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460 lines
15 KiB
Python
460 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Optional
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import flashinfer
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import pytest
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import torch
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from tests.kernels.quantization.nvfp4_utils import (
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dequantize_nvfp4_to_dtype,
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get_nvfp4_global_scale,
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)
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from vllm.platforms import current_platform
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from vllm.utils import round_up
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if not current_platform.is_device_capability(100):
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pytest.skip(
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"This TRTLLM kernel requires NVIDIA Blackwell.", allow_module_level=True
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)
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FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
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FP8_DTYPE = current_platform.fp8_dtype()
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FP4_DTYPE = torch.uint8
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def to_float8(x, dtype=torch.float8_e4m3fn):
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finfo = torch.finfo(dtype)
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min_val, max_val = x.aminmax()
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amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
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scale = finfo.max / amax * 0.1
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x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
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return x_scl_sat.to(dtype), scale.float().reciprocal()
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DTYPE = [torch.bfloat16]
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QUANT_DTYPES = [
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# (q_quant_dtype, kv_quant_dtype, o_quant_dtype)
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(None, None, None),
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(None, FP8_DTYPE, None),
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(FP8_DTYPE, FP8_DTYPE, None),
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(FP8_DTYPE, FP8_DTYPE, FP8_DTYPE),
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(FP8_DTYPE, FP8_DTYPE, FP4_DTYPE),
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]
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BATCH_SIZE = [4, 12]
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MAX_SEQ_LENS = [(1024, 4096)]
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NUM_HEADS = [(64, 8), (40, 8)]
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HEAD_SIZE = [128]
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KV_LAYOUT = ["HND"] # currently only HND is supported
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BLOCK_SIZE = [16]
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WINDOW_LEFT = [-1, 127]
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SOFT_CAP = [None, 50.0]
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HAS_SINKS = [True, False]
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NUM_BLOCKS = 32768 # Large enough to test overflow in index calculation.
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@pytest.mark.parametrize("dtype", DTYPE)
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@pytest.mark.parametrize("quant_dtypes", QUANT_DTYPES)
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@pytest.mark.parametrize("batch_size", BATCH_SIZE)
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@pytest.mark.parametrize("max_seq_lens", MAX_SEQ_LENS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZE)
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@pytest.mark.parametrize("kv_layout", KV_LAYOUT)
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@pytest.mark.parametrize("block_size", BLOCK_SIZE)
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@pytest.mark.parametrize("window_left", WINDOW_LEFT)
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@pytest.mark.parametrize("soft_cap", SOFT_CAP)
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@pytest.mark.parametrize("has_sinks", HAS_SINKS)
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@torch.inference_mode
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def test_flashinfer_trtllm_decode_with_baseline(
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dtype: torch.dtype,
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quant_dtypes: tuple[
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Optional[torch.dtype], Optional[torch.dtype], Optional[torch.dtype]
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],
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batch_size: int,
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max_seq_lens: tuple[int, int],
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num_heads: tuple[int, int],
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head_size: int,
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kv_layout: str,
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block_size: int,
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window_left: int,
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soft_cap: Optional[float],
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has_sinks: bool,
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) -> None:
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torch.set_default_device("cuda")
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current_platform.seed_everything(42)
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q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtypes
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q_quant_dtype = q_quant_dtype or dtype
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kv_quant_dtype = kv_quant_dtype or dtype
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o_quant_dtype = o_quant_dtype or dtype
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_, max_kv_len = max_seq_lens
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num_qo_heads, num_kv_heads = num_heads
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assert num_qo_heads % num_kv_heads == 0
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sm_scale = float(1.0 / (head_size**0.5))
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kv_cache_shape = None
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if kv_layout == "NHD":
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kv_cache_shape = (NUM_BLOCKS, 2, block_size, num_kv_heads, head_size)
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elif kv_layout == "HND":
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kv_cache_shape = (NUM_BLOCKS, 2, num_kv_heads, block_size, head_size)
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else:
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raise ValueError(f"Invalid kv_layout: {kv_layout}")
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# max_q_len = 1
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q_lens = torch.ones((batch_size,), dtype=torch.int32)
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q_indptr = torch.cat(
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[
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torch.tensor([0], dtype=torch.int32),
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torch.cumsum(q_lens, dim=0, dtype=torch.int32),
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]
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)
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query = torch.randn(torch.sum(q_lens).item(), num_qo_heads, head_size, dtype=dtype)
