"""Fused MoE kernel.""" import functools import json import os from typing import Any, Dict, Optional, Tuple import torch import triton import triton.language as tl from loguru import logger from aphrodite._C import ops from aphrodite.common.utils import is_hip @triton.jit def fused_moe_kernel( # Pointers to matrices a_ptr, b_ptr, c_ptr, a_scale_ptr, b_scale_ptr, topk_weights_ptr, sorted_token_ids_ptr, expert_ids_ptr, num_tokens_post_padded_ptr, # Matrix dimensions N, K, EM, num_valid_tokens, # The stride variables represent how much to increase the ptr by when # moving by 1 element in a particular dimension. E.g. `stride_am` is # how much to increase `a_ptr` by to get the element one row down # (A has M rows). stride_am, stride_ak, stride_be, stride_bk, stride_bn, stride_cm, stride_cn, # Meta-parameters BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, MUL_ROUTED_WEIGHT: tl.constexpr, top_k: tl.constexpr, compute_type: tl.constexpr, use_fp8: tl.constexpr, ): """ Implements the fused computation for a Mixture of Experts (MOE) using token and expert matrices. Key Parameters: - A: The input tensor representing tokens with shape (*, K), where '*' can be any shape representing batches and K is the feature dimension of each token. - B: The stacked MOE weight tensor with shape (E, N, K), where E is the number of experts, K is the input feature dimension, and N is the output feature dimension. - C: The output cache tensor with shape (M, topk, N), where M is the total number of tokens post padding, topk is the number of times each token is repeated, and N is the output feature dimension. - sorted_token_ids: A tensor containing the sorted indices of tokens, repeated topk times and arranged by the expert index they are assigned to. - expert_ids: A tensor containing the indices of the expert for each block. It determines which expert matrix from B should be used for each block in A. This kernel performs the multiplication of a token by its corresponding expert matrix as determined by `expert_ids`. The sorting of `sorted_token_ids` by expert index and padding ensures divisibility by BLOCK_SIZE_M, which is necessary to maintain consistency in block matrix multiplication across different blocks processed by the same expert. """ # ----------------------------------------------------------- # Map program ids `pid` to the block of C it should compute. # This is done in a grouped ordering to promote L2 data reuse. pid = tl.program_id(axis=0) num_pid_m = tl.cdiv(EM, BLOCK_SIZE_M) num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) num_pid_in_group = GROUP_SIZE_M * num_pid_n group_id = pid // num_pid_in_group first_pid_m = group_id * GROUP_SIZE_M group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m) pid_n = (pid % num_pid_in_group) // group_size_m # ---------------------------------------------------------- # Create pointers for the first blocks of A and B. # We will advance this pointer as we move in the K direction # and accumulate # `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers # `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers num_tokens_post_padded = tl.load(num_tokens_post_padded_ptr) if pid_m * BLOCK_SIZE_M >= num_tokens_post_padded: return offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_token = tl.load(sorted_token_ids_ptr + offs_token_id) token_mask = offs_token < num_valid_tokens offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N offs_k = tl.arange(0, BLOCK_SIZE_K) a_ptrs = a_ptr + (offs_token[:, None] // top_k * stride_am + offs_k[None, :] * stride_ak) off_experts = tl.load(expert_ids_ptr + pid_m) b_ptrs = b_ptr + off_experts * stride_be + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) if use_fp8: a_scale = tl.load(a_scale_ptr) b_scale = tl.load(b_scale_ptr + off_experts) # ----------------------------------------------------------- # Iterate to compute a block of the C matrix. # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block # of fp32 values for higher accuracy. # `accumulator` will be converted back to fp16 after the loop. accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): # Load the next block of A and B, generate a mask by checking the # K dimension. a = tl.load(a_ptrs, mask=token_mask[:, None] & (offs_k[None, :] < K - k * BLOCK_SIZE_K), other=0.0) b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0) # We accumulate along the K dimension. if use_fp8: accumulator = tl.dot(a, b, acc=accumulator) else: accumulator += tl.dot(a, b) # Advance the ptrs to the next K block. a_ptrs += BLOCK_SIZE_K * stride_ak b_ptrs += BLOCK_SIZE_K * stride_bk if MUL_ROUTED_WEIGHT: moe_weight = tl.load(topk_weights_ptr + offs_token, mask=token_mask, other=0) accumulator = accumulator * moe_weight[:, None] if use_fp8: accumulator = (accumulator * a_scale * b_scale).to(compute_type) else: accumulator = accumulator.to(compute_type) # ----------------------------------------------------------- # Write back the block of the output offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) c_ptrs = c_ptr + stride_cm * offs_token[:, None] + stride_cn * offs_cn[ None, :] c_mask = token_mask[:, None] & (offs_cn[None, :] < N) tl.store(c_ptrs, accumulator, mask=c_mask) def moe_align_block_size( topk_ids: torch.Tensor, block_size: int, num_experts: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Aligns the token distribution across experts to be compatible with block size for matrix multiplication. Parameters: - topk_ids: A tensor of shape [total_tokens, top_k] representing the top-k expert indices for each token. - block_size: The block size used in block matrix multiplication. - num_experts: The total number of experts. Returns: - sorted_token_ids: A tensor containing the sorted token indices according to their allocated expert. - expert_ids: A tensor indicating the assigned expert index for each block. - num_tokens_post_padded: The total number of tokens after padding, ensuring divisibility by block_size. This function pads the number of tokens that each expert needs to process so that it is divisible by block_size. Padding ensures that during block matrix multiplication, the dimensions align correctly. Example: Given topk_ids = [[2, 3, 4], [1, 2, 4], [1, 3, 4], [1, 2, 3]], block_size = 4, and num_experts = 4: - We initially have 12 tokens (after repeating 'top_k' times) and 4 experts, with each expert needing to process 3 tokens. - As block_size is 4, we pad 1 token for each expert. - First, flatten topk_ids to [2, 3, 4, 1, 2, 4, 1, 3, 4, 1, 2, 3]. - Then append padding tokens [12, 12, 12, 12] for each block. - After sorting by expert index, we obtain token_ids [3, 6, 9, 12, 0, 4, 10, 12, 1, 7, 11, 12, 2, 5, 8, 12]. Tokens 12 are non-existent (padding) and are ignored in the subsequent matrix multiplication. - The padding ensures that the total number of tokens is now divisible by block_size for proper block matrix operations. """ sorted_ids = torch.empty( (topk_ids.numel() + num_experts * (block_size - 1), ), dtype=torch.int32, device=topk_ids.device) expert_ids = torch.empty((topk_ids.numel() + num_experts, ), dtype=torch.int32, device=topk_ids.device) sorted_ids.fill_(topk_ids.numel()) num_tokens_post_pad = torch.empty((1), dtype=torch.int32, device=topk_ids.device) ops.moe_align_block_size(topk_ids, num_experts, block_size, sorted_ids, expert_ids, num_tokens_post_pad) return sorted_ids, expert_ids, num_tokens_post_pad def invoke_fused_moe_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor, A_scale: Optional[torch.Tensor], B_scale: Optional[torch.Tensor], topk_weights: torch.Tensor, topk_ids: torch.Tensor, sorted_token_ids: torch.Tensor, expert_ids: torch.Tensor, num_tokens_post_padded: torch.Tensor, mul_routed_weight: bool, top_k: int, config: Dict[str, Any], compute_type: tl.dtype, use_fp8: bool) -> None: assert topk_weights.stride(1) == 1 assert sorted_token_ids.stride(0) == 1 if not use_fp8: assert A_scale is None assert B_scale is None else: A, A_scale = ops.scaled_fp8_quant(A, A_scale) assert B_scale is not None grid = lambda META: (triton.cdiv(sorted_token_ids.shape[0], META[ 'BLOCK_SIZE_M']) * triton.cdiv(B.shape[1], META['BLOCK_SIZE_N']), ) fused_moe_kernel[grid]( A, B, C, A_scale, B_scale, topk_weights, sorted_token_ids, expert_ids, num_tokens_post_padded, B.shape[1], B.shape[2], sorted_token_ids.shape[0], topk_ids.numel(), A.stride(0), A.stride(1), B.stride(0), B.stride(2), B.stride(1), C.stride(1), C.stride(2), MUL_ROUTED_WEIGHT=mul_routed_weight, top_k=top_k, compute_type=compute_type, use_fp8=use_fp8, **config, ) def get_config_file_name(E: int, N: int, dtype: Optional[str]) -> str: device_name = torch.cuda.get_device_name().replace(" ", "_") dtype_selector = "" if not dtype else f",dtype={dtype}" return f"E={E},N={N},device_name={device_name}{dtype_selector}.json" @functools.lru_cache def get_moe_configs(E: int, N: int, dtype: Optional[str]) -> Optional[Dict[int, Any]]: """ Return optimized configurations for the fused MoE kernel. The return value will be a dictionary that maps an irregular grid of batch sizes to configurations of the fused_moe kernel. To evaluate the kernel on a given batch size bs, the closest batch size in the grid should be picked and the associated configuration chosen to invoke the kernel. """ # First look up if an optimized configuration is available in the configs # directory json_file_name = get_config_file_name(E, N, dtype) config_file_path = os.path.join( os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name) if os.path.exists(config_file_path): with