[sgl-kernel][1/N]Support Expert Specialization Grouped GEMM (#11432)
Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com> Co-authored-by: PGFLMG <1106310035@qq.com> Co-authored-by: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com>
This commit is contained in:
Qi Yuhang
@@ -323,6 +323,8 @@ set(SOURCES
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"csrc/speculative/packbit.cu"
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"csrc/speculative/speculative_sampling.cu"
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"csrc/expert_specialization/es_fp8_blockwise.cu"
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"${repo-flashinfer_SOURCE_DIR}/csrc/norm.cu"
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"${repo-flashinfer_SOURCE_DIR}/csrc/renorm.cu"
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"${repo-flashinfer_SOURCE_DIR}/csrc/sampling.cu"
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336
sgl-kernel/benchmark/bench_es_fp8_blockwise_grouped_gemm.py
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336
sgl-kernel/benchmark/bench_es_fp8_blockwise_grouped_gemm.py
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@@ -0,0 +1,336 @@
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import argparse
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import random
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from dataclasses import dataclass
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from typing import List, Tuple
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import numpy as np
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import torch
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from sgl_kernel import (
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es_fp8_blockwise_scaled_grouped_mm,
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fp8_blockwise_scaled_grouped_mm,
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)
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random.seed(28)
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def ceil_div(x: int, y: int) -> int:
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return (x + y - 1) // y
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def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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assert x.dim() == 2
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m, n = x.shape
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pad_size = (128 - (n % 128)) % 128
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x = torch.nn.functional.pad(x, (0, pad_size), value=0) if pad_size > 0 else x
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x_view = x.view(m, -1, 128)
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x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
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fp8_data = (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn)
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return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)
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def per_block_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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assert x.dim() == 2
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m, n = x.shape
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x_padded = torch.zeros(
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(ceil_div(m, 128) * 128, ceil_div(n, 128) * 128), dtype=x.dtype, device=x.device
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)
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x_padded[:m, :n] = x
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x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
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x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
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x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
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return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view(
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x_view.size(0), x_view.size(2)
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)
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def create_unbalanced_expert_token_distribution(max_num_experts):
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ratios = [random.random() for _ in range(max_num_experts)]
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def convert_to_tokens(ratio: float):
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if ratio <= 0.7:
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return random.randint(1, 32)
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elif ratio > 0.7 and ratio <= 0.85:
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return random.randint(32, 64)
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elif ratio > 0.85 and ratio <= 0.95:
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return random.randint(64, 128)
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elif ratio > 0.95:
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return random.randint(128, 1024)
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else:
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return 128
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group_ms = [convert_to_tokens(ratio) for ratio in ratios]
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return group_ms
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group_ms = create_unbalanced_expert_token_distribution(8192)
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# group_ms = [128 for _ in range(8192)]
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# group_ms = [128 if i % 2 == 0 else 64 for i in range(8192)]
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def bench_es(
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n: int,
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k: int,
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num_groups: int,
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num_warmup: int,
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num_run: int,
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) -> Tuple[float, int]:
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device = "cuda"
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alignment = 128
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n_g = ceil_div(n, alignment) * alignment
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k_g = ceil_div(k, alignment) * alignment
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out_dtype = torch.bfloat16
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expert_offsets = torch.zeros((num_groups + 1), device=device, dtype=torch.int32)
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problem_sizes = torch.zeros((num_groups, 3), device=device, dtype=torch.int32)
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a_tensors = []
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b_tensors = []
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a_scales_tensors = []
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b_scales_tensors = []
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if False:
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print("Token Distributtion: ", group_ms[0:num_groups])
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print("Token Count: ", sum(group_ms[0:num_groups]))
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for g in range(num_groups):
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m_g = group_ms[g]
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expert_offsets[g + 1] = expert_offsets[g] + m_g
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problem_sizes[g][:] = torch.tensor([m_g, n_g, k_g], device=device)
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a_g, a_scale = per_token_cast_to_fp8(torch.randn((m_g, k_g), device=device))
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b_g, b_scale = per_block_cast_to_fp8(torch.randn((n_g, k_g), device=device).t())
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a_tensors.append(a_g)
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b_tensors.append(b_g)
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a_scales_tensors.append(a_scale)
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b_scales_tensors.append(b_scale)
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a_stack = torch.empty(
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(expert_offsets[-1], k_g), device=device, dtype=torch.float8_e4m3fn
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)
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b_stack = torch.empty(
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(num_groups, n_g, k_g), device=device, dtype=torch.float8_e4m3fn
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)
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for g in range(num_groups):
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a_stack[expert_offsets[g] : expert_offsets[g + 1]] = a_tensors[g]
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b_stack[g] = b_tensors[g].t()
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b_stack = b_stack.transpose(1, 2)
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a_scale_stack = torch.empty(
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(expert_offsets[-1], k_g // 128), device=device, dtype=torch.float32
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)
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b_scale_stack = torch.empty(
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(num_groups, n_g // 128, k_g // 128), device=device, dtype=torch.float32
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)
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for g in range(num_groups):
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a_scale_stack[expert_offsets[g] : expert_offsets[g + 1]] = a_scales_tensors[g]
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b_scale_stack[g] = b_scales_tensors[g].t()
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b_scale_stack = b_scale_stack.transpose(1, 2)
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c_out = torch.empty((expert_offsets[-1], n_g), device=device, dtype=out_dtype)
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a_strides = torch.full(
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(num_groups,), a_stack.stride(0), device=device, dtype=torch.int64
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)
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d_strides = torch.full(
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(num_groups,), c_out.stride(0), device=device, dtype=torch.int64
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)
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def run_cutlass():
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es_fp8_blockwise_scaled_grouped_mm(
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c_out,
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a_stack,
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b_stack,
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a_scale_stack,
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b_scale_stack,
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a_strides,
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a_strides,
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d_strides,
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problem_sizes,
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expert_offsets[:-1],
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)
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run_cutlass()
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# warmup
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for _ in range(num_warmup):
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run_cutlass()
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torch.cuda.synchronize()
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# run
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start_event = torch.cuda.Event(enable_timing=True)
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end_event = torch.cuda.Event(enable_timing=True)
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start_event.record()
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for _ in range(num_run):
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run_cutlass()
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end_event.record()
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end_event.synchronize()
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torch.cuda.synchronize()
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avg = start_event.elapsed_time(end_event) / num_run * 1000 # us
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return avg, expert_offsets[-1]
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def bench_sgl(
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n: int,
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k: int,
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num_groups: int,
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num_warmup: int,
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num_run: int,
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) -> Tuple[float, int]:
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device = "cuda"
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alignment = 128
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n_g = ceil_div(n, alignment) * alignment
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k_g = ceil_div(k, alignment) * alignment
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out_dtype = torch.bfloat16
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expert_offsets = torch.zeros((num_groups + 1), device=device, dtype=torch.int32)
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problem_sizes = torch.zeros((num_groups, 3), device=device, dtype=torch.int32)
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layout_sfa = torch.zeros((num_groups, 5), device=device, dtype=torch.int32)
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layout_sfb = torch.zeros((num_groups, 5), device=device, dtype=torch.int32)
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a_tensors = []
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b_tensors = []
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a_scales_tensors = []
