[benchmark] fbgemm benchmark support bandwidth report and support fbgemm_cutlass_gmm (#7422)
This commit is contained in:
Xiaoyu Zhang
29
benchmark/fbgemm/README.md
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29
benchmark/fbgemm/README.md
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@@ -0,0 +1,29 @@
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## Benchmark FBGEMM Grouped GEMM
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Benchmark FBGEMM Grouped GEMM in both Triton and CUDA version and SGLang Triton Grouped GEMM, it will be used to compare the bandwidth of different implementations.
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### Requirements
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```shell
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pip install fbgemm-gpu-genai
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```
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### Usage
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```bash
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python3 benchmark/fbgemm/benchmark_fbgemm_grouped_gemm.py --model Qwen/Qwen2-57B-A14B-Instruct --tp-size 4 --use-fp8-w8a8
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```
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For example, in H200, the Qwen2-57B-A14B-Instruct TP4 fp8w8a8 grouped gemm bandwidth result is as follows:
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```shell
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grouped-gemm-performance:
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batch_size FBGEMM Triton Grouped GEMM FP8 FBGEMM CUTLASS F8F8BF16 Rowwise SGLang Grouped GEMM FP8
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0 256.0 3704.841339 3042.626402 2254.725030
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1 512.0 3691.426346 3029.065684 2269.504543
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2 1024.0 3653.938629 2258.471467 2358.319020
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3 2048.0 3596.644313 2271.611904 2476.895397
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4 4096.0 3468.496435 2231.283986 2179.473910
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```
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The theoretical peak bandwidth of H200 is 4.8 TB/s. Taking batch_size 256 as an example, the bandwidth of FBGEMM Triton Grouped GEMM FP8 is 3704.841339 GB/s, the bandwidth of FBGEMM CUTLASS F8F8BF16 Rowwise is 3042.626402 GB/s, and the bandwidth of SGLang Grouped GEMM FP8 is 2254.725030 GB/s. Therefore, FBGEMM Triton Grouped GEMM FP8 achieves 77.9% of H200's theoretical peak bandwidth, FBGEMM CUTLASS F8F8BF16 Rowwise achieves 63.4% of H200's theoretical peak bandwidth, and SGLang Grouped GEMM FP8 achieves 46.9% of H200's theoretical peak bandwidth.
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@@ -1,10 +1,16 @@
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# python3 benchmark/kernels/fbgemm/benchmark_fbgemm_grouped_gemm.py --model Qwen/Qwen2-57B-A14B-Instruct --tp-size 4 --use-fp8-w8a8
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# python3 benchmark/fbgemm/benchmark_fbgemm_grouped_gemm.py --model Qwen/Qwen2-57B-A14B-Instruct --tp-size 4 --use-fp8-w8a8
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import argparse
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import torch
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import triton
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from fbgemm_grouped_gemm import grouped_gemm as fbgemm_grouped_gemm
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from fbgemm_grouped_gemm import (
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from fbgemm_gpu.experimental.gemm.triton_gemm.fp8_gemm import (
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quantize_fp8_row,
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triton_quantize_fp8_row,
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)
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from fbgemm_gpu.experimental.gemm.triton_gemm.grouped_gemm import (
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grouped_gemm as fbgemm_grouped_gemm,
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)
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from fbgemm_gpu.experimental.gemm.triton_gemm.grouped_gemm import (
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grouped_gemm_fp8_rowwise as fbgemm_grouped_gemm_fp8_rowwise,
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)
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from transformers import AutoConfig
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@@ -29,12 +35,11 @@ def get_model_config(model_name: str, tp_size: int):
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elif config.architectures[0] == "Qwen3MoeForCausalLM":
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num_groups = config.num_experts
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intermediate_size = config.moe_intermediate_size
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elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
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num_groups = (
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config.n_routed_experts + 1
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if config.architectures[0] in ["DeepseekV3ForCausalLM"]
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else config.n_routed_experts
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)
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elif config.architectures[0] in [
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"DeepseekV2ForCausalLM",
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"DeepseekV3ForCausalLM",
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]:
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num_groups = config.n_routed_experts
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intermediate_size = config.moe_intermediate_size
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elif config.architectures[0] == "Llama4ForConditionalGeneration":
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num_groups = config.text_config.num_local_experts
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@@ -65,7 +70,7 @@ def create_test_data(batch_size, num_groups, hidden_size, intermediate_size):
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tokens_per_group = batch_size // num_groups
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m_sizes = torch.full(
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(num_groups,), tokens_per_group, dtype=torch.int64, device="cuda"
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(num_groups,), tokens_per_group, dtype=torch.int32, device="cuda"
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)
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x = torch.randn(batch_size, hidden_size, dtype=torch.bfloat16, device="cuda")
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@@ -84,11 +89,11 @@ def create_test_data(batch_size, num_groups, hidden_size, intermediate_size):
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batch_size, intermediate_size, dtype=torch.bfloat16, device="cuda"
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)
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seg_indptr = torch.zeros(num_groups + 1, dtype=torch.int64, device="cuda")
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seg_indptr = torch.zeros(num_groups + 1, dtype=torch.int32, device="cuda")
