# ============================================================================= # Copyright (c) 2024-2026 Luis E. Davila Flores. All rights reserved. # # FireEcho Engine — High-Performance Inference Kernel # Creator & Sole Author: Luis E. Davila Flores # # Licensed under Creative Commons Attribution-NonCommercial 4.0 International # (CC BY-NC 4.0). You may share and adapt this work for non-commercial # purposes with proper attribution. Full license terms: # https://creativecommons.org/licenses/by-nc/4.0/ # ============================================================================= """ FireEcho Goliath — Native FP4/FP8/INT2/FE-XC/FE-XVQ Fused Triton GEMM Kernel =============================================================================== Part of the FireEcho Engine — Custom inference kernel for NVIDIA Blackwell Copyright (c) 2025-2026 Echo (FireEcho Project). All rights reserved. A unified quantized GEMM kernel that dequantizes inside the Triton matmul loop — no separate dequantization step, no global memory materialization. Features: 1. NVFP4 GEMM — Fused dequant inside Triton matmul 2. NVFP8 GEMM — Same approach for FP8 3. INT2 GEMM — 2-bit quantization for cold MoE experts 4. FE-XC GEMM — Codebook 2-bit (2x8) with CodeGEMM psumbook (near-FP16 quality) 5. FE-XVQ GEMM — Hessian-weighted codebook 2-bit (VPTQ-inspired, second-order optimal) 6. Packed MoE — Contiguous [128, K//2, N] expert buffers, GPU expert IDs 7. Fused SwiGLU+Down — Single-kernel gate+up+silu+mul+down 8. Auto-dispatch — Automatically choose FP4/FP8/INT2/FE-XC/FE-XVQ based on expert temperature 9. Unified API: - goliath_quantize(tensor, bits=4 or 8) - goliath_gemm(activations, quantized_weights) Design: - Loads packed FP4/FP8 data directly from global memory - Dequantizes in registers (not global memory) — zero extra traffic - Uses Triton block pointers for efficient memory access - Supports both BF16 and FP16 accumulation - Target: 5-10x over the 10.4 TFLOPS baseline Usage: from goliath_kernel import goliath_quantize, goliath_gemm # FP4 quantization (maximum compression) w_q4 = goliath_quantize(weights, bits=4) out = goliath_gemm(activations, w_q4) # FP8 quantization (higher accuracy) w_q8 = goliath_quantize(weights, bits=8) out = goliath_gemm(activations, w_q8) # Auto mode (let Goliath decide) w_q = goliath_quantize(weights, bits='auto') out = goliath_gemm(activations, w_q) """ import torch import triton import triton.language as tl from typing import Optional, Tuple, Union from dataclasses import dataclass # ============================================================================= # NVFP4 Bridge (cutlass_kernels → native cuBLAS path when available) # ============================================================================= try: from cutlass_kernels import ( NVFP4Weights as _BridgeNVFP4Weights, _fused_nvfp4_matmul as _bridge_fused_nvfp4, _can_use_scaled_mm_fp4 as _bridge_can_use_cublas_fp4, _scaled_mm_fp4 as _bridge_scaled_mm_fp4, ) _NVFP4_BRIDGE_AVAILABLE = True except Exception: _BridgeNVFP4Weights = None _bridge_fused_nvfp4 = None _bridge_can_use_cublas_fp4 = None _bridge_scaled_mm_fp4 = None _NVFP4_BRIDGE_AVAILABLE = False # ============================================================================= # E2M1 Constants (shared with cutlass_kernels) # ============================================================================= _E2M1_VALUES = torch.tensor( [0, 0.5, 1, 1.5, 2, 3, 4, 6, 0, -0.5, -1, -1.5, -2, -3, -4, -6], dtype=torch.float32, ) _E2M1_BOUNDARIES = torch.tensor([0.25, 0.75, 1.25, 1.75, 2.5, 3.5, 5.0]) # ============================================================================= # E4M3 Encode/Decode (PyTorch host-side) # ============================================================================= def _encode_e4m3(values: torch.Tensor) -> torch.Tensor: """Encode FP32 values to E4M3 (FP8) as uint8.""" if hasattr(torch, 'float8_e4m3fn'): return values.clamp(-448.0, 448.0).to(torch.float8_e4m3fn).view(torch.uint8) v = values.float().clamp(-448.0, 448.0) sign = (v < 0).to(torch.uint8) << 7 av = v.abs().clamp(min=0.0) log2_av = torch.log2(av.clamp(min=2**-9)) exp_raw = torch.floor(log2_av).clamp(-6, 8) exp_biased = (exp_raw + 7).clamp(0, 15) mantissa_f = (av / torch.pow(2.0, exp_raw) - 1.0) * 8.0 mantissa = mantissa_f.round().clamp(0, 7).to(torch.uint8) sub_mant = (av / (2**-6) * 8.0).round().clamp(0, 7).to(torch.uint8) is_sub = exp_biased == 0 final_mant = torch.where(is_sub, sub_mant, mantissa) return sign | (exp_biased.to(torch.uint8) << 3) | final_mant def _decode_e4m3(encoded: torch.Tensor) -> torch.Tensor: """Decode E4M3 uint8 back to FP32.""" if hasattr(torch, 'float8_e4m3fn'): return encoded.view(torch.float8_e4m3fn).float() sign = ((encoded >> 7) & 1).float() exp = ((encoded >> 3) & 0xF).long() mant = (encoded & 0x7).long() is_normal = exp > 0 normal_val = (8 + mant).float() * torch.pow(2.0, (exp - 10).float()) subnormal_val = mant.float() * (2.0 ** -9) unsigned = torch.where(is_normal, normal_val, subnormal_val) return torch.where(sign != 0, -unsigned, unsigned) # ============================================================================= # Goliath Quantized Weight Containers # ============================================================================= @dataclass class GoliathFP4Weights: """ Goliath FP4 quantized weights — fused-dequant format. 16-element blocks, E4M3 scales, per-tensor FP32 scale. Packed as 2 nibbles per uint8 byte (same as NVFP4). Optional FP8 residual correction (double-buff) for near-FP16 accuracy. """ packed: torch.Tensor # [K//2, N] uint8 block_scales: torch.Tensor # [K//16, N] uint8 (E4M3) tensor_scale: float # FP32 per-tensor scale shape: Tuple[int, int] # (K, N) bits: int = 4 # FP8 residual correction (optional, "double-buff") residual: Optional[torch.Tensor] = None # [K, N] uint8 — E4M3 encoded residual_scales: Optional[torch.Tensor] = None # [K//16, N] float32 per-block @classmethod def from_float(cls, weights: torch.Tensor, training: bool = False, sr_seed: Optional[int] = None, compute_residual: bool = False) -> 'GoliathFP4Weights': K, N = weights.shape assert K % 16 == 0, f"K ({K}) must be multiple of 16 for FP4" device = weights.device w = weights.float() reshaped = w.view(K // 16, 16, N) absmax = reshaped.abs().amax(dim=1).clamp(min=1e-10) # Two-level scaling global_absmax = absmax.max().clamp(min=1e-10) tensor_scale = (global_absmax / 448.0).item() target = absmax / (tensor_scale * 6.0) target = target.clamp(min=1e-10) block_scales_fp8 = _encode_e4m3(target) actual_scale = _decode_e4m3(block_scales_fp8) * tensor_scale actual_scale = actual_scale.clamp(min=1e-10) normalized = (reshaped / actual_scale.unsqueeze(1)).clamp(-6.0, 6.0) # Vectorized bucketize quantization boundaries = _E2M1_BOUNDARIES.to(device) abs_norm = normalized.abs().reshape(-1) unsigned_idx = torch.bucketize(abs_norm, boundaries) # Stochastic rounding for training (reduces systematic quantization bias) if training and sr_seed is not None: e2m1_unsigned = _E2M1_VALUES[:8].to(device) # [0, 0.5, 1, 1.5, 2, 3, 4, 6] lower_val = e2m1_unsigned[unsigned_idx.clamp(max=7)] upper_val = e2m1_unsigned[(unsigned_idx + 1).clamp(max=7)] spread = (upper_val - lower_val).clamp(min=1e-10) frac = (abs_norm - lower_val) / spread gen = torch.Generator(device=device).manual_seed(sr_seed) rand = torch.rand_like(frac, generator=gen) unsigned_idx = torch.where(rand < frac, unsigned_idx + 1, unsigned_idx).clamp(0, 7) sign_bit = ((normalized.reshape(-1) < 0) & (unsigned_idx > 0)).to(torch.uint8) << 3 indices = (sign_bit | unsigned_idx.to(torch.uint8)).reshape(K, N) packed = (indices[0::2] | (indices[1::2] << 4)) # FP8 residual correction (double-buff) residual_e4m3 = None residual_scales = None if compute_residual: fp4_approx = cls( packed=packed, block_scales=block_scales_fp8, tensor_scale=tensor_scale, shape=(K, N), ).to_float() residual_float = w - fp4_approx res_blocks = residual_float.view(K // 16, 16, N) res_absmax = res_blocks.abs().amax(dim=1).clamp(min=1e-10) res_scale = res_absmax / 448.0 res_normalized = res_blocks / res_scale.unsqueeze(1) res_normalized = res_normalized.clamp(-448.0, 448.0) residual_e4m3 = res_normalized.view(K, N).to(torch.float8_e4m3fn).view(torch.uint8) residual_scales = res_scale return cls( packed=packed, block_scales=block_scales_fp8, tensor_scale=tensor_scale, shape=(K, N), residual=residual_e4m3, residual_scales=residual_scales, ) def to_float(self) -> torch.Tensor: K, N = self.shape device = self.packed.device e2m1 = _E2M1_VALUES.to(device) low = (self.packed & 0xF).long() high = (self.packed >> 4).long() low_vals = e2m1[low.flatten()].view(K // 2, N) high_vals = e2m1[high.flatten()].view(K // 2, N) unpacked = torch.zeros(K, N, device=device, dtype=torch.float32) unpacked[0::2] = low_vals unpacked[1::2] = high_vals block_sf = _decode_e4m3(self.block_scales) scale = block_sf * self.tensor_scale unpacked = unpacked.view(K // 16, 16, N) * scale.unsqueeze(1) return unpacked.view(K, N) @dataclass class GoliathFP4NativeWeights: """ Goliath FP4 native weights for ``tl.dot_scaled`` code path. Layout required by ``tl.dot_scaled(..., "e2m1")``: - packed_col_major: [N, K//2] uint8 — E2M1 nibbles, col-major for RHS - scales_e8m0: [N, K//32] uint8 — E8M0 power-of-two scales (32-element blocks) Created from a GoliathFP4Weights instance via ``from_goliath_fp4()``. """ packed_col_major: torch.Tensor # [N, K//2] uint8 scales_e8m0: torch.Tensor # [N, K//32] uint8 shape: Tuple[int, int] # (K, N) — original weight shape bits: int = 4 @classmethod def from_goliath_fp4(cls, w: GoliathFP4Weights) -> 'GoliathFP4NativeWeights': """Convert GoliathFP4Weights → native dot_scaled layout. Transposes packed data to col-major [N, K//2] and converts E4M3 16-element block scales → E8M0 32-element block scales. """ K, N = w.shape device = w.packed.device # Transpose packed nibbles: [K//2, N] → [N, K//2] packed_col = w.packed.T.contiguous() # Convert E4M3 block scales to E8M0 (power-of-two, 32-element blocks) # w.block_scales: [K//16, N] uint8 E4M3 # E8M0 needs [K//32, N] — merge pairs of 16-element groups scales_f = _decode_e4m3(w.block_scales) # [K//16, N] float32 num_16_groups = K // 16 if num_16_groups >= 2: # Pair consecutive 16-element groups → 32-element blocks scales_paired = scales_f.view(num_16_groups // 2, 2, N) # Effective scale = max of the pair * tensor_scale scales_32 = scales_paired.amax(dim=1) * w.tensor_scale # [K//32, N] else: scales_32 = scales_f * w.tensor_scale # [1, N] (K==16 edge case) # E8M0 encoding: uint8 = round(log2(val / 6.0)) + 127 # 6.0 = max absolute E2M1 value; E8M0 is pure exponent (bias 127) safe_scales = scales_32.clamp(min=1e-20) log2_val = torch.log2(safe_scales / 6.0) e8m0 = (log2_val.round() + 127).clamp(0, 254).to(torch.uint8) # [K//32, N] # Transpose scales to [N, K//32] for col-major RHS access scales_col = e8m0.T.contiguous() return cls( packed_col_major=packed_col, scales_e8m0=scales_col, shape=(K, N), ) @dataclass class GoliathFP8Weights: """ Goliath FP8 quantized weights — fused-dequant format. Per-block FP32 scales, data stored as uint8 E4M3 encoding. Block size: 32 elements (matches Triton tile granularity for shared memory). """ data: torch.Tensor # [K, N] uint8 (E4M3 encoded) block_scales: torch.Tensor # [K//32, N] float32 per-block scale shape: Tuple[int, int] # (K, N) bits: int = 8 @classmethod def from_float(cls, weights: torch.Tensor) -> 'GoliathFP8Weights': K, N = weights.shape assert K % 32 == 0, f"K ({K}) must be multiple of 32 for FP8" device = weights.device w = weights.float() reshaped = w.view(K // 32, 32, N) absmax = reshaped.abs().amax(dim=1).clamp(min=1e-10) # [K//32, N] # Scale so max maps to 448 (E4M3 max) block_scales = absmax / 448.0 # [K//32, N] normalized = reshaped / block_scales.unsqueeze(1) normalized = normalized.clamp(-448.0, 448.0) # Encode as E4M3 data = _encode_e4m3(normalized.reshape(K, N)) return cls( data=data, block_scales=block_scales, shape=(K, N), ) def to_float(self) -> torch.Tensor: K, N = self.shape decoded = _decode_e4m3(self.data) # [K, N] decoded = decoded.view(K // 32, 32, N) * self.block_scales.unsqueeze(1) return decoded.view(K, N) # ============================================================================= # Goliath INT2 Weights — Aggressive 2-bit Quantization for Cold Experts # ============================================================================= # # Simple uniform 2-bit quantization with group scales: # - 4 weights packed per uint8 byte # - 32-element groups with FP16 scales # - Values quantize to {-2, -1, 0, 1} × scale # - 2x smaller than FP4 (0.25 bytes/weight vs 0.5 bytes/weight) # # Use case: Cold MoE experts (rarely routed, <10% of tokens) # Quality: ~2-3% accuracy loss acceptable for cold experts # @dataclass class GoliathINT2Weights: """ Goliath INT2 quantized weights — aggressive compression for cold experts. 