1359 lines
40 KiB
Python
1359 lines
40 KiB
Python
# Copyright 2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import functools
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import json
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import logging
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import os
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from functools import lru_cache
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from typing import Any, Dict, List, Optional, Tuple
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import torch
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import triton
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import triton.language as tl
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from sglang.srt.layers.quantization import deep_gemm_wrapper
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from sglang.srt.utils import (
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align,
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direct_register_custom_op,
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get_bool_env_var,
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get_device_core_count,
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get_device_name,
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is_cpu,
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is_cuda,
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is_hip,
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log_info_on_rank0,
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supports_custom_op,
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)
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_is_hip = is_hip()
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_is_cuda = is_cuda()
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_is_cpu = is_cpu()
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_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
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if _is_cuda:
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from sgl_kernel import (
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sgl_per_tensor_quant_fp8,
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sgl_per_token_group_quant_fp8,
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sgl_per_token_quant_fp8,
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)
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if _is_hip:
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if _use_aiter:
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try:
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from aiter import ( # v0.1.3
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dynamic_per_tensor_quant,
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dynamic_per_token_scaled_quant,
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static_per_tensor_quant,
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)
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except ImportError:
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raise ImportError("aiter is required when SGLANG_USE_AITER is set to True")
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else:
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try:
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import vllm._C
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except ImportError:
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raise ImportError("vllm is required when SGLANG_USE_AITER is set to False")
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logger = logging.getLogger(__name__)
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@lru_cache()
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def is_fp8_fnuz() -> bool:
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if _is_hip:
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# only device 0 is checked, this assumes MI300 platforms are homogeneous
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return "gfx94" in torch.cuda.get_device_properties(0).gcnArchName
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return False
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if is_fp8_fnuz():
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fp8_dtype = torch.float8_e4m3fnuz
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fp8_max = 224.0
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else:
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fp8_dtype = torch.float8_e4m3fn
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fp8_max = torch.finfo(fp8_dtype).max
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fp8_min = -fp8_max
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if supports_custom_op():
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def deep_gemm_fp8_fp8_bf16_nt(
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A: torch.Tensor,
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As: torch.Tensor,
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B: torch.Tensor,
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Bs: torch.Tensor,
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C: torch.Tensor,
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) -> None:
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deep_gemm_wrapper.gemm_nt_f8f8bf16((A, As), (B, Bs), C)
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def deep_gemm_fp8_fp8_bf16_nt_fake(
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A: torch.Tensor,
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As: torch.Tensor,
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B: torch.Tensor,
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Bs: torch.Tensor,
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C: torch.Tensor,
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) -> None:
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return
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direct_register_custom_op(
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op_name="deep_gemm_fp8_fp8_bf16_nt",
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op_func=deep_gemm_fp8_fp8_bf16_nt,
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mutates_args=["C"],
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fake_impl=deep_gemm_fp8_fp8_bf16_nt_fake,
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)
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@triton.jit
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def _per_token_group_quant_fp8(
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# Pointers to inputs and output
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y_ptr,
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y_q_ptr,
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y_s_ptr,
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# Stride of input
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y_stride,
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# Columns of input
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N,
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# Avoid to divide zero
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eps,
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# Information for float8
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fp8_min,
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fp8_max,
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# Meta-parameters
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BLOCK: tl.constexpr,
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):
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"""A Triton-accelerated function to perform per-token-group quantization on a
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tensor.
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This function converts the tensor values into float8 values.
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"""
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# Map the program id to the row of X and Y it should compute.
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g_id = tl.program_id(0)
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y_ptr += g_id * y_stride
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y_q_ptr += g_id * y_stride
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y_s_ptr += g_id
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cols = tl.arange(0, BLOCK) # N <= BLOCK
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mask = cols < N
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y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
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# Quant
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_absmax = tl.maximum(tl.max(tl.abs(y)), eps)
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y_s = _absmax / fp8_max
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y_s_inv = 1.0 / y_s
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y_q = tl.clamp(y * y_s_inv, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
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tl.store(y_q_ptr + cols, y_q, mask=mask)
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tl.store(y_s_ptr, y_s)
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@triton.jit
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def _per_token_group_quant_fp8_colmajor(
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# Pointers to inputs and output
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y_ptr,
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y_q_ptr,
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y_s_ptr,
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group_size,
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# Num columns of y
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y_num_columns,
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# Stride from one column to the next of y_s
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y_s_col_stride,
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# Avoid to divide zero
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eps,
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# Information for float8
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fp8_min,
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fp8_max,
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# Meta-parameters
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BLOCK: tl.constexpr,
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SCALE_UE8M0: tl.constexpr,
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):
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"""A Triton-accelerated function to perform per-token-group
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quantization on a tensor.
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This function converts the tensor values into float8 values.
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"""
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# Map the program id to the row of X and Y it should compute.
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g_id = tl.program_id(0)
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y_ptr += g_id.to(tl.int64) * group_size
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y_q_ptr += g_id.to(tl.int64) * group_size
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# Convert g_id the flattened block coordinate to 2D so we can index
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# into the output y_scales matrix
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blocks_per_row = y_num_columns // group_size
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scale_col = g_id % blocks_per_row
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scale_row = g_id // blocks_per_row
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y_s_ptr += scale_col * y_s_col_stride + scale_row
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cols = tl.arange(0, BLOCK) # group_size <= BLOCK
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mask = cols < group_size
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y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
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# Quant
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_absmax = tl.maximum(tl.max(tl.abs(y)), eps)
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y_s = _absmax / fp8_max
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if SCALE_UE8M0:
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y_s = tl.exp2(tl.ceil(tl.log2(tl.abs(y_s))))
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y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
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tl.store(y_q_ptr + cols, y_q, mask=mask)
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tl.store(y_s_ptr, y_s)
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def per_token_group_quant_fp8(
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x: torch.Tensor,
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group_size: int,
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eps: float = 1e-10,
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column_major_scales: bool = False,
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scale_tma_aligned: bool = False,
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scale_ue8m0: bool = False,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Function to perform per-token-group quantization on an input tensor `x`.
