[2/2] Introduce Chunked-SGMV kernels and corresponding LoRA backend for improved performance (#10286)
This commit is contained in:
Lifu Huang
740
test/srt/lora/test_chunked_sgmv_backend.py
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740
test/srt/lora/test_chunked_sgmv_backend.py
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import random
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import unittest
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from enum import Enum
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from typing import Dict, List, Optional, Tuple
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import torch
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from sglang.srt.lora.backend.chunked_backend import ChunkedSgmvLoRABackend
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from sglang.srt.lora.triton_ops import (
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chunked_sgmv_lora_expand_forward,
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chunked_sgmv_lora_shrink_forward,
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)
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from sglang.srt.lora.utils import LoRABatchInfo
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def safe_matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
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"""Matrix multiplication with mixed precision handling for float16"""
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result = torch.matmul(a.float(), b.float())
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return result.to(a.dtype)
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class BatchComposition(Enum):
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UNIFORM = "uniform"
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MIXED = "mixed"
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SKEWED = "skewed"
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NONE = "_NO_LORA_"
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class BatchMode(Enum):
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PREFILL = "prefill"
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DECODE = "decode"
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def reference_sgmv_shrink(
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x: torch.Tensor,
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weights: torch.Tensor,
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batch_info: LoRABatchInfo,
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seq_lengths: List[int],
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lora_assignments: List[str],
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num_slices: int = 1,
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) -> torch.Tensor:
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"""
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Simple sequence-level reference implementation of SGMV shrink operation.
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Args:
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x: (total_seq_len, input_dim) - Input activations
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weights: (num_loras, num_slices * max_rank, input_dim) - LoRA A weights
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batch_info: Batch information (only used for lora_ranks)
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seq_lengths: Length of each sequence
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lora_assignments: LoRA name for each sequence
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num_slices: Number of slices (3 for QKV, 2 for gate_up, 1 for others)
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Returns:
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output: (total_seq_len, num_slices * max_rank) - Intermediate activations
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"""
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if weights.numel() == 0:
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total_seq_len = x.shape[0]
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return torch.zeros(total_seq_len, 0, dtype=x.dtype, device=x.device)
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total_seq_len, input_dim = x.shape
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num_loras, weight_out_dim, _ = weights.shape
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max_rank = weight_out_dim // num_slices
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output = torch.zeros(
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total_seq_len, num_slices * max_rank, dtype=x.dtype, device=x.device
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)
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unique_loras = sorted(set(lora_assignments))
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lora_name_to_idx = {name: idx for idx, name in enumerate(unique_loras)}
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lora_ranks = batch_info.lora_ranks.cpu().numpy()
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token_offset = 0
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for seq_len, lora_name in zip(seq_lengths, lora_assignments):
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if seq_len == 0:
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continue
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lora_idx = lora_name_to_idx[lora_name]
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rank = lora_ranks[lora_idx]
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if rank > 0:
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x_seq = x[token_offset : token_offset + seq_len, :]
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w_seq = weights[lora_idx, : num_slices * rank, :]
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result = safe_matmul(x_seq, w_seq.t())
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output[token_offset : token_offset + seq_len, : num_slices * rank] = result
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token_offset += seq_len
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return output
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def reference_sgmv_expand(
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x: torch.Tensor,
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weights: torch.Tensor,
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batch_info: LoRABatchInfo,
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seq_lengths: List[int],
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lora_assignments: List[str],
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slice_offsets: torch.Tensor,
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max_slice_size: int,
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base_output: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""
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Simple sequence-level reference implementation of SGMV expand operation.
