biondizzle
0b6ca0df80
P5: Wire up fused mHC pre_block + RMSNorm + NVFP4 quantize kernel - Replaces: pre_block bmm + rmsnorm (4+ launches) + quantize (2 launches) - With: 2 kernel launches (mhc_rmsnorm_amax_gsa + mhc_rmsnorm_quantize_nvfp4) - Both attn and ffn mHC paths now use P5 fused kernel - Savings: ~5 launches/site × 2 sites × 61 layers = 610 launches/token B3: Fused rmsnorm+quant for q_a_norm → q_b path - q_a output → rmsnorm_quantize_nvfp4 → QuantizedActivation → q_b.run_from_quantized - Eliminates BF16 round-trip between q_a_norm and q_b GEMM - Saves: ~6 kernel launches per layer (rmsnorm 4+ + quantize 2 vs fused 2) gsa scalar fix in Nvfp4Linear.run_from_quantized: - CuTeDSL NVFP4 GEMM expects global_scale_a as per-expert scalar (shape (1,)) - Per-row gsa from fused kernels must be reduced to scalar (max) for M>1 - For M=1 decode: already scalar, no reduction needed - Fixes potential correctness issue at prefill (M>1) when using fused paths Cleanup: Remove --ab-compare flag and A/B comparison code (replaced by P5)
278 lines
11 KiB
Python
278 lines
11 KiB
Python
"""CuTeDSL NVFP4 Linear (single GEMM)
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Generic NVFP4 GEMM runner for attention projections and any single
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linear layer. Uses ScaledGroupedGemmKernel with num_groups=1.
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CUDA-graph-compatible: all buffers pre-allocated, no CPU-GPU syncs.
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"""
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import torch
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from dsv4.ops.quantize import (
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quantize_activation_nvfp4,
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quantize_to_nvfp4,
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)
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from dsv4.ops.layouts import (
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make_b_k_major,
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)
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from dsv4.ops.gemm_runner import (
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run_nvfp4_grouped_gemm,
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)
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from dsv4.kernels.gemm.grouped import (
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ceil_div as cutedsl_ceil_div,
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pad_and_swizzle_single,
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)
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from dsv4.ops.custom_ops import register_runner, nvfp4_linear_gemm
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class Nvfp4Linear:
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"""Single NVFP4 GEMM using CuTeDSL (num_groups=1).
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Handles any (K, N) weight matrix in NVFP4 format.
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Simple: quantize activation → GEMM → BF16 output.
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No SiLU, no fusion, no routing.
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CUDA-graph-compatible: all buffers pre-allocated, no CPU-GPU syncs.
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"""
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def __init__(
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self,
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in_features: int,
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out_features: int,
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max_num_tokens: int = 8192,
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device: str = "cuda",
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):
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self.in_features = in_features
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self.out_features = out_features
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self.max_num_tokens = max_num_tokens
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self.device = device
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# Weights (set after construction, then call finalize_weights)
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self.fp4 = None # list of 1 tensor
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self.sf = None # list of 1 tensor
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self.gs = None # list of 1 float
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self.ws2 = None # list of 1 tensor — weight_scale_2 (scalar, folded into global_scale_b)
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# Processed weights
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self._mat_b = None
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self._scale_b = None
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self._gsb = None
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# Activation global scale
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self._activation_global_scale = 1.0 / (6.0 * 448.0)
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# Pre-allocated buffers
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self._padded_x_fp4_buf = None
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self._expert_offsets_buf = None
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self._gsa_buf = None
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self._buffers_allocated = False
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def finalize_weights(self):
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"""Process weights for CuTeDSL GEMM."""
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# Convert uint8 checkpoint weights to float4_e2m1fn_x2 view
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fp4_view = [w.view(torch.float4_e2m1fn_x2) if w.dtype == torch.uint8 else w for w in self.fp4]
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# Checkpoint weight is (out_features//2, in_features//2) = (N_packed, K_packed)
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# make_b_k_major expects (E, K_packed, N_packed), so we need to permute
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stacked = torch.stack(fp4_view).permute(0, 2, 1).contiguous() # (1, K_packed, N_packed)
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self._mat_b = make_b_k_major(stacked)
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# Checkpoint scale is (N_packed, K_sf) — already in the right row order for the
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# kernel's swizzle. Use assemble_raw_scales_2d3d_3d_side (no transpose),
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# NOT assemble_scales_3d_side (which transposes K_sf↔N).