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It converts the tensor values into signed float8 values and returns the
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quantized tensor along with the scaling factor used for quantization.
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Args:
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x: The input tenosr with ndim >= 2.
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group_size: The group size used for quantization.
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eps: The minimum to avoid dividing zero.
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Returns:
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Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization.
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"""
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assert (
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x.shape[-1] % group_size == 0
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), "the last dimension of `x` cannot be divisible by `group_size`"
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assert x.is_contiguous(), "`x` is not contiguous"
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x_q = torch.empty_like(x, device=x.device, dtype=fp8_dtype)
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x_s = create_per_token_group_quant_fp8_output_scale(
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x_shape=x.shape,
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device=x.device,
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group_size=group_size,
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column_major_scales=column_major_scales,
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scale_tma_aligned=scale_tma_aligned,
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scale_ue8m0=False,
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)
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M = x.numel() // group_size
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N = group_size
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BLOCK = triton.next_power_of_2(N)
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# heuristics for number of warps
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num_warps = min(max(BLOCK // 256, 1), 8)
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num_stages = 1
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if column_major_scales:
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_per_token_group_quant_fp8_colmajor[(M,)](
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x,
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x_q,
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x_s,
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group_size,
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x.shape[1],
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x_s.stride(1),
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eps,
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fp8_min=fp8_min,
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fp8_max=fp8_max,
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BLOCK=BLOCK,
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num_warps=num_warps,
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num_stages=num_stages,
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SCALE_UE8M0=scale_ue8m0,
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)
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else:
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assert not scale_ue8m0
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_per_token_group_quant_fp8[(M,)](
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x,
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x_q,
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x_s,
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group_size,
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N,
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eps,
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fp8_min=fp8_min,
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fp8_max=fp8_max,
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BLOCK=BLOCK,
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num_warps=num_warps,
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num_stages=num_stages,
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)
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if scale_ue8m0:
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from deep_gemm.utils.layout import transform_sf_into_required_layout
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assert group_size == 128
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x_s = transform_sf_into_required_layout(
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x_s,
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num_groups=None,
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mn=x_q.shape[0],
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k=x_q.shape[1],
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recipe=(1, group_size, group_size),
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is_sfa=True,
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)
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return x_q, x_s
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def create_per_token_group_quant_fp8_output_scale(
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x_shape,
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device,
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group_size,
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column_major_scales: bool,
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scale_tma_aligned: bool,
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scale_ue8m0: bool,
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):
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if scale_ue8m0:
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assert column_major_scales and scale_tma_aligned
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x_q_mn, x_q_k = x_shape
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x_s_mn, x_s_k = x_q_mn, x_q_k // 128
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aligned_mn = align(x_s_mn, 4)
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aligned_k = align(x_s_k, 4)
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# TODO(FIXME): Fix cuda kernel and recover here to empty.
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return torch.zeros(
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(aligned_k // 4, aligned_mn),
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device=device,
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dtype=torch.int,
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).transpose(0, 1)[:x_s_mn, :]
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elif column_major_scales:
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if scale_tma_aligned:
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# TODO extract "align" function
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# aligned to 4 * sizeof(float)
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aligned_size = (x_shape[-2] + 3) // 4 * 4
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return torch.empty(
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x_shape[:-2] + (x_shape[-1] // group_size, aligned_size),
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device=device,
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dtype=torch.float32,
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).permute(-1, -2)[: x_shape[-2], :]
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else:
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return torch.empty(
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(x_shape[-1] // group_size,) + x_shape[:-1],
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device=device,
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dtype=torch.float32,
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).permute(-1, -2)
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else:
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return torch.empty(
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x_shape[:-1] + (x_shape[-1] // group_size,),
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device=device,
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dtype=torch.float32,
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)
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def sglang_per_token_group_quant_fp8(
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x: torch.Tensor,
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group_size: int,
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eps: float = 1e-10,
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column_major_scales: bool = False,
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scale_tma_aligned: bool = False,
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scale_ue8m0: bool = False,
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):
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assert (
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x.shape[-1] % group_size == 0
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), "the last dimension of `x` cannot be divisible by `group_size`"
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assert x.is_contiguous(), "`x` is not contiguous"
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x_q = torch.empty_like(x, device=x.device, dtype=fp8_dtype)
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x_s = create_per_token_group_quant_fp8_output_scale(
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x_shape=x.shape,
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device=x.device,
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group_size=group_size,
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column_major_scales=column_major_scales,
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scale_tma_aligned=scale_tma_aligned,
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scale_ue8m0=scale_ue8m0,
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)
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if x.shape[0] > 0:
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sgl_per_token_group_quant_fp8(
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x, x_q, x_s, group_size, eps, fp8_min, fp8_max, scale_ue8m0
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)
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return x_q, x_s
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def sglang_per_token_quant_fp8(
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x: torch.Tensor,
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dtype: torch.dtype = fp8_dtype,
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):
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assert x.is_contiguous(), "`x` is not contiguous"
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x_q = torch.empty_like(x, device=x.device, dtype=dtype)
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x_s = torch.empty(
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x.shape[0],
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1,
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device=x.device,
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dtype=torch.float32,
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)
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sgl_per_token_quant_fp8(x, x_q, x_s)
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return x_q, x_s
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@triton.jit
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def _static_quant_fp8(
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# Pointers to inputs and output
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y_ptr,
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y_q_ptr,
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y_s_ptr,
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y_s_repeat_ptr,
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# Stride of input
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y_stride,
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# Columns of input
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N,
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# Information for float8
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fp8_min,
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fp8_max,
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# Meta-parameters
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BLOCK: tl.constexpr,
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REPEAT_SCALE: tl.constexpr,
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):
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"""A Triton-accelerated function to perform quantization using the given scale on a
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tensor
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This function converts the tensor values into float8 values.
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"""
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# Map the program id to the row of X and Y it should compute.
