MoE Expert Parallel Impl

.github/workflows/pr-test.yml

@@ -105,6 +105,12 @@ jobs:
           cd test/srt
           python3 test_update_weights_from_distributed.py
 
+      - name: Evaluate MoE EP accuracy (TP=2)
+        timeout-minutes: 10
+        run: |
+          cd test/srt
+          python3 test_moe_ep.py
+
   performance-test-1-gpu-part-1:
     if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request'
     runs-on: 1-gpu-runner

python/sglang/srt/layers/ep_moe/kernels.py

@@ -0,0 +1,349 @@
+import logging
+from typing import Optional
+
+import torch
+import triton
+import triton.language as tl
+
+logger = logging.getLogger(__name__)
+
+
+@triton.jit
+def compute_seg_indptr_triton_kernel(reorder_topk_ids, seg_indptr, num_toks):
+    expert = tl.program_id(0)
+    low = 0
+    high = num_toks - 1
+    target_location = -1
+    while low <= high:
+        mid = (low + high) // 2
+
+        if tl.load(reorder_topk_ids + mid) > expert:
+            high = mid - 1
+        else:
+            low = mid + 1
+            target_location = mid
+    tl.store(seg_indptr + expert + 1, target_location + 1)
+
+
+@triton.jit
+def compute_src2dst_triton_kernel(
+    reorder_ids, src2dst, num_toks, BLOCK_SIZE: tl.constexpr
+):
+    pid = tl.program_id(axis=0)
+    dst_id = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = dst_id < num_toks
+    src_id = tl.load(reorder_ids + dst_id, mask=mask)
+    tl.store(src2dst + src_id, dst_id, mask=mask)
+
+
+def run_moe_ep_preproess(topk_ids: torch.Tensor, num_experts: int):
+    reorder_topk_ids, reorder_ids = torch.sort(topk_ids.view(-1), stable=True)
+    seg_indptr = torch.zeros(num_experts + 1, device=topk_ids.device, dtype=torch.int64)
+    src2dst = torch.empty(topk_ids.numel(), device=topk_ids.device, dtype=torch.int32)
+
+    compute_seg_indptr_triton_kernel[(num_experts,)](
+        reorder_topk_ids, seg_indptr, topk_ids.numel()
+    )
+
+    BLOCK_SIZE = 512
+    grid = (triton.cdiv(topk_ids.numel(), BLOCK_SIZE),)
+    compute_src2dst_triton_kernel[grid](
+        reorder_ids, src2dst, topk_ids.numel(), BLOCK_SIZE
+    )
+    return reorder_topk_ids, src2dst, seg_indptr
+
+
+@triton.jit
+def pre_reorder_triton_kernel(
+    input_ptr,
+    gateup_input_ptr,
+    src2dst_ptr,
+    topk_ids_ptr,
+    a1_scales_ptr,
+    start_expert_id,
+    end_expert_id,
+    topk,
+    hidden_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    OutDtype = gateup_input_ptr.dtype.element_ty
+
+    src_idx = tl.program_id(0)
+    src2dst_ptr = src2dst_ptr + src_idx * topk
+    topk_ids_ptr = topk_ids_ptr + src_idx * topk
+
+    src_ptr = input_ptr + src_idx * hidden_size
+    for idx in range(topk):
+        expert_id = tl.load(topk_ids_ptr + idx)
+        if expert_id >= start_expert_id and expert_id <= end_expert_id:
+            if a1_scales_ptr is not None:
+                scale = 1.0 / tl.load(a1_scales_ptr + expert_id - start_expert_id)
+            else:
+                scale = 1.0
+
+            dst_idx = tl.load(src2dst_ptr + idx)
+            dst_ptr = gateup_input_ptr + dst_idx * hidden_size
+            for start_offset in tl.range(0, hidden_size, BLOCK_SIZE):
+                offset = start_offset + tl.arange(0, BLOCK_SIZE)
+                mask = offset < hidden_size
+                in_data = tl.load(src_ptr + offset, mask=mask).to(tl.float32)
+                out_data = (in_data * scale).to(OutDtype)
+                tl.store(dst_ptr + offset, out_data, mask=mask)
+
+
+@triton.jit
+def silu_and_mul_triton_kernel(
+    gateup_output,
+    down_input,
+    hidden_size,
+    reorder_topk_ids,
+    scales,
+    start_expert_id,
+    end_expert_id,
+    BLOCK_SIZE: tl.constexpr,
+):
+    InDtype = gateup_output.dtype.element_ty
+    OutDtype = down_input.dtype.element_ty
+
+    half_hidden_size = hidden_size // 2
+
+    pid = tl.program_id(0)
