[fix] fix DeepGEMM blackwell input quant & ut & fix style and log (#7247)

python/sglang/test/test_block_fp8.py

@@ -343,6 +343,7 @@ class TestW8A8BlockFP8Matmul(CustomTestCase):
         OUT_DTYPES = [torch.bfloat16]
         M = [64, 128, 512, 1024, 4096]
         NKs = [
+            (2112, 7168),
             (1536, 7168),
             (3072, 1536),
             (24576, 7168),

python/sglang/test/test_block_fp8_deep_gemm_blackwell.py

@@ -0,0 +1,252 @@
+import itertools
+import os
+import unittest
+from typing import List, Tuple
+
+import torch
+from deep_gemm import fp8_gemm_nt
+
+from sglang.test.test_utils import CustomTestCase
+
+_is_cuda = torch.cuda.is_available() and torch.version.cuda
+
+
+# Modify form DeepGEMM Blackwell
+def ceil_div(x: int, y: int) -> int:
+    return (x + y - 1) // y
+
+
+def align(x: int, y: int) -> int:
+    return ceil_div(x, y) * y
+
+
+def per_token_group_quant_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2 and x.size(1) % 128 == 0
+    m, n = x.shape
+    x_view = x.view(m, -1, 128)
+    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
+    sf = x_amax / 448.0
+    return (x_view * (1.0 / sf.unsqueeze(2))).to(torch.float8_e4m3fn).view(m, n), sf
+
+
+def per_block_quant_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2
+    m, n = x.shape
+    x_padded = torch.zeros(
+        (align(m, 128), align(n, 128)), dtype=x.dtype, device=x.device
+    )
+    x_padded[:m, :n] = x
+    x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
+    x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
+    sf = x_amax / 448.0
+    x_scaled = (x_view * (1.0 / sf)).to(torch.float8_e4m3fn)
+    return x_scaled.view_as(x_padded)[:m, :n].contiguous(), sf.view(
+        x_view.size(0), x_view.size(2)
+    )
+
+
+def ceil_to_ue8m0(x: torch.Tensor):
+    assert x.view(-1).amax().item() > 0
+    return torch.pow(2.0, torch.ceil(torch.log2(x.abs())))
+
+
+def per_token_group_quant_mxfp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2 and x.size(1) % 128 == 0
+    m, n = x.shape
+    x_view = x.view(m, -1, 128)
+    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
+    sf = ceil_to_ue8m0(x_amax / 448.0)
+    return (x_view * (1.0 / sf.unsqueeze(2))).to(torch.float8_e4m3fn).view(m, n), sf
+
+
+def per_block_quant_mxfp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2
+    m, n = x.shape
+    x_padded = torch.zeros(
+        (align(m, 128), align(n, 128)), dtype=x.dtype, device=x.device
+    )
+    x_padded[:m, :n] = x
+    x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
+    x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
+    sf = ceil_to_ue8m0(x_amax / 448.0)
+    x_scaled = (x_view * (1.0 / sf)).to(torch.float8_e4m3fn)
+    return x_scaled.view_as(x_padded)[:m, :n].contiguous(), sf.view(
+        x_view.size(0), x_view.size(2)
+    )
+
+
+# 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.
+
+    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
+    The output is returned in the specified `output_dtype`.
+    """
+
+    A = A.to(torch.float32)
+    B = B.to(torch.float32)
+    assert A.shape[-1] == B.shape[-1]
+    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
+    assert len(block_size) == 2
+    block_n, block_k = block_size[0], block_size[1]
+    assert (A.shape[-1] + block_k - 1) // block_k == As.shape[-1]
+    assert A.shape[:-1] == As.shape[:-1]
+
+    M = A.numel() // A.shape[-1]
+    N, K = B.shape
+    origin_C_shape = A.shape[:-1] + (N,)
+    A = A.reshape(M, A.shape[-1])
+    As = As.reshape(M, As.shape[-1])
+    n_tiles = (N + block_n - 1) // block_n
+    k_tiles = (K + block_k - 1) // block_k
+    assert n_tiles == Bs.shape[0]
+    assert k_tiles == Bs.shape[1]
+
+    C_shape = (M, N)
+    C = torch.zeros(C_shape, dtype=torch.float32, device=A.device)
+
+    A_tiles = [A[:, i * block_k : min((i + 1) * block_k, K)] for i in range(k_tiles)]
+    B_tiles = [
+        [
+            B[
+                j * block_n : min((j + 1) * block_n, N),
+                i * block_k : min((i + 1) * block_k, K),
+            ]
+            for i in range(k_tiles)
+        ]
+        for j in range(n_tiles)
+    ]