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Args:
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x: (total_seq_len, num_slices * max_rank) - Intermediate activations
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weights: (num_loras, output_dim, max_rank) - LoRA B weights
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batch_info: Batch information (only used for lora_ranks)
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seq_lengths: Length of each sequence
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lora_assignments: LoRA name for each sequence
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slice_offsets: Tensor defining slice boundaries
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max_slice_size: Maximum slice size for chunking
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base_output: Optional base output to accumulate into
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Returns:
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output: (total_seq_len, total_output_dim) - Final output
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"""
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if weights.numel() == 0:
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total_seq_len = x.shape[0]
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total_output_dim = slice_offsets[-1].item() if len(slice_offsets) > 0 else 0
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return torch.zeros(
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total_seq_len, total_output_dim, dtype=x.dtype, device=x.device
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)
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total_seq_len, _ = x.shape
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num_slices = len(slice_offsets) - 1
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if base_output is not None:
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output = base_output.clone()
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else:
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total_output_dim = slice_offsets[-1].item()
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output = torch.zeros(
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total_seq_len, total_output_dim, dtype=x.dtype, device=x.device
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)
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unique_loras = sorted(set(lora_assignments))
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lora_name_to_idx = {name: idx for idx, name in enumerate(unique_loras)}
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lora_ranks = batch_info.lora_ranks.cpu().numpy()
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token_offset = 0
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for seq_len, lora_name in zip(seq_lengths, lora_assignments):
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if seq_len == 0:
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continue
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lora_idx = lora_name_to_idx[lora_name]
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lora_rank = lora_ranks[lora_idx]
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if lora_rank > 0:
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# Extract sequence intermediate activations
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x_seq = x[
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token_offset : token_offset + seq_len, : num_slices * lora_rank
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] # (seq_len, num_slices * rank)
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for slice_idx in range(num_slices):
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slice_start_input = slice_idx * lora_rank
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slice_end_input = (slice_idx + 1) * lora_rank
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slice_start_output = slice_offsets[slice_idx].item()
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slice_end_output = slice_offsets[slice_idx + 1].item()
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x_slice = x_seq[:, slice_start_input:slice_end_input] # (seq_len, rank)
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w_slice = weights[
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lora_idx, slice_start_output:slice_end_output, :lora_rank
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] # (slice_dim, rank)
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result = safe_matmul(x_slice, w_slice.t()) # (seq_len, slice_dim)
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output[
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token_offset : token_offset + seq_len,
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slice_start_output:slice_end_output,
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] += result
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token_offset += seq_len
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return output
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class TestChunkedSGMV(unittest.TestCase):
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# Test configuration constants
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RTOL = 1e-3
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ATOL = 1e-3
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DEFAULT_BATCH_SIZE = 8
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def _compare_shrink_outputs(
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self,
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chunked_output: torch.Tensor,
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reference_output: torch.Tensor,
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seq_lengths: List[int],
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lora_assignments: List[str],
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batch_info: LoRABatchInfo,
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num_slices: int,
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test_name: str,
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):
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"""
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Compare only the valid portions of shrink outputs.
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The chunked SGMV shrink kernel only guarantees correctness for
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output[seq_start:seq_end, :rank * num_slices] for each sequence.
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"""
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# Create mapping from LoRA names to indices and ranks
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unique_loras = sorted(set(lora_assignments))
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lora_name_to_idx = {name: idx for idx, name in enumerate(unique_loras)}
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lora_ranks = batch_info.lora_ranks.cpu().numpy()
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token_offset = 0
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for seq_idx, (seq_len, lora_name) in enumerate(
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zip(seq_lengths, lora_assignments)
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):
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if seq_len == 0:
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continue
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lora_idx = lora_name_to_idx[lora_name]
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rank = lora_ranks[lora_idx]
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if rank > 0:
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# Only compare the valid columns for this sequence
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valid_cols = num_slices * rank
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chunked_seq = chunked_output[
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token_offset : token_offset + seq_len, :valid_cols
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]
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reference_seq = reference_output[
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token_offset : token_offset + seq_len, :valid_cols
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]
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torch.testing.assert_close(
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chunked_seq,
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reference_seq,
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rtol=self.RTOL,
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atol=self.ATOL,
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msg=f"Shrink operation failed for {test_name}, sequence {seq_idx} ({lora_name})",
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)
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token_offset += seq_len
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def setUp(self):
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"""Set up common test parameters"""
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torch.manual_seed(42)
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random.seed(42)
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self.device = torch.device("cuda")
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self.dtype = torch.float16