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g_id = tl.program_id(0)
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y_ptr += g_id * y_stride
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y_q_ptr += g_id * y_stride
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if REPEAT_SCALE:
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y_s_repeat_ptr += g_id
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cols = tl.arange(0, BLOCK) # N <= BLOCK
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mask = cols < N
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y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
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y_s = tl.load(y_s_ptr).to(tl.float32)
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y_s_inv = 1.0 / y_s
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y_q = tl.clamp(y * y_s_inv, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
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tl.store(y_q_ptr + cols, y_q, mask=mask)
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if REPEAT_SCALE:
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tl.store(y_s_repeat_ptr, y_s)
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|
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def static_quant_fp8(
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x: torch.Tensor,
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x_s: torch.Tensor,
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repeat_scale: bool = False,
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) -> Tuple[torch.Tensor, torch.Tensor]:
|
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"""Function to perform static quantization using the given scale on an input tensor `x`.
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It converts the tensor values into signed float8 values and returns the
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quantized tensor along with the scaling factor used for quantization.
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|
Args:
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x: The input tenosr with ndim >= 2.
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x_s: The quantization scale.
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repeat_scale: Whether to broadcast per-tensor scale to per-channel scale.
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dtype: The dype of output tensor.
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Returns:
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Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization.
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"""
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assert x.is_contiguous(), "`x` is not contiguous"
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assert x_s.numel() == 1, "only supports per-tensor scale"
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x_q = torch.empty_like(x, device=x.device, dtype=fp8_dtype)
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M = x.numel() // x.shape[-1]
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N = x.shape[-1]
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if repeat_scale:
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x_s_repeat = torch.empty(
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(M, 1),
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device=x.device,
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dtype=torch.float32,
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)
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else:
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x_s_repeat = None
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BLOCK = triton.next_power_of_2(N)
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# heuristics for number of warps
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num_warps = min(max(BLOCK // 256, 1), 8)
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num_stages = 1
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_static_quant_fp8[(M,)](
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x,
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x_q,
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x_s,
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x_s_repeat,
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N,
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N,
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fp8_min=fp8_min,
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fp8_max=fp8_max,
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BLOCK=BLOCK,
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REPEAT_SCALE=repeat_scale,
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num_warps=num_warps,
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num_stages=num_stages,
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)
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x_s = x_s_repeat if repeat_scale else x_s
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return x_q, x_s
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|
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@triton.jit
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|
def _w8a8_block_fp8_matmul(
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|
# Pointers to inputs and output
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A,
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B,
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C,
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As,
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Bs,
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# Shape for matmul
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M,
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N,
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K,
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# Block size for block-wise quantization
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group_n,
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group_k,
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# Stride for inputs and output
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|
stride_am,
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stride_ak,
|
|
stride_bk,
|
|
stride_bn,
|
|
stride_cm,
|
|
stride_cn,
|
|
stride_As_m,
|
|
stride_As_k,
|
|
stride_Bs_k,
|
|
stride_Bs_n,
|
|
# Meta-parameters
|
|
BLOCK_SIZE_M: tl.constexpr,
|
|
BLOCK_SIZE_N: tl.constexpr,
|
|
BLOCK_SIZE_K: tl.constexpr,
|
|
GROUP_SIZE_M: tl.constexpr,
|
|
):
|
|
"""Triton-accelerated function used to perform linear operations (dot
|
|
product) on input tensors `A` and `B` with block-wise quantization, and store the result in output
|
|
tensor `C`.
|
|
"""
|
|
|
|
pid = tl.program_id(axis=0)
|
|
num_pid_m = tl.cdiv(M, 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 % group_size_m)
|
|
pid_n = (pid % num_pid_in_group) // group_size_m
|
|
|
|
offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
|
|
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 + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
|
|
b_ptrs = B + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
|
|
|
|
As_ptrs = As + offs_am * stride_As_m
|
|
offs_bsn = offs_bn // group_n
|
|
Bs_ptrs = Bs + offs_bsn * stride_Bs_n
|
|
|
|
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
|
|
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
|
|
a = tl.load(a_ptrs, mask=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)
|
|
|
|
k_start = k * BLOCK_SIZE_K
|
|
offs_ks = k_start // group_k
|
|
a_s = tl.load(As_ptrs + offs_ks * stride_As_k)
|
|
b_s = tl.load(Bs_ptrs + offs_ks * stride_Bs_k)
|
|
|
|
accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
|
|
a_ptrs += BLOCK_SIZE_K * stride_ak
|
|
b_ptrs += BLOCK_SIZE_K * stride_bk
|
|
|
|
if C.dtype.element_ty == tl.bfloat16:
|
|
c = accumulator.to(tl.bfloat16)
|
|
elif C.dtype.element_ty == tl.float16:
|
|
c = accumulator.to(tl.float16)
|
|
else:
|
|
c = accumulator.to(tl.float32)
|
|
|
|
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
|
|
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
|
|
c_ptrs = C + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
|
|
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
|
|
tl.store(c_ptrs, c, mask=c_mask)
|
|
|
|
|
|
@triton.jit
|
|
def _w8a8_block_fp8_matmul_unrolledx4(
|
|
# Pointers to inputs and output
|
|
A,
|
|
B,
|
|
C,
|
|
As,
|
|
Bs,
|
|
# Shape for matmul
|
|
M,
|
|
N,
|
|
K,
|
|
# Block size for block-wise quantization
|
|
group_n,
|
|
group_k,
|
|
# Stride for inputs and output
|
|
stride_am,
|
|
stride_ak,
|
|
stride_bk,
|
|
stride_bn,
|
|
stride_cm,
|
|
stride_cn,
|
|
stride_As_m,
|
|
stride_As_k,
|
|
stride_Bs_k,
|
|
stride_Bs_n,
|
|
# Meta-parameters
|
|
BLOCK_SIZE_M: tl.constexpr,
|
|
BLOCK_SIZE_N: tl.constexpr,
|
|
BLOCK_SIZE_K: tl.constexpr,
|
|
GROUP_SIZE_M: tl.constexpr,
|
|
):
|
|
"""Triton-accelerated function used to perform linear operations (dot
|
|
product) on input tensors `A` and `B` with block-wise quantization, and store the result in output
|
|
tensor `C`.