32-element groups, FP16 scales, 4 weights packed per uint8 byte. Values map to {-2, -1, 0, 1} × scale (symmetric 2-bit). """ packed: torch.Tensor # [K//4, N] uint8 (4 weights per byte) block_scales: torch.Tensor # [K//32, N] float16 per-block scale shape: Tuple[int, int] # (K, N) bits: int = 2 @classmethod def from_float(cls, weights: torch.Tensor, stochastic: bool = False) -> 'GoliathINT2Weights': """Quantize FP32/BF16 weights to INT2 format. Args: weights: Input [K, N] tensor stochastic: Use stochastic rounding (reduces bias for training) Returns: GoliathINT2Weights with packed 2-bit data """ K, N = weights.shape assert K % 32 == 0, f"K ({K}) must be multiple of 32 for INT2" device = weights.device w = weights.float() # Reshape to 32-element blocks for scaling reshaped = w.view(K // 32, 32, N) absmax = reshaped.abs().amax(dim=1).clamp(min=1e-10) # [K//32, N] # Scale factor: map max to 2.0 (our max quantized value) block_scales = (absmax / 2.0).to(torch.float16) # [K//32, N] # Normalize to [-2, 2] range scale_expanded = block_scales.unsqueeze(1).float() # [K//32, 1, N] normalized = reshaped / scale_expanded.clamp(min=1e-10) # [K//32, 32, N] # Quantize to {-2, -1, 0, 1} (2-bit signed) if stochastic: # Stochastic rounding noise = torch.rand_like(normalized) - 0.5 quantized = torch.round(normalized + noise * 0.5) else: quantized = torch.round(normalized) quantized = quantized.clamp(-2, 1).to(torch.int8) # {-2, -1, 0, 1} # Shift to unsigned {0, 1, 2, 3} for packing unsigned = (quantized + 2).to(torch.uint8) # {0, 1, 2, 3} # Reshape to [K, N] for packing unsigned = unsigned.view(K, N) # Pack 4 weights per byte: w0 | (w1 << 2) | (w2 << 4) | (w3 << 6) packed = (unsigned[0::4] | (unsigned[1::4] << 2) | (unsigned[2::4] << 4) | (unsigned[3::4] << 6)) # [K//4, N] return cls( packed=packed, block_scales=block_scales, shape=(K, N), ) @classmethod def from_fp4(cls, fp4_weights: GoliathFP4Weights) -> 'GoliathINT2Weights': """Convert FP4 weights to INT2 (for demoting cold experts).""" return cls.from_float(fp4_weights.to_float()) def to_float(self) -> torch.Tensor: """Dequantize INT2 weights back to FP32.""" K, N = self.shape device = self.packed.device # Unpack 4 weights per byte w0 = (self.packed & 0x3).to(torch.int8) - 2 # {0,1,2,3} -> {-2,-1,0,1} w1 = ((self.packed >> 2) & 0x3).to(torch.int8) - 2 w2 = ((self.packed >> 4) & 0x3).to(torch.int8) - 2 w3 = ((self.packed >> 6) & 0x3).to(torch.int8) - 2 # Interleave back to [K, N] unpacked = torch.zeros(K, N, device=device, dtype=torch.float32) unpacked[0::4] = w0.float() unpacked[1::4] = w1.float() unpacked[2::4] = w2.float() unpacked[3::4] = w3.float() # Apply block scales unpacked = unpacked.view(K // 32, 32, N) unpacked = unpacked * self.block_scales.unsqueeze(1).float() return unpacked.view(K, N) def memory_bytes(self) -> int: """Return memory usage in bytes.""" return self.packed.numel() + self.block_scales.numel() * 2 # FP16 = 2 bytes @dataclass class GoliathFEXCWeights: """ FE-XC (FireEcho Xtreme Compress) — Codebook-based 2-bit quantization. Uses AQLM-style 2x8 additive codebooks: each group of 8 weights is represented as C0[idx0] + C1[idx1] where C0/C1 are learned codebooks with 256 centroids each. Achieves near-FP16 quality at 2 bits/weight. Combined with CodeGEMM-style psumbook precomputation for fast inference: precompute psumbook[m,c,j] = dot(codebook[m][c], input[j*8:(j+1)*8]) once per token, then the matmul reduces to scalar gathers + adds. Layout: codes: [K, N//8, 2] uint8 — 2 codebook indices per 8-weight group codebooks: [2, 256, 8] float16 — 2 codebooks, 256 centroids, 8 elements scales: [K] float16 — per-output-channel scale """ codes: torch.Tensor # [K, N//8, 2] uint8 codebooks: torch.Tensor # [2, 256, 8] float16 scales: torch.Tensor # [K] float16 shape: Tuple[int, int] # (K, N) bits: int = 2 group_size: int = 8 @classmethod def from_float( cls, weights: torch.Tensor, codebooks: Optional[torch.Tensor] = None, n_centroids: int = 256, n_iters: int = 20, ) -> 'GoliathFEXCWeights': """Quantize FP32/BF16 weights to FE-XC 2x8 codebook format. Uses residual k-means: learn codebook_0 on raw groups, then codebook_1 on residuals. If codebooks are provided (shared), skips k-means and only assigns codes. Args: weights: Input [K, N] tensor codebooks: Optional pre-learned [2, 256, 8] codebooks (shared across experts) n_centroids: Number of codebook entries (default 256) n_iters: K-means iterations (default 20) Returns: GoliathFEXCWeights with codebook indices + shared codebooks """ K, N = weights.shape g = 8 assert N % g == 0, f"N ({N}) must be multiple of group_size {g}" device = weights.device w = weights.float() # Reshape to groups: [K * N//g, g] groups = w.view(-1, g) # [K*N/8, 8] num_groups = groups.shape[0] if codebooks is None: # Learn codebooks via residual k-means codebooks = torch.zeros(2, n_centroids, g, device=device, dtype=torch.float32) for cb_idx in range(2): if cb_idx == 0: data = groups else: # Residual after first codebook nearest_0 = codebooks[0][codes_0.long()] # [num_groups, g] data = groups - nearest_0 # K-means: init with random sample perm = torch.randperm(num_groups, device=device)[:n_centroids] centroids = data[perm].clone() # [256, g] for _ in range(n_iters): # Assign: find nearest centroid for each group # dists[i, c] = ||data[i] - centroids[c]||^2 dists = torch.cdist(data, centroids) # [num_groups, 256] assignments = dists.argmin(dim=1) # [num_groups] # Update centroids for c in range(n_centroids): mask = (assignments == c) if mask.any(): centroids[c] = data[mask].mean(dim=0) codebooks[cb_idx] = centroids if cb_idx == 0: codes_0 = dists.argmin(dim=1).to(torch.uint8) codebooks = codebooks.to(torch.float16) else: codebooks = codebooks.to(device=device) # Assign codes using provided or learned codebooks cb_float = codebooks.float() # Codebook 0: nearest centroid dists_0 = torch.cdist(groups, cb_float[0]) # [num_groups, 256] codes_0 = dists_0.argmin(dim=1).to(torch.uint8) # [num_groups] # Residual after codebook 0 residual = groups - cb_float[0][codes_0.long()] # Codebook 1: nearest centroid on residual dists_1 = torch.cdist(residual, cb_float[1]) codes_1 = dists_1.argmin(dim=1).to(torch.uint8) # Pack codes: [K, N//g, 2] codes = torch.stack([codes_0, codes_1], dim=1).view(K, N // g, 2) # Per-output-channel scale: compensate for reconstruction error reconstructed = (cb_float[0][codes_0.long()] + cb_float[1][codes_1.long()]).view(K, N) row_norms_orig = w.norm(dim=1).clamp(min=1e-10) row_norms_recon = reconstructed.norm(dim=1).clamp(min=1e-10) scales = (row_norms_orig / row_norms_recon).to(torch.float16) return cls( codes=codes, codebooks=codebooks.to(torch.float16), scales=scales, shape=(K, N), ) @classmethod def from_fp4(cls, fp4_weights: GoliathFP4Weights, codebooks: Optional[torch.Tensor] = None) -> 'GoliathFEXCWeights': """Convert FP4 weights to FE-XC (for demoting cold experts).""" return cls.from_float(fp4_weights.to_float(), codebooks=codebooks) def to_float(self) -> torch.Tensor: """Dequantize FE-XC weights back to FP32.""" K, N = self.shape g = self.group_size cb = self.codebooks.float() # [2, 256, 8] codes_flat = self.codes.view(-1, 2).long() # [K*N//8, 2] reconstructed = cb[0][codes_flat[:, 0]] + cb[1][codes_flat[:, 1]] # [K*N//8, 8] reconstructed = reconstructed.view(K, N) # Apply per-row scale reconstructed = reconstructed * self.scales.float().unsqueeze(1) return reconstructed def memory_bytes(self) -> int: """Return memory usage in bytes.""" return (self.codes.numel() # uint8 + self.codebooks.numel() * 2 # float16 + self.scales.numel() * 2) # float16 @dataclass class GoliathFEXVQWeights: """ FE-XVQ (FireEcho XVector Quantization) — Hessian-weighted codebook 2-bit. VPTQ-inspired: uses second-order information (Hessian diagonal from calibration data) to weight the k-means objective. Errors in important dimensions (high Hessian) are penalized more, producing better codebooks than FE-XC's plain MSE k-means. Same storage format as FE-XC — reuses the same inference kernel (psumbook CodeGEMM). Only the codebook LEARNING differs. For a linear layer y = Wx, the Hessian H = X^T X (input covariance). Quantization error δw contributes δw^T H δw to output loss. FE-XVQ minimizes this weighted error instead of plain ||δw||^2. Layout (identical to FE-XC): codes: [K, N//8, 2] uint8 — 2 codebook indices per 8-weight group codebooks: [2, 256, 8] float16 — 2 codebooks, 256 centroids, 8 elements scales: [K] float16 — per-output-channel scale """ codes: torch.Tensor # [K, N//8, 2] uint8 codebooks: torch.Tensor # [2, 256, 8] float16 scales: torch.Tensor # [K] float16 shape: Tuple[int, int] # (K, N) bits: int = 2 group_size: int = 8 @classmethod def from_float( cls, weights: torch.Tensor, hessian_diag: Optional[torch.Tensor] = None, codebooks: Optional[torch.Tensor] = None, n_centroids: int = 256, n_iters: int = 20, ) -> 'GoliathFEXVQWeights': """Quantize weights to FE-XVQ format with Hessian-weighted codebooks. When hessian_diag is provided, uses importance-weighted k-means: distance = sum(h_i * (w_i - c_i)^2) instead of plain MSE. This prioritizes accuracy on dimensions that matter most for output. The Hessian diagonal is averaged within each group of 8 to produce per-element importance weights [8]. Both data and centroids are pre-scaled by sqrt(h_avg), converting Mahalanobis distance to Euclidean — enabling efficient torch.cdist. Falls back to plain k-means (FE-XC equivalent) when hessian_diag=None. Args: weights: Input [K, N] tensor (K=out_features, N=in_features) hessian_diag: Optional [N] tensor — diagonal of H = X^T X codebooks: Optional pre-learned [2, 256, 8] codebooks n_centroids: Number of codebook entries (default 256) n_iters: K-means iterations (default 20) Returns: GoliathFEXVQWeights with Hessian-optimal codebook indices """ K, N = weights.shape g = 8 assert N % g == 0, f"N ({N}) must be multiple of group_size {g}" device = weights.device w = weights.float() # Reshape to groups: [K * N//g, g] groups = w.view(-1, g) # [K*N/8, 8] num_groups = groups.shape[0] # Compute per-element importance weights [g] from Hessian diagonal # Average over group positions to get a single [8] weight vector. # This captures which of the 8 elements within each group matters most. # Pre-scaling by sqrt(h_avg) converts Mahalanobis→Euclidean for cdist. if hessian_diag is not None: h = hessian_diag.float().to(device) h = h / h.mean().clamp(min=1e-10) # normalize mean=1 h_groups = h.view(-1, g) # [N//8, 8] h_avg = h_groups.mean(dim=0) # [8] per-element importance sqrt_h = h_avg.sqrt().unsqueeze(0) # [1, 8] for broadcasting else: sqrt_h = None h_avg = None # Transform groups to weighted space for efficient cdist groups_w = groups * sqrt_h if sqrt_h is not None else groups if codebooks is None: codebooks = torch.zeros(2, n_centroids, g, device=device, dtype=torch.float32) for cb_idx in range(2): if cb_idx == 0: data = groups else: nearest_0 = codebooks[0][codes_0.long()] data = groups - nearest_0 data_w = data * sqrt_h if sqrt_h is not None else data # K-means init: random sample perm = torch.randperm(num_groups, device=device)[:n_centroids] centroids = data[perm].clone() # [256, g] in original space for _ in range(n_iters): # Distances in