[AMD] Add triton awq_dequantize kernel to support AWQ on ROCm (#7661)

python/sglang/srt/layers/quantization/awq.py

@@ -43,11 +43,20 @@ try:
 except ImportError:
     ops = None
 
-from sglang.srt.utils import is_cuda
+from sglang.srt.utils import is_cuda, is_hip
 
 _is_cuda = is_cuda()
+_is_hip = is_hip()
 if _is_cuda:
     from sgl_kernel import awq_dequantize, fused_marlin_moe
+elif _is_hip:
+    from sglang.srt.layers.quantization.awq_triton import (
+        awq_dequantize_triton as awq_dequantize,
+    )
+
+    warnings.warn(f"HIP does not support fused_marlin_moe currently.")
+else:
+    warnings.warn(f"Only CUDA and HIP support AWQ currently.")
 
 logger = logging.getLogger(__name__)
 
@@ -398,7 +407,6 @@ class AWQLinearMethod(LinearMethodBase):
         pack_factor = self.quant_config.pack_factor
         out_shape = x.shape[:-1] + (qweight.shape[-1] * pack_factor,)
         reshaped_x = x.reshape(-1, x.shape[-1])
-
         out = awq_dequantize(qweight, scales, qzeros)
         out = torch.matmul(reshaped_x, out)
 