|
|
"""
|
|
|
|
pid = tl.program_id(axis=0)
|
|
num_pid_m = tl.cdiv(M, 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 % group_size_m)
|
|
pid_n = (pid % num_pid_in_group) // group_size_m
|
|
|
|
offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
|
|
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 + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
|
|
b_ptrs = B + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
|
|
|
|
As_ptrs = As + offs_am * stride_As_m
|
|
offs_bsn = offs_bn // group_n
|
|
Bs_ptrs = Bs + offs_bsn * stride_Bs_n
|
|
|
|
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
|
|
# manually unroll to 4 iterations
|
|
UNROLL_FACTOR = 4
|
|
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * UNROLL_FACTOR)):
|
|
# 1st iteration
|
|
a = tl.load(
|
|
a_ptrs,
|
|
mask=offs_k[None, :] < K - (k * UNROLL_FACTOR) * BLOCK_SIZE_K,
|
|
other=0.0,
|
|
)
|
|
b = tl.load(
|
|
b_ptrs,
|
|
mask=offs_k[:, None] < K - (k * UNROLL_FACTOR) * BLOCK_SIZE_K,
|
|
other=0.0,
|
|
)
|
|
|
|
k_start = (k * UNROLL_FACTOR) * BLOCK_SIZE_K
|
|
offs_ks = k_start // group_k
|
|
a_s = tl.load(As_ptrs + offs_ks * stride_As_k)
|
|
b_s = tl.load(Bs_ptrs + offs_ks * stride_Bs_k)
|
|
|
|
accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
|
|
a_ptrs += BLOCK_SIZE_K * stride_ak
|
|
b_ptrs += BLOCK_SIZE_K * stride_bk
|
|
|
|
# 2nd iteration
|
|
a = tl.load(
|
|
a_ptrs,
|
|
mask=offs_k[None, :] < K - (k * UNROLL_FACTOR + 1) * BLOCK_SIZE_K,
|
|
other=0.0,
|
|
)
|
|
b = tl.load(
|
|
b_ptrs,
|
|
mask=offs_k[:, None] < K - (k * UNROLL_FACTOR + 1) * BLOCK_SIZE_K,
|
|
other=0.0,
|
|
)
|
|
|
|
k_start = k_start + BLOCK_SIZE_K
|
|
offs_ks = k_start // group_k
|
|
a_s = tl.load(As_ptrs + offs_ks * stride_As_k)
|
|
b_s = tl.load(Bs_ptrs + offs_ks * stride_Bs_k)
|
|
|
|
accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
|
|
a_ptrs += BLOCK_SIZE_K * stride_ak
|
|
b_ptrs += BLOCK_SIZE_K * stride_bk
|
|
|
|
# 3rd iteration
|
|
a = tl.load(
|
|
a_ptrs,
|
|
mask=offs_k[None, :] < K - (k * UNROLL_FACTOR + 2) * BLOCK_SIZE_K,
|
|
other=0.0,
|
|
)
|
|
b = tl.load(
|
|
b_ptrs,
|
|
mask=offs_k[:, None] < K - (k * UNROLL_FACTOR + 2) * BLOCK_SIZE_K,
|
|
other=0.0,
|
|
)
|
|
|
|
k_start = k_start + BLOCK_SIZE_K
|
|
offs_ks = k_start // group_k
|
|
a_s = tl.load(As_ptrs + offs_ks * stride_As_k)
|
|
b_s = tl.load(Bs_ptrs + offs_ks * stride_Bs_k)
|
|
|
|
accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
|
|
a_ptrs += BLOCK_SIZE_K * stride_ak
|
|
b_ptrs += BLOCK_SIZE_K * stride_bk
|
|
|
|
# 4th iteration
|
|
a = tl.load(
|
|
a_ptrs,
|
|
mask=offs_k[None, :] < K - (k * UNROLL_FACTOR + 3) * BLOCK_SIZE_K,
|
|
other=0.0,
|
|
)
|
|
b = tl.load(
|
|
b_ptrs,
|
|
mask=offs_k[:, None] < K - (k * UNROLL_FACTOR + 3) * BLOCK_SIZE_K,
|
|
other=0.0,
|
|
)
|
|
|
|
k_start = k_start + BLOCK_SIZE_K
|
|
offs_ks = k_start // group_k
|
|
a_s = tl.load(As_ptrs + offs_ks * stride_As_k)
|
|
b_s = tl.load(Bs_ptrs + offs_ks * stride_Bs_k)
|
|
|
|
accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
|
|
a_ptrs += BLOCK_SIZE_K * stride_ak
|
|
b_ptrs += BLOCK_SIZE_K * stride_bk
|
|
|
|
if C.dtype.element_ty == tl.bfloat16:
|
|
c = accumulator.to(tl.bfloat16)
|
|
elif C.dtype.element_ty == tl.float16:
|
|
c = accumulator.to(tl.float16)
|
|
else:
|
|
c = accumulator.to(tl.float32)
|
|
|
|
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
|
|
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
|
|
c_ptrs = C + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
|
|
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
|
|
tl.store(c_ptrs, c, mask=c_mask)
|
|
|
|
|
|
@functools.lru_cache
|
|
def get_w8a8_block_fp8_configs(
|
|
N: int, K: int, block_n: int, block_k: int
|
|
) -> Optional[Dict[int, Any]]:
|
|
"""
|
|
Return optimized configurations for the w8a8 block fp8 kernel.