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for i in range(1, num_groups + 1):
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seg_indptr[i] = seg_indptr[i - 1] + tokens_per_group
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weight_indices = torch.arange(num_groups, dtype=torch.int64, device="cuda")
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weight_indices = torch.arange(num_groups, dtype=torch.int32, device="cuda")
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return (
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x,
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@@ -102,39 +107,144 @@ def create_test_data(batch_size, num_groups, hidden_size, intermediate_size):
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)
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def create_fp8_test_data(batch_size, num_groups, hidden_size, intermediate_size):
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def create_fp8_test_data(
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batch_size, num_groups, hidden_size, intermediate_size, backend="triton"
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):
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"""
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Create test data for FP8 grouped GEMM operations.
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Args:
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batch_size: Total batch size
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num_groups: Number of groups
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hidden_size: Hidden dimension size
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intermediate_size: Intermediate dimension size
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backend: "triton" for Triton GEMM, "cutlass" for CUTLASS GEMM
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Returns:
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For triton: (x_fp8, w_fp8, m_sizes, x_scale, w_scale)
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For cutlass: (x, wq, w_scale, m_sizes)
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"""
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torch.manual_seed(42)
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tokens_per_group = batch_size // num_groups
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m_sizes = torch.full(
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(num_groups,), tokens_per_group, dtype=torch.int64, device="cuda"
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)
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x_fp16 = torch.randn(batch_size, hidden_size, dtype=torch.float16, device="cuda")
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w_fp16 = torch.randn(
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num_groups * intermediate_size, hidden_size, dtype=torch.float16, device="cuda"
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)
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# Create weight matrices for each group
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w_list = []
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for _ in range(num_groups):
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w = torch.randn(
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intermediate_size, hidden_size, dtype=torch.float16, device="cuda"
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)
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w_list.append(w)
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x_fp8 = x_fp16.to(torch.float8_e4m3fn)
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w_fp8 = w_fp16.to(torch.float8_e4m3fn)
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# Quantize weights using quantize_fp8_row for each group
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wq_list, w_scale_list = zip(*[quantize_fp8_row(w) for w in w_list])
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x_scale = torch.randn(batch_size, dtype=torch.float32, device="cuda").abs() + 1e-4
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w_scale = torch.randn(num_groups, dtype=torch.float32, device="cuda").abs() + 1e-4
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if backend == "triton":
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# Triton format: concatenated weights
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w_fp8 = torch.concat(wq_list, dim=0).contiguous()
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w_scale = torch.concat(w_scale_list, dim=0).contiguous()
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return x_fp8, w_fp8, m_sizes, x_scale, w_scale
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# Create m_sizes as int32 for triton
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m_sizes = torch.full(
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(num_groups,), tokens_per_group, dtype=torch.int32, device="cuda"
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)
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# Create and quantize input
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x_fp16 = torch.randn(
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batch_size, hidden_size, dtype=torch.float16, device="cuda"
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)
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x_fp8, x_scale = triton_quantize_fp8_row(x_fp16)
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x_scale = x_scale.view(batch_size, -1)
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return x_fp8, w_fp8, m_sizes, x_scale, w_scale
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elif backend == "cutlass":
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# CUTLASS format: stacked weights
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wq = torch.stack(wq_list, dim=0).contiguous()
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w_scale = torch.stack(w_scale_list, dim=0).contiguous()
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# Create m_sizes as int64 for cutlass
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m_values = [tokens_per_group] * num_groups
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m_sizes = torch.tensor(m_values).to(dtype=torch.int64, device="cuda")
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# Create input data - separate for each group then concat
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x_list = []
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for _ in range(num_groups):
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x = torch.randn(
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tokens_per_group, hidden_size, dtype=torch.float16, device="cuda"
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)
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x_list.append(x)
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# Concatenate inputs into single tensor
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x = torch.concat(x_list, dim=0).contiguous()
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return x, wq, w_scale, m_sizes
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else:
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raise ValueError(f"Unsupported backend: {backend}")
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def calculate_memory_bandwidth(m_sizes, hidden_size, intermediate_size, dtype):
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"""
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Calculate memory bandwidth based on accessed expert weights.