python/sglang/srt/layers/quantization/awq_triton.py

+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/awq_triton.py
+
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+import triton
+import triton.language as tl
+
+AWQ_TRITON_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
+
+
+@triton.jit
+def awq_dequantize_kernel(
+    qweight_ptr,  # quantized matrix
+    scales_ptr,  # scales, per group
+    zeros_ptr,  # zeros, per group
+    group_size,  # Should always be one of the supported group sizes
+    result_ptr,  # Output matrix
+    num_cols,  # input num cols in qweight
+    num_rows,  # input num rows in qweight
+    BLOCK_SIZE_X: tl.constexpr,
+    BLOCK_SIZE_Y: tl.constexpr,
+):
+    # Setup the pids.
+    pid_x = tl.program_id(axis=0)
+    pid_y = tl.program_id(axis=1)
+
+    # Compute offsets and masks for qweight_ptr.
+    offsets_y = pid_y * BLOCK_SIZE_Y + tl.arange(0, BLOCK_SIZE_Y)
+    offsets_x = pid_x * BLOCK_SIZE_X + tl.arange(0, BLOCK_SIZE_X)
+    offsets = num_cols * offsets_y[:, None] + offsets_x[None, :]
+
+    masks_y = offsets_y < num_rows
+    masks_x = offsets_x < num_cols
+
+    masks = masks_y[:, None] & masks_x[None, :]
+
+    # Compute offsets and masks for result output ptr.
+    result_offsets_y = pid_y * BLOCK_SIZE_Y + tl.arange(0, BLOCK_SIZE_Y)
+    result_offsets_x = pid_x * BLOCK_SIZE_X * 8 + tl.arange(0, BLOCK_SIZE_X * 8)
+    result_offsets = (
+        8 * num_cols * result_offsets_y[:, None] + result_offsets_x[None, :]
+    )
+
+    result_masks_y = result_offsets_y < num_rows
+    result_masks_x = result_offsets_x < num_cols * 8
+    result_masks = result_masks_y[:, None] & result_masks_x[None, :]
+
+    # Load the weights.
+    iweights = tl.load(qweight_ptr + offsets, masks, 0.0)
+    iweights = tl.interleave(iweights, iweights)
+    iweights = tl.interleave(iweights, iweights)
+    iweights = tl.interleave(iweights, iweights)
+
+    # Create reverse AWQ order as tensor: [0, 4, 1, 5, 2, 6, 3, 7]
+    # that will map given indices to the correct order.
+    reverse_awq_order_tensor = (
+        (tl.arange(0, 2) * 4)[None, :] + tl.arange(0, 4)[:, None]
+    ).reshape(8)
+
+    # Use this to compute a set of shifts that can be used to unpack and
+    # reorder the values in iweights and zeros.
+    shifts = reverse_awq_order_tensor * 4
+    shifts = tl.broadcast_to(shifts[None, :], (BLOCK_SIZE_Y * BLOCK_SIZE_X, 8))
+    shifts = tl.reshape(shifts, (BLOCK_SIZE_Y, BLOCK_SIZE_X * 8))
+
+    # Unpack and reorder: shift out the correct 4-bit value and mask.
+    iweights = (iweights >> shifts) & 0xF
+
+    # Compute zero offsets and masks.
+    zero_offsets_y = pid_y * BLOCK_SIZE_Y // group_size + tl.arange(0, 1)
+    zero_offsets_x = pid_x * BLOCK_SIZE_X + tl.arange(0, BLOCK_SIZE_X)
+    zero_offsets = num_cols * zero_offsets_y[:, None] + zero_offsets_x[None, :]
+
+    zero_masks_y = zero_offsets_y < num_rows // group_size
+    zero_masks_x = zero_offsets_x < num_cols
+    zero_masks = zero_masks_y[:, None] & zero_masks_x[None, :]
+
+    # Load the zeros.
+    zeros = tl.load(zeros_ptr + zero_offsets, zero_masks, 0.0)
+    zeros = tl.interleave(zeros, zeros)
+    zeros = tl.interleave(zeros, zeros)
+    zeros = tl.interleave(zeros, zeros)
+    zeros = tl.broadcast_to(zeros, (BLOCK_SIZE_Y, BLOCK_SIZE_X * 8))
+
+    # Unpack and reorder: shift out the correct 4-bit value and mask.
+    zeros = (zeros >> shifts) & 0xF
+
+    # Compute scale offsets and masks.
+    scale_offsets_y = pid_y * BLOCK_SIZE_Y // group_size + tl.arange(0, 1)
+    scale_offsets_x = pid_x * BLOCK_SIZE_X * 8 + tl.arange(0, BLOCK_SIZE_X * 8)
+    scale_offsets = num_cols * 8 * scale_offsets_y[:, None] + scale_offsets_x[None, :]
+    scale_masks_y = scale_offsets_y < num_rows // group_size
+    scale_masks_x = scale_offsets_x < num_cols * 8
+    scale_masks = scale_masks_y[:, None] & scale_masks_x[None, :]
+
+    # Load the scales.
