307 lines
13 KiB
Python
307 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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B200 test: Proves the attention O-projection root cause and fix.
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Loads real model weights from the NVFP4 checkpoint and tests:
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1. OLD path: fused_inv_rope_fp8_quant + FP8 einsum (crashes with BF16 wo_a)
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2. NEW path: BF16 inv RoPE + BMM wo_a + NVFP4 wo_b (should work)
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Also tests the NVFP4 linear kernel (wo_b) with real weights.
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Usage (on B200):
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python3 tests/test_o_projection_b200.py
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Requires: Real model weights at /root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4
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CuTeDSL, CUDA, Blackwell GPU
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"""
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import sys
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import os
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import json
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import torch
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import torch.nn.functional as F
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from safetensors import safe_open
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MODEL_PATH = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
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DEVICE = "cuda:0"
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LAYER_IDX = 0
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# DeepSeek V4 Pro dimensions
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HIDDEN_SIZE = 7168
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NUM_HEADS = 128
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HEAD_DIM = 512
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NOPE_DIM = 448
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ROPE_DIM = 64
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Q_LORA_RANK = 1536
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O_LORA_RANK = 1024
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O_GROUPS = 16 # from config (not TP-sharded)
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HEADS_PER_GROUP = NUM_HEADS // O_GROUPS # 8
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NUM_TOKENS = 4
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_cache = {}
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def load_tensor(key, wm, model_dir):
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if key in _cache:
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return _cache[key]
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shard_path = os.path.join(model_dir, wm[key])
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with safe_open(shard_path, framework="pt") as f:
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t = f.get_tensor(key)
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_cache[key] = t
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return t
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# ── OLD PATH: What the unpatched vLLM forward does ──────────────────
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def old_path_o_projection(o, positions, cos_sin_cache, wo_a_weight_bf16):
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"""Simulates the OLD (broken) attention forward.
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The old path does:
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o_fp8, o_scale = fused_inv_rope_fp8_quant(o, ...)
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wo_a_scale = self.wo_a.weight_scale_inv ← DOESN'T EXIST on BF16 wo_a
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deepseek_v4_fp8_einsum(o_fp8, o_scale, wo_a_fp8, wo_a_scale, z, ...)
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Since wo_a is BF16 (no weight_scale_inv), this crashes.
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We simulate the crash by showing weight_scale_inv doesn't exist.
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"""
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has_scale_inv = hasattr(wo_a_weight_bf16, 'weight_scale_inv') or \
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(isinstance(wo_a_weight_bf16, torch.Tensor) and False)
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# The weight is BF16 — FP8 einsum can't use it
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# The old code does: wo_a_fp8 = self.wo_a.weight (BF16!)
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# Then: wo_a_scale = self.wo_a.weight_scale_inv (AttributeError!)
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# Simulate what would happen: FP8 einsum with BF16 weight
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# If we naively try to quantize BF16 to FP8 without proper scales...
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print(" OLD PATH: wo_a.weight is BF16 (shape={}, dtype={})".format(
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wo_a_weight_bf16.shape, wo_a_weight_bf16.dtype))
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# Try to access weight_scale_inv (this is what the old code does)
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if isinstance(wo_a_weight_bf16, torch.Tensor):
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# This is what vLLM does: self.wo_a.weight_scale_inv
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# Since wo_a is a plain BF16 tensor, this AttributeError crashes the worker
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try:
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_ = wo_a_weight_bf16.weight_scale_inv
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print(" ❌ UNEXPECTED: weight_scale_inv exists (shouldn't for BF16)")
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except AttributeError:
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print(" ✅ CONFIRMED: weight_scale_inv does NOT exist → AttributeError in vLLM")
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print(" This is the root cause: the FP8 einsum path crashes because")
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print(" wo_a has quant_config=None (BF16) but the forward expects FP8.")
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return None # Can't produce valid output
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# ── NEW PATH: Our patched forward ───────────────────────────────────
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def apply_inv_rope_bf16(o, positions, cos_sin_cache, nope_dim=NOPE_DIM, rope_dim=ROPE_DIM):
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"""BF16 inverse RoPE (pure PyTorch)."""
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if rope_dim == 0 or o.numel() == 0:
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return o
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half_rope = rope_dim // 2
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cos_all = cos_sin_cache[positions, :half_rope].unsqueeze(1).to(o.dtype)
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sin_all = cos_sin_cache[positions, half_rope:].unsqueeze(1).to(o.dtype)
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o_rope = o[:, :, nope_dim:]
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o_even = o_rope[:, :, 0::2]
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o_odd = o_rope[:, :, 1::2]
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inv_even = o_even * cos_all + o_odd * sin_all
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inv_odd = -o_even * sin_all + o_odd * cos_all
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result = o.clone()
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result[:, :, nope_dim:][:, :, 0::2] = inv_even
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result[:, :, nope_dim:][:, :, 1::2] = inv_odd
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return result
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def new_path_o_projection(o, positions, cos_sin_cache, wo_a_weight_bf16):
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"""NEW path: BF16 inv RoPE + BMM wo_a.