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Args:
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m_sizes: Tensor containing batch sizes for each group
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hidden_size: Hidden dimension size
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intermediate_size: Intermediate dimension size
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dtype: Data type of weights
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Returns:
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Memory size in bytes for accessed expert weights
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"""
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# Count non-zero groups (active experts)
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if hasattr(m_sizes, "cpu"):
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active_experts = torch.count_nonzero(m_sizes).item()
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else:
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active_experts = sum(1 for m in m_sizes if m > 0)
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# Calculate bytes per element based on dtype
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if dtype in [torch.float16, torch.bfloat16]:
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bytes_per_element = 2
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elif dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
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bytes_per_element = 1
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elif dtype == torch.float32:
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bytes_per_element = 4
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else:
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# Default to 2 bytes for unknown dtypes
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bytes_per_element = 2
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# Memory per expert weight matrix
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memory_per_expert = hidden_size * intermediate_size * bytes_per_element
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# Total memory for active experts
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total_memory_bytes = active_experts * memory_per_expert
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return total_memory_bytes
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def get_benchmark_config(use_fp8_w8a8=False):
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if use_fp8_w8a8:
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return {
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"line_vals": ["fbgemm_grouped_gemm_fp8", "sglang_grouped_gemm"],
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"line_names": ["FBGEMM Grouped GEMM FP8", "SGLang Grouped GEMM FP8"],
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"styles": [("blue", "-"), ("red", "-")],
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"line_vals": [
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"fbgemm_triton_grouped_gemm_fp8",
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"fbgemm_cutlass_f8f8bf16_rowwise",
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"sglang_grouped_gemm",
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],
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"line_names": [
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"FBGEMM Triton Grouped GEMM FP8",
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"FBGEMM CUTLASS F8F8BF16 Rowwise",
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"SGLang Grouped GEMM FP8",
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],
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"styles": [("blue", "-"), ("orange", "-"), ("red", "-")],
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}
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else:
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return {
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"line_vals": ["fbgemm_grouped_gemm", "sglang_grouped_gemm"],
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"line_names": ["FBGEMM Grouped GEMM BF16", "SGLang Grouped GEMM BF16"],
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"line_vals": ["fbgemm_triton_grouped_gemm", "sglang_grouped_gemm"],
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"line_names": [
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"FBGEMM Triton Grouped GEMM BF16",
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"SGLang Grouped GEMM BF16",
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],
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"styles": [("blue", "-"), ("green", "-")],
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}
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@@ -146,12 +256,12 @@ def run_benchmark(
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benchmark_config = triton.testing.Benchmark(
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x_names=["batch_size"],
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x_vals=[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096],
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x_vals=[256, 512, 1024, 2048, 4096],
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line_arg="provider",
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line_vals=config["line_vals"],
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line_names=config["line_names"],
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styles=config["styles"],
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ylabel="Time (ms)",
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ylabel="Bandwidth (GB/s)",
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plot_name="grouped-gemm-performance",
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args={},
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)
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@@ -165,13 +275,22 @@ def run_benchmark(
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hidden_size = model_config["hidden_size"]
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intermediate_size = model_config["intermediate_size"]
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if provider == "fbgemm_grouped_gemm_fp8":
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if provider == "fbgemm_triton_grouped_gemm_fp8":
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try:
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test_data = create_fp8_test_data(
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batch_size, num_groups, hidden_size, intermediate_size
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batch_size,
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num_groups,
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hidden_size,
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intermediate_size,
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backend="triton",
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)
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x_fp8, w_fp8, m_sizes, x_scale, w_scale = test_data
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# Calculate memory bandwidth
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memory_bytes = calculate_memory_bandwidth(
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m_sizes, hidden_size, intermediate_size, torch.float8_e4m3fn
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)
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def run_func():
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return fbgemm_grouped_gemm_fp8_rowwise(
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x_fp8, w_fp8, m_sizes, x_scale, w_scale, use_fast_accum=True
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@@ -180,6 +299,38 @@ def run_benchmark(
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except Exception as e:
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print(f"FP8 not supported, skipping: {e}")