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self.input_dim = 2560 # Hidden dimension
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self.max_seq_len = 1024
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# LoRA configurations: name -> (rank, output_q, output_k, output_v)
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self.lora_configs = {
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"lora_A": (8, 4096, 1024, 1024),
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"lora_B": (16, 4096, 1024, 1024),
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"lora_C": (32, 4096, 1024, 1024),
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"_NO_LORA_": (0, 4096, 1024, 1024),
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}
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# QKV slice offsets: 4096 (Q) + 1024 (K) + 1024 (V) = 6144 total
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self.slice_offsets = torch.tensor(
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[0, 4096, 5120, 6144], dtype=torch.int32, device=self.device
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)
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self.max_slice_size = 4096
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def generate_sequence_lengths(
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self,
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batch_size: int,
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batch_mode: BatchMode = BatchMode.PREFILL,
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min_len: int = 1,
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max_len: int = None,
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) -> List[int]:
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"""Generate sequence lengths for a batch based on mode"""
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if batch_mode == BatchMode.DECODE:
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return [1] * batch_size
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else:
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if max_len is None:
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max_len = self.max_seq_len
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return [random.randint(min_len, max_len) for _ in range(batch_size)]
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def create_lora_weights(
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self, lora_name: str, include_missing_k: bool = False
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Create LoRA A and B weights for given configuration"""
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rank, out_q, out_k, out_v = self.lora_configs[lora_name]
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if rank == 0:
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lora_a = torch.empty(
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0, self.input_dim, dtype=self.dtype, device=self.device
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)
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lora_b = torch.empty(
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out_q + out_k + out_v, 0, dtype=self.dtype, device=self.device
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)
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return lora_a, lora_b
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# Create LoRA A weights (3 slices for QKV)
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lora_a = torch.randn(
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3 * rank, self.input_dim, dtype=self.dtype, device=self.device
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)
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if include_missing_k:
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lora_a[rank : 2 * rank, :] = 0.0
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# Create LoRA B weights (stacked Q, K, V)
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total_output_dim = out_q + out_k + out_v
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lora_b = torch.randn(
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total_output_dim, rank, dtype=self.dtype, device=self.device
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)
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if include_missing_k:
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lora_b[out_q : out_q + out_k, :] = 0.0
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return lora_a, lora_b
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def create_batch_info(
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self,
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seq_lengths: List[int],
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lora_assignments: List[Optional[str]],
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batch_mode: BatchMode = BatchMode.PREFILL,
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) -> LoRABatchInfo:
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"""Create LoRABatchInfo using the same logic as chunked backend"""
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unique_loras = sorted(set(lora_assignments))
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lora_name_to_idx = {name: idx for idx, name in enumerate(unique_loras)}
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seq_weight_indices = [lora_name_to_idx[name] for name in lora_assignments]
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lora_ranks = [self.lora_configs[name][0] for name in unique_loras]
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def create_mock_batch():
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# Create a minimal mock ForwardBatch for the test
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class MockForwardBatch:
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def __init__(self, batch_size, seq_lengths):
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self.batch_size = batch_size
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self.extend_seq_lens_cpu = seq_lengths
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self.forward_mode = MockForwardMode()
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class MockForwardMode:
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def is_extend(self):
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return batch_mode == BatchMode.PREFILL
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return MockForwardBatch(len(seq_lengths), seq_lengths)
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mock_batch = create_mock_batch()
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# Use the same functions as chunked backend
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permutation, weights_reordered = ChunkedSgmvLoRABackend._get_permutation(
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seq_weight_indices, mock_batch
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)
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# Create a minimal backend instance to access _get_segments_info
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mock_backend = ChunkedSgmvLoRABackend(max_loras_per_batch=8, device=self.device)
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weight_indices_list, seg_indptr = mock_backend._get_segments_info(
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weights_reordered
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)
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scalings = [1.0] * len(unique_loras)
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seg_indptr_tensor = seg_indptr.to(self.device)
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weight_indices_tensor = weight_indices_list.to(self.device)
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lora_ranks_tensor = (
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torch.tensor(lora_ranks, dtype=torch.int32, device=self.device)
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if lora_ranks
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else torch.empty(0, dtype=torch.int32, device=self.device)
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)
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scalings_tensor = (
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torch.tensor(scalings, dtype=torch.float32, device=self.device)
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if scalings
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else torch.empty(0, dtype=torch.float32, device=self.device)
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)
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permutation_tensor = permutation.to(
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self.device, dtype=torch.int32
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) # Convert to int32 for LoRABatchInfo
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seq_lens_tensor = torch.tensor(
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seq_lengths, dtype=torch.int32, device=self.device
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)
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return LoRABatchInfo(
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use_cuda_graph=False,
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bs=len(seq_lengths),
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num_segments=len(weight_indices_list), # Number of segments, not sequences!
