biondizzle
09ff5c5b98
cutedsl/moe_pipeline.py: complete pipeline - stage_activation: BF16 → NVFP4 (keeps data in FP4) - L1 GEMM: NVFP4 × NVFP4 → BF16 (gate+up) - SiLU(gate) * up: BF16 (only nonlinear, can't avoid) - Re-quantize: BF16 → NVFP4 (back to native) - L2 GEMM: NVFP4 × NVFP4 → BF16 (down_proj) - Scatter with routing weights → BF16 output layertest.py: now tests the FULL MoE pipeline against BF16 reference. NVFP4-native: both GEMMs use float4_e2m1fn_x2 for A and B, float8_e4m3fn for block scales, float32 for global scales. BF16 only for SiLU activation and final scatter.
302 lines
11 KiB
Python
302 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Layer 0 kernel comparison test: CuTeDSL NVFP4 kernel vs BF16 reference.
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No vLLM, no Docker, no tensor parallelism. Just raw weights + CuTeDSL kernel.
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If cosine < 0.99, the test exits with error.
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Uses the bridge layer in cutedsl/bridge.py for tensor layout conversion.
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"""
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import os
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import sys
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import json
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import glob
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import torch
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from safetensors import safe_open
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# Add repo root to path
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REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, REPO_ROOT)
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from cutedsl.bridge import (
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quantize_to_nvfp4,
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quantize_weight_to_nvfp4,
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assemble_scales_2d_side,
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assemble_scales_3d_side,
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make_b_k_major,
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compute_expert_offsets,
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run_nvfp4_grouped_gemm,
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)
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from cutedsl.moe_pipeline import (
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stage_activation,
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prepare_nvfp4_moe_weights,
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run_nvfp4_moe,
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)
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# ── Constants ──────────────────────────────────────────────────────────
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NVFP4_MODEL_DIR = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
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LAYER_IDX = 0
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DEVICE = "cuda"
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COSINE_THRESHOLD = 0.99
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# E2M1 FP4 lookup table (for BF16 dequant reference)
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E2M1_LUT = torch.tensor([
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0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
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-0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0,
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], dtype=torch.float32)
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# ── Checkpoint loading ─────────────────────────────────────────────────
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def find_shards(model_dir):
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index_path = os.path.join(model_dir, "model.safetensors.index.json")
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key_to_shard = {}
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if os.path.exists(index_path):
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with open(index_path) as f:
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index = json.load(f)
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for key, shard in index["weight_map"].items():
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key_to_shard[key] = os.path.join(model_dir, shard)
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else:
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for sf in glob.glob(os.path.join(model_dir, "*.safetensors")):
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with safe_open(sf, framework="pt") as f:
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for key in f.keys():
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key_to_shard[key] = sf
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return key_to_shard
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def load_layer_tensors(model_dir, layer_idx):
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"""Load all tensors for a specific layer. Keys normalized (no 'model.' prefix)."""
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key_to_shard = find_shards(model_dir)
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layer_prefix = f"layers.{layer_idx}."
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shard_to_keys = {}
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for key, shard in key_to_shard.items():
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norm_key = key.removeprefix("model.")
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if not norm_key.startswith(layer_prefix):
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continue
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shard_to_keys.setdefault(shard, []).append((key, norm_key))
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tensors = {}
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for shard, keys in shard_to_keys.items():
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with safe_open(shard, framework="pt") as f:
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for orig_key, norm_key in keys:
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tensors[norm_key] = f.get_tensor(orig_key)
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return tensors
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# ── NVFP4 Dequantization (BF16 reference) ─────────────────────────────
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def dequantize_nvfp4_weight(packed_uint8, scale_e4m3, global_scale):
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"""Dequantize NVFP4 (E2M1 + E4M3 + global) to BF16."""
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device = packed_uint8.device
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lut = E2M1_LUT.to(device)
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lower = lut[(packed_uint8 & 0x0F).long()]
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upper = lut[((packed_uint8 >> 4) & 0x0F).long()]
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out_features = packed_uint8.shape[0]
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in_features = packed_uint8.shape[1] * 2
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unpacked = torch.empty(out_features, in_features, dtype=torch.float32, device=device)
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unpacked[:, 0::2] = lower
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unpacked[:, 1::2] = upper
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block_scale = scale_e4m3.float()
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block_expanded = block_scale.repeat_interleave(16, dim=1)[:, :in_features]
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return (unpacked * block_expanded * global_scale).to(torch.bfloat16)
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def dequantize_nvfp4_experts(nvfp4_tensors, layer_idx, expert_indices):
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"""Dequantize expert weights from NVFP4 checkpoint → BF16."""