|
|
|
|
The return value will be a dictionary that maps an irregular grid of
|
|
batch sizes to configurations of the w8a8 block fp8 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
|
|
device_name = get_device_name().replace(" ", "_")
|
|
json_file_name = f"N={N},K={K},device_name={device_name},dtype=fp8_w8a8,block_shape=[{block_n}, {block_k}].json"
|
|
|
|
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:
|
|
log_info_on_rank0(
|
|
logger,
|
|
f"Using configuration from {config_file_path} for W8A8 Block FP8 kernel.",
|
|
)
|
|
# 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
|
|
logger.warning(
|
|
(
|
|
"Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! "
|
|
"Config file not found at %s"
|
|
),
|
|
config_file_path,
|
|
)
|
|
return None
|
|
|
|
|
|
def select_w8a8_block_fp8_matmul_kernel(M, N, META):
|
|
return _w8a8_block_fp8_matmul
|
|
|
|
|
|
if _is_hip:
|
|
|
|
def use_w8a8_block_fp8_matmul_unrolledx4(M, N, META):
|
|
# Use manually unrolledx4 kernel on AMD GPU when the grid size is small.
|
|
# Empirical testing shows the sweet spot lies when it's less than the # of
|
|
# compute units available on the device.
|
|
num_workgroups = triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(
|
|
N, META["BLOCK_SIZE_N"]
|
|
)
|
|
num_workgroups <= get_device_core_count()
|
|
|
|
def select_w8a8_block_fp8_matmul_kernel(M, N, META):
|
|
if use_w8a8_block_fp8_matmul_unrolledx4(M, N, META):
|
|
return _w8a8_block_fp8_matmul_unrolledx4
|
|
else:
|
|
return _w8a8_block_fp8_matmul
|
|
|
|
|
|
def prepare_block_fp8_matmul_inputs(
|
|
A: torch.Tensor,
|
|
B: torch.Tensor,
|
|
As: torch.Tensor,
|
|
Bs: torch.Tensor,
|
|
block_size: List[int],
|
|
output_dtype: torch.dtype = torch.float16,
|
|
) -> Tuple[int, int, int]:
|
|
assert len(block_size) == 2
|
|
block_n, block_k = block_size[0], block_size[1]
|
|
|
|
assert A.shape[-1] == B.shape[-1]
|
|
assert A.shape[:-1] == As.shape[:-1]
|
|
assert A.is_contiguous()
|
|
|
|
if As.dtype == torch.float:
|
|
assert triton.cdiv(A.shape[-1], block_k) == As.shape[-1]
|
|
elif As.dtype == torch.int:
|
|
assert (
|
|
triton.cdiv(triton.cdiv(A.shape[-1], block_k), 4) == As.shape[-1]
|
|
), f"{A.shape=} {As.shape=} {block_size=}"
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
M = A.numel() // A.shape[-1]
|
|
|
|
assert B.ndim == 2
|
|
assert B.is_contiguous()
|
|
assert Bs.ndim == 2
|
|
N, K = B.shape
|
|
|
|
if Bs.dtype == torch.float:
|
|
assert triton.cdiv(N, block_n) == Bs.shape[0]
|
|
assert triton.cdiv(K, block_k) == Bs.shape[1]
|
|
elif Bs.dtype == torch.int:
|
|
assert N == Bs.shape[0], f"{B.shape=} {Bs.shape=} {block_size=}"
|
|
assert (
|
|
triton.cdiv(triton.cdiv(K, block_k), 4) == Bs.shape[1]
|
|
), f"{B.shape=} {Bs.shape=} {block_size=}"
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
C_shape = A.shape[:-1] + (N,)
|
|
C = A.new_empty(C_shape, dtype=output_dtype)
|
|
|
|
return M, N, K, C
|
|
|
|
|
|
def w8a8_block_fp8_matmul_deepgemm(
|
|
A: torch.Tensor,
|
|
B: torch.Tensor,
|
|
As: torch.Tensor,
|
|
Bs: torch.Tensor,
|
|
block_size: List[int],
|
|
output_dtype: torch.dtype,
|
|
) -> torch.Tensor:
|
|
M, N, K, C = prepare_block_fp8_matmul_inputs(A, B, As, Bs, block_size, output_dtype)
|
|
|
|
# Deepgemm only supports output tensor type as bfloat16
|
|
assert C.dtype == torch.bfloat16 and deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
|
|
|
|
if supports_custom_op():
|
|
torch.ops.sglang.deep_gemm_fp8_fp8_bf16_nt(A, As, B, Bs, C)
|
|
else:
|
|
deep_gemm_wrapper.gemm_nt_f8f8bf16((A, As), (B, Bs), C)
|
|
|
|
return C
|
|
|
|
|
|
def w8a8_block_fp8_matmul_triton(
|
|
A: torch.Tensor,
|
|
B: torch.Tensor,
|
|
As: torch.Tensor,
|
|
Bs: torch.Tensor,
|
|
block_size: List[int],
|
|
output_dtype: torch.dtype = torch.float16,
|
|
) -> torch.Tensor:
|
|
"""This function performs matrix multiplication with block-wise quantization.
|
|
|
|
It takes two input tensors `A` and `B` with scales `As` and `Bs`.
|
|
The output is returned in the specified `output_dtype`.
|
|
|
|
Args:
|
|
A: The input tensor, e.g., activation.
|
|
B: The input tensor, e.g., weight.
|
|
As: The per-token-group quantization scale for `A`.
|
|
Bs: The per-block quantization scale for `B`.
|
|
block_size: The block size for per-block quantization. It should be 2-dim, e.g., [128, 128].
|
|
output_dytpe: The dtype of the returned tensor.
|
|
|
|
Returns:
|
|
torch.Tensor: The result of matmul.