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seg_indptr=seg_indptr_tensor,
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weight_indices=weight_indices_tensor,
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lora_ranks=lora_ranks_tensor,
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scalings=scalings_tensor,
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seg_lens=seq_lens_tensor, # Original sequence lengths for reference
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max_len=max(seq_lengths) if seq_lengths else 0,
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permutation=permutation_tensor, # Token reordering permutation
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)
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def stack_lora_weights(
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self, weight_list: List[torch.Tensor], is_lora_a: bool
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) -> torch.Tensor:
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"""Stack LoRA weights from different adapters into a single tensor"""
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if not weight_list:
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return torch.empty(0, 0, 0, dtype=self.dtype, device=self.device)
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first_non_empty = next((w for w in weight_list if w.numel() > 0), None)
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if first_non_empty is None:
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return torch.empty(
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len(weight_list), 0, 0, dtype=self.dtype, device=self.device
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)
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if is_lora_a:
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# LoRA A: (slice_num * rank, input_dim) -> (num_loras, slice_num * max_rank, input_dim)
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max_rank = max(w.shape[0] // 3 if w.numel() > 0 else 0 for w in weight_list)
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final_shape = (len(weight_list), 3 * max_rank, self.input_dim)
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else:
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# LoRA B: (output_dim, rank) -> (num_loras, output_dim, max_rank)
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max_rank = max(w.shape[1] if w.numel() > 0 else 0 for w in weight_list)
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output_dim = first_non_empty.shape[0]
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final_shape = (len(weight_list), output_dim, max_rank)
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stacked = torch.zeros(final_shape, dtype=self.dtype, device=self.device)
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for i, weight in enumerate(weight_list):
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if weight.numel() > 0:
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if is_lora_a:
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stacked[i, : weight.shape[0], :] = weight
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else:
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stacked[i, :, : weight.shape[1]] = weight
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return stacked
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def create_test_batch(
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self,
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batch_composition: BatchComposition,
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batch_size: int,
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batch_mode: BatchMode = BatchMode.PREFILL,
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include_missing_k: bool = False,
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) -> Tuple[
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torch.Tensor,
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Dict[str, Tuple[torch.Tensor, torch.Tensor]],
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LoRABatchInfo,
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List[int],
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List[str],
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]:
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"""Create test batch with specified composition and mode"""
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seq_lengths = self.generate_sequence_lengths(
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batch_size, batch_mode, 1, self.max_seq_len