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Returns z of shape (T, n_local_groups, o_lora_rank) ready for wo_b.
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"""
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# Step 1: Inverse RoPE (BF16)
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o_inv = apply_inv_rope_bf16(o, positions, cos_sin_cache)
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print(f" Inverse RoPE: shape={o_inv.shape} amax={o_inv.amax():.4f} NaN={torch.isnan(o_inv).any()}")
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# Step 2: wo_a BMM
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num_tokens = o_inv.shape[0]
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# wo_a weight: (O_GROUPS * O_LORA_RANK, HEADS_PER_GROUP * HEAD_DIM)
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hidden_dim = wo_a_weight_bf16.shape[1] # 4096 = HEADS_PER_GROUP * HEAD_DIM
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out_dim = wo_a_weight_bf16.shape[0] # 16384 = O_GROUPS * O_LORA_RANK
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o_grouped = o_inv.view(num_tokens, O_GROUPS, hidden_dim)
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wo_a_w = wo_a_weight_bf16.view(O_GROUPS, O_LORA_RANK, hidden_dim)
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z = torch.bmm(
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o_grouped.permute(1, 0, 2),
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wo_a_w.transpose(1, 2),
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).permute(1, 0, 2)
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print(f" wo_a BMM: shape={z.shape} amax={z.amax():.4f} NaN={torch.isnan(z).any()}")
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return z
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# ── NVFP4 wo_b test ─────────────────────────────────────────────────
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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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def dequant_nvfp4(packed_uint8, scale_e4m3, global_scale):
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"""Dequantize NVFP4 weight to BF16 for reference."""
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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)[:out_features, :in_features]
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return (unpacked * block_expanded * global_scale).to(torch.bfloat16)
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def test_wo_b_nvfp4(z, wo_b_weight, wo_b_sf, wo_b_gs):
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"""Test wo_b NVFP4 GEMM against BF16 reference."""
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sys.path.insert(0, "/root/nvfp4-megamoe-kernel")
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from cutedsl.nvfp4_linear import CuTeDSLNvfp4Linear
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in_features = wo_b_weight.shape[1] * 2
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out_features = wo_b_weight.shape[0]
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# Convert to CuTeDSL format
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fp4 = [wo_b_weight.view(torch.float4_e2m1fn_x2).permute(1, 0).contiguous()]
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sf = [wo_b_sf.permute(1, 0).contiguous()]
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gs = [wo_b_gs]
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runner = CuTeDSLNvfp4Linear(
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in_features=in_features,
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out_features=out_features,
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max_num_tokens=8192,
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device=DEVICE,
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)
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runner.fp4 = fp4
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runner.sf = sf
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runner.gs = gs
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runner.finalize_weights()
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runner._ensure_initialized()
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# Warmup: compute activation global scale
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z_flat = z.flatten(1) # (T, O_GROUPS * O_LORA_RANK)
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runner.compute_activation_global_scale(z_flat)
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# Run CuTeDSL
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with torch.no_grad():
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output = runner.run(z_flat)
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print(f" wo_b CuTeDSL: shape={output.shape} amax={output.amax():.4f} NaN={torch.isnan(output).any()}")
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# BF16 reference
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bf16_w = dequant_nvfp4(wo_b_weight, wo_b_sf, wo_b_gs)
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with torch.no_grad():
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ref = z_flat @ bf16_w.T
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print(f" wo_b BF16 ref: shape={ref.shape} amax={ref.amax():.4f}")
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cos = F.cosine_similarity(ref.flatten().unsqueeze(0),
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output.flatten().unsqueeze(0)).item()
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mse = (ref - output).pow(2).mean().item()
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status = "✅" if cos >= 0.98 else "❌"
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print(f" wo_b cosine={cos:.6f} MSE={mse:.6e} {status}")
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return cos
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def build_cos_sin_cache(max_pos=4096, rope_dim=ROPE_DIM):
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"""Build cos_sin_cache in the same format as vLLM's RotaryEmbedding."""