Hessian-weighted space via pre-scaled cdist cent_w = centroids * sqrt_h if sqrt_h is not None else centroids dists = torch.cdist(data_w, cent_w) # [G, 256] assignments = dists.argmin(dim=1) # Update centroids (Hessian-weighted mean in original space) for c in range(n_centroids): mask = (assignments == c) if mask.any(): if h_avg is not None: # Weighted centroid: sum(h_avg * x) / sum(h_avg) d_masked = data[mask] # [count, g] centroids[c] = (h_avg * d_masked).sum(0) / (h_avg * mask.sum()).clamp(min=1e-10) else: centroids[c] = data[mask].mean(dim=0) codebooks[cb_idx] = centroids if cb_idx == 0: codes_0 = dists.argmin(dim=1).to(torch.uint8) codebooks = codebooks.to(torch.float16) else: codebooks = codebooks.to(device=device) # Assign codes using Hessian-weighted distances cb_float = codebooks.float() cb0_w = cb_float[0] * sqrt_h if sqrt_h is not None else cb_float[0] dists_0 = torch.cdist(groups_w, cb0_w) codes_0 = dists_0.argmin(dim=1).to(torch.uint8) residual = groups - cb_float[0][codes_0.long()] residual_w = residual * sqrt_h if sqrt_h is not None else residual cb1_w = cb_float[1] * sqrt_h if sqrt_h is not None else cb_float[1] dists_1 = torch.cdist(residual_w, cb1_w) codes_1 = dists_1.argmin(dim=1).to(torch.uint8) # Pack codes: [K, N//g, 2] codes = torch.stack([codes_0, codes_1], dim=1).view(K, N // g, 2) # Per-output-channel scale (Hessian-weighted norm ratio) reconstructed = (cb_float[0][codes_0.long()] + cb_float[1][codes_1.long()]).view(K, N) if hessian_diag is not None: h_row = hessian_diag.float().to(device).unsqueeze(0) # [1, N] row_norms_orig = (w * w * h_row).sum(dim=1).sqrt().clamp(min=1e-10) row_norms_recon = (reconstructed * reconstructed * h_row).sum(dim=1).sqrt().clamp(min=1e-10) else: row_norms_orig = w.norm(dim=1).clamp(min=1e-10) row_norms_recon = reconstructed.norm(dim=1).clamp(min=1e-10) scales = (row_norms_orig / row_norms_recon).to(torch.float16) return cls( codes=codes, codebooks=codebooks.to(torch.float16), scales=scales, shape=(K, N), ) @classmethod def from_fexc(cls, fexc_weights: GoliathFEXCWeights) -> 'GoliathFEXVQWeights': """Promote FE-XC weights to FE-XVQ (same data, different type tag).""" return cls( codes=fexc_weights.codes, codebooks=fexc_weights.codebooks, scales=fexc_weights.scales, shape=fexc_weights.shape, ) def to_fexc(self) -> GoliathFEXCWeights: """Downcast to FE-XC (for using FE-XC inference kernel).""" return GoliathFEXCWeights( codes=self.codes, codebooks=self.codebooks, scales=self.scales, shape=self.shape, ) def to_float(self) -> torch.Tensor: """Dequantize FE-XVQ weights back to FP32 (same as FE-XC).""" K, N = self.shape g = self.group_size cb = self.codebooks.float() codes_flat = self.codes.view(-1, 2).long() reconstructed = cb[0][codes_flat[:, 0]] + cb[1][codes_flat[:, 1]] reconstructed = reconstructed.view(K, N) reconstructed = reconstructed * self.scales.float().unsqueeze(1) return reconstructed def memory_bytes(self) -> int: """Return memory usage in bytes.""" return (self.codes.numel() + self.codebooks.numel() * 2 + self.scales.numel() * 2) # Union type for dispatch GoliathWeights = Union[GoliathFP4Weights, GoliathFP8Weights, GoliathINT2Weights, GoliathFEXCWeights, GoliathFEXVQWeights] # ============================================================================= # Triton JIT Helpers # ============================================================================= @triton.jit def _int2_decode(packed_byte, offset): """Decode 2-bit value from packed byte at given offset (0-3). packed_byte: uint8 with 4 packed 2-bit values offset: which 2-bit value to extract (0=bits 0-1, 1=bits 2-3, etc.) Returns: float32 value in {-2, -1, 0, 1} """ shift = offset * 2 unsigned = (packed_byte >> shift) & 0x3 # Extract 2 bits return (unsigned.to(tl.float32) - 2.0) # Convert {0,1,2,3} -> {-2,-1,0,1} @triton.jit def _e2m1_decode(idx): """Decode 4-bit E2M1 index -> float32.""" sign = (idx >> 3) & 1 exp = (idx >> 1) & 3 mant = idx & 1 is_normal = exp > 0 subnormal_val = mant.to(tl.float32) * 0.5 normal_val = (2 + mant).to(tl.float32) * tl.exp2((exp - 2).to(tl.float32)) unsigned_val = tl.where(is_normal, normal_val, subnormal_val) return tl.where(sign != 0, -unsigned_val, unsigned_val) @triton.jit def _decode_e4m3_triton(raw_uint8): """Decode E4M3 FP8 in Triton registers.""" sign = (raw_uint8 >> 7) & 1 exp = (raw_uint8 >> 3) & 0xF mant = raw_uint8 & 0x7 is_normal = exp > 0 normal_val = (8 + mant).to(tl.float32) * tl.exp2((exp - 10).to(tl.float32)) subnormal_val = mant.to(tl.float32) * tl.exp2(tl.full(mant.shape, -9.0, tl.float32)) unsigned = tl.where(is_normal, normal_val, subnormal_val) return tl.where(sign != 0, -unsigned, unsigned) # ============================================================================= # Goliath FP4 Fused Dequant-MatMul Kernel # ============================================================================= @triton.autotune( configs=[ # --- Blackwell 5090 prefill configs (dual-SM, high occupancy) --- triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128}, num_stages=5, num_warps=16), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128}, num_stages=5, num_warps=16), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128}, num_stages=5, num_warps=8), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 64}, num_stages=4, num_warps=8), triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64}, num_stages=5, num_warps=4), triton.Config({'BLOCK_M': 64, 'BLOCK_N': 64, 'BLOCK_K': 64}, num_stages=5, num_warps=4), # --- Decode-optimized (small M, maximize N-parallelism for 170 SMs) --- triton.Config({'BLOCK_M': 16, 'BLOCK_N': 256, 'BLOCK_K': 128}, num_stages=5, num_warps=8), triton.Config({'BLOCK_M': 16, 'BLOCK_N': 256, 'BLOCK_K': 64}, num_stages=5, num_warps=4), triton.Config({'BLOCK_M': 16, 'BLOCK_N': 128, 'BLOCK_K': 64}, num_stages=5, num_warps=4), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 256, 'BLOCK_K': 128}, num_stages=5, num_warps=8), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 256, 'BLOCK_K': 64}, num_stages=5, num_warps=4), # --- MoE expert small-matrix (768×2048 / 2048×768) --- # N=768: tiles must be 64 or 128 (768/64=12, 768/128=6) # K=768: smaller K needs more pipeline depth triton.Config({'BLOCK_M': 16, 'BLOCK_N': 64, 'BLOCK_K': 128}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 16, 'BLOCK_N': 128, 'BLOCK_K': 128}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 16, 'BLOCK_N': 64, 'BLOCK_K': 64}, num_stages=4, num_warps=2), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 64, 'BLOCK_K': 128}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 128, 'BLOCK_K': 128}, num_stages=4, num_warps=4), ], key=['M', 'N', 'K'], ) @triton.jit def _goliath_fp4_kernel( a_ptr, # [M, K] activations w_packed_ptr, # [K//2, N] uint8 packed FP4 w_scales_ptr, # [K//16, N] uint8 E4M3 scales out_ptr, # [M, N] output bias_ptr, # [N] optional bias tensor_scale, # FP32 per-tensor scale M, N, K, stride_am, stride_ak, stride_wk, stride_wn, stride_sk, stride_sn, stride_om, stride_on, HAS_BIAS: tl.constexpr, ACC_DTYPE: tl.constexpr, # 0=float32, 1=bfloat16 BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, ): """ Goliath FP4 fused dequant-matmul kernel. Loads packed E2M1 nibbles, dequantizes in registers via arithmetic decode, applies two-level E4M3 + FP32 scaling, and accumulates via tl.dot(). The full dequantized weight matrix NEVER exists in global memory. """ pid_m = tl.program_id(0) pid_n = tl.program_id(1) offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) HALF_BLOCK_K: tl.constexpr = BLOCK_K // 2 SCALES_PER_TILE: tl.constexpr = BLOCK_K // 16 for k_start in range(0, K, BLOCK_K): # Load A as even/odd column halves even_k = k_start + tl.arange(0, HALF_BLOCK_K) * 2 odd_k = k_start + tl.arange(0, HALF_BLOCK_K) * 2 + 1 a_even_ptrs = a_ptr + offs_m[:, None] * stride_am + even_k[None, :] * stride_ak a_odd_ptrs = a_ptr + offs_m[:, None] * stride_am + odd_k[None, :] * stride_ak mask_ae = (offs_m[:, None] < M) & (even_k[None, :] < K) mask_ao = (offs_m[:, None] < M) & (odd_k[None, :] < K) a_even = tl.load(a_even_ptrs, mask=mask_ae, other=0.0) a_odd = tl.load(a_odd_ptrs, mask=mask_ao, other=0.0) # Load packed weights pk_start = k_start // 2 offs_pk = pk_start + tl.arange(0, HALF_BLOCK_K) w_ptrs = w_packed_ptr + offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn mask_w = (offs_pk[:, None] < (K // 2)) & (offs_n[None, :] < N) packed = tl.load(w_ptrs, mask=mask_w, other=0).to(tl.int32) # Unpack + E2M1 decode in registers low_f = _e2m1_decode(packed & 0xF) high_f = _e2m1_decode((packed >> 4) & 0xF) # Load E4M3 scales (16-element groups = 8 packed rows each) scale_start = k_start // 16 offs_local = tl.arange(0, HALF_BLOCK_K) group_idx = offs_local // 8 scale_bc = tl.zeros((HALF_BLOCK_K, BLOCK_N), dtype=tl.float32) for sg in tl.static_range(0, SCALES_PER_TILE): sg_row = scale_start + sg sg_ptrs = w_scales_ptr + sg_row * stride_sk + offs_n * stride_sn sg_mask = (sg_row < (K // 16)) & (offs_n < N) sg_raw = tl.load(sg_ptrs, mask=sg_mask, other=0).to(tl.int32) sg_val = _decode_e4m3_triton(sg_raw) * tensor_scale sg_match = (group_idx == sg) scale_bc = tl.where(sg_match[:, None], sg_val[None, :], scale_bc) # Apply scales and cast w_even = (low_f * scale_bc).to(tl.bfloat16) w_odd = (high_f * scale_bc).to(tl.bfloat16) # Two half-sized dot products acc += tl.dot(a_even.to(tl.bfloat16), w_even) acc += tl.dot(a_odd.to(tl.bfloat16), w_odd) # Bias if HAS_BIAS: bias = tl.load(bias_ptr + offs_n, mask=offs_n < N, other=0.0).to(tl.float32) acc += bias[None, :] # Store out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on mask_out = (offs_m[:, None] < M) & (offs_n[None, :] < N) tl.store(out_ptrs, acc.to(tl.bfloat16), mask=mask_out) # ============================================================================= # Goliath FP4 Native dot_scaled Kernel (tcgen05.mma.mxf4) # ============================================================================= # # Activates only when Triton maps tl.dot_scaled to real FP4 tensor cores # (SM >= 10.0 + Triton with MXFP4 support). On fallback hardware the probe # function _can_use_goliath_dot_scaled() returns False and this path is skipped. # @triton.autotune( configs=[ triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128}, num_stages=3, num_warps=8), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 256}, num_stages=3, num_warps=8), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 256}, num_stages=3, num_warps=8), ], key=['M', 'N', 'K'], ) @triton.jit def _goliath_fp4_dot_scaled_kernel( a_ptr, # [M, K] BF16 activations w_packed_ptr, # [N, K//2] uint8 packed E2M1 (col-major) w_scales_ptr, # [N, K//32] uint8 E8M0 scales out_ptr, # [M, N] output bias_ptr, # [N] optional bias M, N, K, stride_am, stride_ak, stride_wn, stride_wk, # col-major: row=N, col=K//2 stride_sn, stride_sk, # col-major: row=N, col=K//32 stride_om, stride_on, HAS_BIAS: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, ): """ Goliath FP4 dot_scaled kernel — uses tl.dot_scaled for native MXFP4 TCs. LHS = BF16 activations (no scale), RHS = E2M1 packed weights with E8M0 scales. When Triton maps this to tcgen05.mma.mxf4, throughput reaches 200-800+ TFLOPS. """ pid_m = tl.program_id(0) pid_n = tl.program_id(1) offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) HALF_BLOCK_K: tl.constexpr = BLOCK_K // 2 SCALES_PER_TILE: tl.constexpr = BLOCK_K // 32 for k_start in range(0, K, BLOCK_K): # --- LHS: load A tile [BLOCK_M, BLOCK_K] BF16 --- offs_k = k_start + tl.arange(0, BLOCK_K) a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak mask_a = (offs_m[:, None] < M) & (offs_k[None, :] < K) a_tile = tl.load(a_ptrs, mask=mask_a, other=0.0).to(tl.bfloat16) # --- RHS: load packed weights [BLOCK_N, BLOCK_K//2] uint8 --- pk_start = k_start // 2 offs_pk = pk_start + tl.arange(0, HALF_BLOCK_K) w_ptrs = w_packed_ptr + offs_n[:, None] * stride_wn + offs_pk[None, :] * stride_wk mask_w = (offs_n[:, None] < N) & (offs_pk[None, :] < (K // 2)) b_tile = tl.load(w_ptrs, mask=mask_w, other=0) # [BLOCK_N, HALF_BLOCK_K] # --- RHS scales: load E8M0 [BLOCK_N, SCALES_PER_TILE] --- sc_start = k_start // 32 offs_sc = sc_start + tl.arange(0, SCALES_PER_TILE) s_ptrs = w_scales_ptr + offs_n[:, None] * stride_sn + offs_sc[None, :] * stride_sk mask_s = (offs_n[:, None] < N) & (offs_sc[None, :] < (K // 32)) b_scale = tl.load(s_ptrs, mask=mask_s, other=127) # [BLOCK_N, SCALES_PER_TILE] # --- tl.dot_scaled: A (bf16, no scale) × B^T (e2m1, e8m0 scale) --- # b_tile is [BLOCK_N, HALF_BLOCK_K], transposed for RHS of dot_scaled acc = tl.dot_scaled(a_tile, None, "bf16", b_tile.T, b_scale, "e2m1", acc) # Bias if HAS_BIAS: bias = tl.load(bias_ptr + offs_n, mask=offs_n < N, other=0.0).to(tl.float32) acc += bias[None, :] # Store out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on mask_out = (offs_m[:, None] < M) & (offs_n[None, :] < N) tl.store(out_ptrs, acc.to(tl.bfloat16), mask=mask_out) # ============================================================================= # Goliath FP8 Fused Dequant-MatMul Kernel # ============================================================================= @triton.autotune( configs=[ # --- Blackwell 5090 prefill configs (dual-SM, high occupancy) --- triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128}, num_stages=5, num_warps=16), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128}, num_stages=5, num_warps=16), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128}, num_stages=5, num_warps=8), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 64}, num_stages=4, num_warps=8), triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64}, num_stages=5, num_warps=4), triton.Config({'BLOCK_M': 64, 'BLOCK_N': 64, 'BLOCK_K': 64}, num_stages=5, num_warps=4), # --- Decode-optimized (small M, maximize N-parallelism for 170 SMs) --- triton.Config({'BLOCK_M': 16, 'BLOCK_N': 256, 'BLOCK_K': 128}, num_stages=5, num_warps=8), triton.Config({'BLOCK_M': 16, 'BLOCK_N': 256, 'BLOCK_K': 64}, num_stages=5, num_warps=4), triton.Config({'BLOCK_M': 16, 'BLOCK_N': 128, 'BLOCK_K': 64}, num_stages=5, num_warps=4), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 256, 'BLOCK_K': 128}, num_stages=5, num_warps=8), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 256, 'BLOCK_K': 64}, num_stages=5, num_warps=4), # --- MoE expert small-matrix (768×2048 / 2048×768) --- triton.Config({'BLOCK_M': 16, 'BLOCK_N': 64, 'BLOCK_K': 128}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 16, 'BLOCK_N': 128, 'BLOCK_K': 128}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 16, 'BLOCK_N': 64, 'BLOCK_K': 64}, num_stages=4, num_warps=2), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 64, 'BLOCK_K': 128}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 128, 'BLOCK_K': 128}, num_stages=4, num_warps=4), ], key=['M', 'N', 'K'], ) @triton.jit def _goliath_fp8_kernel( a_ptr, # [M, K] activations (BF16) w_data_ptr, # [K, N] uint8 E4M3 encoded weights w_scales_ptr, # [K//32, N] float32 per-block scales out_ptr, # [M, N] output bias_ptr, # [N] optional bias M, N, K, stride_am, stride_ak, stride_wk, stride_wn, stride_sk, stride_sn, stride_om, stride_on, HAS_BIAS: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, ): """ Goliath FP8 fused dequant-matmul kernel. Loads E4M3-encoded uint8 weights, dequantizes in registers via _decode_e4m3_triton, applies per-block FP32 scales, accumulates via tl.dot(). No separate dequantization step — everything happens in-register. 32-element scale blocks for shared-memory-friendly tile sizes. """ pid_m = tl.program_id(0) pid_n = tl.program_id(1) offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) SCALES_PER_TILE: tl.constexpr = BLOCK_K // 32 for k_start in range(0, K, BLOCK_K): # Load A tile offs_k = k_start + tl.arange(0, BLOCK_K) a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak mask_a = (offs_m[:, None] < M) & (offs_k[None, :] < K) a_tile = tl.load(a_ptrs, mask=mask_a, other=0.0) # Load weight tile as uint8 w_ptrs = w_data_ptr + offs_k[:, None] * stride_wk + offs_n[None, :] * stride_wn mask_w = (offs_k[:, None] < K) & (offs_n[None, :] < N) w_raw = tl.load(w_ptrs, mask=mask_w, other=0).to(tl.int32) # Decode E4M3 in registers w_decoded = _decode_e4m3_triton(w_raw) # [BLOCK_K, BLOCK_N] float32 # Apply per-block scales (32-element groups) scale_start = k_start // 32 offs_local_k = tl.arange(0, BLOCK_K) group_idx = offs_local_k // 32 scale_bc = tl.zeros((BLOCK_K, BLOCK_N), dtype=tl.float32) for sg in tl.static_range(0, SCALES_PER_TILE): sg_row = scale_start + sg sg_ptrs = w_scales_ptr + sg_row * stride_sk + offs_n * stride_sn sg_mask = (sg_row < (K // 32)) & (offs_n < N) sg_val = tl.load(sg_ptrs, mask=sg_mask, other=1.0) # [BLOCK_N] float32 sg_match = (group_idx == sg) scale_bc = tl.where(sg_match[:, None], sg_val[None, :], scale_bc) w_scaled = (w_decoded * scale_bc).to(tl.bfloat16) # Matmul accumulate acc += tl.dot(a_tile.to(tl.bfloat16), w_scaled) # Bias if HAS_BIAS: bias = tl.load(bias_ptr + offs_n, mask=offs_n < N, other=0.0).to(tl.float32) acc += bias[None, :] # Store out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on mask_out = (offs_m[:, None] < M) & (offs_n[None, :] < N) tl.store(out_ptrs, acc.to(tl.bfloat16), mask=mask_out) # ============================================================================= # Kernel Wrappers # ============================================================================= def _goliath_fp4_matmul( activations: torch.Tensor, weights: GoliathFP4Weights, bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Launch Goliath FP4 fused dequant-matmul kernel. Dispatches to native dot_scaled path when available (SM >= 10.0 + Triton MXFP4 support), otherwise uses the manual dequant kernel. """ # Tier 1: Native dot_scaled path (SM >= 10.0 + real FP4 tensor cores) if _can_use_goliath_dot_scaled(): native_w = GoliathFP4NativeWeights.from_goliath_fp4(weights) return _goliath_fp4_dot_scaled_matmul(activations, native_w, bias) # Tier 1.5: NVFP4 bridge (zero-copy, identical storage layout) # packed [K//2, N] uint8, block_scales [K//16, N] E4M3, tensor_scale FP32 if _NVFP4_BRIDGE_AVAILABLE: nvfp4_w = _BridgeNVFP4Weights( packed=weights.packed, block_scales=weights.block_scales, tensor_scale=weights.tensor_scale, shape=weights.shape, residual=weights.residual, residual_scales=weights.residual_scales, ) # Prefer native cuBLAS FP4 on Blackwell (5th-gen tensor cores) if _bridge_can_use_cublas_fp4 and _bridge_can_use_cublas_fp4(): return _bridge_scaled_mm_fp4(activations, nvfp4_w, bias) return _bridge_fused_nvfp4(activations, nvfp4_w, bias) # Tier 2: Manual dequant fallback M, K = activations.shape _, N = weights.shape a = activations.contiguous() if a.dtype != torch.bfloat16: a = a.to(torch.bfloat16) w_packed = weights.packed.contiguous() w_scales = weights.block_scales.contiguous() out = torch.empty(M, N, device=a.device, dtype=torch.bfloat16) has_bias = bias is not None if has_bias: bias_t = bias.contiguous().float() else: bias_t = torch.empty(0, device=a.device, dtype=torch.float32) grid = lambda META: ( triton.cdiv(M, META['BLOCK_M']), triton.cdiv(N, META['BLOCK_N']), ) _goliath_fp4_kernel[grid]( a, w_packed, w_scales, out, bias_t, weights.tensor_scale, M, N, K, a.stride(0), a.stride(1), w_packed.stride(0), w_packed.stride(1), w_scales.stride(0), w_scales.stride(1), out.stride(0), out.stride(1), HAS_BIAS=has_bias, ACC_DTYPE=0, ) return out def _goliath_fp8_matmul( activations: torch.Tensor, weights: GoliathFP8Weights, bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Launch Goliath FP8 fused dequant-matmul kernel.""" M, K = activations.shape _, N = weights.shape a = activations.contiguous() if a.dtype != torch.bfloat16: a = a.to(torch.bfloat16) w_data = weights.data.contiguous() w_scales = weights.block_scales.contiguous() out = torch.empty(M, N, device=a.device, dtype=torch.bfloat16) has_bias = bias is not None if has_bias: bias_t = bias.contiguous().float() else: bias_t = torch.empty(0, device=a.device, dtype=torch.float32) grid = lambda META: ( triton.cdiv(M, META['BLOCK_M']), triton.cdiv(N, META['BLOCK_N']), ) _goliath_fp8_kernel[grid]( a, w_data, w_scales, out, bias_t, M, N, K, a.stride(0), a.stride(1), w_data.stride(0), w_data.stride(1), w_scales.stride(0), w_scales.stride(1), out.stride(0), out.stride(1), HAS_BIAS=has_bias, ) return out # ============================================================================= # dot_scaled Wrapper + Probe # ============================================================================= def _goliath_fp4_dot_scaled_matmul( activations: torch.Tensor, weights: GoliathFP4NativeWeights, bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Launch Goliath FP4 dot_scaled kernel (native MXFP4 tensor cores).""" M, K = activations.shape _, N = weights.shape a = activations.contiguous() if a.dtype != torch.bfloat16: a = a.to(torch.bfloat16) w_packed = weights.packed_col_major.contiguous() # [N, K//2] w_scales = weights.scales_e8m0.contiguous() # [N, K//32] out = torch.empty(M, N, device=a.device, dtype=torch.bfloat16) has_bias = bias is not None if has_bias: bias_t = bias.contiguous().float() else: bias_t = torch.empty(0, device=a.device, dtype=torch.float32) grid = lambda META: ( triton.cdiv(M, META['BLOCK_M']), triton.cdiv(N, META['BLOCK_N']), ) _goliath_fp4_dot_scaled_kernel[grid]( a, w_packed, w_scales, out, bias_t, M, N, K, a.stride(0), a.stride(1), w_packed.stride(0), w_packed.stride(1), w_scales.stride(0), w_scales.stride(1), out.stride(0), out.stride(1), HAS_BIAS=has_bias, ) return out # Cached probe result _DOT_SCALED_PROBE_RESULT: Optional[bool] = None def _can_use_goliath_dot_scaled() -> bool: """Probe whether tl.dot_scaled maps to real FP4 tensor cores. Checks: 1. SM >= 10.0 (Blackwell or later) 2. tl.dot_scaled exists in Triton 3. Runtime probe: run a small matmul with both the manual-dequant kernel and the dot_scaled kernel. If outputs are bit-identical, Triton is falling back to BF16 MMA → return False. If outputs differ with rel_err < 0.15 → native FP4 TCs are active → return True. """ global _DOT_SCALED_PROBE_RESULT if _DOT_SCALED_PROBE_RESULT is not None: return _DOT_SCALED_PROBE_RESULT _DOT_SCALED_PROBE_RESULT = False # Check 1: SM capability if not torch.cuda.is_available(): return False cap = torch.cuda.get_device_capability(0) if cap[0] < 10: return False # Check 2: Triton API if not hasattr(tl, 'dot_scaled'): return False # Check 3: Runtime probe with small matmul try: M, N, K = 64, 64, 64 torch.manual_seed(42) w_f = torch.randn(K, N, device='cuda', dtype=torch.float32) a = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) # Quantize via Goliath FP4 w_q = GoliathFP4Weights.from_float(w_f) # Path A: manual dequant kernel out_manual = _goliath_fp4_matmul(a, w_q) # Path B: dot_scaled kernel w_native = GoliathFP4NativeWeights.from_goliath_fp4(w_q) out_scaled = _goliath_fp4_dot_scaled_matmul(a, w_native) # Compare if torch.equal(out_manual, out_scaled): # Bit-identical → Triton using BF16 fallback _DOT_SCALED_PROBE_RESULT = False else: ref = out_manual.float() diff = (out_scaled.float() - ref).abs().mean() rel_err = diff / ref.abs().mean().clamp(min=1e-10) _DOT_SCALED_PROBE_RESULT = rel_err.item() < 0.15 except Exception: _DOT_SCALED_PROBE_RESULT = False return _DOT_SCALED_PROBE_RESULT # ============================================================================= # Auto-dispatch: FP4 vs FP8 selection # ============================================================================= def _estimate_accuracy_need(weights: torch.Tensor) -> int: """ Estimate whether FP4 or FP8 is appropriate for these weights. Heuristic: if the weight distribution has high kurtosis (heavy tails) or many outliers, FP8 preserves more fidelity. Otherwise FP4 suffices. Returns: 4 for FP4, 8 for FP8 """ w = weights.float() absmax = w.abs().max() mean_abs = w.abs().mean() # Outlier ratio: if max >> mean, distribution has heavy tails outlier_ratio = absmax / mean_abs.clamp(min=1e-10) # High kurtosis → FP8 for better tail representation if outlier_ratio > 20.0: return 8 # Check what fraction of values fall outside FP4 representable range # after