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)
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if batch_composition == BatchComposition.UNIFORM:
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lora_assignments = ["lora_A"] * batch_size
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elif batch_composition == BatchComposition.MIXED:
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lora_names = ["lora_A", "lora_B", "lora_C", None]
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lora_assignments = [
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lora_names[i % len(lora_names)] for i in range(batch_size)
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]
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elif batch_composition == BatchComposition.SKEWED:
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num_minority = max(1, batch_size // 8)
|
||||
lora_assignments = ["lora_A"] * num_minority + ["lora_B"] * (
|
||||
batch_size - num_minority
|
||||
)
|
||||
random.shuffle(lora_assignments)
|
||||
elif batch_composition == BatchComposition.NONE:
|
||||
lora_assignments = [None] * batch_size
|
||||
else:
|
||||
raise ValueError(f"Unknown batch composition: {batch_composition}")
|
||||
|
||||
total_seq_len = sum(seq_lengths)
|
||||
x = torch.randn(
|
||||
total_seq_len, self.input_dim, dtype=self.dtype, device=self.device
|
||||
)
|
||||
|
||||
normalized_assignments = [
|
||||
name if name is not None else "_NO_LORA_" for name in lora_assignments
|
||||
]
|
||||
unique_loras = set(normalized_assignments)
|
||||
weights = {}
|
||||
for lora_name in unique_loras:
|
||||
weights[lora_name] = self.create_lora_weights(lora_name, include_missing_k)
|
||||
|
||||
batch_info = self.create_batch_info(
|
||||
seq_lengths, normalized_assignments, batch_mode
|
||||
)
|
||||
|
||||
return x, weights, batch_info, seq_lengths, normalized_assignments
|
||||
|
||||
def run_test_comparison(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
weights: Dict[str, Tuple[torch.Tensor, torch.Tensor]],
|
||||
batch_info: LoRABatchInfo,
|
||||
seq_lengths: List[int],
|
||||
lora_assignments: List[str],
|
||||
test_name: str,
|
||||
):
|
||||
"""Run comparison between chunked and reference implementations"""
|
||||
if not weights: # Handle case with no LoRA weights
|
||||
return
|
||||
|
||||
# Stack LoRA A weights
|
||||
lora_a_weights = [weights[name][0] for name in sorted(weights.keys())]
|
||||
stacked_lora_a = self.stack_lora_weights(lora_a_weights, is_lora_a=True)
|
||||
|
||||
# Stack LoRA B weights
|
||||
lora_b_weights = [weights[name][1] for name in sorted(weights.keys())]
|
||||
stacked_lora_b = self.stack_lora_weights(lora_b_weights, is_lora_a=False)
|
||||
|
||||
# Test shrink operation
|
||||
chunked_shrink = chunked_sgmv_lora_shrink_forward(
|
||||
x, stacked_lora_a, batch_info, num_slices=3
|
||||
)
|
||||
reference_shrink = reference_sgmv_shrink(
|
||||
x, stacked_lora_a, batch_info, seq_lengths, lora_assignments, num_slices=3
|
||||
)
|
||||
|
||||
# Only compare valid portions of shrink output (first rank * num_slices columns per sequence)
|
||||
self._compare_shrink_outputs(
|
||||
chunked_shrink,
|
||||
reference_shrink,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
batch_info,
|
||||
num_slices=3,
|
||||
test_name=test_name,
|
||||
)
|
||||
|
||||
# Test expand operation
|
||||
chunked_expand = chunked_sgmv_lora_expand_forward(
|
||||
reference_shrink,
|
||||
stacked_lora_b,
|
||||
batch_info,
|
||||
self.slice_offsets,
|
||||
self.max_slice_size,
|
||||
)
|
||||
reference_expand = reference_sgmv_expand(
|
||||
reference_shrink,
|
||||
stacked_lora_b,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
self.slice_offsets,
|
||||
self.max_slice_size,
|
||||
)
|
||||
|
||||
torch.testing.assert_close(
|
||||
chunked_expand,
|
||||
reference_expand,
|
||||
rtol=self.RTOL,
|
||||
atol=self.ATOL,
|
||||
msg=f"Expand operation failed for {test_name}",
|
||||
)
|
||||
|
||||
# === Basic Operations Tests ===
|
||||
|
||||
def test_shrink_basic(self):
|
||||
"""Test basic shrink operation against PyTorch reference"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(BatchComposition.UNIFORM, batch_size)
|
||||
)
|
||||
|
||||
lora_a_weights = [weights[name][0] for name in sorted(weights.keys())]
|
||||
stacked_lora_a = self.stack_lora_weights(lora_a_weights, is_lora_a=True)
|
||||
|
||||
chunked_shrink = chunked_sgmv_lora_shrink_forward(
|
||||
x, stacked_lora_a, batch_info, num_slices=3
|
||||
)
|
||||
reference_shrink = reference_sgmv_shrink(