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from dsv4.ops.layouts import assemble_raw_scales_2d3d_3d_side
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self._scale_b = assemble_raw_scales_2d3d_3d_side(self.sf)
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self._gsb = torch.tensor(self.gs, dtype=torch.float32, device=self.device)
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# Fold weight_scale_2 into global_scale_b
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# Dequant formula: w = lut[w_packed] * weight_scale * weight_scale_2
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# Production GEMM: y = (x * scale_a * gsa) @ (w * scale_b * gsb)
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# So gsb = input_scale * weight_scale_2
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if self.ws2 is not None and len(self.ws2) > 0 and self.ws2[0] is not None:
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ws2_val = self.ws2[0].float().item()
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self._gsb = self._gsb * ws2_val
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# Free raw weights
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self.fp4 = None
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self.sf = None
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self.gs = None
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self.ws2 = None
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# Eagerly JIT-compile the GEMM kernel for this (K, N) shape.
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# Uses num_groups=1 since this is a single linear layer.
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K_packed = self.in_features // 2
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N_packed = self.out_features // 2
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# warmup_compilation(1, K_packed, N_packed, self.device) # Lazy compile on first real forward
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def _ensure_buffer_size(self, num_tokens: int):
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"""Ensure the padded buffer is large enough for num_tokens."""
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needed_rows = cutedsl_ceil_div(num_tokens, 128) * 128
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if self._padded_x_fp4_buf is not None and self._padded_x_fp4_buf.shape[0] >= needed_rows:
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return # Already big enough
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self._padded_x_fp4_buf = torch.zeros(
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needed_rows, self.in_features // 2, dtype=torch.uint8, device=self.device
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).view(torch.float4_e2m1fn_x2)
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self._expert_offsets_buf = torch.zeros(1, dtype=torch.int32, device=self.device)
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self._gsa_buf = torch.full((1,), self._activation_global_scale, dtype=torch.float32, device=self.device)
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def _ensure_initialized(self):
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if self._mat_b is None:
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self.finalize_weights()
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def _assemble_scales_single_group(self, x_sf):
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"""Assemble 2D-side activation scales for num_groups=1."""
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num_rows, num_cols = x_sf.shape
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padded_rows = cutedsl_ceil_div(num_rows, 128) * 128
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padded_cols = cutedsl_ceil_div(num_cols, 4) * 4
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buf = torch.zeros(padded_rows, padded_cols, dtype=torch.float16, device=x_sf.device).to(torch.float8_e4m3fn)
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buf[:num_rows, :num_cols] = x_sf
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swizzled_flat = pad_and_swizzle_single(buf)
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return swizzled_flat.reshape(padded_rows, padded_cols)
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def compute_activation_global_scale(self, hidden_states_sample):
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"""Compute activation global scale from a warmup forward."""
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self._ensure_initialized()
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with torch.no_grad():
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_, _, gs = quantize_to_nvfp4(hidden_states_sample)
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self._activation_global_scale = gs
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def run(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""Forward: BF16 input → NVFP4 GEMM → BF16 output.
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Uses torch.library.custom_op (nvfp4::linear_gemm) so torch.compile
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treats this as an opaque op. The custom op calls _run_impl internally.
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"""
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if not hasattr(self, '_runner_id'):
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self._runner_id = register_runner(self)
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return nvfp4_linear_gemm(
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hidden_states, self._runner_id, self.out_features,
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)
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def _run_impl(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""Actual implementation — called via custom autograd to be torch.compile-safe."""
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self._ensure_initialized()
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num_tokens = hidden_states.shape[0]
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padded_rows = cutedsl_ceil_div(num_tokens, 128) * 128
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# Ensure buffer is large enough
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self._ensure_buffer_size(num_tokens)
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# Fused amax + quantize: single kernel launch, zero CPU-GPU syncs.
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# Computes amax on GPU → derives gsa → quantizes to NVFP4.
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# gsa written to GPU buffer for downstream GEMM global_scale_a.
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#
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# This replaces the two-step path:
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# compute_amax_gsa_gpu(hidden_states) → .item() sync
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# quantize_nvfp4_gpu(hidden_states, gsa_float) → another kernel launch
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#
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# Old path: ~2 kernel launches + 1 .item() sync per projection.
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# New path: 1 kernel launch + 0 .item() syncs per projection.
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# Total across 61 layers: ~486 .item() syncs eliminated.