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if q_quant_dtype == FP8_DTYPE:
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query, q_scale = to_float8(query)
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ref_query = query.to(dtype) * q_scale
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else:
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q_scale = 1.0
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ref_query = query
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kv_lens = torch.randint(1, max_kv_len, (batch_size,), dtype=torch.int32)
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kv_lens[-1] = max_kv_len
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seq_lens = kv_lens + q_lens
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max_seq_len = torch.max(seq_lens).item()
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kv_cache = torch.randn(kv_cache_shape, dtype=dtype)
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if kv_quant_dtype == FP8_DTYPE:
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kv_cache, kv_scale = to_float8(kv_cache)
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ref_kv_cache = kv_cache.to(dtype) * kv_scale
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else:
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kv_scale = 1.0
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ref_kv_cache = kv_cache
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k_scale = v_scale = kv_scale
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max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
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block_tables = torch.randint(
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0, NUM_BLOCKS, (batch_size, max_num_blocks_per_seq), dtype=torch.int32
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)
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kv_indptr = [0]
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kv_indices = []
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kv_last_page_lens = []
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for i in range(batch_size):
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seq_len = seq_lens[i]
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assert seq_len > 0
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num_blocks = (seq_len + block_size - 1) // block_size
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kv_indices.extend(block_tables[i, :num_blocks])
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kv_indptr.append(kv_indptr[-1] + num_blocks)
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kv_last_page_len = seq_len % block_size
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if kv_last_page_len == 0:
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kv_last_page_len = block_size
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kv_last_page_lens.append(kv_last_page_len)
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kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
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kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
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kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
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workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
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# Baseline Decode
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if has_sinks:
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sinks = torch.rand(num_qo_heads, dtype=torch.float32) * 5
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wrapper = flashinfer.BatchAttentionWithAttentionSinkWrapper(
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float_workspace_buffer=workspace_buffer, kv_layout=kv_layout, backend="fa2"
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)
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else:
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sinks = None
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wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
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float_workspace_buffer=workspace_buffer, kv_layout=kv_layout, backend="fa2"
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)
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wrapper.plan(
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qo_indptr=q_indptr,
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paged_kv_indptr=kv_indptr,
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paged_kv_indices=kv_indices,
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paged_kv_last_page_len=kv_last_page_lens,
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num_qo_heads=num_qo_heads,
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num_kv_heads=num_kv_heads,
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head_dim_qk=head_size,
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page_size=block_size,
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causal=True,
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sm_scale=sm_scale,
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window_left=window_left,
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logits_soft_cap=soft_cap,
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q_data_type=dtype,
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kv_data_type=dtype,
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)
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output = torch.empty(ref_query.shape, dtype=dtype)
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wrapper.run(ref_query, ref_kv_cache, sinks, sm_scale, out=output)
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o_scale = 1.0
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o_sf_scale_float = None
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if o_quant_dtype == FP8_DTYPE:
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_, o_scale = to_float8(output)
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elif o_quant_dtype == FP4_DTYPE:
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o_sf_scale = get_nvfp4_global_scale(output)
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o_sf_scale_float = o_sf_scale.item()
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# TRTLLM Decode
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if o_quant_dtype == FP4_DTYPE:
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output_trtllm = flashinfer.utils.FP4Tensor(
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torch.empty(query.shape[:-1] + (query.shape[-1] // 2,), dtype=torch.uint8),
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torch.empty(
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(
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round_up(query.shape[0], 128),
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round_up(query.shape[1] * query.shape[2] // 16, 4),
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),
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dtype=torch.float8_e4m3fn,
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),
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)
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else:
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output_trtllm = torch.empty(query.shape, dtype=o_quant_dtype)
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flashinfer.decode.trtllm_batch_decode_with_kv_cache(
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query=query,
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kv_cache=kv_cache,
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workspace_buffer=workspace_buffer,
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block_tables=block_tables,
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seq_lens=seq_lens,
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max_seq_len=max_seq_len,
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bmm1_scale=q_scale * k_scale * sm_scale,