scaling. FP4 has only 16 levels — coarse quantization. std = w.std() # If std is very small relative to max, distribution is spiky → FP8 if std / absmax.clamp(min=1e-10) < 0.05: return 8 return 4 # ============================================================================= # Public API # ============================================================================= def goliath_quantize( weights: torch.Tensor, bits: Union[int, str] = 4, training: bool = False, sr_seed: Optional[int] = None, compute_residual: bool = False, ) -> GoliathWeights: """ Quantize weights for Goliath fused GEMM. Args: weights: Input tensor [K, N] in any float dtype bits: 4 for FP4, 8 for FP8, 'auto' for automatic selection training: If True, use stochastic rounding for unbiased quantization sr_seed: Seed for stochastic rounding RNG compute_residual: Compute FP8 residual correction for FP4 weights (double-buff) Returns: GoliathFP4Weights or GoliathFP8Weights """ if bits == 'auto': bits = _estimate_accuracy_need(weights) if bits == 4: return GoliathFP4Weights.from_float(weights, training=training, sr_seed=sr_seed, compute_residual=compute_residual) elif bits == 8: return GoliathFP8Weights.from_float(weights) else: raise ValueError(f"bits must be 4, 8, or 'auto', got {bits}") # ============================================================================= # Goliath Multi-Expert Fused Kernel (all active experts in ONE launch) # ============================================================================= # # For MoE single-token decode: instead of launching N separate Goliath kernels # (one per active expert), this kernel processes ALL active experts' matmuls # in a single kernel launch. Grid dim 0 = expert index, dim 1 = output col tiles. # Each expert has its own packed weight, block_scales, and tensor_scale. # # This eliminates kernel launch overhead which dominates at M=1: # - Before: 8 experts × 2 projections = 16 launches per MoE layer × 48 = 768 # - After: 1 launch for gate_up + 1 launch for down = 2 per layer × 48 = 96 # MAX_EXPERTS: int = 16 # max active experts per launch (Qwen3: 8) @triton.jit def _goliath_fp4_multi_expert_kernel( a_ptr, # [E*M, K] or [M, K] activations # Expert weight pointers (padded to MAX_EXPERTS) w0_ptr, w1_ptr, w2_ptr, w3_ptr, w4_ptr, w5_ptr, w6_ptr, w7_ptr, # Expert scale pointers s0_ptr, s1_ptr, s2_ptr, s3_ptr, s4_ptr, s5_ptr, s6_ptr, s7_ptr, # Per-expert tensor scales (passed as array in global memory) tscale_ptr, out_ptr, # [num_experts, M, N] output (expert-batched) M, N, K, num_experts, stride_am, stride_ak, stride_wk, stride_wn, stride_sk, stride_sn, stride_oe, stride_om, stride_on, a_expert_stride, # 0 = shared input, >0 = per-expert input (row offset) BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, ): """Multi-expert FP4 fused dequant-matmul: all experts in ONE launch. When a_expert_stride=0: all experts share the same input (gate_up case). When a_expert_stride>0: each expert reads from row pid_expert*M of a_ptr (down case). """ pid_expert = tl.program_id(0) pid_n = tl.program_id(1) if pid_expert >= num_experts: return # Per-expert activation offset a_base = a_ptr + pid_expert * a_expert_stride # Select weight pointer for this expert w_packed_ptr = w0_ptr w_scales_ptr = s0_ptr if pid_expert == 1: w_packed_ptr = w1_ptr; w_scales_ptr = s1_ptr elif pid_expert == 2: w_packed_ptr = w2_ptr; w_scales_ptr = s2_ptr elif pid_expert == 3: w_packed_ptr = w3_ptr; w_scales_ptr = s3_ptr elif pid_expert == 4: w_packed_ptr = w4_ptr; w_scales_ptr = s4_ptr elif pid_expert == 5: w_packed_ptr = w5_ptr; w_scales_ptr = s5_ptr elif pid_expert == 6: w_packed_ptr = w6_ptr; w_scales_ptr = s6_ptr elif pid_expert == 7: w_packed_ptr = w7_ptr; w_scales_ptr = s7_ptr tensor_scale = tl.load(tscale_ptr + pid_expert) offs_m = tl.arange(0, BLOCK_M) offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) HALF_BLOCK_K: tl.constexpr = BLOCK_K // 2 SCALES_PER_TILE: tl.constexpr = BLOCK_K // 16 for k_start in range(0, K, BLOCK_K): even_k = k_start + tl.arange(0, HALF_BLOCK_K) * 2 odd_k = k_start + tl.arange(0, HALF_BLOCK_K) * 2 + 1 a_even_ptrs = a_base + offs_m[:, None] * stride_am + even_k[None, :] * stride_ak a_odd_ptrs = a_base + offs_m[:, None] * stride_am + odd_k[None, :] * stride_ak mask_ae = (offs_m[:, None] < M) & (even_k[None, :] < K) mask_ao = (offs_m[:, None] < M) & (odd_k[None, :] < K) a_even = tl.load(a_even_ptrs, mask=mask_ae, other=0.0) a_odd = tl.load(a_odd_ptrs, mask=mask_ao, other=0.0) pk_start = k_start // 2 offs_pk = pk_start + tl.arange(0, HALF_BLOCK_K) w_ptrs = w_packed_ptr + offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn mask_w = (offs_pk[:, None] < (K // 2)) & (offs_n[None, :] < N) packed = tl.load(w_ptrs, mask=mask_w, other=0).to(tl.int32) low_f = _e2m1_decode(packed & 0xF) high_f = _e2m1_decode((packed >> 4) & 0xF) scale_start = k_start // 16 offs_local = tl.arange(0, HALF_BLOCK_K) group_idx = offs_local // 8 scale_bc = tl.zeros((HALF_BLOCK_K, BLOCK_N), dtype=tl.float32) for sg in tl.static_range(0, SCALES_PER_TILE): sg_row = scale_start + sg sg_ptrs = w_scales_ptr + sg_row * stride_sk + offs_n * stride_sn sg_mask = (sg_row < (K // 16)) & (offs_n < N) sg_raw = tl.load(sg_ptrs, mask=sg_mask, other=0).to(tl.int32) sg_val = _decode_e4m3_triton(sg_raw) * tensor_scale sg_match = (group_idx == sg) scale_bc = tl.where(sg_match[:, None], sg_val[None, :], scale_bc) w_even = (low_f * scale_bc).to(tl.bfloat16) w_odd = (high_f * scale_bc).to(tl.bfloat16) acc += tl.dot(a_even.to(tl.bfloat16), w_even) acc += tl.dot(a_odd.to(tl.bfloat16), w_odd) # Store to expert-batched output [num_experts, M, N] out_ptrs = out_ptr + pid_expert * stride_oe + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on mask_out = (offs_m[:, None] < M) & (offs_n[None, :] < N) tl.store(out_ptrs, acc.to(tl.bfloat16), mask=mask_out) def goliath_multi_expert_gemm( activations: torch.Tensor, expert_weights: list, num_experts: int = 8, per_expert_input: bool = False, ) -> torch.Tensor: """ Fused multi-expert FP4 GEMM: all experts in ONE kernel launch. Args: activations: Input in BF16. If per_expert_input=False: [M, K] shared across all experts (gate_up case) If per_expert_input=True: [num_experts*M, K] stacked per-expert inputs (down case) expert_weights: List of GoliathFP4Weights (one per active expert) num_experts: Number of active experts per_expert_input: If True, each expert reads from its own M rows of activations Returns: Output [num_experts, M, N] in BF16 """ assert num_experts <= MAX_EXPERTS, f"max {MAX_EXPERTS} experts, got {num_experts}" assert all(isinstance(w, GoliathFP4Weights) for w in expert_weights) _, N = expert_weights[0].shape a = activations.contiguous() if a.dtype != torch.bfloat16: a = a.to(torch.bfloat16) if per_expert_input: # activations is [num_experts*M, K], each expert gets M rows total_rows, K = a.shape M = total_rows // num_experts a_expert_stride = M * a.stride(0) # byte offset between expert inputs else: M, K = a.shape a_expert_stride = 0 # shared input out = torch.empty(num_experts, M, N, device=a.device, dtype=torch.bfloat16) # Collect tensor scales into a GPU tensor tscales = torch.tensor( [w.tensor_scale for w in expert_weights], dtype=torch.float32, device=a.device) packed_ptrs = [w.packed.contiguous() for w in expert_weights] scale_ptrs = [w.block_scales.contiguous() for w in expert_weights] # Pad to 8 with dummy (first expert repeated) while len(packed_ptrs) < 8: packed_ptrs.append(packed_ptrs[0]) scale_ptrs.append(scale_ptrs[0]) grid = (num_experts, triton.cdiv(N, 64)) _goliath_fp4_multi_expert_kernel[grid]( a, packed_ptrs[0], packed_ptrs[1], packed_ptrs[2], packed_ptrs[3], packed_ptrs[4], packed_ptrs[5], packed_ptrs[6], packed_ptrs[7], scale_ptrs[0], scale_ptrs[1], scale_ptrs[2], scale_ptrs[3], scale_ptrs[4], scale_ptrs[5], scale_ptrs[6], scale_ptrs[7], tscales, out, M, N, K, num_experts, a.stride(0), a.stride(1), packed_ptrs[0].stride(0), packed_ptrs[0].stride(1), scale_ptrs[0].stride(0), scale_ptrs[0].stride(1), out.stride(0), out.stride(1), out.stride(2), a_expert_stride, BLOCK_M=16, BLOCK_N=64, BLOCK_K=128, ) return out def goliath_gemm( activations: torch.Tensor, weights: GoliathWeights, bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ Goliath fused dequant-matmul GEMM. Automatically dispatches to FP4 or FP8 kernel based on weight type. All dequantization happens in Triton registers — zero extra memory traffic. Args: activations: Input [M, K] in BF16/FP16/FP32 weights: GoliathFP4Weights or GoliathFP8Weights bias: Optional bias [N] Returns: Output [M, N] in BF16 """ if not activations.is_cuda: # CPU fallback w_deq = weights.to_float() d = torch.matmul(activations.float(), w_deq) if bias is not None: d = d + bias.float() return d.to(torch.bfloat16) if isinstance(weights, GoliathFP4Weights): return _goliath_fp4_matmul(activations, weights, bias) elif isinstance(weights, GoliathFP8Weights): return _goliath_fp8_matmul(activations, weights, bias) else: raise TypeError(f"Expected GoliathFP4Weights or GoliathFP8Weights, got {type(weights)}") # ============================================================================= # Packed MoE Kernel — contiguous expert buffer + GPU-resident expert IDs # ============================================================================= # # Instead of passing 8 separate weight pointers, pack ALL 128 experts' FP4 # weights into contiguous [E, K//2, N] buffers. Expert selection reads from # a GPU tensor — zero .item() calls, zero CPU-GPU sync, CUDA-graph-safe. # # Benefits over goliath_multi_expert_gemm: # 1. No .item() calls (8 per layer × 48 layers = 384 CPU syncs eliminated) # 2. No Python weight-collection loops (~100μs × 48 layers saved) # 3. CUDA-graph-capturable (all inputs are static-address GPU tensors) # 4. Better L2 locality from contiguous weight storage # @triton.jit def _goliath_fp4_packed_moe_kernel( a_ptr, # [M, K] or [num_active*M, K] activations packed_w_ptr, # [E_total, K//2, N] contiguous FP4 weights packed_s_ptr, # [E_total, K//16, N] contiguous FP8 scales tscale_ptr, # [E_total] tensor scales (float32) expert_ids_ptr, # [num_active] selected expert indices (GPU tensor!) out_ptr, # [num_active, M, N] output M, N, K, num_active, # number of active experts (e.g. 8) stride_ew, # expert stride for packed weights (K//2 * N) stride_wk, stride_wn, stride_es, # expert stride for packed scales (K//16 * N) stride_sk, stride_sn, stride_oe, stride_om, stride_on, stride_am, stride_ak, a_expert_stride, # 0 = shared input, >0 = per-expert activation stride BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, ): """Packed MoE FP4 fused dequant-matmul: GPU-resident expert selection. Grid: (num_active, ceil(N/BLOCK_N), ceil(M/BLOCK_M)) Each program handles one active expert's matmul for one tile of output rows × columns. Expert IDs are read from a GPU tensor — no CPU involvement. """ pid_active = tl.program_id(0) # which active expert (0..num_active-1) pid_n = tl.program_id(1) # output column tile pid_m = tl.program_id(2) # output row tile if pid_active >= num_active: return # Read expert index from GPU tensor (NO .item()!) expert_id = tl.load(expert_ids_ptr + pid_active) # Compute base pointers for this expert w_packed_ptr = packed_w_ptr + expert_id * stride_ew w_scales_ptr = packed_s_ptr + expert_id * stride_es tensor_scale = tl.load(tscale_ptr + expert_id) # Activation base (shared or per-expert) a_base = a_ptr + pid_active * a_expert_stride offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) HALF_BLOCK_K: tl.constexpr = BLOCK_K // 2 SCALES_PER_TILE: tl.constexpr = BLOCK_K // 16 for k_start in range(0, K, BLOCK_K): even_k = k_start + tl.arange(0, HALF_BLOCK_K) * 2 odd_k = k_start + tl.arange(0, HALF_BLOCK_K) * 2 + 1 a_even_ptrs = a_base + offs_m[:, None] * stride_am + even_k[None, :] * stride_ak a_odd_ptrs = a_base + offs_m[:, None] * stride_am + odd_k[None, :] * stride_ak mask_ae = (offs_m[:, None] < M) & (even_k[None, :] < K) mask_ao = (offs_m[:, None] < M) & (odd_k[None, :] < K) a_even = tl.load(a_even_ptrs, mask=mask_ae, other=0.0) a_odd = tl.load(a_odd_ptrs, mask=mask_ao, other=0.0) pk_start = k_start // 2 offs_pk = pk_start + tl.arange(0, HALF_BLOCK_K) w_ptrs = w_packed_ptr + offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn mask_w = (offs_pk[:, None] < (K // 2)) & (offs_n[None, :] < N) packed = tl.load(w_ptrs, mask=mask_w, other=0).to(tl.int32) low_f = _e2m1_decode(packed & 0xF) high_f = _e2m1_decode((packed >> 4) & 0xF) scale_start = k_start // 16 offs_local = tl.arange(0, HALF_BLOCK_K) group_idx = offs_local // 8 scale_bc = tl.zeros((HALF_BLOCK_K, BLOCK_N), dtype=tl.float32) for sg in tl.static_range(0, SCALES_PER_TILE): sg_row = scale_start + sg sg_ptrs = w_scales_ptr + sg_row * stride_sk + offs_n * stride_sn sg_mask = (sg_row < (K // 16)) & (offs_n < N) sg_raw = tl.load(sg_ptrs, mask=sg_mask, other=0).to(tl.int32) sg_val = _decode_e4m3_triton(sg_raw) * tensor_scale sg_match = (group_idx == sg) scale_bc = tl.where(sg_match[:, None], sg_val[None, :], scale_bc) w_even = (low_f * scale_bc).to(tl.bfloat16) w_odd = (high_f * scale_bc).to(tl.bfloat16) acc += tl.dot(a_even.to(tl.bfloat16), w_even) acc += tl.dot(a_odd.to(tl.bfloat16), w_odd) # Store to expert-batched output [num_active, M, N] out_ptrs = out_ptr + pid_active * stride_oe + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on mask_out = (offs_m[:, None] < M) & (offs_n[None, :] < N) tl.store(out_ptrs, acc.to(tl.bfloat16), mask=mask_out) def goliath_packed_moe_gemm( activations: torch.Tensor, packed_w: torch.Tensor, # [E_total, K//2, N] uint8 packed_s: torch.Tensor, # [E_total, K//16, N] uint8 packed_ts: torch.Tensor, # [E_total] float32 expert_ids: torch.Tensor, # [num_active] int64 on GPU num_active: int = 8, per_expert_input: bool = False, ) -> torch.Tensor: """Packed MoE FP4 GEMM: contiguous expert buffer + GPU-resident expert IDs. All expert selection happens on GPU — zero .item() calls, CUDA-graph-safe. Args: activations: Input in BF16. If per_expert_input=False: [M, K] shared across all experts If per_expert_input=True: [num_active*M, K] stacked per-expert inputs packed_w: [E_total, K//2, N] contiguous packed FP4 weights for all experts packed_s: [E_total, K//16, N] contiguous FP8 block scales packed_ts: [E_total] per-expert tensor scales (float32) expert_ids: [num_active] selected expert indices (GPU tensor, int64) num_active: Number of active experts per_expert_input: If True, each expert reads from its own M rows Returns: Output [num_active, M, N] in BF16 """ N = packed_w.shape[2] # N is the last dim of packed weights a = activations.contiguous() if a.dtype != torch.bfloat16: a = a.to(torch.bfloat16) if per_expert_input: total_rows, K = a.shape M = total_rows // num_active a_expert_stride = M * a.stride(0) else: M, K = a.shape a_expert_stride = 0 out = torch.empty(num_active, M, N, device=a.device, dtype=torch.bfloat16) BLOCK_M = 16 BLOCK_N = 64 grid = (num_active, triton.cdiv(N, BLOCK_N), triton.cdiv(M, BLOCK_M)) _goliath_fp4_packed_moe_kernel[grid]( a, packed_w, packed_s, packed_ts, expert_ids, out, M, N, K, num_active, packed_w.stride(0), # stride_ew packed_w.stride(1), packed_w.stride(2), # stride_wk, stride_wn packed_s.stride(0), # stride_es packed_s.stride(1), packed_s.stride(2), # stride_sk, stride_sn out.stride(0), out.stride(1), out.stride(2), a.stride(0), a.stride(1), a_expert_stride, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=128, ) return out # ============================================================================= # Fused SwiGLU + Down Projection — Eliminates Intermediate Tensor # ============================================================================= # # Instead of: gate_up → split → SiLU*up → down_proj (3 kernel launches) # We do: gate_up → fused_swiglu_down (2 kernel launches, no hidden tensor) # # The key insight: apply SwiGLU as input preprocessing in the down_proj kernel, # reading gate_up output and applying SiLU(gate)*up inline before GEMM. @triton.jit def _goliath_fp4_swiglu_down_kernel( gate_up_ptr, # [num_active, M, 2*intermediate] gate_up output (BF16) packed_w_ptr, # [E_total, K//2, N] down_proj FP4 weights (K=intermediate) packed_s_ptr, # [E_total, K//16, N] down_proj FP8 scales tscale_ptr, # [E_total] tensor scales (float32) expert_ids_ptr, # [num_active] selected expert indices (GPU tensor) out_ptr, # [num_active, M, N] output M, N, K, # K = intermediate_size, N = dim num_active, inter_size, # intermediate_size (K) stride_ge, stride_gm, stride_gk, # gate_up strides stride_ew, stride_wk, stride_wn, stride_es, stride_sk, stride_sn, stride_oe, stride_om, stride_on, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, ): """Fused SwiGLU + FP4 down_proj GEMM. Reads gate_up output [num_active, M, 2*inter], applies SiLU(gate)*up inline, then performs FP4 GEMM with down_proj weights. Eliminates hidden tensor write/read. """ pid_active = tl.program_id(0) pid_n = tl.program_id(1) if pid_active >= num_active: return expert_id = tl.load(expert_ids_ptr + pid_active) w_packed_ptr = packed_w_ptr + expert_id * stride_ew w_scales_ptr = packed_s_ptr + expert_id * stride_es tensor_scale = tl.load(tscale_ptr + expert_id) gate_up_base = gate_up_ptr + pid_active * stride_ge offs_m = tl.arange(0, BLOCK_M) offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) HALF_BLOCK_K: tl.constexpr = BLOCK_K // 2 SCALES_PER_TILE: tl.constexpr = BLOCK_K // 16 for k_start in range(0, K, BLOCK_K): even_k = k_start + tl.arange(0, HALF_BLOCK_K) * 2 odd_k = k_start + tl.arange(0, HALF_BLOCK_K) * 2 + 1 # Load gate (from first half) and up (from second half) gate_even_ptrs = gate_up_base + offs_m[:, None] * stride_gm + even_k[None, :] * stride_gk gate_odd_ptrs = gate_up_base + offs_m[:, None] * stride_gm + odd_k[None, :] * stride_gk up_even_ptrs = gate_up_base + offs_m[:, None] * stride_gm + (inter_size + even_k[None, :]) * stride_gk up_odd_ptrs = gate_up_base + offs_m[:, None] * stride_gm + (inter_size + odd_k[None, :]) * stride_gk mask_e = (offs_m[:, None] < M) & (even_k[None, :] < K) mask_o = (offs_m[:, None] < M) & (odd_k[None, :] < K) gate_even = tl.load(gate_even_ptrs, mask=mask_e, other=0.0).to(tl.float32) gate_odd = tl.load(gate_odd_ptrs, mask=mask_o, other=0.0).to(tl.float32) up_even = tl.load(up_even_ptrs, mask=mask_e, other=0.0).to(tl.float32) up_odd = tl.load(up_odd_ptrs, mask=mask_o, other=0.0).to(tl.float32) # Apply SiLU(gate) * up inline a_even = (tl.sigmoid(gate_even) * gate_even * up_even).to(tl.bfloat16) a_odd = (tl.sigmoid(gate_odd) * gate_odd * up_odd).to(tl.bfloat16) # Load FP4 down_proj weights pk_start = k_start // 2 offs_pk = pk_start + tl.arange(0, HALF_BLOCK_K) w_ptrs = w_packed_ptr + offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn mask_w = (offs_pk[:, None] < (K // 2)) & (offs_n[None, :] < N) packed = tl.load(w_ptrs, mask=mask_w, other=0).to(tl.int32) low_f = _e2m1_decode(packed & 0xF) high_f = _e2m1_decode((packed >> 4) & 0xF) scale_start = k_start // 16 offs_local = tl.arange(0, HALF_BLOCK_K) group_idx = offs_local // 8 scale_bc = tl.zeros((HALF_BLOCK_K, BLOCK_N), dtype=tl.float32) for sg in tl.static_range(0, SCALES_PER_TILE): sg_row = scale_start + sg sg_ptrs = w_scales_ptr + sg_row * stride_sk + offs_n * stride_sn sg_mask = (sg_row < (K // 16)) & (offs_n < N) sg_raw = tl.load(sg_ptrs, mask=sg_mask, other=0).to(tl.int32) sg_val = _decode_e4m3_triton(sg_raw) * tensor_scale sg_match = (group_idx == sg) scale_bc = tl.where(sg_match[:, None], sg_val[None, :], scale_bc) w_even = (low_f * scale_bc).to(tl.bfloat16) w_odd = (high_f * scale_bc).to(tl.bfloat16) acc += tl.dot(a_even, w_even) acc += tl.dot(a_odd, w_odd) out_ptrs = out_ptr + pid_active * stride_oe + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on mask_out = (offs_m[:, None] < M) & (offs_n[None, :] < N) tl.store(out_ptrs, acc.to(tl.bfloat16), mask=mask_out) def goliath_packed_moe_swiglu_down( gate_up_output: torch.Tensor, # [num_active, M, 2*inter] from gate_up GEMM packed_w: torch.Tensor, # [E_total, K//2, N] down_proj FP4 weights packed_s: torch.Tensor, # [E_total, K//16, N] down_proj FP8 scales packed_ts: torch.Tensor, # [E_total] tensor scales expert_ids: torch.Tensor, # [num_active] on GPU intermediate_size: int, # K for down_proj num_active: int = 8, ) -> torch.Tensor: """Fused SwiGLU + FP4 down_proj GEMM. Takes gate_up output, applies SiLU(gate)*up inline, performs down_proj. Eliminates the hidden tensor write/read cycle. Args: gate_up_output: [num_active, M, 2*intermediate] output from gate_up GEMM packed_w: Down projection FP4 weights [E_total, K//2, N] packed_s: Down projection FP8 block scales packed_ts: Down projection tensor scales expert_ids: Selected expert indices on GPU intermediate_size: K dimension (matches gate_up output's second half) num_active: Number of active experts Returns: [num_active, M, N] final output """ num_active_in, M, gu_dim = gate_up_output.shape assert gu_dim == 2 * intermediate_size, f"gate_up dim {gu_dim} != 2*{intermediate_size}" K = intermediate_size _, _, N = packed_w.shape[0], packed_w.shape[1] * 2, packed_w.shape[2] # N is the output dim (dim), K is intermediate_size gate_up = gate_up_output.contiguous() if gate_up.dtype != torch.bfloat16: gate_up = gate_up.to(torch.bfloat16) out = torch.empty(num_active, M, N, device=gate_up.device, dtype=torch.bfloat16) grid = (num_active, triton.cdiv(N, 64)) _goliath_fp4_swiglu_down_kernel[grid]( gate_up, packed_w, packed_s, packed_ts, expert_ids, out, M, N, K, num_active, intermediate_size, gate_up.stride(0), gate_up.stride(1), gate_up.stride(2), packed_w.stride(0), packed_w.stride(1), packed_w.stride(2), packed_s.stride(0), packed_s.stride(1), packed_s.stride(2), out.stride(0), out.stride(1), out.stride(2), BLOCK_M=1 if M == 1 else 16, BLOCK_N=64, BLOCK_K=32, ) return out # ============================================================================= # INT2 Packed MoE Kernel — For Cold Experts (2x smaller than FP4) # ============================================================================= # # Same structure as FP4 packed MoE kernel, but with 2-bit unpacking. # 4 weights per byte instead of 2 = 2x less bandwidth for cold experts. # @triton.jit def _goliath_int2_packed_moe_kernel( a_ptr, # [M, K] or [num_active*M, K] activations packed_w_ptr, # [E_total, K//4, N] contiguous INT2 weights (4 per byte) packed_s_ptr, # [E_total, K//32, N] contiguous FP16 scales expert_ids_ptr, # [num_active] selected expert indices (GPU tensor) out_ptr, # [num_active, M, N] output M, N, K, num_active, stride_ew, # expert stride for packed weights (K//4 * N) stride_wk, stride_wn, stride_es, # expert stride for packed scales (K//32 * N) stride_sk, stride_sn, stride_oe, stride_om, stride_on, stride_am, stride_ak, a_expert_stride, # 0 = shared input, >0 = per-expert activation stride BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, ): """Packed MoE INT2 fused dequant-matmul: GPU-resident expert selection. Similar to FP4 kernel but unpacks 4 weights per byte instead of 2. Used for cold experts where 2-bit precision is acceptable. """ pid_active = tl.program_id(0) pid_n = tl.program_id(1) if pid_active >= num_active: return expert_id = tl.load(expert_ids_ptr + pid_active) w_packed_ptr = packed_w_ptr + expert_id * stride_ew w_scales_ptr = packed_s_ptr + expert_id * stride_es a_base = a_ptr + pid_active * a_expert_stride offs_m = tl.arange(0, BLOCK_M) offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) # INT2: 4 weights per byte, so QUARTER_BLOCK_K QUARTER_BLOCK_K: tl.constexpr = BLOCK_K // 4 SCALES_PER_TILE: tl.constexpr = BLOCK_K // 32 for k_start in range(0, K, BLOCK_K): # Load packed INT2 weights [QUARTER_BLOCK_K, BLOCK_N] pk_start = k_start // 4 offs_pk = pk_start + tl.arange(0, QUARTER_BLOCK_K) w_ptrs = w_packed_ptr + offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn mask_w = (offs_pk[:, None] < (K // 4)) & (offs_n[None, :] < N) packed = tl.load(w_ptrs, mask=mask_w, other=0).to(tl.int32) # Unpack 4 weights per byte: {0,1,2,3} -> {-2,-1,0,1} w0 = ((packed >> 0) & 0x3).to(tl.float32) - 2.0 # [QUARTER_BLOCK_K, BLOCK_N] w1 = ((packed >> 2) & 