+    scales = tl.load(scales_ptr + scale_offsets, scale_masks, 0.0)
+    scales = tl.broadcast_to(scales, (BLOCK_SIZE_Y, BLOCK_SIZE_X * 8))
+
+    # Dequantize.
+    iweights = (iweights - zeros) * scales
+    iweights = iweights.to(result_ptr.type.element_ty)
+
+    # Finally, store.
+    tl.store(result_ptr + result_offsets, iweights, result_masks)
+
+
+@triton.jit
+def awq_gemm_kernel(
+    a_ptr,
+    b_ptr,
+    c_ptr,
+    zeros_ptr,
+    scales_ptr,
+    M,
+    N,
+    K,
+    group_size,
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+    SPLIT_K: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    pid_z = tl.program_id(1)
+
+    # NOTE: This doesn't work in TRITON_INTERPRET=1 mode.  Use below instead.
+    # num_pid_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+
+    pid_m = pid // num_pid_n
+    pid_n = pid % num_pid_n
+
+    accumulator_dtype = c_ptr.type.element_ty
+
+    # NOTE: This doesn't work in TRITON_INTERPRET=1 mode.  Use below instead.
+    # accumulator = tl.arange(0, BLOCK_SIZE_N)
+    # accumulator = tl.broadcast_to(accumulator[None, :],
+    # (BLOCK_SIZE_M, BLOCK_SIZE_N))
+    # accumulator = accumulator & 0x0
+    # accumulator = accumulator.to(accumulator_dtype)
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=accumulator_dtype)
+
+    # Create reverse AWQ order as tensor: [0, 4, 1, 5, 2, 6, 3, 7]
+    # that will map given indices to the correct order.
+    reverse_awq_order_tensor = (
+        (tl.arange(0, 2) * 4)[None, :] + tl.arange(0, 4)[:, None]
+    ).reshape(8)
+
+    # Create the necessary shifts to use to unpack.
+    shifts = reverse_awq_order_tensor * 4
+    shifts = tl.broadcast_to(shifts[None, :], (BLOCK_SIZE_K * (BLOCK_SIZE_N // 8), 8))
+    shifts = tl.reshape(shifts, (BLOCK_SIZE_K, BLOCK_SIZE_N))
+
+    # Offsets and masks.
+    offsets_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    masks_am = offsets_am < M
+
+    offsets_bn = pid_n * (BLOCK_SIZE_N // 8) + tl.arange(0, BLOCK_SIZE_N // 8)
+    masks_bn = offsets_bn < N // 8
+
+    offsets_zn = pid_n * (BLOCK_SIZE_N // 8) + tl.arange(0, BLOCK_SIZE_N // 8)
+    masks_zn = offsets_zn < N // 8
+
+    offsets_sn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    masks_sn = offsets_sn < N
+
+    offsets_k = pid_z * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
+    offsets_a = K * offsets_am[:, None] + offsets_k[None, :]
+    offsets_b = (N // 8) * offsets_k[:, None] + offsets_bn[None, :]
+
+    a_ptrs = a_ptr + offsets_a
+    b_ptrs = b_ptr + offsets_b
+
+    # NOTE: Use this in TRITON_INTERPRET=1 mode instead of tl.cdiv
+    # block_offset = BLOCK_SIZE_K * SPLIT_K
+    # for k in range(0, (K + block_offset - 1) // (block_offset)):
+    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * SPLIT_K)):
+        masks_k = offsets_k < K
+        masks_a = masks_am[:, None] & masks_k[None, :]
+        a = tl.load(a_ptrs, mask=masks_a, other=0.0)
+
+        masks_b = masks_k[:, None] & masks_bn[None, :]
+        b = tl.load(b_ptrs, mask=masks_b, other=0.0)
+        b = tl.interleave(b, b)
+        b = tl.interleave(b, b)
+        b = tl.interleave(b, b)
+
+        # Dequantize b.
+        offsets_szk = (
+            BLOCK_SIZE_K * SPLIT_K * k + pid_z * BLOCK_SIZE_K
+        ) // group_size + tl.arange(0, 1)
+        offsets_z = (N // 8) * offsets_szk[:, None] + offsets_zn[None, :]
+        masks_zk = offsets_szk < K // group_size
+        masks_z = masks_zk[:, None] & masks_zn[None, :]
+        zeros_ptrs = zeros_ptr + offsets_z
+        zeros = tl.load(zeros_ptrs, mask=masks_z, other=0.0)
+        zeros = tl.interleave(zeros, zeros)
+        zeros = tl.interleave(zeros, zeros)
+        zeros = tl.interleave(zeros, zeros)
+        zeros = tl.broadcast_to(zeros, (BLOCK_SIZE_K, BLOCK_SIZE_N))
+
+        offsets_s = N * offsets_szk[:, None] + offsets_sn[None, :]
+        masks_sk = offsets_szk < K // group_size
+        masks_s = masks_sk[:, None] & masks_sn[None, :]
