = do2.cast(paddle.bfloat16)
+        do1, _ = paddle._C_ops.swiglu_grad(o1, None, do2)
+        probs_grad = (do2_s.cast(paddle.float32) * (o2.cast(paddle.float32))).sum(axis=-1)
+        return do1, probs_grad, o2_s
+
+    do1_gold, pg_gold, o2_s_gold = fn_gold()
+    paddle.base.core.eager._for_test_check_cuda_error()
+
+    np.testing.assert_allclose(do1.astype('float32'), do1_gold.astype('float32'), atol=1e-2, rtol=1e-2)
+    np.testing.assert_allclose(pg, pg_gold.flatten(), atol=1e-2, rtol=1e-3)
+    np.testing.assert_allclose(o2_s.astype('float32'), o2_s_gold, atol=1e-2, rtol=1e-2)
+
+
+if __name__ == '__main__':
+    for height in [8192, 16384, 32768, 128000, 510336]:
+        for width in [4096, 7168]:
+            test_fused_spaq(width, height)
+
+    for height in [4096, 16384, 32768, 128000, 510336]:
+        for width in [4096, 7168]:
+            test_fused_act_dequant(height, width)
+
+    for topk in [8]:
+        for seq_len in [4096, 7168]:
+            for moe_intermediate_size in [2048, 20480, 40960]:
+                test_fused_swiglu_probs_bwd(topk, seq_len, moe_intermediate_size)