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bmm2_scale=v_scale / o_scale,
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window_left=window_left,
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sinks=sinks,
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o_sf_scale=o_sf_scale_float,
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out=output_trtllm,
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)
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if o_quant_dtype == FP8_DTYPE:
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output_trtllm = output_trtllm.to(dtype) * o_scale
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elif o_quant_dtype == FP4_DTYPE:
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output_trtllm.data = output_trtllm.data.reshape(
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-1, query.shape[1] * query.shape[2] // 2
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)
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output_trtllm = dequantize_nvfp4_to_dtype(
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output_trtllm.data, output_trtllm.scale, o_sf_scale, dtype, query.device
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)
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output_trtllm = output_trtllm.reshape(-1, query.shape[1], query.shape[2])
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if q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP4_DTYPE:
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rtol, atol = 7e-2, 9e-2
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elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP8_DTYPE:
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rtol, atol = 2e-2, 4e-2
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elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == dtype:
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rtol, atol = 1e-2, 2e-2
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else:
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rtol, atol = 1e-2, 1e-2
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(
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torch.testing.assert_close(output, output_trtllm, atol=atol, rtol=rtol),
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f"{torch.max(torch.abs(output - output_trtllm))}",
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)
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@pytest.mark.parametrize("dtype", DTYPE)
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@pytest.mark.parametrize("quant_dtypes", QUANT_DTYPES)
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@pytest.mark.parametrize("batch_size", BATCH_SIZE)
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@pytest.mark.parametrize("max_seq_lens", MAX_SEQ_LENS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZE)
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@pytest.mark.parametrize("kv_layout", KV_LAYOUT)
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@pytest.mark.parametrize("block_size", BLOCK_SIZE)
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@pytest.mark.parametrize("window_left", WINDOW_LEFT)
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@pytest.mark.parametrize("soft_cap", [None])
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@pytest.mark.parametrize("has_sinks", HAS_SINKS)
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@torch.inference_mode
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def test_flashinfer_trtllm_prefill_with_baseline(
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dtype: torch.dtype,
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quant_dtypes: tuple[
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Optional[torch.dtype], Optional[torch.dtype], Optional[torch.dtype]
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],
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batch_size: int,
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max_seq_lens: tuple[int, int],
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num_heads: tuple[int, int],
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head_size: int,
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kv_layout: str,
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block_size: int,
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window_left: int,
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soft_cap: Optional[float],
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has_sinks: bool,
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) -> None:
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torch.set_default_device("cuda")
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current_platform.seed_everything(42)
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q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtypes
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q_quant_dtype = q_quant_dtype or dtype
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kv_quant_dtype = kv_quant_dtype or dtype
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o_quant_dtype = o_quant_dtype or dtype
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if q_quant_dtype != kv_quant_dtype:
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pytest.skip("Skipped mixed QKV dtypes for prefill")
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max_q_len, max_kv_len = max_seq_lens
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num_qo_heads, num_kv_heads = num_heads
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assert num_qo_heads % num_kv_heads == 0
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sm_scale = float(1.0 / (head_size**0.5))
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kv_cache_shape = None
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if kv_layout == "NHD":
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kv_cache_shape = (NUM_BLOCKS, 2, block_size, num_kv_heads, head_size)
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elif kv_layout == "HND":
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kv_cache_shape = (NUM_BLOCKS, 2, num_kv_heads, block_size, head_size)
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else:
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raise ValueError(f"Invalid kv_layout: {kv_layout}")
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q_lens = torch.randint(1, max_q_len, (batch_size,), dtype=torch.int32)
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q_lens[-1] = max_q_len
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q_indptr = torch.cat(
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[
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torch.tensor([0], dtype=torch.int32),
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torch.cumsum(q_lens, dim=0, dtype=torch.int32),
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]
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)
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query = torch.randn(torch.sum(q_lens).item(), num_qo_heads, head_size, dtype=dtype)
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if q_quant_dtype == FP8_DTYPE:
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query, q_scale = to_float8(query)
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ref_query = query.to(dtype) * q_scale
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else:
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q_scale = 1.0
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ref_query = query
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kv_lens = torch.randint(1, max_kv_len, (batch_size,), dtype=torch.int32)
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kv_lens[-1] = max_kv_len
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seq_lens = kv_lens + q_lens
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max_seq_len = torch.max(seq_lens).item()
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kv_cache = torch.randn(kv_cache_shape, dtype=dtype)
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if kv_quant_dtype == FP8_DTYPE:
