Prototype Vortex NVFP4 quantizer seam

src/h3_blackwell_runtime/nvfp4_quant.py

@@ -0,0 +1,88 @@
+"""Experimental NVFP4 activation quantization helpers for profiling."""
+
+from __future__ import annotations
+
+import time
+
+import torch
+
+
+def _sync() -> None:
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+
+
+def _record_timing(timings: dict[str, list[float]] | None, name: str, fn):
+    if timings is None:
+        return fn()
+    _sync()
+    started = time.perf_counter()
+    value = fn()
+    _sync()
+    timings.setdefault(name, []).append(time.perf_counter() - started)
+    return value
+
+
+def nvfp4_activation_scale(tensor: torch.Tensor, *, timings: dict[str, list[float]] | None = None) -> torch.Tensor:
+    """Compute Comfy Kitchen's current per-tensor NVFP4 activation scale."""
+    from comfy_kitchen.float_utils import F4_E2M1_MAX, F8_E4M3_MAX
+
+    amax = _record_timing(timings, "scale_absmax", lambda: torch.amax(tensor.abs()))
+    return _record_timing(timings, "scale_finalize", lambda: amax / (F8_E4M3_MAX * F4_E2M1_MAX))
+
+
+def vortex_quantize_nvfp4(
+    tensor: torch.Tensor,
+    *,
+    scale: torch.Tensor | float | None = None,
+    timings: dict[str, list[float]] | None = None,
+):
+    """Create a TensorCoreNVFP4 QuantizedTensor through an explicit Vortex seam.
+
+    This prototype still uses Comfy Kitchen's low-level ``quantize_nvfp4`` CUDA op
+    for the pack/block-scale step, but bypasses ``QuantizedTensor.from_float`` and
+    lets callers provide a precomputed/global scale. It is intentionally isolated so a
+    native Vortex quantizer can replace this implementation without touching the
+    linear call sites or benchmarks.
+    """
+    if tensor.dim() != 2:
+        raise ValueError(f"NVFP4 activation quantization requires a 2D tensor, got {tensor.dim()}D")
+    if not tensor.is_contiguous():
+        raise ValueError("vortex_quantize_nvfp4 requires contiguous input; fix the caller rather than hiding a copy here")
+
+    import comfy_kitchen as ck
+    from comfy_kitchen.tensor import QuantizedTensor, TensorCoreNVFP4Layout
+
+    orig_dtype = tensor.dtype
+    orig_shape = tuple(tensor.shape)
+    if scale is None:
+        scale = nvfp4_activation_scale(tensor, timings=timings)
+    scale = _record_timing(
+        timings,
+        "scale_to_device",
+        lambda: torch.as_tensor(scale, device=tensor.device, dtype=torch.float32),
+    )
+
+    qdata, block_scale = _record_timing(
+        timings,
+        "ck_quantize_nvfp4",
+        lambda: ck.quantize_nvfp4(
+            tensor,
+            scale,
+            pad_16x=TensorCoreNVFP4Layout.get_padded_shape(orig_shape) != orig_shape,
+        ),
+    )
+    return _record_timing(
+        timings,
+        "params_wrap",
+        lambda: QuantizedTensor(
+            qdata,
+            "TensorCoreNVFP4Layout",
+            TensorCoreNVFP4Layout.Params(
+                scale=scale,
+                orig_dtype=orig_dtype,
+                orig_shape=orig_shape,
+                block_scale=block_scale,
+            ),
+        ),
+    )

tools/profile_nvfp4_linear.py

@@ -19,6 +19,7 @@ from h3_blackwell_runtime.attention import AVAILABLE_BACKENDS, rms_norm, rms_rop
 from h3_blackwell_runtime.block import H3DiTBlock, gate_segments, modulate_segments
 from h3_blackwell_runtime.checkpoint import H3Checkpoint
 from h3_blackwell_runtime.nvfp4 import Nvfp4Linear
+from h3_blackwell_runtime.nvfp4_quant import nvfp4_activation_scale, vortex_quantize_nvfp4
 from h3_blackwell_runtime.packing import H3PromptPacker
 from h3_blackwell_runtime.rope import h3_rope_rotation
 from h3_blackwell_runtime.sampler import _audio_sigma, _model_sigma, beta_sigmas
@@ -41,18 +42,47 @@ def timed(stats: dict[str, list[float]], name: str, fn):
 