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b_scales_tensors = []
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for g in range(num_groups):
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m_g = group_ms[g]
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expert_offsets[g + 1] = expert_offsets[g] + m_g
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problem_sizes[g][:] = torch.tensor([m_g, n_g, k_g], device=device)
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a_g, a_scale = per_token_cast_to_fp8(torch.randn((m_g, k_g), device=device))
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b_g, b_scale = per_block_cast_to_fp8(torch.randn((n_g, k_g), device=device).t())
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a_tensors.append(a_g)
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b_tensors.append(b_g)
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a_scales_tensors.append(a_scale)
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b_scales_tensors.append(b_scale)
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a_stack = torch.empty(
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(expert_offsets[-1], k_g), device=device, dtype=torch.float8_e4m3fn
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)
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b_stack = torch.empty(
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(num_groups, n_g, k_g), device=device, dtype=torch.float8_e4m3fn
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)
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for g in range(num_groups):
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a_stack[expert_offsets[g] : expert_offsets[g + 1]] = a_tensors[g]
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b_stack[g] = b_tensors[g].t()
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b_stack = b_stack.transpose(1, 2)
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a_scale_stack = torch.empty(
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(expert_offsets[-1], k_g // 128), device=device, dtype=torch.float32
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)
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b_scale_stack = torch.empty(
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(num_groups, n_g // 128, k_g // 128), device=device, dtype=torch.float32
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)
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for g in range(num_groups):
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a_scale_stack[expert_offsets[g] : expert_offsets[g + 1]] = a_scales_tensors[g]
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b_scale_stack[g] = b_scales_tensors[g].t()
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b_scale_stack = b_scale_stack.transpose(1, 2)
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c_out = torch.empty((expert_offsets[-1], n_g), device=device, dtype=out_dtype)
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a_strides = torch.full(
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(num_groups,), a_stack.stride(0), device=device, dtype=torch.int64
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)
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c_strides = torch.full(
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(num_groups,), c_out.stride(0), device=device, dtype=torch.int64
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)
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workspace = torch.empty((1024 * 1024 * 1024), device=device, dtype=torch.uint8)
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a_ptrs = torch.empty((num_groups,), device=device, dtype=torch.int64)
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b_ptrs = torch.empty((num_groups,), device=device, dtype=torch.int64)
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out_ptrs = torch.empty((num_groups,), device=device, dtype=torch.int64)
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a_scales_ptrs = torch.empty((num_groups,), device=device, dtype=torch.int64)
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b_scales_ptrs = torch.empty((num_groups,), device=device, dtype=torch.int64)
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def run_cutlass():
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fp8_blockwise_scaled_grouped_mm(
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c_out,
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a_ptrs,
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b_ptrs,
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out_ptrs,
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a_scales_ptrs,
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b_scales_ptrs,
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a_stack,
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b_stack,
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a_scale_stack,
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b_scale_stack,
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a_strides,
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a_strides,
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c_strides,
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layout_sfa,
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layout_sfb,
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problem_sizes,
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expert_offsets[:-1],
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workspace,
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)
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# warmup
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for _ in range(num_warmup):
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run_cutlass()
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torch.cuda.synchronize()
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# run
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start_event = torch.cuda.Event(enable_timing=True)
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end_event = torch.cuda.Event(enable_timing=True)
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start_event.record()
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for _ in range(num_run):
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run_cutlass()
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end_event.record()
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end_event.synchronize()
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torch.cuda.synchronize()
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avg = start_event.elapsed_time(end_event) / num_run * 1000 # us
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return avg, expert_offsets[-1]
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benchmark_kernels = {"es": bench_es, "sgl-kernel": bench_sgl}
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@dataclass
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class ShapeArg:
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n: int
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k: int
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num_groups: int
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def benchmark_one_shape(
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shape_args: List[ShapeArg],
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num_warmup: int,
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num_run: int,
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):
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for shape in shape_args:
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print(f"\nBenchmark: n={shape.n}, k={shape.k}, num_groups={shape.num_groups}")
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for kernel_name, kernel_func in benchmark_kernels.items():
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average_time, m = kernel_func(
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shape.n,
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shape.k,
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shape.num_groups,
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num_warmup,
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num_run,
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)
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print(f"{kernel_name}: {average_time} us")
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--num-warmup", type=int, default=3)
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parser.add_argument("--num-run", type=int, default=20)
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shape_args = [
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# Prefill, DeepSeek-R1, gateup, chunk_size = 4096, TP = 8
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ShapeArg(n=512, k=7168, num_groups=256),
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# Prefill, DeepSeek-R1, down, chunk_size = 4096, TP = 8
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ShapeArg(n=7168, k=256, num_groups=256),
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# Prefill, Qwen3-235B-A22B-FP8, gateup, TP = 4
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ShapeArg(n=768, k=4096, num_groups=128),
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# Prefill, Qwen3-235B-A22B-FP8, down, TP = 4
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ShapeArg(n=4096, k=384, num_groups=128),
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# Decode, DeepSeek-R1, gateup, bs = 128, EP = 8
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ShapeArg(n=4096, k=7168, num_groups=32),
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# Decode, DeepSeek-R1, gateup, bs = 256, EP = 16
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ShapeArg(n=4096, k=7168, num_groups=16),
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]
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args = parser.parse_args()
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benchmark_one_shape(shape_args, args.num_warmup, args.num_run)
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if __name__ == "__main__":
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main()
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@@ -531,6 +531,14 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
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"bool silu_activation,"
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"int pad_slot_id) -> ()");
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m.impl("causal_conv1d_fwd", torch::kCUDA, &causal_conv1d_fwd);
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/*
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* From csrc/expert_sepcialization
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*/
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m.def(
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"es_fp8_blockwise_scaled_grouped_mm(Tensor output, Tensor a, Tensor b, Tensor scales_a, Tensor scales_b, Tensor "
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"stride_a, Tensor stride_b, Tensor stride_d, Tensor problem_sizes, Tensor expert_offsets) -> ()");
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m.impl("es_fp8_blockwise_scaled_grouped_mm", &es_fp8_blockwise_scaled_grouped_mm);
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}
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REGISTER_EXTENSION(common_ops)
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126
sgl-kernel/csrc/expert_specialization/es_fp8_blockwise.cu
Normal file
126
sgl-kernel/csrc/expert_specialization/es_fp8_blockwise.cu
Normal file
@@ -0,0 +1,126 @@
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#include <torch/all.h>
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#include <tuple>
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#include "es_fp8_blockwise_launcher.cuh"
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/**
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* @brief Performs blockwise grouped matrix multiplication on FP8 quantized inputs,
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* with per-block scaling.
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*
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* This function dispatches to hardware-specific implementations (e.g., SM100 FP8)
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* to compute:
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* C_i = scale_a[i] * A_i * scale_b[i] * B_i
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* for each expert group `i`, using input `problem_sizes` and `expert_offsets`
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* to describe the individual matrix dimensions and their offsets.
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*
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* Input tensors A and B must be quantized to 8-bit formats and dequantized before multiplication.
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* The output tensor is written with bfloat16 or half precision.
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*
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* @param output Output tensor (must be of type bfloat16 or half).
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* @param a Input tensor A (must be kFloat8_e4m3fn).
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* @param b Input tensor B (must be kFloat8_e4m3fn).
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* @param scales_a Scaling factors for tensor A, float32 per expert group.
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* @param scales_b Scaling factors for tensor B, float32 per expert group.
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* @param stride_a Stride information for tensor A (int32).
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* @param stride_b Stride information for tensor B (int32).
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* @param stride_c Stride information for output tensor C (int32).
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* @param problem_sizes 2D int32 tensor of shape (num_experts, 3), specifying (M, N, K)
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* for each grouped matrix multiplication problem.
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* @param expert_offsets 1D int32 tensor of size (num_experts), used to index into
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* the grouped input tensors for dispatch.