|
||||
x,
|
||||
stacked_lora_a,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
num_slices=3,
|
||||
)
|
||||
|
||||
torch.testing.assert_close(
|
||||
chunked_shrink, reference_shrink, rtol=self.RTOL, atol=self.ATOL
|
||||
)
|
||||
|
||||
def test_expand_basic(self):
|
||||
"""Test basic expand operation against PyTorch reference"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(BatchComposition.UNIFORM, batch_size)
|
||||
)
|
||||
|
||||
lora_a_weights = [weights[name][0] for name in sorted(weights.keys())]
|
||||
stacked_lora_a = self.stack_lora_weights(lora_a_weights, is_lora_a=True)
|
||||
|
||||
intermediate = reference_sgmv_shrink(
|
||||
x,
|
||||
stacked_lora_a,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
num_slices=3,
|
||||
)
|
||||
|
||||
lora_b_weights = [weights[name][1] for name in sorted(weights.keys())]
|
||||
stacked_lora_b = self.stack_lora_weights(
|
||||
lora_b_weights, is_lora_a=False
|
||||
)
|
||||
|
||||
chunked_expand = chunked_sgmv_lora_expand_forward(
|
||||
intermediate,
|
||||
stacked_lora_b,
|
||||
batch_info,
|
||||
self.slice_offsets,
|
||||
self.max_slice_size,
|
||||
)
|
||||
reference_expand = reference_sgmv_expand(
|
||||
intermediate,
|
||||
stacked_lora_b,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
self.slice_offsets,
|
||||
self.max_slice_size,
|
||||
)
|
||||
|
||||
torch.testing.assert_close(
|
||||
chunked_expand, reference_expand, rtol=self.RTOL, atol=self.ATOL
|
||||
)
|
||||
|
||||
# === QKV Operations Test ===
|
||||
|
||||
def test_qkv_missing_projections(self):
|
||||
"""Test QKV operations with missing k_proj (Qwen3 scenario)"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(
|
||||
BatchComposition.MIXED, batch_size, include_missing_k=True
|
||||
)
|
||||
)
|
||||
self.run_test_comparison(
|
||||
x,
|
||||
weights,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
f"QKV missing k_proj batch_size={batch_size}",
|
||||
)
|
||||
|
||||
# === Batch Composition Tests ===
|
||||
|
||||
def test_uniform_lora_batch(self):
|
||||
"""All sequences use same LoRA, random sequence lengths"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(BatchComposition.UNIFORM, batch_size)
|
||||
)
|
||||
self.run_test_comparison(
|
||||
x,
|
||||
weights,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
f"uniform batch_size={batch_size}",
|
||||
)
|
||||
|
||||
def test_evenly_mixed_lora_batch(self):
|
||||
"""Sequences evenly distributed across LoRAs, random lengths"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(BatchComposition.MIXED, batch_size)
|
||||
)
|
||||
self.run_test_comparison(
|
||||
x,
|
||||
weights,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
f"mixed batch_size={batch_size}",
|
||||
)
|
||||
|
||||
def test_highly_skewed_lora_batch(self):
|
||||
"""Highly uneven LoRA distribution, random lengths"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(BatchComposition.SKEWED, batch_size)
|
||||
)
|
||||
self.run_test_comparison(
|
||||
x,
|
||||
weights,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
f"skewed batch_size={batch_size}",
|
||||
)
|
||||
|
||||
# === Decode Mode Tests ===
|
||||
|
||||
def test_decode_uniform_lora_batch(self):
|
||||
"""Decode mode: All sequences use same LoRA, all length 1"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(
|
||||
BatchComposition.UNIFORM, batch_size, BatchMode.DECODE
|
||||
)
|
||||
)
|
||||
self.run_test_comparison(
|
||||
x,
|
||||
weights,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
f"decode uniform batch_size={batch_size}",
|
||||
)
|
||||
|
||||
def test_decode_mixed_lora_batch(self):
|
||||
"""Decode mode: Sequences distributed across LoRAs, all length 1"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(
|
||||
BatchComposition.MIXED, batch_size, BatchMode.DECODE
|
||||
)
|
||||
)
|
||||
self.run_test_comparison(
|
||||
x,
|
||||
weights,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
f"decode mixed batch_size={batch_size}",
|
||||
)
|
||||
|
||||
def test_decode_skewed_lora_batch(self):
|
||||
"""Decode mode: Highly uneven LoRA distribution, all length 1"""
|
||||
for batch_size in [1, 2, 16, 64]:
|
||||
with self.subTest(batch_size=batch_size):
|
||||
x, weights, batch_info, seq_lengths, lora_assignments = (
|
||||
self.create_test_batch(
|
||||
BatchComposition.SKEWED, batch_size, BatchMode.DECODE
|
||||
)
|
||||
)
|
||||
self.run_test_comparison(
|
||||
x,
|
||||
weights,
|
||||
batch_info,
|
||||
seq_lengths,
|
||||
lora_assignments,
|
||||
f"decode skewed batch_size={batch_size}",
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user