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kv_cache, kv_scale = to_float8(kv_cache)
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ref_kv_cache = kv_cache.to(dtype) * kv_scale
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else:
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kv_scale = 1.0
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ref_kv_cache = kv_cache
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k_scale = v_scale = kv_scale
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max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
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block_tables = torch.randint(
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0, NUM_BLOCKS, (batch_size, max_num_blocks_per_seq), dtype=torch.int32
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)
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kv_indptr = [0]
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kv_indices = []
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kv_last_page_lens = []
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for i in range(batch_size):
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seq_len = seq_lens[i]
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assert seq_len > 0
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num_blocks = (seq_len + block_size - 1) // block_size
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kv_indices.extend(block_tables[i, :num_blocks])
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kv_indptr.append(kv_indptr[-1] + num_blocks)
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kv_last_page_len = seq_len % block_size
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if kv_last_page_len == 0:
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kv_last_page_len = block_size
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kv_last_page_lens.append(kv_last_page_len)
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kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
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kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
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kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
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workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
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# Baseline Prefill
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if has_sinks:
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sinks = torch.rand(num_qo_heads, dtype=torch.float32) * 5
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wrapper = flashinfer.BatchAttentionWithAttentionSinkWrapper(
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float_workspace_buffer=workspace_buffer, kv_layout=kv_layout, backend="fa2"
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)
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else:
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sinks = None
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wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
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float_workspace_buffer=workspace_buffer, kv_layout=kv_layout, backend="fa2"
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)
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wrapper.plan(
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qo_indptr=q_indptr,
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paged_kv_indptr=kv_indptr,
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paged_kv_indices=kv_indices,
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paged_kv_last_page_len=kv_last_page_lens,
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num_qo_heads=num_qo_heads,
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num_kv_heads=num_kv_heads,
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head_dim_qk=head_size,
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page_size=block_size,
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causal=True,
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sm_scale=sm_scale,
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window_left=window_left,
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logits_soft_cap=soft_cap,
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q_data_type=dtype,
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kv_data_type=dtype,
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)
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output = torch.empty(ref_query.shape, dtype=dtype)
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wrapper.run(ref_query, ref_kv_cache, sinks, sm_scale, out=output)
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o_scale = 1.0
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o_sf_scale_float = None
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if o_quant_dtype == FP8_DTYPE:
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_, o_scale = to_float8(output)
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elif o_quant_dtype == FP4_DTYPE:
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o_sf_scale = get_nvfp4_global_scale(output)
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o_sf_scale_float = o_sf_scale.item()
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# TRTLLM Prefill
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if o_quant_dtype == FP4_DTYPE:
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output_trtllm = flashinfer.utils.FP4Tensor(
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torch.empty(query.shape[:-1] + (query.shape[-1] // 2,), dtype=torch.uint8),
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torch.empty(
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(
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round_up(query.shape[0], 128),
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round_up(query.shape[1] * query.shape[2] // 16, 4),
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),
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dtype=torch.float8_e4m3fn,
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),
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)
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else:
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output_trtllm = torch.empty(query.shape, dtype=o_quant_dtype)
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flashinfer.prefill.trtllm_batch_context_with_kv_cache(
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query=query,
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kv_cache=kv_cache,
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workspace_buffer=workspace_buffer,
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block_tables=block_tables,
|
|
seq_lens=seq_lens,
|
|
max_q_len=max_q_len,
|
|
max_kv_len=max_seq_len,
|
|
bmm1_scale=q_scale * k_scale * sm_scale,
|
|
bmm2_scale=v_scale / o_scale,
|
|
batch_size=batch_size,
|
|
cum_seq_lens_q=q_indptr,
|
|
cum_seq_lens_kv=kv_indptr,
|
|
window_left=window_left,
|
|
sinks=sinks,
|
|
o_sf_scale=o_sf_scale_float,
|
|
out=output_trtllm,
|
|
)
|
|
if o_quant_dtype == FP8_DTYPE:
|
|
output_trtllm = output_trtllm.to(dtype) * o_scale
|
|
elif o_quant_dtype == FP4_DTYPE:
|
|
output_trtllm.data = output_trtllm.data.reshape(
|
|
-1, query.shape[1] * query.shape[2] // 2
|
|
)
|
|
output_trtllm = dequantize_nvfp4_to_dtype(
|
|
output_trtllm.data, output_trtllm.scale, o_sf_scale, dtype, query.device
|
|
)
|
|
output_trtllm = output_trtllm.reshape(-1, query.shape[1], query.shape[2])
|
|
|
|
if q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP4_DTYPE:
|
|
rtol, atol = 1e-1, 2e-1
|
|
elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP8_DTYPE:
|
|
rtol, atol = 4e-2, 6e-2
|
|
elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == dtype:
|
|
rtol, atol = 2e-2, 3e-2
|
|
else:
|
|
rtol, atol = 1e-2, 1e-2
|
|
|
|
(
|
|
torch.testing.assert_close(output, output_trtllm, atol=atol, rtol=rtol),
|
|
f"{torch.max(torch.abs(output - output_trtllm))}",
|
|
)
|