+    expert_id = tl.load(reorder_topk_ids + pid)
+    if expert_id >= start_expert_id and expert_id <= end_expert_id:
+        gateup_output_ptr = gateup_output + pid * hidden_size
+        gate_output_ptr = gateup_output_ptr
+        up_output_ptr = gateup_output_ptr + half_hidden_size
+        down_input_ptr = down_input + pid * half_hidden_size
+
+        if scales is not None:
+            scale = tl.load(scales + expert_id - start_expert_id)
+            scale = (1 / scale).to(InDtype)
+        else:
+            scale = 1
+
+        for start_offset in tl.range(0, half_hidden_size, BLOCK_SIZE):
+            offset = start_offset + tl.arange(0, BLOCK_SIZE)
+            mask = offset < half_hidden_size
+
+            gate_output = tl.load(gate_output_ptr + offset, mask=mask).to(tl.float32)
+            up_output = tl.load(up_output_ptr + offset, mask=mask)
+
+            # silu & mul & quantize
+            gate_output = gate_output * tl.sigmoid(gate_output)
+            gate_output = gate_output.to(InDtype)
+
+            silu_mul_output = gate_output * up_output * scale
+            silu_mul_output = silu_mul_output.to(OutDtype)
+            tl.store(down_input_ptr + offset, silu_mul_output, mask=mask)
+
+
+@triton.jit
+def post_reorder_triton_kernel(
+    down_output_ptr,
+    output_ptr,
+    src2dst_ptr,
+    topk_ids_ptr,
+    topk_weights_ptr,
+    start_expert_id,
+    end_expert_id,
+    topk,
+    hidden_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    InDtype = down_output_ptr.dtype.element_ty
+
+    src_idx = tl.program_id(0)
+    src2dst_ptr = src2dst_ptr + src_idx * topk
+    topk_ids_ptr = topk_ids_ptr + src_idx * topk
+    topk_weights_ptr = topk_weights_ptr + src_idx * topk
+
+    computed = False
+    store_ptr = output_ptr + src_idx * hidden_size
+    for start_offset in tl.range(0, hidden_size, BLOCK_SIZE):
+        offset = start_offset + tl.arange(0, BLOCK_SIZE)
+        mask = offset < hidden_size
+
+        sum_vec = tl.zeros([BLOCK_SIZE], dtype=InDtype)
+        for idx in range(topk):
+            expert_id = tl.load(topk_ids_ptr + idx)
+            if expert_id >= start_expert_id and expert_id <= end_expert_id:
+                computed = True
+                dst_idx = tl.load(src2dst_ptr + idx)
+                weigh_scale = tl.load(topk_weights_ptr + idx).to(InDtype)
+                load_ptr = down_output_ptr + dst_idx * hidden_size
+                in_data = tl.load(load_ptr + offset, mask=mask)
+                sum_vec += in_data * weigh_scale
+        tl.store(store_ptr + offset, sum_vec, mask=mask)
+
+    if computed == False:
+        for start_offset in tl.range(0, hidden_size, BLOCK_SIZE):
+            offset = start_offset + tl.arange(0, BLOCK_SIZE)
+            mask = offset < hidden_size
+            tl.store(
+                store_ptr + offset, tl.zeros([BLOCK_SIZE], dtype=InDtype), mask=mask
+            )
+
+
+@triton.jit
+def compute_m_range(
+    pid,
+    batch_size,
+    seg_indptr,
+    weight_indices,
+    m_num_tiles_indptr,
+    BLOCK_SIZE_M: tl.constexpr,
+):
+    idx = 0
+    for bs in range(batch_size):
+        tiles = tl.load(m_num_tiles_indptr + bs)
+        if pid >= tiles:
+            idx = bs
+
+    idx_start = tl.load(m_num_tiles_indptr + idx)
+
+    m_range_start = tl.load(seg_indptr + idx) + (pid - idx_start) * BLOCK_SIZE_M
+    m_range_end = min(tl.load(seg_indptr + idx + 1), m_range_start + BLOCK_SIZE_M)
+    expert_id = tl.load(weight_indices + idx)
+    return m_range_start, m_range_end, expert_id
+
+
+@triton.jit
+def grouped_gemm_triton_kernel(
+    a,
+    b,
+    c,
+    batch_size,
+    N,
+    K,
+    seg_indptr,
+    weight_indices,
+    m_num_tiles_indptr,
+    use_fp8_w8a8,
+    scale_a,
+    scale_b,
+    a_stride_0: tl.constexpr,
+    b_stride_0: tl.constexpr,
+    b_stride_1: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+):