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half_rope = rope_dim // 2
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base = 10000.0
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inv_freq = 1.0 / (base ** (torch.arange(0, half_rope, dtype=torch.float32) / half_rope))
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t = torch.arange(max_pos, dtype=torch.float32)
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freqs = torch.outer(t, inv_freq) # (max_pos, half_rope)
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return torch.cat([freqs.cos(), freqs.sin()], dim=-1) # (max_pos, rope_dim)
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def main():
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torch.cuda.set_device(0)
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torch.manual_seed(42)
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print("=" * 70)
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print(" B200 Test: O-Projection Root Cause + Fix")
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print("=" * 70)
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# Load weight map
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with open(os.path.join(MODEL_PATH, "model.safetensors.index.json")) as f:
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wm = json.load(f)["weight_map"]
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P = lambda key: load_tensor(key, wm, MODEL_PATH).to(DEVICE)
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prefix = f"model.layers.{LAYER_IDX}.self_attn"
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# Load wo_a (BF16) and wo_b (NVFP4) weights
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print("\n--- Loading weights ---")
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wo_a_w = P(f"{prefix}.o_a_proj.weight")
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print(f" wo_a: shape={wo_a_w.shape} dtype={wo_a_w.dtype}")
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wo_b_w = P(f"{prefix}.o_b_proj.weight")
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wo_b_sf = P(f"{prefix}.o_b_proj.weight_scale")
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wo_b_gs = P(f"{prefix}.o_b_proj.weight_scale_2").item()
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print(f" wo_b: shape={wo_b_w.shape} dtype={wo_b_w.dtype} gs={wo_b_gs:.8f}")
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# Check: wo_a should NOT have weight_scale_inv
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# (it's a plain BF16 tensor, not a quantized layer)
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# Build cos_sin_cache
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cos_sin_cache = build_cos_sin_cache().to(DEVICE)
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# Simulate attention output (what FlashMLA would produce)
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print("\n--- Simulating attention output ---")
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positions = torch.tensor([0, 1, 2, 3], dtype=torch.int64, device=DEVICE)
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o = torch.randn(NUM_TOKENS, NUM_HEADS, HEAD_DIM, dtype=torch.bfloat16, device=DEVICE) * 0.1
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print(f" Attention output: shape={o.shape} amax={o.amax():.4f}")
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# ═══════════════════════════════════════════════════════════════════
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# TEST 1: OLD PATH (should show crash/AttributeError)
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# ═══════════════════════════════════════════════════════════════════
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print("\n" + "=" * 70)
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print(" TEST 1: OLD PATH (FP8 einsum — should crash)")
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print("=" * 70)
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old_path_o_projection(o, positions, cos_sin_cache, wo_a_w)
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# ═══════════════════════════════════════════════════════════════════
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# TEST 2: NEW PATH (BF16 inv RoPE + BMM wo_a — should work)
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# ═══════════════════════════════════════════════════════════════════
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print("\n" + "=" * 70)
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print(" TEST 2: NEW PATH (BF16 inv RoPE + BMM wo_a)")
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print("=" * 70)
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z = new_path_o_projection(o, positions, cos_sin_cache, wo_a_w)
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if z is not None and not torch.isnan(z).any():
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# ═══════════════════════════════════════════════════════════════
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# TEST 3: wo_b NVFP4 GEMM
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# ═══════════════════════════════════════════════════════════════
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print("\n" + "=" * 70)
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print(" TEST 3: wo_b NVFP4 GEMM (CuTeDSL vs BF16 reference)")
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print("=" * 70)
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cos_wo_b = test_wo_b_nvfp4(z, wo_b_w, wo_b_sf, wo_b_gs)
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else:
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print("\n❌ z is invalid (NaN or None), skipping wo_b test")
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cos_wo_b = 0.0
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# ═══════════════════════════════════════════════════════════════════
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# SUMMARY
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# ═══════════════════════════════════════════════════════════════════
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print("\n" + "=" * 70)
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print(" SUMMARY")
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print("=" * 70)
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print(" OLD PATH (FP8 einsum): CRASHES — wo_a has no weight_scale_inv")
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print(f" NEW PATH (BF16 inv RoPE + BMM): z amax={z.amax():.4f} NaN={torch.isnan(z).any()}")
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print(f" wo_b NVFP4 cosine: {cos_wo_b:.6f} {'✅' if cos_wo_b >= 0.98 else '❌'}")
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if cos_wo_b >= 0.98 and z is not None and not torch.isnan(z).any():
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print("\n✅ ROOT CAUSE CONFIRMED + FIX VALIDATED")
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print(" The attention forward was crashing because wo_a is BF16")
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print(" but the FP8 einsum path expected weight_scale_inv.")
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print(" Our patched forward (BF16 inv RoPE + BMM) fixes this.")
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else:
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print("\n❌ FIX INCOMPLETE — further investigation needed")
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if __name__ == "__main__":
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main()
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