open(config_file_path) as f: logger.info( f"Using configuration from {config_file_path} for MoE layer.") # If a configuration has been found, return it return {int(key): val for key, val in json.load(f).items()} # If no optimized configuration is available, we will use the default # configuration return None def fused_moe( hidden_states: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor, gating_output: torch.Tensor, topk: int, renormalize: bool, inplace: bool = False, override_config: Optional[Dict[str, Any]] = None, use_fp8: bool = False, w1_scale: Optional[torch.Tensor] = None, w2_scale: Optional[torch.Tensor] = None, a1_scale: Optional[torch.Tensor] = None, a2_scale: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ This function computes a Mixture of Experts (MoE) layer using two sets of weights, w1 and w2, and top-k gating mechanism. Parameters: - hidden_states (torch.Tensor): The input tensor to the MoE layer. - w1 (torch.Tensor): The first set of expert weights. - w2 (torch.Tensor): The second set of expert weights. - gating_output (torch.Tensor): The output of the gating operation (before softmax). - topk (int): The number of top-k experts to select. - renormalize (bool): If True, renormalize the top-k weights to sum to 1. - inplace (bool): If True, perform the operation in-place. Defaults to False. - override_config (Optional[Dict[str, Any]]): Optional override for the kernel configuration. - use_fp8 (bool): If True, use fp8 arithmetic to compute the inner products for w1 and w2. Defaults to False. - w1_scale (Optional[torch.Tensor]): Optional scale to be used for w1. - w2_scale (Optional[torch.Tensor]): Optional scale to be used for w2. Returns: - torch.Tensor: The output tensor after applying the MoE layer. """ # Check constraints. assert hidden_states.shape[0] == gating_output.shape[0], ( "Number of tokens mismatch") assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch" assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch" assert hidden_states.is_contiguous(), "Hidden_states must be contiguous" assert w1.is_contiguous(), "Expert weights1 must be contiguous" assert w2.is_contiguous(), "Expert weights2 must be contiguous" assert hidden_states.dtype in [ torch.float32, torch.float16, torch.bfloat16 ] M, _ = hidden_states.shape E, N, _ = w1.shape if is_hip(): # The MoE kernels are not yet supported on ROCm. routing_weights = torch.softmax(gating_output, dim=-1, dtype=torch.float32) topk_weights, topk_ids = torch.topk(routing_weights, topk, dim=-1) else: import aphrodite._moe_C as moe_kernels topk_weights = torch.empty(M, topk, dtype=torch.float32, device=hidden_states.device) topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device) token_expert_indicies = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device) moe_kernels.topk_softmax( topk_weights, topk_ids, token_expert_indicies, gating_output.float(), # TODO(woosuk): Optimize this. ) del token_expert_indicies # Not used. Will be used in the future. if renormalize: topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True) if override_config: config = override_config else: # First try to load optimal config from the file configs = get_moe_configs(E, w2.shape[2], "float8" if use_fp8 else None) if configs: # If an optimal configuration map has been found, look up the # optimal config config = configs[min(configs.keys(), key=lambda x: abs(x - M))] else: # Else use the default config config = { 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8 } if M <= E: config = { 'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1 } intermediate_cache1 = torch.empty((M, topk_ids.shape[1], N), device=hidden_states.device, dtype=hidden_states.dtype) intermediate_cache2 = torch.empty((M * topk_ids.shape[1], N // 2), device=hidden_states.device, dtype=hidden_states.dtype) intermediate_cache3 = torch.empty((M, topk_ids.shape[1], w2.shape[1]), device=hidden_states.device, dtype=hidden_states.dtype) sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size( topk_ids, config['BLOCK_SIZE_M'], E) invoke_fused_moe_kernel(hidden_states, w1, intermediate_cache1, a1_scale, w1_scale, topk_weights, topk_ids, sorted_token_ids, expert_ids, num_tokens_post_padded, False, topk_ids.shape[1], config, compute_type=tl.float16, use_fp8=use_fp8) ops.silu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, N)) invoke_fused_moe_kernel(intermediate_cache2, w2, intermediate_cache3, a2_scale, w2_scale, topk_weights, topk_ids, sorted_token_ids, expert_ids, num_tokens_post_padded, True, 1, config, compute_type=tl.float16, use_fp8=use_fp8) if inplace: return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape), dim=1, out=hidden_states) return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape), dim=1)