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*/
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void es_fp8_blockwise_scaled_grouped_mm(
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torch::Tensor& output,
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const torch::Tensor& a,
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const torch::Tensor& b,
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const torch::Tensor& scales_a,
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const torch::Tensor& scales_b,
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const torch::Tensor& stride_a,
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const torch::Tensor& stride_b,
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||||
const torch::Tensor& stride_d,
|
||||
const torch::Tensor& problem_sizes,
|
||||
const torch::Tensor& expert_offsets) {
|
||||
#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
|
||||
TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
|
||||
TORCH_CHECK(problem_sizes.size(1) == 3, "problem_sizes must have shape (num_experts, 3)");
|
||||
TORCH_CHECK(
|
||||
problem_sizes.size(0) == expert_offsets.size(0), "Number of experts in problem_sizes must match expert_offsets");
|
||||
TORCH_CHECK(problem_sizes.dtype() == torch::kInt32, "problem_sizes must be int32");
|
||||
TORCH_CHECK(a.scalar_type() == torch::kFloat8_e4m3fn, "a must be kFloat8_e4m3fn");
|
||||
TORCH_CHECK(b.scalar_type() == torch::kFloat8_e4m3fn, "b must be kFloat8_e4m3fn");
|
||||
TORCH_CHECK(
|
||||
output.scalar_type() == torch::kBFloat16 || output.scalar_type() == torch::kHalf,
|
||||
"output must be bfloat16 or half");
|
||||
|
||||
int num_experts = (int)problem_sizes.size(0);
|
||||
torch::TensorOptions options_int64 = torch::TensorOptions().dtype(torch::kInt64).device(a.device());
|
||||
torch::TensorOptions options_int32 = torch::TensorOptions().dtype(torch::kInt32).device(a.device());
|
||||
torch::Tensor out_ptrs = torch::empty(num_experts, options_int64);
|
||||
torch::Tensor a_ptrs = torch::empty(num_experts, options_int64);
|
||||
torch::Tensor b_ptrs = torch::empty(num_experts, options_int64);
|
||||
torch::Tensor a_scales_ptrs = torch::empty(num_experts, options_int64);
|
||||
torch::Tensor b_scales_ptrs = torch::empty(num_experts, options_int64);
|
||||
|
||||
torch::Tensor layout_sfa = torch::empty({num_experts, 5}, options_int32);
|
||||
torch::Tensor layout_sfb = torch::empty({num_experts, 5}, options_int32);
|
||||
|
||||
torch::Tensor lm_problem_sizes = torch::empty({num_experts, 3}, options_int32);
|
||||
torch::Tensor mm_problem_sizes = torch::empty({num_experts, 3}, options_int32);
|
||||
torch::Tensor hm_problem_sizes = torch::empty({num_experts, 3}, options_int32);
|
||||
expert_specialization::es_sm90_fp8_blockwise_scaled_group_mm_pre_compute(
|
||||
out_ptrs,
|
||||
a_ptrs,
|
||||
b_ptrs,
|
||||
a_scales_ptrs,
|
||||
b_scales_ptrs,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
lm_problem_sizes,
|
||||
mm_problem_sizes,
|
||||
hm_problem_sizes,
|
||||
output,
|
||||
a,
|
||||
b,
|
||||
scales_a,
|
||||
scales_b,
|
||||
problem_sizes,
|
||||
expert_offsets);
|
||||
if (output.dtype() == torch::kBFloat16) {
|
||||
expert_specialization::es_sm90_fp8_blockwise_scaled_group_mm_distpatch_out_dtype<cutlass::bfloat16_t>(
|
||||
out_ptrs,
|
||||
a_ptrs,
|
||||
b_ptrs,
|
||||
a_scales_ptrs,
|
||||
b_scales_ptrs,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_d,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
lm_problem_sizes,
|
||||
mm_problem_sizes,
|
||||
hm_problem_sizes);
|
||||
} else if (output.dtype() == torch::kFloat16) {
|
||||
expert_specialization::es_sm90_fp8_blockwise_scaled_group_mm_distpatch_out_dtype<cutlass::half_t>(
|
||||
out_ptrs,
|
||||
a_ptrs,
|
||||
b_ptrs,
|
||||
a_scales_ptrs,
|
||||
b_scales_ptrs,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_d,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
lm_problem_sizes,
|
||||
mm_problem_sizes,
|
||||
hm_problem_sizes);
|
||||
} else {
|
||||
TORCH_CHECK(false, "Invalid output type (must be float16 or bfloat16)");
|
||||
}
|
||||
#else
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(
|
||||
can_implement, "No implemented fp8_blockwise_scaled_grouped_mm for current compute capability: ", sm_version);
|
||||
#endif
|
||||
}
|
||||
@@ -0,0 +1,268 @@
|
||||
#pragma once
|
||||
#include <cuda.h>
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cute/tensor.hpp"
|
||||
#include "es_fp8_blockwise_traits.cuh"
|
||||
|
||||
namespace expert_specialization {
|
||||
|
||||
using namespace cute;
|
||||
|
||||
template <typename ElementAB, typename ElementSF, typename ElementD>
|
||||
struct Fp8BlockwiseGroupedGemmOffsetFunctor {
|
||||
// Input
|
||||
int* expert_offsets{nullptr};
|
||||
// Base pointers
|
||||
ElementAB* a_base{nullptr};
|
||||
ElementAB* b_base{nullptr};
|
||||
ElementD* out_base{nullptr};
|
||||
ElementSF* a_scales_base{nullptr};
|
||||
ElementSF* b_scales_base{nullptr};
|
||||
|
||||
// Output
|
||||
// Pointer Array for A/B
|
||||
ElementAB** a_offsets{nullptr};
|
||||
ElementAB** b_offsets{nullptr};
|
||||
ElementSF** a_scales_offsets{nullptr};
|
||||
ElementSF** b_scales_offsets{nullptr};
|
||||
ElementD** out_offsets{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmOffsetFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmOffsetFunctor(
|
||||
int* _expert_offsets,
|
||||
ElementAB* _a_base,
|
||||
ElementAB* _b_base,
|
||||
ElementD* _out_base,
|
||||
ElementSF* _a_scales_base,
|
||||
ElementSF* _b_scales_base,
|
||||
ElementAB** _a_offsets,
|
||||
ElementAB** _b_offsets,
|
||||
ElementSF** _a_scales_offsets,
|
||||
ElementSF** _b_scales_offsets,
|
||||
ElementD** _out_offsets)
|
||||
: expert_offsets(_expert_offsets),
|
||||
a_base(_a_base),
|
||||
b_base(_b_base),
|
||||
out_base(_out_base),
|
||||
a_scales_base(_a_scales_base),
|
||||
b_scales_base(_b_scales_base),
|
||||
a_offsets(_a_offsets),
|
||||
b_offsets(_b_offsets),
|
||||
a_scales_offsets(_a_scales_offsets),
|
||||
b_scales_offsets(_b_scales_offsets),
|
||||
out_offsets(_out_offsets) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
int64_t expert_offset = static_cast<int64_t>(expert_offsets[expert_id]);
|
||||
int64_t a_stride = 0;
|
||||
int64_t b_stride = 0;
|
||||
int64_t a_scale_stride = 0;
|
||||
int64_t b_scale_stride = 0;
|
||||
|
||||
a_stride = expert_offset * k;
|
||||
b_stride = expert_id * k * n;
|
||||
a_scale_stride = expert_offset * k / 128;
|
||||
b_scale_stride = expert_id * k * n / 128 / 128;
|
||||
|
||||
a_offsets[expert_id] = a_base + a_stride;
|
||||
b_offsets[expert_id] = b_base + b_stride;
|
||||
a_scales_offsets[expert_id] = a_scales_base + a_scale_stride;
|
||||
b_scales_offsets[expert_id] = b_scales_base + b_scale_stride;
|
||||
out_offsets[expert_id] = out_base + expert_offset * n;
|
||||
}
|
||||
};
|
||||
|
||||
template <typename PerfConfig>
|
||||
struct Fp8BlockwiseGroupedGemmSFLayoutFunctor {
|
||||
using ScaleConfig = typename PerfConfig::ScaleConfig;
|
||||
using LayoutSFA = typename PerfConfig::LayoutSFA;
|
||||
using LayoutSFB = typename PerfConfig::LayoutSFB;