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if getattr(self, '_use_runtime_gsa', False):
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from dsv4.ops.quantize import quantize_nvfp4_gpu_fused
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x_fp4, x_sf, gsa_gpu = quantize_nvfp4_gpu_fused(hidden_states)
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self._gsa_buf.copy_(gsa_gpu[:1].reshape(1)) # GPU → GPU, no sync
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else:
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# P2 FIX: No per-call fill_(). The _gsa_buf already has the correct
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# value — set either during initialization (via _ensure_buffer_size)
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# or by the first GPU compute when _use_runtime_gsa was True.
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# Old path: self._gsa_buf.fill_(self._activation_global_scale)
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# — H2D transfer every call (~5µs each × 244 calls = ~1.2ms/token).
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# New path: zero H2D transfers on the hot path.
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from dsv4.ops.quantize import quantize_nvfp4_gpu
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x_fp4, x_sf = quantize_nvfp4_gpu(hidden_states, self._activation_global_scale)
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# Scatter x_fp4 into padded buffer
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padded_x_fp4 = self._padded_x_fp4_buf
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padded_x_fp4.view(torch.uint8).zero_()
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padded_x_fp4.view(torch.uint8)[:x_fp4.shape[0]] = x_fp4.view(torch.uint8)
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# Assemble A-side scales
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scale_a = self._assemble_scales_single_group(x_sf)
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# Expert offsets: [padded_rows] for 1 group
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expert_offsets = self._expert_offsets_buf
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expert_offsets.fill_(padded_rows)
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# Global scales — GPU-computed gsa already in _gsa_buf (no CPU sync)
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gsa = self._gsa_buf
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# Run GEMM
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out = run_nvfp4_grouped_gemm(
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mat_a=padded_x_fp4,
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mat_b=self._mat_b,
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scale_a=scale_a,
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scale_b=self._scale_b,
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expert_offsets=expert_offsets,
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global_scale_a=gsa,
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global_scale_b=self._gsb,
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)
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return out[:num_tokens]
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def run_from_quantized(self, quant: 'QuantizedActivation') -> torch.Tensor:
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"""Run GEMM with pre-quantized activation (skip quantize step).
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Used when the input has already been quantized by a fused
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RMSNorm+quantize kernel. Saves 2 kernel launches per call.
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Args:
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quant: QuantizedActivation with x_fp4, x_sf, gsa
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"""
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from dsv4.ops.quantize import QuantizedActivation
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assert isinstance(quant, QuantizedActivation)
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self._ensure_initialized()
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num_tokens = quant.num_tokens
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padded_rows = cutedsl_ceil_div(num_tokens, 128) * 128
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self._ensure_buffer_size(num_tokens)
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# Scatter pre-quantized x_fp4 into padded buffer
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padded_x_fp4 = self._padded_x_fp4_buf
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padded_x_fp4.view(torch.uint8).zero_()
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padded_x_fp4.view(torch.uint8)[:quant.x_fp4.shape[0]] = quant.x_fp4.view(torch.uint8)
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# Assemble A-side scales from pre-quantized sf
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scale_a = self._assemble_scales_single_group(quant.x_sf)
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# Expert offsets
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expert_offsets = self._expert_offsets_buf
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expert_offsets.fill_(padded_rows)
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# Global scales — the CuTeDSL NVFP4 GEMM expects global_scale_a as a
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# per-expert scalar (shape (1,) for single linear). The fused
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# rmsnorm/mhc kernels compute per-row gsa, but we must reduce to a
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# scalar. Using max reduction: gsa = max(per_row_gsa) ensures no
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# E4M3 block scale overflow (rows with smaller magnitude get slightly
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# less FP4 precision, but all rows stay within E4M3 range).
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#
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# For M=1 decode: per-row gsa is already scalar, no reduction needed.
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# For M>1 prefill: reduce per-row gsa to a single scalar (max).
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if quant.gsa.shape[0] == 1:
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gsa = quant.gsa[:1].reshape(1) # Already scalar
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else:
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# Reduce per-row gsa to scalar (max) for GEMM compatibility.
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# Per-row gsa is mathematically more precise, but the GEMM only
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# supports a single global scale per expert.
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gsa = quant.gsa.max().reshape(1)
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self._gsa_buf.copy_(gsa)
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# Run GEMM
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out = run_nvfp4_grouped_gemm(
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mat_a=padded_x_fp4,
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mat_b=self._mat_b,
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scale_a=scale_a,
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scale_b=self._scale_b,
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expert_offsets=expert_offsets,
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global_scale_a=self._gsa_buf,
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global_scale_b=self._gsb,
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)
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return out[:num_tokens]
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def __call__(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return self.run(hidden_states)
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