|
|
"""
|
|
|
|
M, N, K, C = prepare_block_fp8_matmul_inputs(A, B, As, Bs, block_size, output_dtype)
|
|
|
|
block_n, block_k = block_size
|
|
|
|
configs = get_w8a8_block_fp8_configs(N, K, block_size[0], block_size[1])
|
|
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:
|
|
# Default config
|
|
# Block-wise quant: BLOCK_SIZE_K must be divisible by block_size[1]
|
|
config = {
|
|
"BLOCK_SIZE_M": 64,
|
|
"BLOCK_SIZE_N": block_size[0],
|
|
"BLOCK_SIZE_K": block_size[1],
|
|
"GROUP_SIZE_M": 32,
|
|
"num_warps": 4,
|
|
"num_stages": 3,
|
|
}
|
|
|
|
def grid(META):
|
|
return (
|
|
triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
|
|
)
|
|
|
|
kernel = select_w8a8_block_fp8_matmul_kernel(M, N, config)
|
|
|
|
kernel[grid](
|
|
A,
|
|
B,
|
|
C,
|
|
As,
|
|
Bs,
|
|
M,
|
|
N,
|
|
K,
|
|
block_n,
|
|
block_k,
|
|
A.stride(-2),
|
|
A.stride(-1),
|
|
B.stride(1),
|
|
B.stride(0),
|
|
C.stride(-2),
|
|
C.stride(-1),
|
|
As.stride(-2),
|
|
As.stride(-1),
|
|
Bs.stride(1),
|
|
Bs.stride(0),
|
|
**config,
|
|
)
|
|
|
|
return C
|
|
|
|
|
|
# universal entry point, for testing purposes
|
|
def w8a8_block_fp8_matmul(
|
|
A: torch.Tensor,
|
|
B: torch.Tensor,
|
|
As: torch.Tensor,
|
|
Bs: torch.Tensor,
|
|
block_size: List[int],
|
|
output_dtype: torch.dtype = torch.float16,
|
|
) -> torch.Tensor:
|
|
if output_dtype == torch.bfloat16 and deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM:
|
|
return w8a8_block_fp8_matmul_deepgemm(
|
|
A, B, As, Bs, block_size, output_dtype=output_dtype
|
|
)
|
|
|
|
return w8a8_block_fp8_matmul_triton(
|
|
A, B, As, Bs, block_size, output_dtype=output_dtype
|
|
)
|
|
|
|
|
|
@triton.jit
|
|
def _per_tensor_quant_mla_fp8_stage1(
|
|
x_ptr,
|
|
x_s_ptr,
|
|
head_size,
|
|
x_stride_h,
|
|
x_stride_s,
|
|
eps,
|
|
fp8_max,
|
|
BLOCK_SIZE: tl.constexpr,
|
|
):
|
|
seq_id = tl.program_id(0)
|
|
head_id = tl.program_id(1)
|
|
offset = tl.arange(0, BLOCK_SIZE)
|
|
mask = offset < head_size
|
|
|
|
x_ptr += head_id * x_stride_h + seq_id * x_stride_s
|
|
x = tl.load(x_ptr + offset, mask=mask, other=0.0).to(tl.float32)
|
|
_absmax = tl.maximum(tl.max(tl.abs(x)), eps)
|
|
|
|
tl.atomic_max(x_s_ptr, _absmax / fp8_max)
|
|
|
|
|
|
@triton.jit
|
|
def _per_tensor_quant_mla_fp8_stage2(
|
|
x_ptr,
|
|
x_s_ptr,
|
|
x_q_ptr,
|
|
num_seq,
|
|
head_size,
|
|
x_stride_h,
|
|
x_stride_s,
|
|
fp8_min,
|
|
fp8_max,
|
|
BLOCK_SIZE: tl.constexpr,
|
|
):
|
|
seq_id = tl.program_id(0)
|
|
head_id = tl.program_id(1)
|
|
offset = tl.arange(0, BLOCK_SIZE)
|
|
mask = offset < head_size
|
|
|
|
x_s = tl.load(x_s_ptr)
|
|
x_s_inv = 1.0 / x_s
|
|
|
|
x_ptr += head_id * x_stride_h + seq_id * x_stride_s
|
|
x_q_ptr += head_id * num_seq * head_size + seq_id * head_size
|
|
|
|
x = tl.load(x_ptr + offset, mask=mask, other=0.0).to(tl.float32)
|
|
x_q = tl.clamp(x * x_s_inv, fp8_min, fp8_max).to(x_q_ptr.dtype.element_ty)
|
|
tl.store(x_q_ptr + offset, x_q, mask=mask)
|
|
|
|
|
|
def per_tensor_quant_mla_fp8(
|
|
x: torch.Tensor, x_s_out: torch.Tensor, eps: float = 1e-12
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
This function quantizes input values to float8 values with tensor-wise quantization
|
|
and specialized for mla absorbed case.
|
|
"""
|
|
assert x.dim() == 3, "`x` is not a 3d-tensor"
|
|
assert (
|
|
x_s_out.shape == (1,)
|
|
and x_s_out.dtype == torch.float32
|
|
and x_s_out.device == x.device
|
|
)
|
|
|
|
x_q = x.new_empty(x.size(), dtype=fp8_dtype)
|
|
|
|
num_head, num_seq, head_size = x.shape
|
|
BLOCK_SIZE = triton.next_power_of_2(head_size)
|
|
grid = (num_seq, num_head)
|
|
|
|
_per_tensor_quant_mla_fp8_stage1[grid](
|
|
x,
|
|
x_s_out,
|
|
head_size,
|
|
x.stride(0),
|
|
x.stride(1),
|
|
eps,
|
|
fp8_max,
|
|
BLOCK_SIZE,
|
|
)
|
|
_per_tensor_quant_mla_fp8_stage2[grid](
|
|
x,
|
|
x_s_out,
|
|
x_q,
|
|
num_seq,
|
|
head_size,
|
|
x.stride(0),
|
|
x.stride(1),
|
|
fp8_min,
|
|
fp8_max,
|
|
BLOCK_SIZE,
|
|
)
|
|
|
|
return x_q, x_s_out
|
|
|
|
|
|
@triton.jit
|
|
def _per_token_group_quant_mla_deep_gemm_masked_fp8(
|
|
y_ptr,
|
|
y_q_ptr,
|
|
y_s_ptr,
|
|
masked_m_ptr,
|
|
group_size,
|
|
y_stride_b,
|
|
y_stride_t,
|
|
y_q_stride_b,
|
|
y_q_stride_t,
|
|
y_s_stride_b,
|
|
y_s_stride_g,
|
|
eps,
|
|
fp8_min,
|
|
fp8_max,
|
|
NUM_GROUP: tl.constexpr,
|
|
BLOCK: tl.constexpr,
|
|
):
|
|
"""A Triton-accelerated function to perform per-token-group
|
|
quantization on a tensor for deep_gemm grouped_gemm_masked.