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experts = {}
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for e in expert_indices:
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expert = {}
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for proj in ["gate_proj", "up_proj", "down_proj"]:
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weight_key = f"layers.{layer_idx}.mlp.experts.{e}.{proj}.weight"
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scale_key = f"layers.{layer_idx}.mlp.experts.{e}.{proj}.weight_scale"
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gs_key = f"layers.{layer_idx}.mlp.experts.{e}.{proj}.weight_scale_2"
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if weight_key not in nvfp4_tensors:
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if proj == "down_proj" and e == 211:
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continue
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raise KeyError(f"Missing {weight_key}")
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weight = nvfp4_tensors[weight_key].to(DEVICE)
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scale = nvfp4_tensors[scale_key].to(DEVICE)
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global_scale = nvfp4_tensors[gs_key].item()
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expert[proj] = dequantize_nvfp4_weight(weight, scale, global_scale)
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experts[e] = expert
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return experts
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# ── BF16 MoE Forward ───────────────────────────────────────────────────
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def moe_forward_bf16(hidden_states, experts, expert_ids, expert_weights):
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"""Run MoE forward pass in pure BF16 (torch.matmul)."""
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num_tokens, hidden_size = hidden_states.shape
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top_k = expert_ids.shape[1]
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output = torch.zeros(num_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE)
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for t in range(num_tokens):
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for k in range(top_k):
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e = expert_ids[t, k].item()
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w = expert_weights[t, k].item()
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if e not in experts:
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continue
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x = hidden_states[t]
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gate = x @ experts[e]["gate_proj"].T
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up = x @ experts[e]["up_proj"].T
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activated = torch.nn.functional.silu(gate) * up
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if "down_proj" in experts[e]:
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y = activated @ experts[e]["down_proj"].T
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else:
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y = activated[:hidden_size]
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output[t] += w * y
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return output
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# ── CuTeDSL NVFP4 Kernel MoE Forward ──────────────────────────────────
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def moe_forward_nvfp4_l1_only(slot_hidden, nvfp4_tensors, layer_idx, expert_indices, tokens_per_expert):
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"""Run L1 (gate+up) GEMM using CuTeDSL.
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slot_hidden is already laid out slot-major: [expert0_tokens | expert1_tokens | ...]
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"""
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num_slots, hidden_size = slot_hidden.shape
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num_experts = len(expert_indices)
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# Quantize activation
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x_fp4, x_sf, x_igs = quantize_to_nvfp4(slot_hidden)
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# Load and quantize weights
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w_fp4_list = []
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w_sf_list = []
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w_gs_list = []
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for e in expert_indices:
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gate_w_key = f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight"
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gate_sf_key = f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight_scale"
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gate_gs_key = f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight_scale_2"
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up_w_key = f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight"
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up_sf_key = f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight_scale"
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up_gs_key = f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight_scale_2"
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gate_w_bf16 = dequantize_nvfp4_weight(
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nvfp4_tensors[gate_w_key].to(DEVICE),
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nvfp4_tensors[gate_sf_key].to(DEVICE),
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nvfp4_tensors[gate_gs_key].item(),
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)
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up_w_bf16 = dequantize_nvfp4_weight(
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nvfp4_tensors[up_w_key].to(DEVICE),
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nvfp4_tensors[up_sf_key].to(DEVICE),
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nvfp4_tensors[up_gs_key].item(),
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)
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# Fuse gate + up, transpose to (K=hidden, N=6144)
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fused = torch.cat([gate_w_bf16, up_w_bf16], dim=0) # (6144, 7168)
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l1_w_bf16 = fused.T # (7168, 6144)
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l1_w_fp4, l1_w_sf, l1_w_gs = quantize_weight_to_nvfp4(l1_w_bf16)
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w_fp4_list.append(l1_w_fp4)
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w_sf_list.append(l1_w_sf)
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w_gs_list.append(l1_w_gs)
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# Stack and convert to K-major
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mat_b = make_b_k_major(torch.stack(w_fp4_list))
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# Assemble scale factors
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# scale_a: per-expert activation scales, split by expert offsets
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x_sf_parts = []