|
||||
LayoutSFA* layout_sfa_base{nullptr};
|
||||
LayoutSFB* layout_sfb_base{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmSFLayoutFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmSFLayoutFunctor(LayoutSFA* _layout_sfa_base, LayoutSFB* _layout_sfb_base)
|
||||
: layout_sfa_base(_layout_sfa_base), layout_sfb_base(_layout_sfb_base) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
LayoutSFA* layout_sfa_ptr = layout_sfa_base + expert_id;
|
||||
LayoutSFB* layout_sfb_ptr = layout_sfb_base + expert_id;
|
||||
*layout_sfa_ptr = ScaleConfig::tile_atom_to_shape_SFA(cute::make_shape(m, n, k, 1));
|
||||
*layout_sfb_ptr = ScaleConfig::tile_atom_to_shape_SFB(cute::make_shape(m, n, k, 1));
|
||||
}
|
||||
};
|
||||
|
||||
// [Unused]: Specialization for Swap A/B
|
||||
template <>
|
||||
struct Fp8BlockwiseGroupedGemmSFLayoutFunctor<PerfConfigLowMH20> {
|
||||
using ScaleConfig = typename PerfConfigLowMH20::ScaleConfig;
|
||||
using LayoutSFA = typename PerfConfigLowMH20::LayoutSFA;
|
||||
using LayoutSFB = typename PerfConfigLowMH20::LayoutSFB;
|
||||
LayoutSFA* layout_sfa_base{nullptr};
|
||||
LayoutSFB* layout_sfb_base{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmSFLayoutFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmSFLayoutFunctor(LayoutSFA* _layout_sfa_base, LayoutSFB* _layout_sfb_base)
|
||||
: layout_sfa_base(_layout_sfa_base), layout_sfb_base(_layout_sfb_base) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
LayoutSFA* layout_sfa_ptr = layout_sfa_base + expert_id;
|
||||
LayoutSFB* layout_sfb_ptr = layout_sfb_base + expert_id;
|
||||
*layout_sfa_ptr = ScaleConfig::tile_atom_to_shape_SFA(cute::make_shape(n, m, k, 1));
|
||||
*layout_sfb_ptr = ScaleConfig::tile_atom_to_shape_SFB(cute::make_shape(n, m, k, 1));
|
||||
}
|
||||
};
|
||||
|
||||
template <typename PerfConfig>
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor;
|
||||
|
||||
template <>
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigLowMH20> {
|
||||
int* problem_sizes{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor(int* _problem_sizes) : problem_sizes(_problem_sizes) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
if (m <= 48) {
|
||||
// Swap A/B
|
||||
problem_sizes[expert_id * 3 + 0] = n;
|
||||
problem_sizes[expert_id * 3 + 1] = m;
|
||||
problem_sizes[expert_id * 3 + 2] = k;
|
||||
} else {
|
||||
problem_sizes[expert_id * 3 + 0] = 0;
|
||||
problem_sizes[expert_id * 3 + 1] = 0;
|
||||
problem_sizes[expert_id * 3 + 2] = 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigLowMHx00> {
|
||||
int* problem_sizes{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor(int* _problem_sizes) : problem_sizes(_problem_sizes) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
if (m <= 32) {
|
||||
// Swap A/B
|
||||
problem_sizes[expert_id * 3 + 0] = n;
|
||||
problem_sizes[expert_id * 3 + 1] = m;
|
||||
problem_sizes[expert_id * 3 + 2] = k;
|
||||
} else {
|
||||
problem_sizes[expert_id * 3 + 0] = 0;
|
||||
problem_sizes[expert_id * 3 + 1] = 0;
|
||||
problem_sizes[expert_id * 3 + 2] = 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigMiddleMH20> {
|
||||
int* problem_sizes{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor(int* _problem_sizes) : problem_sizes(_problem_sizes) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
if (m > 48 && m <= 96) {
|
||||
problem_sizes[expert_id * 3 + 0] = m;
|
||||
problem_sizes[expert_id * 3 + 1] = n;
|
||||
problem_sizes[expert_id * 3 + 2] = k;
|
||||
} else {
|
||||
problem_sizes[expert_id * 3 + 0] = 0;
|
||||
problem_sizes[expert_id * 3 + 1] = 0;
|
||||
problem_sizes[expert_id * 3 + 2] = 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigMiddleMHx00> {
|
||||
int* problem_sizes{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor(int* _problem_sizes) : problem_sizes(_problem_sizes) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
if (m > 32 && m <= 64) {
|
||||
problem_sizes[expert_id * 3 + 0] = n;
|
||||
problem_sizes[expert_id * 3 + 1] = m;
|
||||
problem_sizes[expert_id * 3 + 2] = k;
|
||||
} else {
|
||||
problem_sizes[expert_id * 3 + 0] = 0;
|
||||
problem_sizes[expert_id * 3 + 1] = 0;
|
||||
problem_sizes[expert_id * 3 + 2] = 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigHighMH20> {
|
||||
int* problem_sizes{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor(int* _problem_sizes) : problem_sizes(_problem_sizes) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
if (m > 96) {
|
||||
problem_sizes[expert_id * 3 + 0] = m;
|
||||
problem_sizes[expert_id * 3 + 1] = n;
|
||||
problem_sizes[expert_id * 3 + 2] = k;
|
||||
} else {
|
||||
problem_sizes[expert_id * 3 + 0] = 0;
|
||||
problem_sizes[expert_id * 3 + 1] = 0;
|
||||
problem_sizes[expert_id * 3 + 2] = 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigHighMHx00> {
|
||||
int* problem_sizes{nullptr};
|
||||
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor() = default;
|
||||
Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor(int* _problem_sizes) : problem_sizes(_problem_sizes) {}
|
||||
|
||||
void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
|
||||
if (m > 64) {
|
||||
problem_sizes[expert_id * 3 + 0] = m;
|
||||
problem_sizes[expert_id * 3 + 1] = n;
|
||||
problem_sizes[expert_id * 3 + 2] = k;
|
||||
} else {
|
||||
problem_sizes[expert_id * 3 + 0] = 0;
|
||||
problem_sizes[expert_id * 3 + 1] = 0;
|
||||
problem_sizes[expert_id * 3 + 2] = 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <
|
||||
typename OffsetFunctor,
|
||||
typename ScaleLayoutFunctor,
|
||||
typename LowMProblemSizeFilterFunctor,
|
||||
typename MiddleMProblemSizeFilterFunctor,
|
||||
typename HighMProblemSizeFilterFunctor>
|
||||
__global__ void groupedGemmPreComputeKernel(
|
||||
int* problem_sizes,
|
||||
OffsetFunctor offset_functor,
|
||||
ScaleLayoutFunctor sf_functor,
|
||||
LowMProblemSizeFilterFunctor lm_psf_functor,
|
||||
MiddleMProblemSizeFilterFunctor mm_psf_functor,
|
||||
HighMProblemSizeFilterFunctor hm_psf_functor) {
|
||||
int64_t expert_id = static_cast<int64_t>(threadIdx.x);
|
||||
int m = problem_sizes[expert_id * 3 + 0];
|
||||
int n = problem_sizes[expert_id * 3 + 1];
|
||||
int k = problem_sizes[expert_id * 3 + 2];
|
||||
|
||||
offset_functor(expert_id, m, n, k);
|
||||
sf_functor(expert_id, m, n, k);
|
||||
lm_psf_functor(expert_id, m, n, k);
|
||||
mm_psf_functor(expert_id, m, n, k);
|
||||
hm_psf_functor(expert_id, m, n, k);
|
||||
}
|
||||
|
||||
} // namespace expert_specialization
|
||||
@@ -0,0 +1,312 @@
|
||||
#pragma once
|
||||
#include <ATen/cuda/CUDAContext.h>
|
||||
#include <c10/cuda/CUDAGuard.h>
|
||||
#include <torch/all.h>
|
||||
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "es_fp8_blockwise_functor.cuh"
|
||||
|
||||
namespace expert_specialization {
|
||||
|
||||
using namespace cute;
|
||||
|
||||
void es_sm90_fp8_blockwise_scaled_group_mm_pre_compute(
|
||||
// Output
|
||||
torch::Tensor& out_ptrs,
|
||||
torch::Tensor& a_ptrs,
|
||||
torch::Tensor& b_ptrs,
|
||||
torch::Tensor& a_scales_ptrs,
|
||||
torch::Tensor& b_scales_ptrs,
|
||||
torch::Tensor& layout_sfa,
|
||||
torch::Tensor& layout_sfb,
|
||||