|
|
This function converts the tensor values into float8 values.
|
|
y and y_q: (b, t, k)
|
|
y_s: (b, k//group_size, t)
|
|
"""
|
|
t_id = tl.program_id(0)
|
|
b_id = tl.program_id(1)
|
|
|
|
y_ptr += b_id * y_stride_b + t_id * y_stride_t
|
|
y_q_ptr += b_id * y_q_stride_b + t_id * y_q_stride_t
|
|
y_s_ptr += b_id * y_s_stride_b + t_id
|
|
|
|
if t_id == 0:
|
|
tl.store(masked_m_ptr + b_id, tl.num_programs(0))
|
|
|
|
cols = tl.arange(0, BLOCK) # group_size <= BLOCK
|
|
mask = cols < group_size
|
|
|
|
for gid in range(NUM_GROUP):
|
|
y = tl.load(y_ptr + gid * group_size + cols, mask=mask, other=0.0).to(
|
|
tl.float32
|
|
)
|
|
_absmax = tl.maximum(tl.max(tl.abs(y)), eps)
|
|
y_s = _absmax / fp8_max
|
|
y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
|
|
|
|
tl.store(y_q_ptr + gid * group_size + cols, y_q, mask=mask)
|
|
tl.store(y_s_ptr + gid * y_s_stride_g, y_s)
|
|
|
|
|
|
def per_token_group_quant_mla_deep_gemm_masked_fp8(
|
|
x: torch.Tensor,
|
|
group_size: int = 128,
|
|
eps: float = 1e-12,
|
|
dtype: torch.dtype = fp8_dtype,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
This function quantizes input values to float8 values with per-token-group-quantization
|
|
for deep_gemm grouped_gemm_masked and specialized for mla absorbed case.
|
|
"""
|
|
assert x.dim() == 3, "`x` is not a 3d-tensor"
|
|
|
|
b, m, k = x.shape
|
|
aligned_m = (m + 255) // 256 * 256 # 256 is the max block_m of the gemm kernel
|
|
num_tiles_k = k // group_size
|
|
assert num_tiles_k * group_size == k, f"k % {group_size} must be zero"
|
|
|
|
x_q = x.new_empty((b, aligned_m, k), dtype=dtype)
|
|
x_s = x.new_empty((b, num_tiles_k, aligned_m), dtype=torch.float32)
|
|
masked_m = x.new_empty((b,), dtype=torch.int32)
|
|
|
|
BLOCK_SIZE = triton.next_power_of_2(group_size)
|
|
grid = (m, b)
|
|
|
|
_per_token_group_quant_mla_deep_gemm_masked_fp8[grid](
|
|
x,
|
|
x_q,
|
|
x_s,
|
|
masked_m,
|
|
group_size,
|
|
x.stride(0),
|
|
x.stride(1),
|
|
x_q.stride(0),
|
|
x_q.stride(1),
|
|
x_s.stride(0),
|
|
x_s.stride(1),
|
|
eps,
|
|
-fp8_max,
|
|
fp8_max,
|
|
num_tiles_k,
|
|
BLOCK_SIZE,
|
|
)
|
|
|
|
return x_q, x_s.transpose(1, 2), masked_m, m, aligned_m
|
|
|
|
|
|
"""
|
|
Quantize input tensor to FP8 (8-bit floating point) format.
|
|
|
|
Args:
|
|
input (torch.Tensor): Input tensor to be quantized
|
|
scale (Optional[torch.Tensor]): Pre-computed scaling factor for static quantization.
|
|
If None, scales will be computed dynamically.
|
|
num_token_padding (Optional[int]): If specified, pad the first dimension
|
|
of the output to at least this value.
|
|
use_per_token_if_dynamic (bool): When using dynamic scaling (scale=None),
|
|
determines the quantization granularity:
|
|
- True: compute scale per token
|
|
- False: compute single scale per tensor
|
|
|
|
Returns:
|
|
Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
|
|
- quantized_tensor: The FP8 quantized version of input
|
|
- scale_tensor: The scaling factors used for quantization
|
|
|
|
Raises:
|
|
AssertionError: If input is not 2D or if static scale's numel != 1
|
|
"""
|
|
if _is_hip:
|
|
|
|
def scaled_fp8_quant(
|
|
input: torch.Tensor,
|
|
scale: Optional[torch.Tensor] = None,
|
|
num_token_padding: Optional[int] = None,
|
|
use_per_token_if_dynamic: bool = False,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
assert input.ndim == 2, f"Expected 2D input tensor, got {input.ndim}D"
|
|
shape = input.shape
|
|
if num_token_padding:
|
|
shape = (max(num_token_padding, input.shape[0]), shape[1])
|
|
output = torch.empty(shape, device=input.device, dtype=fp8_dtype)
|
|
|
|
if scale is None:
|
|
# Dynamic scaling
|
|
if use_per_token_if_dynamic:
|
|
scale = torch.empty(
|
|
(shape[0], 1), device=input.device, dtype=torch.float32
|
|
)
|
|
if _use_aiter:
|
|
dynamic_per_token_scaled_quant(output, input, scale)
|
|
else:
|
|
torch.ops._C.dynamic_per_token_scaled_fp8_quant(
|
|
output, input.contiguous(), scale, None
|
|
)
|
|
else:
|
|
scale = torch.zeros(1, device=input.device, dtype=torch.float32)
|
|
if _use_aiter:
|
|
dynamic_per_tensor_quant(output, input, scale)
|
|
else:
|
|
torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
|
|
else:
|
|
# Static scaling
|
|
assert (
|
|
scale.numel() == 1
|
|