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offset = 0
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for tpe in tokens_per_expert:
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x_sf_parts.append(x_sf[offset:offset+tpe])
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offset += tpe
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scale_a = assemble_scales_2d_side(x_sf_parts)
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scale_b = assemble_scales_3d_side(w_sf_list)
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# Expert offsets
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expert_offsets = compute_expert_offsets(tokens_per_expert, num_experts)
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# Global scales
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global_scale_a = torch.tensor([x_igs] * num_experts, dtype=torch.float32, device=DEVICE)
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global_scale_b = torch.tensor(w_gs_list, dtype=torch.float32, device=DEVICE)
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# Run kernel
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out = run_nvfp4_grouped_gemm(
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mat_a=x_fp4, mat_b=mat_b,
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scale_a=scale_a, scale_b=scale_b,
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expert_offsets=expert_offsets,
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global_scale_a=global_scale_a, global_scale_b=global_scale_b,
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)
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return out
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def main():
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torch.manual_seed(42)
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expert_indices = [0, 1, 2]
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top_k = 2
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num_tokens = 4
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hidden_size = 7168
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# ── Load NVFP4 checkpoint ──
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print("=" * 70)
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print(" Loading NVFP4 checkpoint layer 0")
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print("=" * 70)
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nvfp4_tensors = load_layer_tensors(NVFP4_MODEL_DIR, LAYER_IDX)
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expert_keys = [k for k in sorted(nvfp4_tensors.keys()) if 'experts.0.' in k]
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print(f" {len(nvfp4_tensors)} tensors loaded")
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for key in expert_keys[:3]:
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t = nvfp4_tensors[key]
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print(f" {key}: dtype={t.dtype} shape={tuple(t.shape)}")
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# ── Prepare NVFP4 weights ──
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print("
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Preparing NVFP4 weights (dequant → re-quant)...")
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weights = prepare_nvfp4_moe_weights(nvfp4_tensors, LAYER_IDX, expert_indices)
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print(f" L1: {len(weights['l1_fp4'])} experts, shape {weights['l1_fp4'][0].shape}")
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print(f" L2: {len(weights['l2_fp4'])} experts, shape {weights['l2_fp4'][0].shape}")
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# ── Dequantize → BF16 reference ──
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print("
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Dequantizing NVFP4 → BF16 reference...")
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nvfp4_experts_bf16 = dequantize_nvfp4_experts(nvfp4_tensors, LAYER_IDX, expert_indices)
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# ── Create test input ──
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hidden_states = torch.randn(num_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE) * 2.0
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expert_ids = torch.tensor([[0, 1]] * num_tokens, dtype=torch.int32, device=DEVICE)
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expert_weights = torch.tensor([[0.6, 0.4]] * num_tokens, dtype=torch.float32, device=DEVICE)
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# ── BF16 full MoE reference ──
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print("
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Running BF16 MoE reference...")
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ref_output = moe_forward_bf16(hidden_states, nvfp4_experts_bf16, expert_ids, expert_weights)
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print(f" BF16 ref: amax={ref_output.abs().max():.4f} mean={ref_output.float().mean():.6f}")
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del nvfp4_experts_bf16
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torch.cuda.empty_cache()
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# ── CuTeDSL NVFP4 full MoE pipeline ──
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print("
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Running CuTeDSL NVFP4 MoE pipeline (first run compiles, ~1-2 min)...")
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kernel_output = run_nvfp4_moe(
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hidden_states, expert_ids, expert_weights,
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weights, expert_indices,
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)
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print(f" Kernel: amax={kernel_output.abs().max():.4f} mean={kernel_output.float().mean():.6f}")
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# ── Compare ──
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cosine = torch.nn.functional.cosine_similarity(
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kernel_output.flatten().unsqueeze(0).float(),
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ref_output.flatten().unsqueeze(0).float(),
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).item()
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mse = (kernel_output.float() - ref_output.float()).pow(2).mean().item()
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print(f"
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{'=' * 70}")
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print(f" RESULT: cosine={cosine:.6f} MSE={mse:.6e}")
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print(f"{'=' * 70}")
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if cosine < COSINE_THRESHOLD:
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print(f" ❌ FAIL: cosine {cosine:.6f} < {COSINE_THRESHOLD}")
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sys.exit(1)
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else:
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print(f" ✅ PASS: cosine {cosine:.6f} >= {COSINE_THRESHOLD}")
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if __name__ == "__main__":
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main()
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