from typing import Any, Dict, List, Optional from contextlib import suppress import torch from torch.nn.parameter import Parameter from aphrodite.modeling.layers.fused_moe import (moe_align_block_size, fused_moe, fused_topk) from aphrodite.modeling.layers.linear import (LinearMethodBase, set_weight_attrs) from aphrodite.quantization.base_config import (QuantizationConfig) HAS_QUANTS = False with suppress(ImportError): from aphrodite._quant_C import quant_ops as ops HAS_QUANTS = True class AWQConfig(QuantizationConfig): """Config class for AWQ. Reference: https://arxiv.org/abs/2306.00978 """ def __init__( self, weight_bits: int, group_size: int, zero_point: bool, ) -> None: if not HAS_QUANTS: raise ImportError("Could not find the quantization kernels.") self.weight_bits = weight_bits self.group_size = group_size self.zero_point = zero_point if self.weight_bits != 4: raise ValueError( "Currently, only 4-bit weight quantization is supported for " f"AWQ, but got {self.weight_bits} bits.") self.pack_factor = 32 // self.weight_bits def __repr__(self) -> str: return (f"AWQConfig(weight_bits={self.weight_bits}, " f"group_size={self.group_size}, " f"zero_point={self.zero_point})") def get_name(self) -> str: return "awq" def get_supported_act_dtypes(self) -> List[torch.dtype]: return [torch.half] def get_min_capability(self) -> int: # The AWQ kernel only supports Turing or newer GPUs. return 75 @staticmethod def get_config_filenames() -> List[str]: return [ "quant_config.json", "quantize_config.json", ] @classmethod def from_config(cls, config: Dict[str, Any]) -> "AWQConfig": weight_bits = cls.get_from_keys(config, ["w_bit", "bits"]) group_size = cls.get_from_keys(config, ["q_group_size", "group_size"]) zero_point = cls.get_from_keys(config, ["zero_point"]) return cls(weight_bits, group_size, zero_point) def get_linear_method(self) -> "AWQLinearMethod": return AWQLinearMethod(self) def get_scaled_act_names(self) -> List[str]: return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"] def merge_weight(self) -> bool: return True def rope_style(self) -> Optional[bool]: return None def quant_vocab(self) -> List[bool]: return [False, False] def support_fused_moe(self) -> bool: return True class AWQLinearMethod(LinearMethodBase): """Linear method for AWQ. Args: quant_config: The AWQ quantization config. """ def __init__(self, quant_config: AWQConfig): self.quant_config = quant_config def create_weights( self, input_size_per_partition: int, output_partition_sizes: List[int], input_size: int, output_size: int, params_dtype: torch.dtype, ) -> Dict[str, Any]: if input_size_per_partition % self.quant_config.group_size != 0: raise ValueError( "The input size is not aligned with the quantized " "weight shape. This can be caused by too large " "tensor parallel size.") output_size_per_partition = sum(output_partition_sizes) if output_size_per_partition % self.quant_config.pack_factor != 0: raise ValueError( "The output size is not aligned with the quantized " "weight shape. This can be caused by too large " "tensor parallel size.") qweight = Parameter( torch.empty( input_size_per_partition, output_size_per_partition // self.quant_config.pack_factor, dtype=torch.int32, ), requires_grad=False, ) set_weight_attrs( qweight, { "input_dim": 0, "output_dim": 1, "packed_dim": 1, "pack_factor": self.quant_config.pack_factor, }) qzeros = Parameter( torch.empty( input_size_per_partition // self.quant_config.group_size, output_size_per_partition // self.quant_config.pack_factor, dtype=torch.int32, ), requires_grad=False, ) set_weight_attrs( qzeros, { "input_dim": 0, "output_dim": 1, "packed_dim": 1, "pack_factor": self.quant_config.pack_factor, }) scales = Parameter( torch.empty( input_size_per_partition // self.quant_config.group_size, output_size_per_partition, dtype=params_dtype, ), requires_grad=False, ) set_weight_attrs(scales, { "input_dim": 0, "output_dim": 1, }) return { "qweight": qweight, "qzeros": qzeros, "scales": scales, } def apply_weights(self, weights: Dict[str, Any], x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: qweight = weights["qweight"] qzeros = weights["qzeros"] scales = weights["scales"] 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]) # num_tokens >= threshold FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 256 if FP16_MATMUL_HEURISTIC_CONDITION: out = ops.awq_dequantize(qweight, scales, qzeros, 0, 0, 0) out = torch.matmul(reshaped_x, out) else: out = ops.awq_gemm(reshaped_x, qweight, scales, qzeros, pack_factor) if bias is not None: out = out + bias return out.reshape(out_shape) def apply_moe_weights(self, w1: Dict[str, torch.Tensor], w2: Dict[str, torch.Tensor], x: torch.Tensor, gating_output: torch.Tensor, topk: int, renormalize: bool) -> torch.Tensor: FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 1024 if FP16_MATMUL_HEURISTIC_CONDITION: dequant_w1 = ops.awq_dequantize(w1["qweight"], w1["scales"], w1["qzeros"], 0, 0, 0).permute(0, 2, 1) dequant_w2 = ops.awq_dequantize(w2["qweight"], w2["scales"], w2["qzeros"], 0, 0, 0).permute(0, 2, 1) return fused_moe(x, dequant_w1, dequant_w2, gating_output, topk, renormalize) topk_weights, topk_ids = fused_topk(gating_output, topk, renormalize) (sorted_token_ids, expert_ids, num_tokens_post_padded) = moe_align_block_size( topk_ids, 16, w1["qweight"].shape[0]) x = x.view(x.shape[0], 1, *x.shape[1:]) pack_factor = self.quant_config.pack_factor gate_up = ops.awq_group_gemm(x, w1["qweight"], w1["scales"], w1["qzeros"], topk_weights, sorted_token_ids, expert_ids, num_tokens_post_padded, False, pack_factor) out = torch.empty((gate_up.shape[:-1] + (gate_up.shape[-1] // 2, )), dtype=x.dtype, device=x.device) ops.silu_and_mul(out, gate_up) out = ops.awq_group_gemm(out, w2["qweight"], w2["scales"], w2["qzeros"], topk_weights, sorted_token_ids, expert_ids, num_tokens_post_padded, True, pack_factor) return torch.sum(out, dim=1)