 def summarize(values: list[float]) -> dict[str, float]:
     ordered = sorted(values)
+    def percentile(percent: float) -> float:
+        if len(ordered) == 1:
+            return ordered[0]
+        rank = (len(ordered) - 1) * percent
+        low = int(rank)
+        high = min(low + 1, len(ordered) - 1)
+        weight = rank - low
+        return ordered[low] * (1.0 - weight) + ordered[high] * weight
+
     return {
         "count": len(values),
         "mean_s": sum(values) / len(values),
+        "p50_s": percentile(0.50),
+        "p90_s": percentile(0.90),
+        "p95_s": percentile(0.95),
+        "p99_s": percentile(0.99),
         "min_s": ordered[0],
         "max_s": ordered[-1],
     }
 
 
-def profile_linear_stages(module: Nvfp4Linear, x: torch.Tensor, *, iterations: int) -> dict[str, dict[str, float]]:
+def quantize_activation(flat_x: torch.Tensor, quantizer: str, scale: torch.Tensor | None, timings: dict[str, list[float]] | None = None):
     from comfy_kitchen.tensor import QuantizedTensor
 
+    if quantizer == "comfy":
+        return QuantizedTensor.from_float(flat_x, "TensorCoreNVFP4Layout")
+    if quantizer == "vortex_recalculate":
+        return vortex_quantize_nvfp4(flat_x, timings=timings)
+    if quantizer == "vortex_precomputed_scale":
+        if scale is None:
+            raise ValueError("vortex_precomputed_scale requires a precomputed scale")
+        return vortex_quantize_nvfp4(flat_x, scale=scale, timings=timings)
+    raise ValueError(f"Unsupported quantizer: {quantizer}")
+
+
+def profile_linear_stages(module: Nvfp4Linear, x: torch.Tensor, *, iterations: int, quantizer: str) -> dict[str, dict[str, float]]:
+
     stats: dict[str, list[float]] = {}
+    precomputed_scale = None
+    if quantizer == "vortex_precomputed_scale":
+        precomputed_scale = timed(stats, "precomputed_scale_calibration", lambda: nvfp4_activation_scale(x.reshape(-1, module.in_features).contiguous()))
     for _ in range(iterations):
         original_shape = x.shape[:-1]
         flat_x = timed(stats, "flatten_contiguous", lambda: x.reshape(-1, module.in_features).contiguous())
@@ -66,23 +96,38 @@ def profile_linear_stages(module: Nvfp4Linear, x: torch.Tensor, *, iterations: i
             weight = timed(stats, "weight_dequantize", lambda: packed_weight.dequantize().to(flat_x))
             output = timed(stats, "linear", lambda: functional.linear(flat_x, weight, bias))
         else:
-            packed_x = timed(stats, "activation_quantize", lambda: QuantizedTensor.from_float(flat_x, "TensorCoreNVFP4Layout"))
+            packed_x = timed(stats, "activation_quantize", lambda: quantize_activation(flat_x, quantizer, precomputed_scale, stats))
             output = timed(stats, "linear", lambda: functional.linear(packed_x, packed_weight, bias))
         timed(stats, "slice_reshape", lambda: output[:flat_x.shape[0], :module.out_features].reshape(*original_shape, module.out_features))
     return {name: summarize(values) for name, values in stats.items()}
 
