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prain: int8/int4 experts quantized once on the GPU, rounds through vLLM's expert_map, [miss] min_weight skips experts not in VRAM; scripts: tune_moe, quality (answer NLL on identical token ids), bench --context, measure --quant

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fabolous005
2026-10-28 22:07:25 +01:00
parent 3ae9bd73a7
commit 708e152549
15 changed files with 703 additions and 120 deletions
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tests/test_kernel_match.py
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@@ -31,3 +31,64 @@ class KernelMatch(unittest.TestCase):
global_num_experts=experts).float().cpu()
cpu = cpu_experts(x, topk_weights, topk_ids, set(range(experts)), lambda e: (w13[e], w2[e]))
torch.testing.assert_close(cpu, gpu, rtol=3e-2, atol=3e-2)
def test_a_round_computes_only_the_experts_its_slot_table_maps(self):
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.fused_moe.activation import MoEActivation
from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
from prain.tiered import cpu_experts, slot_table
torch.manual_seed(0)
tokens, hidden, inter, experts = 64, 256, 128, 6
x = torch.randn(tokens, hidden, dtype=torch.bfloat16)
w13 = torch.randn(experts, 2 * inter, hidden, dtype=torch.bfloat16) * 0.1
w2 = torch.randn(experts, hidden, inter, dtype=torch.bfloat16) * 0.1
topk_ids = torch.stack([torch.randperm(experts)[:2] for _ in range(tokens)]).int()
topk_ids[0, 1] = experts # a padding route
topk_weights = torch.rand(tokens, 2)
table = slot_table({1: 0, 4: 1}, experts, "cuda")
with set_current_vllm_config(VllmConfig()):
gpu = fused_experts(x.cuda(), w13[[1, 4]].cuda(), w2[[1, 4]].cuda(), topk_weights.cuda(),
topk_ids.cuda(), activation=MoEActivation.GELU_TANH,
global_num_experts=table.numel(), expert_map=table).float().cpu()
cpu = cpu_experts(x, topk_weights, topk_ids, {1, 4}, lambda e: (w13[e], w2[e]))
torch.testing.assert_close(cpu, gpu, rtol=3e-2, atol=3e-2)
def test_quantized_slots_compute_what_the_cpu_path_computes(self):
"""Pack layout -> slot layout on the GPU -> vLLM's int8/int4 kernel for a round that
holds experts 1 and 3, against the CPU kernels on the pack layout and against the
dequantized weights."""
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.fused_moe.activation import MoEActivation
from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
from prain.quant import (cpu_matmul, dequantize, moe_quant_config, quantize, to_cpu_int4,
to_gpu_int4)
from prain.tiered import cpu_experts, slot_table
torch.manual_seed(0)
tokens, hidden, inter, experts = 8, 256, 128, 4
x = torch.randn(tokens, hidden, dtype=torch.bfloat16)
w13 = torch.randn(experts, 2 * inter, hidden) * 0.1
w2 = torch.randn(experts, hidden, inter) * 0.1
topk_ids = torch.stack([torch.randperm(experts)[:2] for _ in range(tokens)]).int()
topk_weights = torch.rand(tokens, 2)
table = slot_table({1: 0, 3: 1}, experts, "cuda")
for quant in ("int8", "int4"):
parts = [quantize(w, quant) for w in (w13, w2)] # [q, scale], gpu layout
pack = [[to_cpu_int4(q) if quant == "int4" else q, s] for q, s in parts]
slots = [[to_gpu_int4(q[[1, 3]].cuda()) if quant == "int4" else q[[1, 3]].cuda(),
s[[1, 3]].cuda()] for q, s in pack]
with set_current_vllm_config(VllmConfig()):
gpu = fused_experts(x.cuda(), slots[0][0], slots[1][0], topk_weights.cuda(),
topk_ids.cuda(), activation=MoEActivation.GELU_TANH,
global_num_experts=table.numel(), expert_map=table,
quant_config=moe_quant_config(quant, slots[0][1], slots[1][1]))
cpu = cpu_experts(x, topk_weights, topk_ids, {1, 3}, lambda e: tuple(
cpu_matmul(quant, q[e], s[e]) for q, s in pack))
exact = cpu_experts(x, topk_weights, topk_ids, {1, 3}, lambda e: tuple(
dequantize(q, s, quant)[e] for q, s in parts))
self.assertGreater(exact.norm().item(), 1.0)
torch.testing.assert_close(cpu, exact, rtol=3e-2, atol=3e-2, msg=quant)
torch.testing.assert_close(gpu.float().cpu(), exact, rtol=3e-2, atol=3e-2, msg=quant)