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return float("inf"), float("inf"), float("inf")
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elif provider == "fbgemm_cutlass_f8f8bf16_rowwise":
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try:
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test_data = create_fp8_test_data(
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batch_size,
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num_groups,
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hidden_size,
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intermediate_size,
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backend="cutlass",
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)
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x, wq, w_scale, m_sizes = test_data
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# Calculate memory bandwidth
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memory_bytes = calculate_memory_bandwidth(
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m_sizes, hidden_size, intermediate_size, torch.float8_e4m3fn
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)
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# Quantize input using triton_quantize_fp8_row
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xq, x_scale = triton_quantize_fp8_row(x)
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x_scale = x_scale.view(batch_size, -1)
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def run_func():
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return torch.ops.fbgemm.f8f8bf16_rowwise_grouped_stacked(
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xq, wq, x_scale, w_scale, m_sizes
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)
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except Exception as e:
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print(
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f"CUTLASS f8f8bf16_rowwise_grouped_stacked not supported, "
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f"skipping: {e}"
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)
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return float("inf"), float("inf"), float("inf")
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else:
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test_data = create_test_data(
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batch_size, num_groups, hidden_size, intermediate_size
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@@ -195,7 +346,12 @@ def run_benchmark(
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weight_indices,
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) = test_data
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if provider == "fbgemm_grouped_gemm":
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# Calculate memory bandwidth for BF16 operations
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memory_bytes = calculate_memory_bandwidth(
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m_sizes, hidden_size, intermediate_size, torch.bfloat16
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)
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if provider == "fbgemm_triton_grouped_gemm":
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def run_func():
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return fbgemm_grouped_gemm(
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@@ -228,10 +384,19 @@ def run_benchmark(
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try:
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quantiles = [0.5, 0.2, 0.8]
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ms, min_ms, max_ms = triton.testing.do_bench(run_func, quantiles=quantiles)
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return ms, min_ms, max_ms
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# Convert time (ms) to bandwidth (GB/s)
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# Bandwidth = Memory (bytes) / Time (seconds)
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# Convert ms to seconds and bytes to GB (1e9)
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gb_per_s = (memory_bytes / 1e9) / (ms / 1000)
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# min bandwidth = max time, max bandwidth = min time
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min_gb_per_s = (memory_bytes / 1e9) / (max_ms / 1000)
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max_gb_per_s = (memory_bytes / 1e9) / (min_ms / 1000)
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return gb_per_s, min_gb_per_s, max_gb_per_s
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except Exception as e:
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print(f"Error during benchmarking for {provider}: {e}")
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return float("inf"), float("inf"), float("inf")
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return 0.0, 0.0, 0.0
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dynamic_benchmark.run(
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show_plots=True,
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@@ -242,7 +407,7 @@ def run_benchmark(
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)
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def verify_correctness(model_config, use_fp8_w8a8):
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def verify_correctness(model_config):
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print("Verifying correctness...")
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batch_size = 128
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num_groups = model_config["num_groups"]
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@@ -250,54 +415,39 @@ def verify_correctness(model_config, use_fp8_w8a8):
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intermediate_size = model_config["intermediate_size"]
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test_data = create_test_data(batch_size, num_groups, hidden_size, intermediate_size)
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(x, w_fbgemm, w_sglang, c_fbgemm, c_sglang, m_sizes, seg_indptr, weight_indices) = (
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test_data
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(
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x,
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w_fbgemm,
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w_sglang,
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c_fbgemm,
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c_sglang,
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m_sizes,
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seg_indptr,
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weight_indices,
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) = test_data
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result_fbgemm = fbgemm_grouped_gemm(x, w_fbgemm, m_sizes, use_fast_accum=True)
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result_sglang = sglang_grouped_gemm(
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x,
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w_sglang,
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c_sglang,
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num_groups,
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weight_column_major=True,
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seg_indptr=seg_indptr,
|
||||
weight_indices=weight_indices,
|
||||
c_dtype=c_sglang.dtype,
|
||||
)
|
||||
|
||||
try:
|
||||
result_fbgemm = fbgemm_grouped_gemm(x, w_fbgemm, m_sizes, use_fast_accum=True)
|
||||
|
||||
result_sglang = sglang_grouped_gemm(
|
||||
x,
|
||||
w_sglang,
|
||||
c_sglang,
|
||||
num_groups,
|
||||
weight_column_major=True,
|
||||
seg_indptr=seg_indptr,
|
||||
weight_indices=weight_indices,
|
||||
c_dtype=c_sglang.dtype,
|
||||
)
|
||||
|
||||
if torch.allclose(result_fbgemm, result_sglang, rtol=1e-3, atol=1e-3):
|
||||
print("✓ BF16 Correctness verification passed!")