torch::Tensor& lm_problem_sizes,
|
||||
torch::Tensor& mm_problem_sizes,
|
||||
torch::Tensor& hm_problem_sizes,
|
||||
// Input
|
||||
torch::Tensor& out_tensors,
|
||||
torch::Tensor const& a_tensors,
|
||||
torch::Tensor const& b_tensors,
|
||||
torch::Tensor const& a_scales,
|
||||
torch::Tensor const& b_scales,
|
||||
torch::Tensor const& problem_sizes,
|
||||
torch::Tensor const& expert_offsets) {
|
||||
TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn);
|
||||
TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
|
||||
TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
|
||||
TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
|
||||
|
||||
const std::string H20_device_type_str("NVIDIA H20");
|
||||
bool is_h20_device = std::string(at::cuda::getCurrentDeviceProperties()->name) == H20_device_type_str;
|
||||
|
||||
// Creat Scale Factor Layout Functor
|
||||
using LayoutSFA = typename PerfConfigMiddleMH20::LayoutSFA;
|
||||
using LayoutSFB = typename PerfConfigMiddleMH20::LayoutSFB;
|
||||
struct Fp8BlockwiseGroupedGemmSFLayoutFunctor<PerfConfigMiddleMH20> sf_layout(
|
||||
reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()), reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr()));
|
||||
|
||||
int num_experts = (int)expert_offsets.size(0);
|
||||
auto stream = at::cuda::getCurrentCUDAStream(a_tensors.device().index());
|
||||
// Dispatch
|
||||
if (out_tensors.dtype() == torch::kBFloat16) {
|
||||
struct Fp8BlockwiseGroupedGemmOffsetFunctor<cutlass::float_e4m3_t, float, cutlass::bfloat16_t> of(
|
||||
static_cast<int*>(expert_offsets.data_ptr()),
|
||||
static_cast<cutlass::float_e4m3_t*>(a_tensors.data_ptr()),
|
||||
static_cast<cutlass::float_e4m3_t*>(b_tensors.data_ptr()),
|
||||
static_cast<cutlass::bfloat16_t*>(out_tensors.data_ptr()),
|
||||
static_cast<float*>(a_scales.data_ptr()),
|
||||
static_cast<float*>(b_scales.data_ptr()),
|
||||
static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()),
|
||||
static_cast<cutlass::float_e4m3_t**>(b_ptrs.data_ptr()),
|
||||
static_cast<float**>(a_scales_ptrs.data_ptr()),
|
||||
static_cast<float**>(b_scales_ptrs.data_ptr()),
|
||||
static_cast<cutlass::bfloat16_t**>(out_ptrs.data_ptr()));
|
||||
if (!is_h20_device) {
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigLowMHx00> lm_psf(
|
||||
static_cast<int*>(lm_problem_sizes.data_ptr()));
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigMiddleMHx00> mm_psf(
|
||||
static_cast<int*>(mm_problem_sizes.data_ptr()));
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigHighMHx00> hm_psf(
|
||||
static_cast<int*>(hm_problem_sizes.data_ptr()));
|
||||
groupedGemmPreComputeKernel<<<1, num_experts, 0, stream>>>(
|
||||
static_cast<int*>(problem_sizes.data_ptr()), of, sf_layout, lm_psf, mm_psf, hm_psf);
|
||||
} else {
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigLowMH20> lm_psf(
|
||||
static_cast<int*>(lm_problem_sizes.data_ptr()));
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigMiddleMH20> mm_psf(
|
||||
static_cast<int*>(mm_problem_sizes.data_ptr()));
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigHighMH20> hm_psf(
|
||||
static_cast<int*>(hm_problem_sizes.data_ptr()));
|
||||
groupedGemmPreComputeKernel<<<1, num_experts, 0, stream>>>(
|
||||
static_cast<int*>(problem_sizes.data_ptr()), of, sf_layout, lm_psf, mm_psf, hm_psf);
|
||||
}
|
||||
} else if (out_tensors.dtype() == torch::kFloat16) {
|
||||
struct Fp8BlockwiseGroupedGemmOffsetFunctor<cutlass::float_e4m3_t, float, cutlass::half_t> of(
|
||||
static_cast<int*>(expert_offsets.data_ptr()),
|
||||
static_cast<cutlass::float_e4m3_t*>(a_tensors.data_ptr()),
|
||||
static_cast<cutlass::float_e4m3_t*>(b_tensors.data_ptr()),
|
||||
static_cast<cutlass::half_t*>(out_tensors.data_ptr()),
|
||||
static_cast<float*>(a_scales.data_ptr()),
|
||||
static_cast<float*>(b_scales.data_ptr()),
|
||||
static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()),
|
||||
static_cast<cutlass::float_e4m3_t**>(b_ptrs.data_ptr()),
|
||||
static_cast<float**>(a_scales_ptrs.data_ptr()),
|
||||
static_cast<float**>(b_scales_ptrs.data_ptr()),
|
||||
static_cast<cutlass::half_t**>(out_ptrs.data_ptr()));
|
||||
if (!is_h20_device) {
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigLowMHx00> lm_psf(
|
||||
static_cast<int*>(lm_problem_sizes.data_ptr()));
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigMiddleMHx00> mm_psf(
|
||||
static_cast<int*>(mm_problem_sizes.data_ptr()));
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigHighMHx00> hm_psf(
|
||||
static_cast<int*>(hm_problem_sizes.data_ptr()));
|
||||
groupedGemmPreComputeKernel<<<1, num_experts, 0, stream>>>(
|
||||
static_cast<int*>(problem_sizes.data_ptr()), of, sf_layout, lm_psf, mm_psf, hm_psf);
|
||||
} else {
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigLowMH20> lm_psf(
|
||||
static_cast<int*>(lm_problem_sizes.data_ptr()));
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigMiddleMH20> mm_psf(
|
||||
static_cast<int*>(mm_problem_sizes.data_ptr()));
|
||||
struct Fp8BlockwiseGroupedGemmProblemSizeFilterFunctor<PerfConfigHighMH20> hm_psf(
|
||||
static_cast<int*>(hm_problem_sizes.data_ptr()));
|
||||
groupedGemmPreComputeKernel<<<1, num_experts, 0, stream>>>(
|
||||
static_cast<int*>(problem_sizes.data_ptr()), of, sf_layout, lm_psf, mm_psf, hm_psf);
|
||||
}
|
||||
} else {
|
||||
TORCH_CHECK(false, "Invalid output type (must be float16 or bfloat16)");
|
||||
}
|
||||
}
|
||||
|
||||
template <typename GemmTraits>
|
||||
void launch_sm90_fp8_blockwise_scaled_group_mm(
|
||||
torch::Tensor& out_ptrs,
|
||||
const torch::Tensor& a_ptrs,
|
||||
const torch::Tensor& b_ptrs,
|
||||
const torch::Tensor& a_scales_ptrs,
|
||||
const torch::Tensor& b_scales_ptrs,
|
||||
const torch::Tensor& stride_a,
|
||||
const torch::Tensor& stride_b,
|
||||
const torch::Tensor& stride_d,
|
||||
const torch::Tensor& layout_sfa,
|
||||
const torch::Tensor& layout_sfb,
|
||||
const torch::Tensor& problem_sizes) {
|
||||
using ElementA = typename GemmTraits::ElementA;
|
||||
using StrideA = typename GemmTraits::StrideA;
|
||||
using ElementB = typename GemmTraits::ElementB;
|
||||
using StrideB = typename GemmTraits::StrideB;
|
||||
using ElementAccumulator = typename GemmTraits::ElementAccumulator;
|
||||
using LayoutSFA = typename GemmTraits::LayoutSFA;
|
||||
using LayoutSFB = typename GemmTraits::LayoutSFB;
|
||||
using ElementD = typename GemmTraits::ElementD;
|
||||
using StrideD = typename GemmTraits::StrideD;
|
||||
using UnderlyingProblemShape = typename GemmTraits::ProblemShape::UnderlyingProblemShape;
|
||||
using Gemm = typename GemmTraits::Gemm;
|
||||
using GemmKernel = typename GemmTraits::GemmKernel;
|
||||
|
||||
int num_experts = (int)problem_sizes.size(0);
|
||||
Gemm gemm_op;
|
||||
|
||||
typename GemmKernel::MainloopArguments mainloop_args{
|
||||
static_cast<const ElementA**>(a_ptrs.data_ptr()),
|
||||
static_cast<StrideA*>(stride_a.data_ptr()),
|
||||
static_cast<const ElementB**>(b_ptrs.data_ptr()),
|
||||
static_cast<StrideB*>(stride_b.data_ptr()),
|
||||
static_cast<const ElementAccumulator**>(a_scales_ptrs.data_ptr()),
|
||||
reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()),
|
||||
static_cast<const ElementAccumulator**>(b_scales_ptrs.data_ptr()),
|
||||
reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr())};
|
||||
|
||||
cutlass::KernelHardwareInfo hw_info;
|
||||
hw_info.device_id = c10::cuda::current_device();
|
||||
hw_info.sm_count = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
|
||||
|
||||
typename GemmKernel::EpilogueArguments epilogue_args{
|
||||
{}, nullptr, nullptr, static_cast<ElementD**>(out_ptrs.data_ptr()), static_cast<StrideD*>(stride_d.data_ptr())};
|
||||
|
||||
UnderlyingProblemShape* problem_sizes_as_shapes = static_cast<UnderlyingProblemShape*>(problem_sizes.data_ptr());
|
||||
typename GemmKernel::Arguments args{
|
||||
cutlass::gemm::GemmUniversalMode::kGrouped,
|
||||
{num_experts, problem_sizes_as_shapes, nullptr},
|
||||
mainloop_args,
|
||||
epilogue_args,
|
||||
hw_info};
|
||||
|
||||
at::cuda::CUDAGuard device_guard{(char)a_ptrs.get_device()};
|
||||
const cudaStream_t stream = at::cuda::getCurrentCUDAStream(a_ptrs.get_device());
|
||||
|
||||
auto can_implement_status = gemm_op.can_implement(args);
|
||||
TORCH_CHECK(can_implement_status == cutlass::Status::kSuccess, "Failed to implement GEMM");
|
||||
|
||||
torch::TensorOptions options_uint8 = torch::TensorOptions().dtype(torch::kUInt8).device(out_ptrs.device());
|
||||
size_t workspace_size = gemm_op.get_workspace_size(args);
|
||||
torch::Tensor workspace = torch::empty(workspace_size, options_uint8);
|
||||
|
||||
auto status = gemm_op.initialize(args, workspace.data_ptr(), stream);
|
||||
TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to initialize GEMM");
|
||||
|
||||
status = gemm_op.run(stream, nullptr, true); // Enable PDL
|
||||
TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to run GEMM");
|
||||
}
|
||||
|
||||
template <typename OutType>
|
||||
void es_sm90_fp8_blockwise_scaled_group_mm_distpatch_out_dtype(
|
||||
torch::Tensor& out_ptrs,
|
||||
const torch::Tensor& a_ptrs,
|
||||
const torch::Tensor& b_ptrs,
|
||||
const torch::Tensor& a_scales_ptrs,
|
||||
const torch::Tensor& b_scales_ptrs,
|
||||
const torch::Tensor& stride_a,
|
||||
const torch::Tensor& stride_b,
|
||||
const torch::Tensor& stride_d,
|
||||
const torch::Tensor& layout_sfa,
|
||||
const torch::Tensor& layout_sfb,
|
||||
const torch::Tensor& lm_problem_sizes,
|
||||
const torch::Tensor& mm_problem_sizes,
|
||||
const torch::Tensor& hm_problem_sizes) {
|
||||
using LowMGemmH20Traits =
|
||||
ExpertSpecializationSm90FP8BlockwiseGroupedGemmTraits<OutType, cutlass::layout::ColumnMajor, PerfConfigLowMH20>;
|
||||
using LowMGemmHx00Traits =
|
||||
ExpertSpecializationSm90FP8BlockwiseGroupedGemmTraits<OutType, cutlass::layout::ColumnMajor, PerfConfigLowMHx00>;
|
||||
using MiddleMGemmH20Traits =
|
||||
ExpertSpecializationSm90FP8BlockwiseGroupedGemmTraits<OutType, cutlass::layout::RowMajor, PerfConfigMiddleMH20>;
|
||||
using MiddleMGemmHx00Traits = ExpertSpecializationSm90FP8BlockwiseGroupedGemmTraits<
|
||||
OutType,
|
||||
cutlass::layout::ColumnMajor,
|
||||
PerfConfigMiddleMHx00>;
|
||||
using HighMGemmH20Traits =
|
||||
ExpertSpecializationSm90FP8BlockwiseGroupedGemmTraits<OutType, cutlass::layout::RowMajor, PerfConfigHighMH20>;
|
||||
using HighMGemmHx00Traits =
|
||||
ExpertSpecializationSm90FP8BlockwiseGroupedGemmTraits<OutType, cutlass::layout::RowMajor, PerfConfigHighMHx00>;
|
||||
|
||||
const std::string H20_device_type_str("NVIDIA H20");
|
||||
bool is_h20_device = std::string(at::cuda::getCurrentDeviceProperties()->name) == H20_device_type_str;
|
||||
|
||||
if (!is_h20_device) {
|
||||
launch_sm90_fp8_blockwise_scaled_group_mm<LowMGemmHx00Traits>(
|
||||
out_ptrs,
|
||||
b_ptrs,
|
||||
a_ptrs,
|
||||
b_scales_ptrs,
|
||||
a_scales_ptrs,
|
||||
stride_b,
|
||||
stride_a,
|
||||
stride_d,
|
||||
layout_sfb,
|
||||
layout_sfa,
|
||||
lm_problem_sizes);
|
||||
} else {
|
||||
launch_sm90_fp8_blockwise_scaled_group_mm<LowMGemmH20Traits>(
|
||||
out_ptrs,
|
||||
b_ptrs,
|
||||
a_ptrs,
|
||||
b_scales_ptrs,
|
||||
a_scales_ptrs,
|
||||
stride_b,
|
||||
stride_a,
|
||||
stride_d,
|
||||
layout_sfb,
|
||||
layout_sfa,
|
||||
lm_problem_sizes);
|
||||
}
|
||||
|
||||
if (!is_h20_device) {
|
||||
launch_sm90_fp8_blockwise_scaled_group_mm<MiddleMGemmHx00Traits>(
|
||||
out_ptrs,
|
||||
b_ptrs,
|
||||
a_ptrs,
|
||||
b_scales_ptrs,
|
||||
a_scales_ptrs,
|
||||
stride_b,
|
||||
stride_a,
|
||||
stride_d,
|
||||
layout_sfb,
|
||||
layout_sfa,
|
||||
mm_problem_sizes);
|
||||
} else {
|
||||
launch_sm90_fp8_blockwise_scaled_group_mm<HighMGemmHx00Traits>(
|
||||
out_ptrs,
|
||||
a_ptrs,
|
||||
b_ptrs,
|
||||
a_scales_ptrs,
|
||||
b_scales_ptrs,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_d,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
mm_problem_sizes);
|
||||
}
|
||||
|
||||
if (!is_h20_device) {
|
||||
launch_sm90_fp8_blockwise_scaled_group_mm<HighMGemmHx00Traits>(
|
||||
out_ptrs,
|
||||
a_ptrs,
|
||||
b_ptrs,
|
||||
a_scales_ptrs,
|
||||
b_scales_ptrs,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_d,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
hm_problem_sizes);
|
||||
} else {
|
||||
launch_sm90_fp8_blockwise_scaled_group_mm<HighMGemmH20Traits>(
|
||||
out_ptrs,
|
||||
a_ptrs,
|
||||
b_ptrs,
|
||||
a_scales_ptrs,
|
||||
b_scales_ptrs,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_d,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
hm_problem_sizes);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace expert_specialization
|
||||
@@ -0,0 +1,174 @@
|
||||
#pragma once
|
||||
|
||||
// Misc
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cutlass/arch/arch.h"
|
||||
#include "cutlass/arch/mma.h"
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/detail/blockwise_scale_layout.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/gemm/dispatch_policy.hpp"
|
||||
#include "cutlass/gemm/group_array_problem_shape.hpp"
|
||||
#include "cutlass/layout/layout.h"
|
||||
#include "cutlass/numeric_conversion.h"
|
||||
#include "cutlass/numeric_size.h"
|
||||
|
||||
// Collective Builder
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/fusion/sm90_callbacks_tma_warpspecialized.hpp"
|
||||
#include "cutlass/epilogue/thread/activation.h"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
|
||||
// Integration
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
|
||||
namespace expert_specialization {
|
||||
|
||||
using namespace cute;
|
||||
|
||||
struct PerfConfigLowMH20 {
|
||||
// Swap A/B
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using MmaTileShape = Shape<_128, _32, _128>;
|
||||
using ClusterShape = Shape<_2, _1, _1>;
|
||||
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8Blockwise;
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
|
||||
using ScaleConfig =
|
||||
cutlass::detail::Sm90BlockwiseScaleConfig<128, 1, 128, cute::GMMA::Major::K, cute::GMMA::Major::K>;
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
};
|
||||
|
||||
struct PerfConfigLowMHx00 {
|
||||
// Swap A/B
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using MmaTileShape = Shape<_256, _32, _128>;
|
||||