), f"Expected scalar scale, got numel={scale.numel()}"
|
|
if _use_aiter:
|
|
static_per_tensor_quant(output, input, scale)
|
|
else:
|
|
torch.ops._C.static_scaled_fp8_quant(output, input, scale)
|
|
|
|
return output, scale
|
|
|
|
else:
|
|
|
|
def scaled_fp8_quant(
|
|
input: torch.Tensor,
|
|
scale: Optional[torch.Tensor] = None,
|
|
num_token_padding: Optional[int] = None,
|
|
use_per_token_if_dynamic: bool = False,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
|
|
assert input.ndim == 2, f"Expected 2D input tensor, got {input.ndim}D"
|
|
shape = input.shape
|
|
if num_token_padding:
|
|
shape = (max(num_token_padding, input.shape[0]), shape[1])
|
|
output = torch.empty(shape, device=input.device, dtype=fp8_dtype)
|
|
|
|
if scale is None:
|
|
# Dynamic scaling
|
|
if use_per_token_if_dynamic:
|
|
scale = torch.empty(
|
|
(shape[0], 1), device=input.device, dtype=torch.float32
|
|
)
|
|
sgl_per_token_quant_fp8(input, output, scale)
|
|
else:
|
|
scale = torch.zeros(1, device=input.device, dtype=torch.float32)
|
|
sgl_per_tensor_quant_fp8(
|
|
input, output, scale, is_static=False
|
|
) # False for dynamic
|
|
else:
|
|
# Static scaling
|
|
assert (
|
|
scale.numel() == 1
|
|
), f"Expected scalar scale, got numel={scale.numel()}"
|
|
sgl_per_tensor_quant_fp8(
|
|
input, output, scale, is_static=True
|
|
) # True for static
|
|
|
|
return output, scale
|
|
|
|
|
|
fp8_autotune = triton.autotune(
|
|
configs=[
|
|
triton.Config({"BLOCK_M": block_m}, num_warps=num_warps)
|
|
for block_m in [16, 32, 64, 128]
|
|
for num_warps in [2, 4, 8]
|
|
],
|
|
key=["K", "BLOCK_K", "M_ALIGNMENT"],
|
|
)
|
|
|
|
|
|
@triton.jit
|
|
def _per_token_group_quant_fp8_hopper_moe_mn_major(
|
|
a, # (M, K):(K, 1)
|
|
expert_offsets, # (num_experts,)
|
|
problem_sizes, # (num_experts, 3)
|
|
a_fp8, # (M, K):(K, 1)
|
|
sfa, # (M, k)
|
|
K: tl.constexpr,
|
|
BLOCK_K: tl.constexpr,
|
|
M_ALIGNMENT: tl.constexpr,
|
|
BLOCK_M: tl.constexpr, # tune
|
|
):
|
|
k_offset = tl.program_id(0)
|
|
expert_id = tl.program_id(1)
|
|
|
|
m = tl.load(problem_sizes + expert_id * 3)
|
|
current_expert_offset = tl.load(expert_offsets + expert_id).to(tl.int64)
|
|
tl.multiple_of(m, M_ALIGNMENT)
|
|
tl.multiple_of(current_expert_offset, M_ALIGNMENT)
|
|
|
|
coord_k = k_offset * BLOCK_K + tl.arange(0, BLOCK_K)
|
|
for i in tl.range(tl.cdiv(m, BLOCK_M)):
|
|
coord_m = i * BLOCK_M + tl.arange(0, BLOCK_M)
|
|
a_ptrs = a + current_expert_offset * K + coord_m[:, None] * K + coord_k[None, :]
|
|
a_mask = (coord_m < m)[:, None] & (coord_k < K)[None, :]
|
|
|
|
inp = tl.load(a_ptrs, mask=a_mask).to(tl.float32) # [BLOCK_M, BLOCK_K]
|
|
inp_amax = tl.max(tl.abs(inp), axis=1) # [BLOCK_M,]
|
|
inp_amax = tl.clamp(inp_amax, min=1e-4, max=float("inf"))
|
|
inp_fp8 = (inp * (448.0 / inp_amax[:, None])).to(tl.float8e4nv)
|
|
|
|
# Store fp8
|
|
a_fp8_ptrs = (
|
|
a_fp8 + current_expert_offset * K + coord_m[:, None] * K + coord_k[None, :]
|
|
)
|
|
tl.store(a_fp8_ptrs, inp_fp8, mask=a_mask)
|
|
|
|
# Store sfa
|
|
k = tl.cdiv(K, BLOCK_K)
|
|
sfa_ptrs = (
|
|
sfa + current_expert_offset * k + k_offset * m + coord_m
|
|
) # MN-Major with sfa
|
|
tl.store(sfa_ptrs, inp_amax / 448.0, mask=coord_m < m)
|
|
|
|
|
|
if not _is_cpu:
|
|
_per_token_group_quant_fp8_hopper_moe_mn_major = fp8_autotune(
|
|
_per_token_group_quant_fp8_hopper_moe_mn_major
|
|
)
|
|
|
|
|
|
def per_token_group_quant_fp8_hopper_moe_mn_major(
|
|
A: torch.Tensor,
|
|
expert_offsets: torch.Tensor,
|
|
problem_sizes: torch.Tensor,
|
|
group_size: int,
|
|
expert_tokens_alignment: int = 1,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
assert A.dim() == 2
|
|
assert A.is_contiguous(), "`A` is not contiguous"
|
|
assert (
|
|
A.shape[-1] % group_size == 0
|
|
), "the last dimension of `A` cannot be divisible by `group_size`"
|
|
|
|
a_q = torch.empty_like(A, device=A.device, dtype=fp8_dtype)
|
|
M, K = A.shape[0], A.shape[1]
|
|
k = K // group_size
|
|
sfa = torch.empty((M, k), device=A.device, dtype=torch.float32)
|
|
num_experts = problem_sizes.shape[0]
|
|
grid = (k, num_experts)
|
|
_per_token_group_quant_fp8_hopper_moe_mn_major[grid](
|
|
A,
|
|
expert_offsets,
|
|
problem_sizes,
|
|
a_q,
|
|
sfa,
|
|
K,
|
|
group_size,
|
|
expert_tokens_alignment,
|
|
)
|
|
return a_q, sfa
|