|
||||
else:
|
||||
max_diff = torch.max(torch.abs(result_fbgemm - result_sglang))
|
||||
print(f"✗ BF16 Correctness verification failed! Max diff: {max_diff}")
|
||||
return False
|
||||
|
||||
if use_fp8_w8a8:
|
||||
try:
|
||||
fp8_data = create_fp8_test_data(
|
||||
batch_size, num_groups, hidden_size, intermediate_size
|
||||
)
|
||||
x_fp8, w_fp8, m_sizes_fp8, x_scale, w_scale = fp8_data
|
||||
|
||||
result_fp8 = fbgemm_grouped_gemm_fp8_rowwise(
|
||||
x_fp8, w_fp8, m_sizes_fp8, x_scale, w_scale, use_fast_accum=True
|
||||
)
|
||||
|
||||
assert result_fp8.shape == (batch_size, intermediate_size)
|
||||
print("✓ FP8 functionality test passed!")
|
||||
except Exception as e:
|
||||
print(f"FP8 test failed (possibly unsupported): {e}")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"✗ Error during correctness verification: {e}")
|
||||
if torch.allclose(result_fbgemm, result_sglang, rtol=1e-3, atol=1e-3):
|
||||
print("✓ BF16 Correctness verification passed!")
|
||||
else:
|
||||
max_diff = torch.max(torch.abs(result_fbgemm - result_sglang))
|
||||
print(f"✗ BF16 Correctness verification failed! Max diff: {max_diff}")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
@@ -348,7 +498,7 @@ def main():
|
||||
print(f" use_fp8_w8a8: {args.use_fp8_w8a8}")
|
||||
|
||||
if args.verify_correctness:
|
||||
if not verify_correctness(model_config, args.use_fp8_w8a8):
|
||||
if not verify_correctness(model_config):
|
||||
print("Correctness verification failed. Exiting...")
|
||||
return
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,323 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
try:
|
||||
from fbgemm_grouped_gemm import grouped_gemm as fbgemm_grouped_gemm
|
||||
from fbgemm_grouped_gemm import (
|
||||
grouped_gemm_fp8_rowwise as fbgemm_grouped_gemm_fp8_rowwise,
|
||||
)
|
||||
|
||||
FBGEMM_AVAILABLE = True
|
||||
print("✓ Successfully imported FBGEMM grouped GEMM")
|
||||
except ImportError as e:
|
||||
print(f"✗ Failed to import FBGEMM grouped GEMM: {e}")
|
||||
FBGEMM_AVAILABLE = False
|
||||
|
||||
try:
|
||||
from sglang.srt.layers.moe.ep_moe.kernels import (
|
||||
grouped_gemm_triton as sglang_grouped_gemm,
|
||||
)
|
||||
|
||||
SGLANG_AVAILABLE = True
|
||||
print("✓ Successfully imported SGLang grouped GEMM")
|
||||
except ImportError as e:
|
||||
print(f"✗ Failed to import SGLang grouped GEMM: {e}")
|
||||
SGLANG_AVAILABLE = False
|
||||
|
||||
|
||||
def create_uniform_groups(batch_size, num_groups, device):
|
||||
tokens_per_group = batch_size // num_groups
|
||||
return torch.full((num_groups,), tokens_per_group, dtype=torch.int64, device=device)
|
||||
|
||||
|
||||
def create_non_uniform_groups(batch_size, num_groups, device):
|
||||
remaining = batch_size
|
||||
m_sizes = []
|
||||
|
||||
for i in range(num_groups - 1):
|
||||
if remaining <= 1:
|
||||
size = 1
|
||||
else:
|
||||
max_size = remaining - (num_groups - i - 1) + 1
|
||||
size = torch.randint(1, max_size, (1,)).item()
|
||||
m_sizes.append(size)
|
||||
remaining -= size
|
||||
|
||||
m_sizes.append(remaining)
|
||||
return torch.tensor(m_sizes, dtype=torch.int64, device=device)
|
||||
|
||||
|
||||
def create_sglang_inputs(x, w, m_sizes, num_groups, intermediate_size, device):
|
||||
batch_size = x.shape[0]
|
||||
|
||||
c_sglang = torch.empty(
|
||||
batch_size, intermediate_size, dtype=torch.bfloat16, device=device