+        scales_ptrs = scales_ptr + offsets_s
+        scales = tl.load(scales_ptrs, mask=masks_s, other=0.0)
+        scales = tl.broadcast_to(scales, (BLOCK_SIZE_K, BLOCK_SIZE_N))
+
+        b = (b >> shifts) & 0xF
+        zeros = (zeros >> shifts) & 0xF
+        b = (b - zeros) * scales
+        b = b.to(c_ptr.type.element_ty)
+
+        # Accumulate results.
+        accumulator = tl.dot(a, b, accumulator, out_dtype=accumulator_dtype)
+
+        offsets_k += BLOCK_SIZE_K * SPLIT_K
+        a_ptrs += BLOCK_SIZE_K * SPLIT_K
+        b_ptrs += BLOCK_SIZE_K * SPLIT_K * (N // 8)
+
+    c = accumulator.to(c_ptr.type.element_ty)
+    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = c_ptr + pid_z * N * M + N * offs_cm[:, None] + offs_cn[None, :]
+    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
+    tl.store(c_ptrs, c, mask=c_mask)
+
+
+# qweights - [K     , M // 8], int32
+# scales   - [K // G, M     ], float16
+# zeros    - [K // G, M // 8], int32
+def awq_dequantize_triton(
+    qweight: torch.Tensor,
+    scales: torch.Tensor,
+    zeros: torch.Tensor,
+    block_size_x: int = 32,
+    block_size_y: int = 32,
+) -> torch.Tensor:
+    K = qweight.shape[0]
+    M = scales.shape[1]
+    group_size = qweight.shape[0] // scales.shape[0]
+
+    assert K > 0 and M > 0
+    assert scales.shape[0] == K // group_size and scales.shape[1] == M
+    assert zeros.shape[0] == K // group_size and zeros.shape[1] == M // 8
+    assert group_size <= K
+    assert group_size in AWQ_TRITON_SUPPORTED_GROUP_SIZES or group_size == K
+
+    # Result tensor:
+    # number of rows = same as input tensor
+    # number of cols = 8 x input tensor num cols
+    result = torch.empty(
+        qweight.shape[0],
+        qweight.shape[1] * 8,
+        device=qweight.device,
+        dtype=scales.dtype,
+    )
+
+    Y = qweight.shape[0]  # num rows
+    X = qweight.shape[1]  # num cols
+
+    grid = lambda META: (
+        triton.cdiv(X, META["BLOCK_SIZE_X"]),
+        triton.cdiv(Y, META["BLOCK_SIZE_Y"]),
+    )
+    awq_dequantize_kernel[grid](
+        qweight,
+        scales,
+        zeros,
+        group_size,
+        result,
+        X,
+        Y,
+        BLOCK_SIZE_X=block_size_x,
+        BLOCK_SIZE_Y=block_size_y,
+    )
+
+    return result
+
+
+# input   - [M, K]
+# qweight - [K, N // 8]
+# qzeros  - [K // G, N // 8]
+# scales  - [K // G, N]
+# split_k_iters - parallelism along K-dimension, int, power of 2.
+def awq_gemm_triton(
+    input: torch.Tensor,
+    qweight: torch.Tensor,
+    scales: torch.Tensor,
+    qzeros: torch.Tensor,
+    split_k_iters: int,
+    block_size_m: int = 32,
+    block_size_n: int = 32,
+    block_size_k: int = 32,
+) -> torch.Tensor:
+    M, K = input.shape
+    N = qweight.shape[1] * 8
+    group_size = qweight.shape[0] // qzeros.shape[0]
+
+    assert N > 0 and K > 0 and M > 0
+    assert qweight.shape[0] == K and qweight.shape[1] == N // 8
+    assert qzeros.shape[0] == K // group_size and qzeros.shape[1] == N // 8
+    assert scales.shape[0] == K // group_size and scales.shape[1] == N
+    assert split_k_iters & (split_k_iters - 1) == 0 and split_k_iters != 0
+    assert split_k_iters <= 32
+    assert group_size <= K
+    assert group_size in AWQ_TRITON_SUPPORTED_GROUP_SIZES or group_size == K
+
+    grid = lambda META: (
+        triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
+        split_k_iters,
+    )
+
+    result = torch.zeros((split_k_iters, M, N), dtype=scales.dtype, device=input.device)
+
+    # A = input, B = qweight, C = result
+    # A = M x K, B = K x N, C = M x N
+    awq_gemm_kernel[grid](
+        input,
+        qweight,
+        result,
+        qzeros,
+        scales,
+        M,
+        N,
+        K,
+        group_size,
+        BLOCK_SIZE_M=block_size_m,
+        BLOCK_SIZE_N=block_size_n,
+        BLOCK_SIZE_K=block_size_k,
+        SPLIT_K=split_k_iters,
+    )
+
+    result = result.sum(0)
+
+    return result