+    C_tiles = [C[:, j * block_n : min((j + 1) * block_n, N)] for j in range(n_tiles)]
+    As_tiles = [As[:, i : i + 1] for i in range(k_tiles)]
+
+    for i in range(k_tiles):
+        for j in range(n_tiles):
+            a = A_tiles[i]
+            b = B_tiles[j][i]
+            c = C_tiles[j]
+            s = As_tiles[i] * Bs[j][i]
+            c[:, :] += torch.matmul(a, b.t()) * s
+
+    C = C.reshape(origin_C_shape).to(output_dtype)
+    return C
+
+
+def block_quant_dequant(
+    x_q_block: torch.Tensor,
+    x_s: torch.Tensor,
+    block_size: List[int],
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """This function converts block-wise quantization to unquantized.
+    The inputs are block-wise quantization tensor `x_q_block`, block-wise quantization scale
+    and the block size.
+    The output is an unquantized tensor with dtype.
+    """
+    block_n, block_k = block_size[0], block_size[1]
+    n, k = x_q_block.shape
+    n_tiles = (n + block_n - 1) // block_n
+    k_tiles = (k + block_k - 1) // block_k
+    assert n_tiles == x_s.shape[0]
+    assert k_tiles == x_s.shape[1]
+
+    x_dq_block = torch.empty_like(x_q_block, dtype=dtype)
+
+    for j in range(n_tiles):
+        for i in range(k_tiles):
+            x_q_block_tile = x_q_block[
+                j * block_n : min((j + 1) * block_n, n),
+                i * block_k : min((i + 1) * block_k, k),
+            ]
+            x_dq_block_tile = x_dq_block[
+                j * block_n : min((j + 1) * block_n, n),
+                i * block_k : min((i + 1) * block_k, k),
+            ]
+            x_dq_block_tile[:, :] = x_q_block_tile.to(torch.float32) * x_s[j][i]
+
+    return x_dq_block
+
+
+class TestDeepGemmBlackwell(CustomTestCase):
+
+    if not _is_cuda:
+        OUT_DTYPES = [torch.float32, torch.half, torch.bfloat16]
+        M = [1, 7, 83, 512, 2048]
+        NKs = [
+            (N, K)
+            for N in [128, 512, 1024, 4096, 7748, 13824]
+            for K in [256, 4096, 5120, 3884, 13824]
+        ]
+        # BLOCK_SIZE = [[64, 64], [64, 128], [128, 64], [128, 128]]
+        BLOCK_SIZE = [[128, 128]]
+        SEEDS = [0]
+    else:
+        # use practical shape in DeepSeek V3 for test
+        OUT_DTYPES = [torch.bfloat16]
+        M = [64, 128, 512, 1024, 4096]
+        NKs = [
+            (2112, 7168),
+            (1536, 7168),
+            # (3072, 1536),
+            # (24576, 7168),
+            # (4096, 512),
+            # (7168, 2048),
+            # (4608, 7168),
+            # (512, 7168),
+            # (7168, 2304),
+            # (7168, 512),
+        ]
+        BLOCK_SIZE = [[128, 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 _test_deep_gemm_blackwell(self, M, NK, block_size, out_dtype, seed):
+        N, K = NK
+        torch.manual_seed(seed)
+
+        A = torch.empty((M, K), dtype=torch.bfloat16).normal_(0, 0.2)
+        B = torch.empty((N, K), dtype=torch.bfloat16).normal_(0, 0.2)
+
+        A_q, A_s = per_token_group_quant_fp8(A)
+        B_q, B_s = per_block_quant_fp8(B)
+
+        A_dq = block_quant_dequant(A_q, A_s, [1, block_size[1]], out_dtype)
+        B_dq = block_quant_dequant(B_q, B_s, block_size, out_dtype)
+
+        A_qu = per_token_group_quant_mxfp8(A_dq)
+        B_qu = per_block_quant_mxfp8(B_dq)
+        out = None
+
+        with torch.inference_mode():
+            ref_out = native_w8a8_block_fp8_matmul(
+                A_q, B_q, A_s, B_s, block_size, out_dtype
+            )
+            out = torch.empty_like(ref_out)
+            fp8_gemm_nt(A_qu, B_qu, out)
+
+        torch.testing.assert_close(out, ref_out, atol=1e-1, rtol=1e-2)
+
+    def test_deep_gemm_blackwell(self):
+        for params in itertools.product(
+            self.M,
+            self.NKs,
+            self.BLOCK_SIZE,
+            self.OUT_DTYPES,
+            self.SEEDS,
+        ):
+            with self.subTest(
+                M=params[0],
+                NKs=params[1],
+                block_size=params[2],
+                out_dtype=params[3],
+                seed=params[4],
+            ):
+                self._test_deep_gemm_blackwell(*params)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)