|
||||
)
|
||||
|
||||
seg_indptr = torch.zeros(num_groups + 1, dtype=torch.int64, device=device)
|
||||
current_pos = 0
|
||||
for i, size in enumerate(m_sizes):
|
||||
current_pos += size
|
||||
seg_indptr[i + 1] = current_pos
|
||||
|
||||
weight_indices = torch.arange(num_groups, dtype=torch.int64, device=device)
|
||||
w_sglang = w.view(num_groups, intermediate_size, -1)
|
||||
|
||||
return c_sglang, seg_indptr, weight_indices, w_sglang
|
||||
|
||||
|
||||
def create_fp8_data(batch_size, num_groups, hidden_size, intermediate_size, device):
|
||||
torch.manual_seed(42)
|
||||
|
||||
x_fp16 = torch.randn(batch_size, hidden_size, dtype=torch.float16, device=device)
|
||||
w_fp16 = torch.randn(
|
||||
num_groups * intermediate_size, hidden_size, dtype=torch.float16, device=device
|
||||
)
|
||||
|
||||
x_fp8 = x_fp16.to(torch.float8_e4m3fn)
|
||||
w_fp8 = w_fp16.to(torch.float8_e4m3fn)
|
||||
|
||||
x_scale = torch.randn(batch_size, dtype=torch.float32, device=device).abs() + 1e-4
|
||||
w_scale = torch.randn(num_groups, dtype=torch.float32, device=device).abs() + 1e-4
|
||||
|
||||
return x_fp8, w_fp8, x_scale, w_scale
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def device():
|
||||
if not torch.cuda.is_available():
|
||||
pytest.skip("CUDA not available")
|
||||
return torch.device("cuda")
|
||||
|
||||
|
||||
@pytest.mark.skipif(not FBGEMM_AVAILABLE, reason="FBGEMM not available")
|
||||
@pytest.mark.skipif(not SGLANG_AVAILABLE, reason="SGLang not available")
|
||||
@pytest.mark.parametrize("batch_size", [32])
|
||||
@pytest.mark.parametrize("num_groups", [2, 4, 8])
|
||||
@pytest.mark.parametrize("hidden_size", [512, 1024])
|
||||
@pytest.mark.parametrize("intermediate_size", [1024, 2048])
|
||||
def test_uniform_groups(batch_size, num_groups, hidden_size, intermediate_size, device):
|
||||
if batch_size % num_groups != 0:
|
||||
pytest.skip(f"batch_size {batch_size} not divisible by num_groups {num_groups}")
|
||||
|
||||
torch.manual_seed(42)
|
||||
|
||||
m_sizes = create_uniform_groups(batch_size, num_groups, device)
|
||||
|
||||
x = torch.randn(batch_size, hidden_size, dtype=torch.bfloat16, device=device)
|
||||
w = torch.randn(
|
||||
num_groups * intermediate_size, hidden_size, dtype=torch.bfloat16, device=device
|
||||
)
|
||||
|
||||
result_fbgemm = fbgemm_grouped_gemm(x, w, m_sizes, use_fast_accum=True)
|
||||
|
||||
c_sglang, seg_indptr, weight_indices, w_sglang = create_sglang_inputs(
|
||||
x, w, m_sizes, num_groups, intermediate_size, device
|
||||
)
|
||||
|
||||
result_sglang = sglang_grouped_gemm(
|
||||
x,
|
||||
w_sglang,
|
||||
c_sglang,
|
||||
num_groups,
|
||||
weight_column_major=True,
|
||||
seg_indptr=seg_indptr,
|
||||
weight_indices=weight_indices,
|
||||
c_dtype=c_sglang.dtype,
|
||||
)
|
||||
|
||||
assert torch.allclose(result_fbgemm, result_sglang, rtol=1e-3, atol=1e-3)
|
||||
|
||||
|
||||
@pytest.mark.skipif(not FBGEMM_AVAILABLE, reason="FBGEMM not available")
|
||||
@pytest.mark.skipif(not SGLANG_AVAILABLE, reason="SGLang not available")
|
||||
@pytest.mark.parametrize("batch_size", [63, 100, 127])
|
||||
@pytest.mark.parametrize("num_groups", [3, 5, 7])
|
||||
@pytest.mark.parametrize("hidden_size", [512, 1024])
|
||||
@pytest.mark.parametrize("intermediate_size", [1024, 2048])
|
||||
def test_non_uniform_groups(
|