python/sglang/srt/models/deepseek_v2.py

@@ -127,6 +127,10 @@ if _is_cuda:
     )
 elif _is_cpu and _is_cpu_amx_available:
     pass
+elif _is_hip:
+    from sglang.srt.layers.quantization.awq_triton import (
+        awq_dequantize_triton as awq_dequantize,
+    )
 else:
     from vllm._custom_ops import awq_dequantize
 
@@ -2176,7 +2180,7 @@ class DeepseekV2ForCausalLM(nn.Module):
             )
             if hasattr(self_attn.kv_b_proj, "qweight"):
                 # AWQ compatible
-                if _is_cuda:
+                if _is_cuda or _is_hip:
                     w = awq_dequantize(
                         self_attn.kv_b_proj.qweight,
                         self_attn.kv_b_proj.scales,

test/srt/run_suite.py

@@ -147,6 +147,7 @@ suites = {
         # TestFile("test_vision_chunked_prefill.py", 175), # Disabled temporarily and track in #7701
         TestFile("test_reasoning_parser.py", 5),
         TestFile("test_rope_rocm.py", 3),
+        TestFile("test_awq_dequant.py", 2),
     ],
     "per-commit-npu": [
         TestFile("test_ascend_attention_backend.py", 400),

test/srt/test_awq_dequant.py

+# Adapted from https://github.com/vllm-project/vllm/blob/main/tests/kernels/quantization/test_awq_triton.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+unittest version of the AWQ Triton kernel tests.
+
+Run with:
+    python -m unittest test_awq_dequant.py
+"""
+import unittest
+
+import torch
+
+from sglang.srt.layers.quantization.awq_triton import (
+    AWQ_TRITON_SUPPORTED_GROUP_SIZES,
+    awq_dequantize_triton,
+    awq_gemm_triton,
+)
+from sglang.test.test_utils import CustomTestCase
+
+device = "cuda"
+
+
+def reverse_awq_order(t: torch.Tensor) -> torch.Tensor:
+    bits = 4
+    AWQ_REVERSE_ORDER = [0, 4, 1, 5, 2, 6, 3, 7]
+    idx = torch.arange(t.shape[-1], dtype=torch.int32, device=t.device)
+    idx = idx.view(-1, 32 // bits)[:, AWQ_REVERSE_ORDER].view(-1)
+    return (t[:, idx] & 0xF).contiguous()
+
+
+def awq_dequantize_torch(
+    qweight: torch.Tensor,
+    scales: torch.Tensor,
+    qzeros: torch.Tensor,
+    group_size: int,
+) -> torch.Tensor:
+    if group_size == -1:
+        group_size = qweight.shape[0]
+
+    bits = 4
+    shifts = torch.arange(0, 32, bits, device=qzeros.device)
+
+    iweights = torch.bitwise_right_shift(qweight[:, :, None], shifts[None, None, :]).to(
+        torch.int8
+    )
+    iweights = reverse_awq_order(iweights.view(iweights.shape[0], -1))
+
+    zeros = torch.bitwise_right_shift(qzeros[:, :, None], shifts[None, None, :]).to(
+        torch.int8
+    )
+    zeros = reverse_awq_order(zeros.view(qzeros.shape[0], -1))
+
+    iweights = torch.bitwise_and(iweights, (2**bits) - 1)
+    zeros = torch.bitwise_and(zeros, (2**bits) - 1)
+
+    scales = scales.repeat_interleave(group_size, dim=0)
+    zeros = zeros.repeat_interleave(group_size, dim=0)
+    return (iweights - zeros) * scales
+
+
+class TestAWQTriton(CustomTestCase):
+    def test_dequantize(self):
+        rows_list = [3584, 18944, 128, 256, 512, 1024]
+        cols_list = [448, 576, 4736, 16, 32, 64, 128]
+
+        for qweight_rows in rows_list:
+            for qweight_cols in cols_list:
+                for group_size in AWQ_TRITON_SUPPORTED_GROUP_SIZES:
+                    with self.subTest(
+                        rows=qweight_rows, cols=qweight_cols, g=group_size
+                    ):