using ClusterShape = Shape<_2, _1, _1>;
|
||||
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperativeFP8Blockwise;
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedCooperative;
|
||||
using ScaleConfig =
|
||||
cutlass::detail::Sm90BlockwiseScaleConfig<128, 1, 128, cute::GMMA::Major::K, cute::GMMA::Major::K>;
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
};
|
||||
|
||||
struct PerfConfigMiddleMH20 {
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using MmaTileShape = Shape<_64, _128, _128>;
|
||||
using ClusterShape = Shape<_1, _2, _1>;
|
||||
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8Blockwise;
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
|
||||
using ScaleConfig =
|
||||
cutlass::detail::Sm90BlockwiseScaleConfig<1, 128, 128, cute::GMMA::Major::K, cute::GMMA::Major::K>;
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
};
|
||||
|
||||
struct PerfConfigMiddleMHx00 {
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using MmaTileShape = Shape<_256, _64, _128>;
|
||||
using ClusterShape = Shape<_2, _1, _1>;
|
||||
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperativeFP8Blockwise;
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedCooperative;
|
||||
using ScaleConfig =
|
||||
cutlass::detail::Sm90BlockwiseScaleConfig<128, 1, 128, cute::GMMA::Major::K, cute::GMMA::Major::K>;
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
};
|
||||
|
||||
struct PerfConfigHighMH20 {
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using MmaTileShape = Shape<_64, _128, _128>;
|
||||
using ClusterShape = Shape<_2, _1, _1>;
|
||||
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8Blockwise;
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
|
||||
using ScaleConfig =
|
||||
cutlass::detail::Sm90BlockwiseScaleConfig<1, 128, 128, cute::GMMA::Major::K, cute::GMMA::Major::K>;
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
};
|
||||
|
||||
struct PerfConfigHighMHx00 {
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using MmaTileShape = Shape<_128, _128, _128>;
|
||||
using ClusterShape = Shape<_1, _2, _1>;
|
||||
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperativeFP8Blockwise;
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedCooperative;
|
||||
using ScaleConfig =
|
||||
cutlass::detail::Sm90BlockwiseScaleConfig<1, 128, 128, cute::GMMA::Major::K, cute::GMMA::Major::K>;
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
};
|
||||
|
||||
template <typename OutType, typename LayoutD, typename PerfConfig>
|
||||
struct ExpertSpecializationSm90FP8BlockwiseGroupedGemmTraits {
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using ElementB = cutlass::float_e4m3_t;
|
||||
using ElementC = void;
|
||||
using ElementD = OutType;
|
||||
using ElementAccumulator = float;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
using LayoutC = LayoutD;
|
||||
using LayoutSFA = typename PerfConfig::LayoutSFA;
|
||||
using LayoutSFB = typename PerfConfig::LayoutSFB;
|
||||
using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int, int, int>>;
|
||||
|
||||
static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
|
||||
static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
|
||||
static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementD>::value;
|
||||
static constexpr int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
|
||||
|
||||
using ArchTag = cutlass::arch::Sm90;
|
||||
using OperatorClass = cutlass::arch::OpClassTensorOp;
|
||||
static constexpr auto RoundStyle = cutlass::FloatRoundStyle::round_to_nearest;
|
||||
using CustomEVTIdentity = // acc
|
||||
cutlass::epilogue::fusion::Sm90EVT<
|
||||
cutlass::epilogue::fusion::
|
||||
Sm90Compute<cutlass::epilogue::thread::Identity, ElementD, ElementAccumulator, RoundStyle>,
|
||||
cutlass::epilogue::fusion::Sm90AccFetch>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
ArchTag,
|
||||
OperatorClass,
|
||||
typename PerfConfig::MmaTileShape,
|
||||
typename PerfConfig::ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
ElementAccumulator,
|
||||
ElementAccumulator,
|
||||
ElementC, // Use void to avoid load Matrix C
|
||||
LayoutC*,
|
||||
AlignmentC,
|
||||
ElementD,
|
||||
LayoutD*,
|
||||
AlignmentD,
|
||||
typename PerfConfig::EpilogueSchedule,
|
||||
CustomEVTIdentity>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
ArchTag,
|
||||
OperatorClass,
|
||||
ElementA,
|
||||
cute::tuple<LayoutA*, typename PerfConfig::LayoutSFA*>,
|
||||
AlignmentA,
|
||||
ElementB,
|
||||
cute::tuple<LayoutB*, typename PerfConfig::LayoutSFB*>,
|
||||
AlignmentB,
|
||||
ElementAccumulator,
|
||||
typename PerfConfig::MmaTileShape,
|
||||
typename PerfConfig::ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
|
||||
sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
typename PerfConfig::KernelSchedule>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop, CollectiveEpilogue, void>;
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
using UnderlyingProblemShape = ProblemShape::UnderlyingProblemShape;
|
||||
using StrideA = typename Gemm::GemmKernel::InternalStrideA;
|
||||
using StrideB = typename Gemm::GemmKernel::InternalStrideB;
|
||||
using StrideC = typename Gemm::GemmKernel::InternalStrideC;
|
||||
using StrideD = typename Gemm::GemmKernel::InternalStrideD;
|
||||
};
|
||||
|
||||
} // namespace expert_specialization
|
||||
@@ -821,3 +821,18 @@ void causal_conv1d_fwd(
|
||||
const std::optional<at::Tensor>& has_initial_state,
|
||||
bool silu_activation,
|
||||
int64_t pad_slot_id);
|
||||
|
||||
/*
|
||||
* From csrc/expert_specialization
|
||||
*/
|
||||
void es_fp8_blockwise_scaled_grouped_mm(
|
||||
torch::Tensor& output,
|
||||
const torch::Tensor& a,
|
||||
const torch::Tensor& b,
|
||||
const torch::Tensor& scales_a,
|
||||
const torch::Tensor& scales_b,
|
||||
const torch::Tensor& stride_a,
|
||||
const torch::Tensor& stride_b,
|
||||
const torch::Tensor& stride_d,
|
||||
const torch::Tensor& problem_sizes,
|
||||
const torch::Tensor& expert_offsets);
|
||||
|
||||
@@ -244,6 +244,7 @@ from sgl_kernel.elementwise import (
|
||||
rmsnorm,
|
||||
silu_and_mul,
|
||||
)
|
||||
from sgl_kernel.expert_specilization import es_fp8_blockwise_scaled_grouped_mm
|
||||
from sgl_kernel.fused_moe import fused_marlin_moe
|
||||
from sgl_kernel.gemm import (
|
||||
awq_dequantize,
|
||||
|
||||
27
sgl-kernel/python/sgl_kernel/expert_specilization.py
Normal file
27
sgl-kernel/python/sgl_kernel/expert_specilization.py
Normal file
@@ -0,0 +1,27 @@
|
||||
import torch
|
||||
|
||||
|
||||
def es_fp8_blockwise_scaled_grouped_mm(
|
||||
output,
|
||||
a,
|
||||
b,
|
||||
scales_a,
|
||||
scales_b,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_d,
|
||||
problem_sizes,
|
||||
expert_offsets,
|
||||
):
|
||||
torch.ops.sgl_kernel.es_fp8_blockwise_scaled_grouped_mm.default(
|
||||
output,
|
||||
a,
|
||||
b,
|
||||
scales_a,
|
||||
scales_b,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_d,
|
||||
problem_sizes,
|
||||
expert_offsets,
|
||||
)
|
||||
204
sgl-kernel/tests/test_es_fp8_blockwise_moe.py
Normal file
204
sgl-kernel/tests/test_es_fp8_blockwise_moe.py
Normal file