||||
batch_size, num_groups, hidden_size, intermediate_size, device
|
||||
):
|
||||
torch.manual_seed(42)
|
||||
|
||||
m_sizes = create_non_uniform_groups(batch_size, num_groups, device)
|
||||
|
||||
x = torch.randn(batch_size, hidden_size, dtype=torch.bfloat16, device=device)
|
||||
w = torch.randn(
|
||||
num_groups * intermediate_size, hidden_size, dtype=torch.bfloat16, device=device
|
||||
)
|
||||
|
||||
result_fbgemm = fbgemm_grouped_gemm(x, w, m_sizes, use_fast_accum=True)
|
||||
|
||||
c_sglang, seg_indptr, weight_indices, w_sglang = create_sglang_inputs(
|
||||
x, w, m_sizes, num_groups, intermediate_size, device
|
||||
)
|
||||
|
||||
result_sglang = sglang_grouped_gemm(
|
||||
x,
|
||||
w_sglang,
|
||||
c_sglang,
|
||||
num_groups,
|
||||
weight_column_major=True,
|
||||
seg_indptr=seg_indptr,
|
||||
weight_indices=weight_indices,
|
||||
c_dtype=c_sglang.dtype,
|
||||
)
|
||||
|
||||
assert torch.allclose(result_fbgemm, result_sglang, rtol=1e-3, atol=1e-3)
|
||||
|
||||
|
||||
@pytest.mark.skipif(not FBGEMM_AVAILABLE, reason="FBGEMM not available")
|
||||
@pytest.mark.skipif(not SGLANG_AVAILABLE, reason="SGLang not available")
|
||||
@pytest.mark.parametrize("batch_size,num_groups", [(64, 4), (128, 8), (256, 16)])
|
||||
@pytest.mark.parametrize("hidden_size", [768, 2048, 4096])
|
||||
@pytest.mark.parametrize("intermediate_size", [2048, 4096, 8192])
|
||||
def test_large_dimensions(
|
||||
batch_size, num_groups, hidden_size, intermediate_size, device
|
||||
):
|
||||
torch.manual_seed(42)
|
||||
|
||||
m_sizes = create_uniform_groups(batch_size, num_groups, device)
|
||||
|
||||
x = torch.randn(batch_size, hidden_size, dtype=torch.bfloat16, device=device)
|
||||
w = torch.randn(
|
||||
num_groups * intermediate_size, hidden_size, dtype=torch.bfloat16, device=device
|
||||
)
|
||||
|
||||
result_fbgemm = fbgemm_grouped_gemm(x, w, m_sizes, use_fast_accum=True)
|
||||
|
||||
c_sglang, seg_indptr, weight_indices, w_sglang = create_sglang_inputs(
|
||||
x, w, m_sizes, num_groups, intermediate_size, device
|
||||
)
|
||||
|
||||
result_sglang = sglang_grouped_gemm(
|
||||
x,
|
||||
w_sglang,
|
||||
c_sglang,
|
||||
num_groups,
|
||||
weight_column_major=True,
|
||||
seg_indptr=seg_indptr,
|
||||
weight_indices=weight_indices,
|
||||
c_dtype=c_sglang.dtype,
|
||||
)
|
||||
|
||||
assert torch.allclose(result_fbgemm, result_sglang, rtol=1e-3, atol=1e-3)
|
||||
|
||||
|
||||
@pytest.mark.skipif(not FBGEMM_AVAILABLE, reason="FBGEMM not available")
|
||||
@pytest.mark.parametrize("batch_size", [32, 64])
|
||||
@pytest.mark.parametrize("num_groups", [2, 4])
|
||||
@pytest.mark.parametrize("hidden_size", [512, 1024])
|
||||
@pytest.mark.parametrize("intermediate_size", [1024, 2048])
|
||||
def test_fp8_uniform_groups(
|
||||
batch_size, num_groups, hidden_size, intermediate_size, device
|
||||
):
|
||||
if batch_size % num_groups != 0:
|
||||
pytest.skip(f"batch_size {batch_size} not divisible by num_groups {num_groups}")
|
||||
|
||||
torch.manual_seed(42)
|
||||
|
||||
m_sizes = create_uniform_groups(batch_size, num_groups, device)
|
||||
x_fp8, w_fp8, x_scale, w_scale = create_fp8_data(
|
||||
batch_size, num_groups, hidden_size, intermediate_size, device
|
||||
)
|
||||
|
||||
try:
|
||||
result_fp8 = fbgemm_grouped_gemm_fp8_rowwise(
|
||||
x_fp8, w_fp8, m_sizes, x_scale, w_scale, use_fast_accum=True