0x3).to(tl.float32) - 2.0 w2 = ((packed >> 4) & 0x3).to(tl.float32) - 2.0 w3 = ((packed >> 6) & 0x3).to(tl.float32) - 2.0 # Load FP16 scales [SCALES_PER_TILE groups, each covers 32 elements] scale_start = k_start // 32 offs_local_k = tl.arange(0, QUARTER_BLOCK_K) # Each packed byte covers 4 elements scale_bc = tl.zeros((QUARTER_BLOCK_K, BLOCK_N), dtype=tl.float32) for sg in tl.static_range(0, SCALES_PER_TILE): sg_row = scale_start + sg sg_ptrs = w_scales_ptr + sg_row * stride_sk + offs_n * stride_sn sg_mask = (sg_row < (K // 32)) & (offs_n < N) sg_val = tl.load(sg_ptrs, mask=sg_mask, other=1.0).to(tl.float32) sg_match = (offs_local_k // 8 == sg) scale_bc = tl.where(sg_match[:, None], sg_val[None, :], scale_bc) # Apply scales to unpacked weights w0_scaled = (w0 * scale_bc).to(tl.bfloat16) w1_scaled = (w1 * scale_bc).to(tl.bfloat16) w2_scaled = (w2 * scale_bc).to(tl.bfloat16) w3_scaled = (w3 * scale_bc).to(tl.bfloat16) # Load activations with stride-4 pattern (Triton doesn't support ::4 slicing) # Each packed INT2 byte covers 4 consecutive K elements: [4i, 4i+1, 4i+2, 4i+3] offs_qk = tl.arange(0, QUARTER_BLOCK_K) mask_a_qk = (offs_m[:, None] < M) & ((k_start + offs_qk[None, :] * 4) < K) a0 = tl.load(a_base + offs_m[:, None] * stride_am + (k_start + offs_qk[None, :] * 4 + 0) * stride_ak, mask=mask_a_qk, other=0.0).to(tl.bfloat16) a1 = tl.load(a_base + offs_m[:, None] * stride_am + (k_start + offs_qk[None, :] * 4 + 1) * stride_ak, mask=mask_a_qk, other=0.0).to(tl.bfloat16) a2 = tl.load(a_base + offs_m[:, None] * stride_am + (k_start + offs_qk[None, :] * 4 + 2) * stride_ak, mask=mask_a_qk, other=0.0).to(tl.bfloat16) a3 = tl.load(a_base + offs_m[:, None] * stride_am + (k_start + offs_qk[None, :] * 4 + 3) * stride_ak, mask=mask_a_qk, other=0.0).to(tl.bfloat16) acc += tl.dot(a0, w0_scaled) acc += tl.dot(a1, w1_scaled) acc += tl.dot(a2, w2_scaled) acc += tl.dot(a3, w3_scaled) out_ptrs = out_ptr + pid_active * stride_oe + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on mask_out = (offs_m[:, None] < M) & (offs_n[None, :] < N) tl.store(out_ptrs, acc.to(tl.bfloat16), mask=mask_out) def goliath_packed_moe_int2_gemm( activations: torch.Tensor, packed_w: torch.Tensor, # [E_total, K//4, N] uint8 (4 weights per byte) packed_s: torch.Tensor, # [E_total, K//32, N] float16 expert_ids: torch.Tensor, # [num_active] int64 on GPU num_active: int = 8, per_expert_input: bool = False, ) -> torch.Tensor: """Packed MoE INT2 GEMM: 2-bit weights for cold experts. 2x smaller than FP4 = 2x less bandwidth. Use for rarely-routed experts. Args: activations: Input in BF16. [M, K] or [num_active*M, K] packed_w: [E_total, K//4, N] INT2 packed weights packed_s: [E_total, K//32, N] FP16 block scales expert_ids: [num_active] selected expert indices on GPU num_active: Number of active experts per_expert_input: If True, each expert reads from its own M rows Returns: Output [num_active, M, N] in BF16 """ N = packed_w.shape[2] a = activations.contiguous() if a.dtype != torch.bfloat16: a = a.to(torch.bfloat16) if per_expert_input: total_rows, K = a.shape M = total_rows // num_active a_expert_stride = M * a.stride(0) else: M, K = a.shape a_expert_stride = 0 out = torch.empty(num_active, M, N, device=a.device, dtype=torch.bfloat16) grid = (num_active, triton.cdiv(N, 64)) _goliath_int2_packed_moe_kernel[grid]( a, packed_w, packed_s, expert_ids, out, M, N, K, num_active, packed_w.stride(0), packed_w.stride(1), packed_w.stride(2), packed_s.stride(0), packed_s.stride(1), packed_s.stride(2), out.stride(0), out.stride(1), out.stride(2), a.stride(0), a.stride(1), a_expert_stride, BLOCK_M=16 if M > 1 else 1, BLOCK_N=64, BLOCK_K=128, ) return out def pack_experts_int2( expert_weights: list, # List of GoliathINT2Weights ) -> Tuple[torch.Tensor, torch.Tensor]: """Pack INT2 expert weights into contiguous buffers. Args: expert_weights: List of GoliathINT2Weights (one per expert) Returns: packed_w: [E, K//4, N] contiguous INT2 weights packed_s: [E, K//32, N] contiguous FP16 scales """ E = len(expert_weights) K, N = expert_weights[0].shape device = expert_weights[0].packed.device packed_w = torch.zeros(E, K // 4, N, dtype=torch.uint8, device=device) packed_s = torch.zeros(E, K // 32, N, dtype=torch.float16, device=device) for i, w in enumerate(expert_weights): packed_w[i] = w.packed packed_s[i] = w.block_scales return packed_w, packed_s # ============================================================================= # FE-XC (FireEcho Xtreme Compress) — Codebook 2-bit with CodeGEMM Psumbook # ============================================================================= def fexc_precompute_psumbook( codebooks: torch.Tensor, # [2, 256, 8] float16 x: torch.Tensor, # [N] or [1, N] bfloat16 — single token input ) -> torch.Tensor: """Precompute psumbook: dot products of all codebook centroids with input groups. psumbook[m, c, j] = dot(codebooks[m][c], x[j*8:(j+1)*8]) This is computed ONCE per token and reused across all 8 active experts. For N=2048: psumbook is [2, 256, 256] float32 = 512KB. Args: codebooks: [2, 256, 8] float16 — shared codebooks for this layer x: [N] or [1, N] input vector Returns: psumbook: [2, 256, N//8] float32 """ x_flat = x.view(-1).float() # [N] N = x_flat.shape[0] g = 8 x_groups = x_flat.view(N // g, g) # [N//8, 8] cb = codebooks.float() # [2, 256, 8] # Batched matmul: [2, 256, 8] × [8, N//8] → [2, 256, N//8] psumbook = torch.bmm(cb, x_groups.T.unsqueeze(0).expand(2, -1, -1)) return psumbook # [2, 256, N//8] float32 @triton.jit def _goliath_fexc_packed_moe_kernel( psumbook_ptr, # [2, 256, num_groups] float32 — precomputed partial sums codes_ptr, # [E_total, K, num_groups, 2] uint8 — codebook indices scales_ptr, # [E_total, K] float16 — per-output-channel scales expert_ids_ptr, # [num_active] int64 — selected expert indices (GPU tensor) out_ptr, # [num_active, K] float32 output (M=1 only) K: tl.constexpr, num_groups: tl.constexpr, # N // 8 num_active, stride_ec, # expert stride for codes: K * num_groups * 2 stride_ck, # code stride per output row: num_groups * 2 stride_cg, # code stride per group: 2 stride_es, # expert stride for scales: K stride_oe, # output expert stride: K BLOCK_K: tl.constexpr, BLOCK_G: tl.constexpr, ): """FE-XC packed MoE kernel: CodeGEMM-style psumbook gather for M=1. For each active expert and each output row, gathers precomputed partial sums from psumbook using codebook indices. This replaces the traditional dequant-matmul with scalar gather+add operations. Grid: (num_active, ceil(K / BLOCK_K)) """ pid_expert = tl.program_id(0) pid_k = tl.program_id(1) if pid_expert >= num_active: return expert_id = tl.load(expert_ids_ptr + pid_expert) offs_k = pid_k * BLOCK_K + tl.arange(0, BLOCK_K) mask_k = offs_k < K # Accumulator for output values [BLOCK_K] acc = tl.zeros((BLOCK_K,), dtype=tl.float32) # Code base pointer for this expert code_base = codes_ptr + expert_id * stride_ec # Psumbook layout: [2, 256, num_groups] row-major # psumbook[m, c, j] at offset: m * 256 * num_groups + c * num_groups + j ps_stride_m = 256 * num_groups ps_stride_c = num_groups # Loop over input groups for g_start in range(0, num_groups, BLOCK_G): offs_g = g_start + tl.arange(0, BLOCK_G) mask_g = offs_g < num_groups # Load codes for [BLOCK_K, BLOCK_G, 2] # codes[expert_id, k, g, 0] and codes[expert_id, k, g, 1] code_ptrs_0 = code_base + offs_k[:, None] * stride_ck + offs_g[None, :] * stride_cg + 0 code_ptrs_1 = code_base + offs_k[:, None] * stride_ck + offs_g[None, :] * stride_cg + 1 mask_kg = mask_k[:, None] & mask_g[None, :] codes_0 = tl.load(code_ptrs_0, mask=mask_kg, other=0).to(tl.int32) # [BLOCK_K, BLOCK_G] codes_1 = tl.load(code_ptrs_1, mask=mask_kg, other=0).to(tl.int32) # Gather from psumbook: psumbook[0, code0, g] + psumbook[1, code1, g] ps_ptrs_0 = psumbook_ptr + 0 * ps_stride_m + codes_0 * ps_stride_c + offs_g[None, :] ps_ptrs_1 = psumbook_ptr + 1 * ps_stride_m + codes_1 * ps_stride_c + offs_g[None, :] ps_vals_0 = tl.load(ps_ptrs_0, mask=mask_kg, other=0.0) # [BLOCK_K, BLOCK_G] ps_vals_1 = tl.load(ps_ptrs_1, mask=mask_kg, other=0.0) # Accumulate partial sums across groups acc += tl.sum(ps_vals_0 + ps_vals_1, axis=1) # reduce over BLOCK_G → [BLOCK_K] # Apply per-output-channel scale scale_ptrs = scales_ptr + expert_id * stride_es + offs_k scales = tl.load(scale_ptrs, mask=mask_k, other=1.0).to(tl.float32) acc = acc * scales # Store output out_ptrs = out_ptr + pid_expert * stride_oe + offs_k tl.store(out_ptrs, acc.to(tl.bfloat16), mask=mask_k) def goliath_packed_moe_fexc_gemm( activations: torch.Tensor, # [M, N] bfloat16 (M=1 for decode) packed_codes: torch.Tensor, # [E_total, K, N//8, 2] uint8 codebooks: torch.Tensor, # [2, 256, 8] float16 (shared per layer) packed_scales: torch.Tensor, # [E_total, K] float16 expert_ids: torch.Tensor, # [num_active] int64 on GPU psumbook: torch.Tensor = None, # [2, 256, N//8] float32 (precomputed, optional) num_active: int = 8, ) -> torch.Tensor: """FE-XC packed MoE GEMM: codebook 2-bit with CodeGEMM psumbook. Near-FP16 quality at 2 bits/weight. Precomputes psumbook once per token, then uses scalar gather+add instead of dequant+matmul. Args: activations: [M, N] or [1, N] input in BF16 packed_codes: [E_total, K, N//8, 2] codebook indices codebooks: [2, 256, 8] shared codebooks for this layer packed_scales: [E_total, K] per-output-channel scales expert_ids: [num_active] expert indices on GPU psumbook: Precomputed [2, 256, N//8] (computed if None) num_active: Number of active experts Returns: Output [num_active, 1, K] in BF16 (M=1) """ M, N = activations.shape K = packed_codes.shape[1] num_groups = N // 8 # Precompute psumbook if not provided if psumbook is None: psumbook = fexc_precompute_psumbook(codebooks, activations[0]) out = torch.empty(num_active, K, device=activations.device, dtype=torch.bfloat16) BLOCK_K = min(64, K) BLOCK_G = min(64, num_groups) grid = (num_active, triton.cdiv(K, BLOCK_K)) _goliath_fexc_packed_moe_kernel[grid]( psumbook, packed_codes, packed_scales, expert_ids, out, K, num_groups, num_active, packed_codes.stride(0), # stride_ec packed_codes.stride(1), # stride_ck packed_codes.stride(2), # stride_cg packed_scales.stride(0), # stride_es out.stride(0), # stride_oe BLOCK_K=BLOCK_K, BLOCK_G=BLOCK_G, ) return out.unsqueeze(1) # [num_active, 1, K] def pack_experts_fexc( expert_weights: list, # List of GoliathFEXCWeights ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Pack FE-XC expert weights into contiguous buffers. Args: expert_weights: List of GoliathFEXCWeights (one per expert) Returns: packed_codes: [E, K, N//8, 2] contiguous uint8 packed_scales: [E, K] contiguous float16 codebooks: [2, 256, 8] float16 (shared, from first expert) """ E = len(expert_weights) K, N = expert_weights[0].shape g = expert_weights[0].group_size device = expert_weights[0].codes.device packed_codes = torch.zeros(E, K, N // g, 2, dtype=torch.uint8, device=device) packed_scales = torch.zeros(E, K, dtype=torch.float16, device=device) for i, w in enumerate(expert_weights): packed_codes[i] = w.codes packed_scales[i] = w.scales # Codebooks are shared across experts — take from first codebooks = expert_weights[0].codebooks return packed_codes, packed_scales, codebooks # ============================================================================= # Benchmark # ============================================================================= def benchmark_goliath(M=4096, N=4096, K=4096, warmup=10, iters=100): """Benchmark Goliath FP4/FP8 vs BF16 cuBLAS.""" import time if not torch.cuda.is_available(): print("No CUDA available.") return print("=" * 60) print("Goliath FP4/FP8 Benchmark") print("=" * 60) w = torch.randn(K, N, device='cuda', dtype=torch.float32) w_fp4 = goliath_quantize(w, bits=4) w_fp8 = goliath_quantize(w, bits=8) w_bf16 = w.to(torch.bfloat16) a = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) # Memory comparison fp4_bytes = w_fp4.packed.numel() + w_fp4.block_scales.numel() fp8_bytes = w_fp8.data.numel() + w_fp8.block_scales.numel() * 4 bf16_bytes = w_bf16.numel() * 2 print(f"Weight memory:") print(f" FP4: {fp4_bytes / 1e6:.1f} MB ({bf16_bytes / fp4_bytes:.1f}x compression)") print(f" FP8: {fp8_bytes / 1e6:.1f} MB ({bf16_bytes / fp8_bytes:.1f}x compression)") print(f" BF16: {bf16_bytes / 1e6:.1f} MB") print() # Warmup for _ in range(warmup): goliath_gemm(a, w_fp4) goliath_gemm(a, w_fp8) torch.matmul(a, w_bf16) torch.cuda.synchronize() # FP4 start = time.perf_counter() for _ in range(iters): goliath_gemm(a, w_fp4) torch.cuda.synchronize() fp4_t = (time.perf_counter() - start) / iters # FP8 start = time.perf_counter() for _ in range(iters): goliath_gemm(a, w_fp8) torch.cuda.synchronize() fp8_t = (time.perf_counter() - start) / iters # BF16 cuBLAS start = time.perf_counter() for _ in range(iters): torch.matmul(a, w_bf16) torch.cuda.synchronize() bf16_t = (time.perf_counter() - start) / iters flops = 2 * M * N * K print(f"{M}x{N}x{K} GEMM:") print(f" Goliath FP4: {flops/fp4_t/1e12:.1f} TFLOPS ({fp4_t*1000:.2f}ms)") print(f" Goliath FP8: {flops/fp8_t/1e12:.1f} TFLOPS ({fp8_t*1000:.2f}ms)") print(f" BF16 cuBLAS: {flops/bf16_t/1e12:.1f} TFLOPS ({bf16_t*1000:.2f}ms)") # Accuracy out_fp4 = goliath_gemm(a, w_fp4) out_fp8 = goliath_gemm(a, w_fp8) out_ref = torch.matmul(a.float(), w).bfloat16() err_fp4 = (out_fp4 - out_ref).abs().mean() / out_ref.abs().mean() err_fp8 = (out_fp8 - out_ref).abs().mean() / out_ref.abs().mean() print(f" FP4 vs FP32 ref: rel_err={err_fp4:.4f}") print(f" FP8 vs FP32 ref: rel_err={err_fp8:.4f}") # Auto mode w_auto = goliath_quantize(w, bits='auto') print(f" Auto-selected: FP{w_auto.bits}") # ============================================================================= # GoliathLinear — Training Module with Custom Autograd # ============================================================================= import torch.nn as nn class _GoliathLinearFunction(torch.autograd.Function): """Custom autograd for fused FP4/FP8 forward + FP32 backward.""" @staticmethod def forward(ctx, input, goliath_weights, bias, weight_fp32, bits): # Forward uses quantized Goliath GEMM # input: [M, K], goliath_weights: GoliathFP4Weights or GoliathFP8Weights out = goliath_gemm(input, goliath_weights, bias) ctx.save_for_backward(input, weight_fp32, bias) ctx.bits = bits return out @staticmethod def backward(ctx, grad_output): input, weight_fp32, bias = ctx.saved_tensors # grad_output: [M, N] # dA = grad_output @ W^T (dequant from master weights for accuracy) # weight_fp32: [out_features, in_features] = [N, K] dA = torch.matmul(grad_output.float(), weight_fp32.float()) # [M, K] # dW = grad_output^T @ input (FP32 accumulation) dW = torch.matmul(grad_output.float().T, input.float()) # [N, K] # db = grad_output.sum(dim=0) if bias exists db = None if bias is not None: db = grad_output.float().sum(dim=0) # Return gradients for: input, goliath_weights, bias, weight_fp32, bits return dA.to(input.dtype), None, db, dW, None class GoliathLinear(nn.Module): """Linear layer with Goliath fused FP4/FP8 quantized forward + FP32 backward. Stores FP32 master weights as ``nn.Parameter`` for training. On forward, weights are quantized via Goliath and the fused kernel runs the matmul. Backward uses FP32 master weights for gradient computation. Args: in_features: Input dimension (K) out_features: Output dimension (N) bias: Whether to include a bias term bits: Quantization bits — 4, 8, or 'auto' """ def __init__(self, in_features: int, out_features: int, bias: bool = True, bits: Union[int, str] = 4): super().__init__() self.in_features = in_features self.out_features = out_features self.bits = bits # FP32 master weights self.weight = nn.Parameter(torch.empty(out_features, in_features)) if bias: self.bias = nn.Parameter(torch.zeros(out_features)) else: self.register_parameter('bias', None) nn.init.kaiming_uniform_(self.weight, a=2.23606797749979) # sqrt(5) # Quantization cache self._goliath_weights: Optional[GoliathWeights] = None self._weight_version: int = -1 def _ensure_quantized(self): """Re-quantize from master weights when weight data has changed.""" # Check if weight tensor has been updated (via _version counter) current_version = self.weight._version if self._goliath_weights is not None and self._weight_version == current_version: return # weight is [out_features, in_features] = [N, K] # Goliath expects [K, N] w_kn = self.weight.data.T.contiguous().float() self._goliath_weights = goliath_quantize(w_kn, bits=self.bits) self._weight_version = current_version def forward(self, x: torch.Tensor) -> torch.Tensor: orig_shape = x.shape[:-1] x_flat = x.reshape(-1, self.in_features) if x_flat.is_cuda and not self.training: # Inference: pure quantized forward (no autograd overhead) self._ensure_quantized() out = goliath_gemm(x_flat, self._goliath_weights, self.bias) return out.view(*orig_shape, self.out_features) if x_flat.is_cuda: # Training: quantized forward + FP32 backward via custom autograd self._ensure_quantized() out = _GoliathLinearFunction.apply( x_flat, self._goliath_weights, self.bias, self.weight, self.bits, ) return out.view(*orig_shape, self.out_features) # CPU fallback return torch.nn.functional.linear(x, self.weight, self.bias) def extra_repr(self) -> str: return (f'in_features={self.in_features}, out_features={self.out_features}, ' f'bias={self.bias is not None}, bits={self.bits}') # ============================================================================= # GoliathQuantumLinear — Hybrid Training Module (FP8 Forward + Quantum Backward) # ============================================================================= # # FireEcho training-optimized linear layer that combines: # Forward: BF16 master → quantize to FP8 → _goliath_fp8_kernel (2x bandwidth) # Backward: quantum_optimized_matmul (L2-swizzled Triton GEMM for gradients) # Master: BF16 weights (saves 2x memory vs FP32 GoliathLinear) # # Designed for EAGLE-3 draft head training where: # - Forward needs to be fast (inference-like, quantized) # - Backward needs to be accurate (BF16 master weights for gradient flow) # - Memory is tight (BF16 master saves 50% vs FP32) # # Hardware-agnostic: uses Triton (compiles to NVIDIA/AMD/Intel), no cuQuantum. # Lazy import for quantum module (lives at kernel/quantum/, parent of Engine/) _quantum_matmul = None _quantum_import_attempted = False def _ensure_quantum_import(): """Lazily import quantum_optimized_matmul from kernel/quantum/.""" global _quantum_matmul, _quantum_import_attempted if _quantum_import_attempted: return _quantum_matmul is not None _quantum_import_attempted = True try: from quantum import quantum_optimized_matmul _quantum_matmul = quantum_optimized_matmul return True except ImportError: import sys as _sys import os as _os # kernel/ is parent of FireEcho Engine/ _kernel_dir = _os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))) if _kernel_dir not in _sys.path: _sys.path.insert(0, _kernel_dir) try: from quantum import quantum_optimized_matmul _quantum_matmul = quantum_optimized_matmul return True except ImportError: return False class _GoliathQuantumFunction(torch.autograd.Function): """Custom autograd: Goliath FP8 forward + Quantum Gold L2-swizzled backward. Forward: Quantize BF16 master → FP8, run fused dequant-matmul (2x bandwidth) Backward: quantum_optimized_matmul for dX and dW (L2 cache swizzle, Triton GEMM) """ @staticmethod def forward(ctx, input, weight_bf16, bias, goliath_fp8): # Forward: use pre-quantized Goliath FP8 weights for 2x bandwidth out = goliath_gemm(input, goliath_fp8, bias) ctx.save_for_backward(input, weight_bf16, bias) return out @staticmethod def backward(ctx, grad_output): input, weight_bf16, bias = ctx.saved_tensors # grad_output: [M, N], weight_bf16: [N, K], input: [M, K] if _quantum_matmul is not None: # Quantum Gold backward: L2-swizzled Triton GEMM # dX = grad_output @ weight_bf16 — [M, N] × [N, K] → [M, K] # quantum_optimized_matmul expects [M, K] × [K, N] → [M, N] # So: dX = quantum_optimized_matmul(grad_output, weight_bf16.T) # but weight_bf16 is [N, K], so weight_bf16.T is [K, N]... # We need grad_output [M, N] × weight [N, K] = [M, K] # = quantum_optimized_matmul(grad_output, weight_bf16.T.contiguous()) won't work # We need: dX[M,K] = grad[M,N] @ W[N,K] # quantum_optimized_matmul(a[M,K], b[K,N]) → [M,N] # So: dX = quantum_optimized_matmul(grad_output.contiguous(), # weight_bf16.contiguous()) # grad[M,N] × W[N,K] → [M,K] # Wait — that's [M,N]×[N,K] which has inner dim N, not K. # quantum expects a[M,K]×b[K,N]. Here K_inner=N. # So this IS valid: a=[M,N_inner], b=[N_inner,K_out] dX = _quantum_matmul( grad_output.contiguous(), weight_bf16.contiguous() # [N, K] — inner dim N matches ) # [M, K] # dW = grad_output.T @ input — [N, M] × [M, K] → [N, K] dW = _quantum_matmul( grad_output.T.contiguous(), # [N, M] input.contiguous() # [M, K] ) # [N, K] else: # Fallback: standard PyTorch matmul (still BF16, still fast) dX = torch.matmul(grad_output, weight_bf16) # [M,N] × [N,K] → [M,K] dW = torch.matmul(grad_output.T, input) # [N,M] × [M,K] → [N,K] db = None if bias is not None: db = grad_output.sum(dim=0) # Gradients for: input, weight_bf16, bias, goliath_fp8 return dX.to(input.dtype), dW.to(weight_bf16.dtype), db, None class GoliathQuantumLinear(nn.Module): """FireEcho training-optimized linear layer. Combines Goliath FP8 forward (2x bandwidth savings) with Quantum Gold backward (L2-swizzled Triton GEMM for gradients). BF16 master weights save 50% memory vs FP32. This is the training counterpart of GoliathLinear. GoliathLinear uses FP32 master weights and standard torch.matmul backward. GoliathQuantumLinear uses BF16 master weights and quantum_optimized_matmul backward. Args: in_features: Input dimension (K) out_features: Output dimension (N) bias: Whether to include a bias term """ def __init__(self, in_features: int, out_features: int, bias: bool = False): super().__init__() self.in_features = in_features self.out_features = out_features # BF16 master weights (saves 2x vs FP32) self.weight = nn.Parameter( torch.empty(out_features, in_features, dtype=torch.bfloat16)) if bias: self.bias = nn.Parameter(torch.zeros(out_features, dtype=torch.bfloat16)) else: self.register_parameter('bias', None) nn.init.kaiming_uniform_(self.weight, a=2.23606797749979) # sqrt(5) # FP8 quantization cache (reused every forward, re-quantized when weight changes) self._goliath_fp8: Optional[GoliathFP8Weights] = None self._weight_version: int = -1 # Ensure quantum module is available _ensure_quantum_import() def _ensure_quantized(self): """Re-quantize BF16 master → FP8 when weights have changed.""" current_version = self.weight._version if self._goliath_fp8 is not None and self._weight_version == current_version: return # weight is [out_features, in_features] = [N, K] # Goliath expects [K, N] w_kn = self.weight.data.T.contiguous().float() self._goliath_fp8 = GoliathFP8Weights.from_float(w_kn) self._weight_version = current_version def forward(self, x: torch.Tensor) -> torch.Tensor: orig_shape = x.shape[:-1] x_flat = x.reshape(-1, self.in_features) if x_flat.is_cuda and not self.training: # Inference: pure FP8 forward (no autograd overhead) self._ensure_quantized() out = goliath_gemm(x_flat, self._goliath_fp8, self.bias) return out.view(*orig_shape, self.out_features) if x_flat.is_cuda: # Training: FP8 forward + Quantum Gold backward self._ensure_quantized() out = _GoliathQuantumFunction.apply( x_flat, self.weight, self.bias, self._goliath_fp8, ) return out.view(*orig_shape, self.out_features) # CPU fallback return torch.nn.functional.linear(x, self.weight, self.bias) def extra_repr(self) -> str: quantum_str = "quantum" if _quantum_matmul is not None else "fallback" return (f'in_features={self.in_features}, out_features={self.out_features}, ' f'bias={self.bias is not None}, backward={quantum_str}') if __name__ == "__main__": print("Goliath — Native FP4/FP8 Fused Triton GEMM Kernel") print("=" * 60) if torch.cuda.is_available(): print(f"GPU: {torch.cuda.get_device_name(0)}") benchmark_goliath(M=2048, N=2048, K=2048) print() benchmark_goliath(M=4096, N=4096, K=4096) else: print("No CUDA available.")