@@ -0,0 +1,204 @@
|
||||
import random
|
||||
from typing import Tuple
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
from sgl_kernel import es_fp8_blockwise_scaled_grouped_mm
|
||||
|
||||
|
||||
def cdiv(a: int, b: int) -> int:
|
||||
return -(a // -b)
|
||||
|
||||
|
||||
def scale_shape(shape, group_shape):
|
||||
return tuple(cdiv(shape[i], group_shape[i]) for i in range(len(group_shape)))
|
||||
|
||||
|
||||
def to_fp8(tensor: torch.Tensor) -> torch.Tensor:
|
||||
finfo = torch.finfo(torch.float8_e4m3fn)
|
||||
return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
|
||||
dtype=torch.float8_e4m3fn
|
||||
)
|
||||
|
||||
|
||||
# Copy from: https://github.com/deepseek-ai/DeepGEMM/blob/main/deep_gemm/utils.py
|
||||
def calc_diff(x, y):
|
||||
x, y = x.double(), y.double()
|
||||
denominator = (x * x + y * y).sum()
|
||||
sim = 2 * (x * y).sum() / denominator
|
||||
return 1 - sim
|
||||
|
||||
|
||||
def ceil_div(x: int, y: int) -> int:
|
||||
return (x + y - 1) // y
|
||||
|
||||
|
||||
def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
assert x.dim() == 2
|
||||
m, n = x.shape
|
||||
pad_size = (128 - (n % 128)) % 128
|
||||
x = torch.nn.functional.pad(x, (0, pad_size), value=0) if pad_size > 0 else x
|
||||
x_view = x.view(m, -1, 128)
|
||||
x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
|
||||
fp8_data = (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn)
|
||||
return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)
|
||||
|
||||
|
||||
def per_block_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
assert x.dim() == 2
|
||||
m, n = x.shape
|
||||
x_padded = torch.zeros(
|
||||
(ceil_div(m, 128) * 128, ceil_div(n, 128) * 128), dtype=x.dtype, device=x.device
|
||||
)
|
||||
x_padded[:m, :n] = x
|
||||
x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
|
||||
x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
|
||||
x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
|
||||
return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view(
|
||||
x_view.size(0), x_view.size(2)
|
||||
)
|
||||
|
||||
|
||||
def baseline_scaled_mm(
|
||||
a: torch.Tensor,
|
||||
b: torch.Tensor,
|
||||
scale_a: torch.Tensor,
|
||||
scale_b: torch.Tensor,
|
||||
out_dtype: type[torch.dtype],
|
||||
) -> torch.Tensor:
|
||||
|
||||
def group_broadcast(t, shape):
|
||||
for i, s in enumerate(shape):
|
||||
if t.shape[i] != s and t.shape[i] != 1:
|
||||
assert s % t.shape[i] == 0
|
||||
t = (
|
||||
t.unsqueeze(i + 1)
|
||||
.expand(*t.shape[: i + 1], s // t.shape[i], *t.shape[i + 1 :])
|
||||
.flatten(i, i + 1)
|
||||
)
|
||||
return t
|
||||
|
||||
scale_a = group_broadcast(scale_a, a.shape)
|
||||
scale_b = group_broadcast(scale_b, b.shape)
|
||||
|
||||
return torch.mm(
|
||||
(scale_a * a.to(dtype=torch.float32)), (scale_b * b.to(dtype=torch.float32))
|
||||
).to(out_dtype)
|
||||
|
||||
|
||||
def is_sm100_supported(device=None) -> bool:
|
||||
return (torch.cuda.get_device_capability(device)[0] == 10) and (
|
||||
torch.version.cuda >= "12.8"
|
||||
)
|
||||
|
||||
|
||||
def is_sm90_supported(device=None) -> bool:
|
||||
return (torch.cuda.get_device_capability(device)[0] == 9) and (
|
||||
torch.version.cuda >= "12.3"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
not (is_sm100_supported() or is_sm90_supported()),
|
||||
reason="fp8_blockwise_scaled_grouped_mm at sgl-kernel is only supported on sm100 or sm90",
|
||||
)
|
||||
@pytest.mark.parametrize("num_experts", [8, 16, 32, 64, 128])
|
||||
@pytest.mark.parametrize("out_dtype", [torch.half, torch.bfloat16])
|
||||
def test_fp8_blockwise_scaled_grouped_mm(num_experts, out_dtype):
|
||||
device = "cuda"
|
||||
alignment = 128
|
||||
n_g = random.randint(1, 64) * alignment
|
||||
k_g = random.randint(1, 64) * alignment
|
||||
|
||||
expert_offsets = torch.zeros((num_experts + 1), device=device, dtype=torch.int32)
|
||||
problem_sizes = torch.zeros((num_experts, 3), device=device, dtype=torch.int32)
|
||||
a_tensors = []
|
||||
b_tensors = []
|
||||
a_scales_tensors = []
|
||||
b_scales_tensors = []
|
||||
baseline_tensors = []
|
||||
|
||||
for g in range(num_experts):
|
||||
m_g = random.randint(1, 256)
|
||||
expert_offsets[g + 1] = expert_offsets[g] + m_g
|
||||
problem_sizes[g][:] = torch.tensor([m_g, n_g, k_g], device=device)
|
||||
|
||||
a = torch.randn((m_g, k_g), device=device, dtype=out_dtype) # (M, K):(K, 1)
|
||||
b = torch.randn((n_g, k_g), device=device, dtype=out_dtype).t() # (K, N):(1, K)
|
||||
|
||||
a_g, a_scale = per_token_cast_to_fp8(
|
||||
a
|
||||
) # ag -- (M, K):(K, 1), a_scale() -- (M, k):(k, 1)
|
||||
b_g, b_scale = per_block_cast_to_fp8(
|
||||
b
|
||||
) # bg -- (K, N):(N, 1), b_scale() -- (k, n):(n, 1)
|
||||
a_tensors.append(a_g)
|
||||
b_tensors.append(b_g)
|
||||
a_scales_tensors.append(a_scale)
|
||||
b_scales_tensors.append(b_scale)
|
||||
|
||||
baseline = torch.mm(a, b)
|
||||
baseline_tensors.append(baseline)
|
||||
a_stack = torch.empty(
|
||||
(expert_offsets[-1], k_g), device=device, dtype=torch.float8_e4m3fn
|
||||
)
|
||||
b_stack = torch.empty(
|
||||
(num_experts, n_g, k_g), device=device, dtype=torch.float8_e4m3fn
|
||||
)
|
||||
a_scale_stack = torch.empty(
|
||||
(expert_offsets[-1], (k_g // 128)), device=device, dtype=torch.float32
|
||||
)
|
||||
b_scale_stack = torch.empty(
|
||||
(num_experts, n_g // 128, k_g // 128), device=device, dtype=torch.float32
|
||||
)
|
||||
|
||||
for g in range(num_experts):
|
||||
# Matrix A is Row-Major.
|
||||
a_stack[expert_offsets[g] : expert_offsets[g + 1], :] = a_tensors[
|
||||
g
|
||||
] # a_stack[expert_offsets[g] : expert_offsets[g + 1], :] -- (M, K):(K, 1)
|
||||
b_stack[g] = b_tensors[g].t() # b_stack[g] -- (N, K):(K, 1)
|
||||
|
||||
# We need K-Major scale factor
|
||||
a_scale_stack[expert_offsets[g] : expert_offsets[g + 1], :] = a_scales_tensors[
|
||||
g
|
||||
]
|
||||
b_scale_stack[g] = b_scales_tensors[
|
||||
g
|
||||
].t() # b_scale_stack[g] -- (k, n):(n, 1), we need transpose & contiguous later
|
||||
b_stack = b_stack.transpose(1, 2) # Transpose Matrix B to Column-Major.
|
||||
b_scale_stack = b_scale_stack.transpose(1, 2)
|
||||
|
||||
c_out = torch.empty((expert_offsets[-1], n_g), device=device, dtype=out_dtype)
|
||||
a_strides = torch.full(
|
||||
(num_experts,), a_stack.stride(0), device=device, dtype=torch.int64
|
||||
)
|
||||
d_strides = torch.full(
|
||||
(num_experts,), c_out.stride(0), device=device, dtype=torch.int64
|
||||
)
|
||||
|
||||
es_fp8_blockwise_scaled_grouped_mm(
|
||||
c_out,
|
||||
a_stack,
|
||||
b_stack,
|
||||
a_scale_stack,
|
||||
b_scale_stack,
|
||||
a_strides,
|
||||
a_strides,
|
||||
d_strides,
|
||||
problem_sizes,
|
||||
expert_offsets[:-1],
|
||||
)
|
||||
|
||||
for g in range(num_experts):
|
||||
baseline = baseline_tensors[g]
|
||||
actual = c_out[expert_offsets[g] : expert_offsets[g + 1]]
|
||||
diff = calc_diff(actual, baseline)
|
||||
assert diff < 0.001
|
||||
print(
|
||||
f"m_g={baseline.shape[0]} n_g={n_g} k_g={k_g} num_experts={num_experts}, out_dtype={out_dtype}, diff={diff:.5f}: OK"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__])
|
||||
Reference in New Issue
Block a user