+                        self._run_dequant_case(
+                            qweight_rows=qweight_rows,
+                            qweight_cols=qweight_cols,
+                            group_size=group_size,
+                        )
+
+    def _run_dequant_case(self, qweight_rows, qweight_cols, group_size):
+        if group_size == -1:
+            group_size = qweight_rows
+
+        torch.manual_seed(0)
+
+        qweight = torch.randint(
+            0,
+            torch.iinfo(torch.int32).max,
+            (qweight_rows, qweight_cols),
+            dtype=torch.int32,
+            device=device,
+        )
+        scales = torch.rand(
+            qweight_rows // group_size,
+            qweight_cols * 8,
+            dtype=torch.float16,
+            device=device,
+        )
+        zeros = torch.randint(
+            0,
+            torch.iinfo(torch.int32).max,
+            (qweight_rows // group_size, qweight_cols),
+            dtype=torch.int32,
+            device=device,
+        )
+
+        ref = awq_dequantize_torch(qweight, scales, zeros, group_size)
+        tri = awq_dequantize_triton(qweight, scales, zeros)
+
+        # sanity
+        self.assertFalse(torch.any(torch.isinf(tri)) or torch.any(torch.isnan(tri)))
+        torch.testing.assert_close(ref, tri)
+
+    # GEMM
+    def test_gemm(self):
+        N_list = [1, 2, 4, 8, 14, 17, 23, 32]
+        K_list = [128]
+        M_list = [16, 24, 32]
+        splitK_list = [1, 8]
+
+        for N in N_list:
+            for K in K_list:
+                for M in M_list:
+                    for group_size in AWQ_TRITON_SUPPORTED_GROUP_SIZES:
+                        for splitK in splitK_list:
+                            with self.subTest(N=N, K=K, M=M, g=group_size, sk=splitK):
+                                self._run_gemm_case(
+                                    N=N,
+                                    K=K,
+                                    M=M,
+                                    group_size=group_size,
+                                    splitK=splitK,
+                                )
+
+    def _run_gemm_case(self, N, K, M, group_size, splitK):
+        if group_size == -1:
+            group_size = K
+
+        torch.manual_seed(0)
+
+        x = torch.rand((N, K), dtype=torch.float32, device=device)
+        qweight = torch.randint(
+            0,
+            torch.iinfo(torch.int32).max,
+            (K, M // 8),
+            dtype=torch.int32,
+            device=device,
+        )
+        qzeros = torch.randint(
+            0,
+            torch.iinfo(torch.int32).max,
+            (K // group_size, M // 8),
+            dtype=torch.int32,
+            device=device,
+        )
+        scales = torch.rand((K // group_size, M), dtype=torch.float32, device=device)
+
+        tri_out = awq_gemm_triton(x, qweight, scales, qzeros, splitK)
+
+        self.assertFalse(
+            torch.any(torch.isinf(tri_out)) or torch.any(torch.isnan(tri_out))
+        )
+
+        # dequantize & compare
+        w_deq = awq_dequantize_triton(qweight, scales, qzeros)
+        ref_out = torch.matmul(x, w_deq)
+
+        self.assertFalse(
+            torch.any(torch.isinf(ref_out)) or torch.any(torch.isnan(ref_out))
+        )
+
+        torch.testing.assert_close(tri_out.cpu(), ref_out.cpu(), atol=1e-1, rtol=1e-1)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)