 
-def profile_cuda_kernels(module: Nvfp4Linear, x: torch.Tensor, *, warmup: int, iterations: int, row_limit: int) -> list[dict]:
+def run_linear_with_quantizer(module: Nvfp4Linear, x: torch.Tensor, quantizer: str, precomputed_scale: torch.Tensor | None = None) -> torch.Tensor:
+    original_shape = x.shape[:-1]
+    flat_x = x.reshape(-1, module.in_features).contiguous()
+    if module.pre_quant_scale is not None:
+        flat_x = flat_x * module.pre_quant_scale.to(flat_x)
+    packed_weight = module._packed_weight()
+    bias = module.bias.to(flat_x) if module.bias is not None else None
+    if module.full_precision_matrix_mult:
+        output = functional.linear(flat_x, packed_weight.dequantize().to(flat_x), bias)
+    else:
+        output = functional.linear(quantize_activation(flat_x, quantizer, precomputed_scale), packed_weight, bias)
+    return output[:flat_x.shape[0], :module.out_features].reshape(*original_shape, module.out_features)
+
+
+def profile_cuda_kernels(module: Nvfp4Linear, x: torch.Tensor, *, warmup: int, iterations: int, row_limit: int, quantizer: str) -> list[dict]:
     with torch.inference_mode():
+        precomputed_scale = nvfp4_activation_scale(x.reshape(-1, module.in_features).contiguous()) if quantizer == "vortex_precomputed_scale" else None
         for _ in range(warmup):
-            module(x)
+            run_linear_with_quantizer(module, x, quantizer, precomputed_scale)
         sync()
         activities = [ProfilerActivity.CPU]
         if torch.cuda.is_available():
             activities.append(ProfilerActivity.CUDA)
         with profile(activities=activities, record_shapes=True) as prof:
             for _ in range(iterations):
-                module(x)
+                run_linear_with_quantizer(module, x, quantizer, precomputed_scale)
         sync()
     rows = []
     for event in prof.key_averages(group_by_input_shape=True):
@@ -200,6 +245,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--block-index", type=int, default=24)
     parser.add_argument("--attention", choices=AVAILABLE_BACKENDS, default="sage2")
     parser.add_argument("--linears", nargs="+", choices=("attn_qkv_proj", "attn_out_proj", "mlp_fc1", "mlp_fc2"), default=("attn_qkv_proj", "attn_out_proj", "mlp_fc1", "mlp_fc2"))
+    parser.add_argument("--quantizers", nargs="+", choices=("comfy", "vortex_recalculate", "vortex_precomputed_scale"), default=("comfy", "vortex_recalculate", "vortex_precomputed_scale"))
     parser.add_argument("--warmup", type=int, default=2)
     parser.add_argument("--iterations", type=int, default=5)
     parser.add_argument("--profiler-iterations", type=int, default=2)
@@ -216,18 +262,25 @@ def main() -> None:
         for name in args.linears:
             module = module_for_name(block, name)
             x = inputs[name]
-            for _ in range(args.warmup):
+            reference = run_linear_with_quantizer(module, x, "comfy")
-                module(x)
+            for quantizer in args.quantizers:
-            stage_timings = profile_linear_stages(module, x, iterations=args.iterations)
+                precomputed_scale = nvfp4_activation_scale(x.reshape(-1, module.in_features).contiguous()) if quantizer == "vortex_precomputed_scale" else None
-            kernels = profile_cuda_kernels(module, x, warmup=args.warmup, iterations=args.profiler_iterations, row_limit=args.profiler_row_limit)
+                for _ in range(args.warmup):
-            results.append(
+                    run_linear_with_quantizer(module, x, quantizer, precomputed_scale)
-                {
+                stage_timings = profile_linear_stages(module, x, iterations=args.iterations, quantizer=quantizer)
-                    "name": name,
+                candidate = run_linear_with_quantizer(module, x, quantizer, precomputed_scale)
-                    "module": module_info(module, x),
+                diff = (candidate.float() - reference.float()).abs()
-                    "stage_timings": stage_timings,
+                kernels = profile_cuda_kernels(module, x, warmup=args.warmup, iterations=args.profiler_iterations, row_limit=args.profiler_row_limit, quantizer=quantizer)
-                    "profiler_top_events": kernels,
+                results.append(
-                }
+                    {
-            )
+                        "name": name,
+                        "quantizer": quantizer,
+                        "module": module_info(module, x),
+                        "stage_timings": stage_timings,
+                        "reference_diff": {"max": diff.max().item(), "mean": diff.mean().item()},
+                        "profiler_top_events": kernels,
+                    }
+                )
 
     output = {
         "model_path": args.model_path,
@@ -236,6 +289,7 @@ def main() -> None:
         "warmup": args.warmup,
         "iterations": args.iterations,
         "profiler_iterations": args.profiler_iterations,
+        "quantizers": args.quantizers,
         "metadata": metadata,
         "results": results,
     }