[perf] introduce deep gemm group_gemm_masked as bmm (#5432)

python/sglang/test/test_block_fp8.py

@@ -7,6 +7,7 @@ import torch
 from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_moe
 from sglang.srt.layers.quantization.fp8_kernel import (
+    per_tensor_quant_mla_deep_gemm_masked_fp8,
     per_tensor_quant_mla_fp8,
     per_token_group_quant_fp8,
     static_quant_fp8,
@@ -212,6 +213,62 @@ class TestPerTensorQuantMlaFP8(CustomTestCase):
                 self._per_tensor_quant_mla_fp8(*params)
 
 
+class TestPerTokenGroupQuantMlaDeepGemmMaskedFP8(CustomTestCase):
+    DTYPES = [torch.half, torch.bfloat16, torch.float32]
+    B = [128]
+    NUM_TOKENS = [7, 83, 2048, 1024 * 16]
+    D = [512, 128]
+    GROUP_SIZE = [128]
+    SEEDS = [0]
+
+    @classmethod
+    def setUpClass(cls):
+        if not torch.cuda.is_available():
+            raise unittest.SkipTest("CUDA is not available")
+        torch.set_default_device("cuda")
+
+    def _per_token_group_quant_mla_deep_gemm_masked_fp8(
+        self, b, num_tokens, d, dtype, group_size, seed
+    ):
+        torch.manual_seed(seed)
+
+        x = torch.rand(b, num_tokens, d, dtype=dtype)
+
+        with torch.inference_mode():
+            ref_out, ref_scale = native_per_token_group_quant_fp8(x, group_size, 1e-12)
+            out, scale, _, _, _ = per_tensor_quant_mla_deep_gemm_masked_fp8(
+                x, group_size
+            )
+            out = out[:, :num_tokens, :]
+            scale = scale[:, :num_tokens, :]
+
+        self.assertTrue(
+            torch.allclose(
+                out.to(torch.float32), ref_out.to(torch.float32), rtol=0.20, atol=1e-2
+            )
+        )
+        self.assertTrue(torch.allclose(scale, ref_scale))
+
+    def test_per_token_group_quant_mla_deep_gemm_masked_fp8(self):
+        for params in itertools.product(
+            self.B,
+            self.NUM_TOKENS,
+            self.D,
+            self.DTYPES,
+            self.GROUP_SIZE,
+            self.SEEDS,
+        ):
+            with self.subTest(
+                b=params[0],
+                num_tokens=params[1],
+                d=params[2],
+                dtype=params[3],
+                group_size=params[4],
+                seed=params[5],
+            ):
+                self._per_token_group_quant_mla_deep_gemm_masked_fp8(*params)
+
+
 # For test
 def native_w8a8_block_fp8_matmul(A, B, As, Bs, block_size, output_dtype=torch.float16):
     """This function performs matrix multiplication with block-wise quantization using native torch.
@@ -485,5 +542,115 @@ class TestW8A8BlockFP8FusedMoE(CustomTestCase):
                 self._w8a8_block_fp8_fused_moe(*params)
 
 
+# For test
+def torch_w8a8_block_fp8_bmm(a, a_s, w, w_s, block_shape, out_dtype):
+    """This function performs bmm with block-wise quantization using native torch."""
+
+    B, N, _ = w.shape
+    _, M, _ = a.shape
+    out = torch.empty((B, M, N), dtype=out_dtype, device=a.device)
+
+    for i in range(B):
+        out[i] = native_w8a8_block_fp8_matmul(
+            a[i], w[i], a_s[i], w_s[i], block_shape, output_dtype=out_dtype
+        )
+
+    return out
+
+
+class TestW8A8BlockFP8BatchedDeepGemm(CustomTestCase):
+    DTYPES = [torch.bfloat16]
+    M = [1, 33, 64, 222, 8192]
+    N = [128, 512]
+    K = [128, 512]
+    BATCH = [128]
+    BLOCK_SIZE = [[128, 128]]
+    SEEDS = [0]
+
+    @classmethod
+    def setUpClass(cls):
+        if not torch.cuda.is_available():
+            raise unittest.SkipTest("CUDA is not available")
+        try:
+            import deep_gemm
+        except ImportError:
+            raise unittest.SkipTest("DeepGEMM is not available")
+        torch.set_default_device("cuda")
+
+    def _w8a8_block_fp8_batched_deep_gemm(self, M, N, K, B, block_size, dtype, seed):
+        torch.manual_seed(seed)
+        factor_for_scale = 1e-2
+        fp8_info = torch.finfo(torch.float8_e4m3fn)
+        fp8_max, fp8_min = fp8_info.max, fp8_info.min
+
+        a_fp32 = torch.randn((B, M, K), dtype=torch.float32) / 10
+        a = a_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+        w_fp32 = (torch.rand((B, N, K), dtype=torch.float32) - 0.5) * 2 * fp8_max
+        w = w_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+        block_n, block_k = block_size[0], block_size[1]
+        n_tiles_w = (N + block_n - 1) // block_n
+        k_tiles_w = (K + block_k - 1) // block_k
+
+        w_s = (
+            torch.rand((B, n_tiles_w, k_tiles_w), dtype=torch.float32)
+            * factor_for_scale
+        )
+        a_s = torch.rand((B, M, k_tiles_w), dtype=torch.float32) * factor_for_scale
+
+        ae = a.new_empty(B, (M + 255) // 256 * 256, K)
+        ae_s = a_s.new_empty(B, (M + 255) // 256 * 256, k_tiles_w)
+        oe = torch.empty((B, (M + 255) // 256 * 256, N), dtype=dtype)
+        ae[:, :M, :] = a
+        ae_s[:, :M, :] = a_s
+
+        masked_m = torch.full((B,), M, dtype=torch.int)
+        expected_m = M
+        lhs = (
+            ae,
+            ae_s,
+        )
+        rhs = (
+            w,
+            w_s,
+        )
+
+        from deep_gemm import m_grouped_gemm_fp8_fp8_bf16_nt_masked
+
+        with torch.inference_mode():
+            ref_out = torch_w8a8_block_fp8_bmm(a, a_s, w, w_s, block_size, dtype)
+            m_grouped_gemm_fp8_fp8_bf16_nt_masked(lhs, rhs, oe, masked_m, expected_m)
+            out = oe[:, :M, :]
+
+        self.assertTrue(
+            torch.mean(torch.abs(out.to(torch.float32) - ref_out.to(torch.float32)))
+            / torch.mean(torch.abs(ref_out.to(torch.float32)))
+            < 0.0001
+        )
+
+    def test_w8a8_block_fp8_batched_deep_gemm(self):
+
+        for params in itertools.product(
+            self.M,
+            self.N,
+            self.K,
+            self.BATCH,
+            self.BLOCK_SIZE,
+            self.DTYPES,
+            self.SEEDS,
+        ):
+            with self.subTest(
+                M=params[0],
+                N=params[1],
+                K=params[2],
+                B=params[3],
+                block_size=params[4],
+                dtype=params[5],
+                seed=params[6],
+            ):
+                self._w8a8_block_fp8_batched_deep_gemm(*params)
+
+
 if __name__ == "__main__":
     unittest.main(verbosity=2)