|
||||
)
|
||||
assert result_fp8.shape == (batch_size, intermediate_size)
|
||||
assert result_fp8.dtype == torch.bfloat16
|
||||
except Exception as e:
|
||||
pytest.skip(f"FP8 test failed (possibly unsupported): {e}")
|
||||
|
||||
|
||||
@pytest.mark.skipif(not FBGEMM_AVAILABLE, reason="FBGEMM not available")
|
||||
@pytest.mark.parametrize("batch_size", [63, 100])
|
||||
@pytest.mark.parametrize("num_groups", [3, 5])
|
||||
@pytest.mark.parametrize("hidden_size", [512, 1024])
|
||||
@pytest.mark.parametrize("intermediate_size", [1024, 2048])
|
||||
def test_fp8_non_uniform_groups(
|
||||
batch_size, num_groups, hidden_size, intermediate_size, device
|
||||
):
|
||||
torch.manual_seed(42)
|
||||
|
||||
m_sizes = create_non_uniform_groups(batch_size, num_groups, device)
|
||||
x_fp8, w_fp8, x_scale, w_scale = create_fp8_data(
|
||||
batch_size, num_groups, hidden_size, intermediate_size, device
|
||||
)
|
||||
|
||||
try:
|
||||
result_fp8 = fbgemm_grouped_gemm_fp8_rowwise(
|
||||
x_fp8, w_fp8, m_sizes, x_scale, w_scale, use_fast_accum=True
|
||||
)
|
||||
assert result_fp8.shape == (batch_size, intermediate_size)
|
||||
assert result_fp8.dtype == torch.bfloat16
|
||||
except Exception as e:
|
||||
pytest.skip(f"FP8 test failed (possibly unsupported): {e}")
|
||||
|
||||
|
||||
@pytest.mark.skipif(not FBGEMM_AVAILABLE, reason="FBGEMM not available")
|
||||
def test_fbgemm_only_uniform(device):
|
||||
torch.manual_seed(42)
|
||||
|
||||
batch_size, num_groups = 64, 4
|
||||
hidden_size, intermediate_size = 512, 1024
|
||||
|
||||
m_sizes = create_uniform_groups(batch_size, num_groups, device)
|
||||
x = torch.randn(batch_size, hidden_size, dtype=torch.bfloat16, device=device)
|
||||
w = torch.randn(
|
||||
num_groups * intermediate_size, hidden_size, dtype=torch.bfloat16, device=device
|
||||
)
|
||||
|
||||
result = fbgemm_grouped_gemm(x, w, m_sizes, use_fast_accum=True)
|
||||
|
||||
assert result.shape == (batch_size, intermediate_size)
|
||||
assert result.dtype == torch.bfloat16
|
||||
|
||||
|
||||
@pytest.mark.skipif(not SGLANG_AVAILABLE, reason="SGLang not available")
|
||||
def test_sglang_only_uniform(device):
|
||||
torch.manual_seed(42)
|
||||
|
||||
batch_size, num_groups = 64, 4
|
||||
hidden_size, intermediate_size = 512, 1024
|
||||
|
||||
m_sizes = create_uniform_groups(batch_size, num_groups, device)
|
||||
x = torch.randn(batch_size, hidden_size, dtype=torch.bfloat16, device=device)
|
||||
w = torch.randn(
|
||||
num_groups * intermediate_size, hidden_size, dtype=torch.bfloat16, device=device
|
||||
)
|
||||
|
||||
c_sglang, seg_indptr, weight_indices, w_sglang = create_sglang_inputs(
|
||||
x, w, m_sizes, num_groups, intermediate_size, device
|
||||
)
|
||||
|
||||
result = sglang_grouped_gemm(
|
||||
x,
|
||||
w_sglang,
|
||||
c_sglang,
|
||||
num_groups,
|
||||
weight_column_major=True,
|
||||
seg_indptr=seg_indptr,
|
||||
weight_indices=weight_indices,
|
||||
c_dtype=c_sglang.dtype,
|
||||
)
|
||||
|
||||
assert result.shape == (batch_size, intermediate_size)
|
||||
assert result.dtype == torch.bfloat16
|
||||
|
||||
|
||||
def test_imports():
|
||||
assert (
|
||||
FBGEMM_AVAILABLE or SGLANG_AVAILABLE
|
||||
), "Neither FBGEMM nor SGLang is available"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__, "-v"])
|
||||
Reference in New Issue
Block a user