+    c_dtype = c.dtype.element_ty
+
+    pid_m = tl.program_id(0)
+    pid_n = tl.program_id(1)
+    total_m_block = tl.load(m_num_tiles_indptr + batch_size)
+    if pid_m >= total_m_block:
+        return
+
+    m_range_start, m_range_end, expert_id = compute_m_range(
+        pid_m, batch_size, seg_indptr, weight_indices, m_num_tiles_indptr, BLOCK_SIZE_M
+    )
+    if m_range_end - m_range_start == 0:
+        return
+
+    n_range_start = pid_n * BLOCK_SIZE_N
+    n_range_end = min(n_range_start + BLOCK_SIZE_N, N)
+
+    offs_am = tl.arange(0, BLOCK_SIZE_M)
+    offs_bn = tl.arange(0, BLOCK_SIZE_N)
+
+    offs_am = tl.where(offs_am < m_range_end - m_range_start, offs_am, 0)
+    offs_bn = tl.where(offs_bn < n_range_end - n_range_start, offs_bn, 0)
+    offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M)
+    offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N)
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+
+    a_ptr = a + (m_range_start + offs_am[:, None]) * a_stride_0 + offs_k[None, :]
+    b_ptr = b + (
+        (expert_id * b_stride_0)
+        + (n_range_start + offs_bn[:, None]) * b_stride_1
+        + offs_k[None, :]
+    )
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
+        a_tile = tl.load(
+            a_ptr, mask=offs_k[None, :] < (K - k * BLOCK_SIZE_K), other=0.0
+        )
+        b_tile = tl.load(
+            b_ptr, mask=offs_k[None, :] < (K - k * BLOCK_SIZE_K), other=0.0
+        )
+        accumulator = tl.dot(a_tile, b_tile.T, accumulator)
+        a_ptr += BLOCK_SIZE_K
+        b_ptr += BLOCK_SIZE_K
+
+    if use_fp8_w8a8:
+        scale_a_value = tl.load(scale_a + expert_id)
+        scale_b_value = tl.load(scale_b + expert_id)
+        accumulator *= scale_a_value * scale_b_value
+    c_tile = accumulator.to(c_dtype)
+
+    offs_cm = m_range_start + tl.arange(0, BLOCK_SIZE_M)
+    offs_cn = n_range_start + tl.arange(0, BLOCK_SIZE_N)
+    c_ptr = c + offs_cm[:, None] * N + offs_cn[None, :]
+    c_mask = (offs_cm[:, None] < m_range_end) & (offs_cn[None, :] < n_range_end)
+    tl.store(c_ptr, c_tile, mask=c_mask)
+
+
+@triton.jit
+def compute_m_num_tiles_indptr(
+    m_num_tiles_indptr, seg_indptr, batch_size: tl.constexpr, BLOCK_SIZE_M: tl.constexpr
+):
+    for bs in range(batch_size):
+        m = tl.load(seg_indptr + bs + 1) - tl.load(seg_indptr + bs)
+        cur_num_tiles = tl.cdiv(m, BLOCK_SIZE_M)
+        pre_num_tiles = tl.load(m_num_tiles_indptr + bs)
+        tl.store(m_num_tiles_indptr + bs + 1, pre_num_tiles + cur_num_tiles)
+
+
+def grouped_gemm_triton(
+    a: torch.Tensor,
+    b: torch.Tensor,
+    c: torch.Tensor,
+    batch_size: int,
+    weight_column_major: bool,
+    seg_indptr: Optional[torch.Tensor] = None,
+    weight_indices: Optional[torch.Tensor] = None,
+    use_fp8_w8a8: bool = False,
+    scale_a: torch.Tensor = None,
+    scale_b: torch.Tensor = None,
+):
+    assert weight_column_major == True  # TODO: more
+    if use_fp8_w8a8:
+        assert scale_a is not None and scale_b is not None
+
+    config = {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+    }
+
+    m_num_tiles_indptr = torch.zeros(batch_size + 1, device=a.device, dtype=torch.int64)
+    compute_m_num_tiles_indptr[(1,)](
+        m_num_tiles_indptr, seg_indptr, batch_size, config["BLOCK_SIZE_M"]
+    )
+
+    grid = lambda META: (
+        triton.cdiv(a.size(0), META["BLOCK_SIZE_M"]) + batch_size,
+        triton.cdiv(b.size(1), META["BLOCK_SIZE_N"]),
+    )
+
+    grouped_gemm_triton_kernel[grid](
+        a,
+        b,
+        c,
+        batch_size,
+        b.size(1),
+        b.size(2),
+        seg_indptr,
+        weight_indices,
+        m_num_tiles_indptr,
+        use_fp8_w8a8,
+        scale_a,
+        scale_b,
+        a.stride(0),
+        b.stride(0),
+        b.stride(1),
+        **config,
+    )
+    return c