Error list: Line: 1: Optional is imported but never referenc...

Prompt

Error list: Line: 1: Optional is imported but never referenced. This is a verified unused import. Fix: remove from typing import Optional. 11: except Exception suppresses non-availability failures such as a broken or binary-incompatible Triton installation and silently changes execution to the fallback implementation. Fix: catch ImportError or ModuleNotFoundError; log or re-raise other exceptions. 29-30: tl.program_id(0) is an int32, so row * n_cols can overflow when the flattened tensor offset exceeds the signed 32-bit range. The resulting pointer can address the wrong memory. Fix: convert row or the complete offset calculation to tl.int64. 59-60: pid * BLOCK and the resulting offsets are based on an int32 program ID and can overflow for flattened tensors exceeding 2^31 - 1 elements. Fix: cast pid to tl.int64 before multiplication. 79-80: The GELU kernel has the same signed 32-bit flattened-index overflow. Fix: cast pid to tl.int64 before computing offsets. 38-39: eps is not validated. For an all-zero row, eps == 0 produces rstd = inf and then 0 * inf = NaN; a negative or NaN epsilon can also produce NaN. Fix: require a finite eps > 0 before either implementation executes. 45, 87-97: No exact weight-shape validation exists. If weight.numel() < x.shape[-1], line 45 reads beyond the weight allocation because its mask only checks cols < n_cols. If the weight has extra or multidimensional elements, the Triton path treats its storage as a one-dimensional array while the fallback applies PyTorch broadcasting. Fix: require weight.ndim == 1 and weight.numel() == x.shape[-1]. 87-97: No device check exists for weight. A CPU weight or weight on another GPU can be passed to a kernel launched for x, while the fallback also fails during cross-device multiplication. Fix: require weight.device == x.device. 54, 101: The public default levels_max=3.75 is not the FP4 E2M1 maximum. NVFP4 E2M1 represents magnitudes through 6. Fix: use the actual FP4 E2M1 format and its maximum of 6; do not expose an arbitrary incompatible maximum under an NVFP4 API name. 64-75: The quantization kernel does not implement a four-bit value set. With step=0.125 and limits [-3.75, 3.75], it can produce 61 distinct normalized values, whereas four bits encode only 16 patterns. Fix: convert to the actual FP4 E2M1 representable values. 65-68: min_scale is applied before power-of-two rounding, so it is not actually preserved. With the supplied min_scale=1e-6, flooring produces 2^-20, approximately 9.5367e-7, which is below the configured minimum. Fix: perform the final bounds check after scale-format conversion. 64, 67-74: Flooring the decode scale rounds it downward. Unless the original scale is already a power of two, the block maximum becomes larger than levels_max after normalization and is clipped. Fix: remove power-of-two scaling for NVFP4, or use an upward scale rounding rule if implementing a separate power-of-two fake quantizer. 67-68: Power-of-two scales do not implement the E4M3 block-scale format used by NVIDIA’s hardware NVFP4 recipe. Fix: either implement the intended NVFP4 scale representation or rename the operation as a custom power-of-two fake quantizer. 71-74: The hard-coded uniform 0.125 step is independent of levels_max. Changing levels_max changes clipping endpoints without changing the quantization lattice and can generate clipped endpoint values that are not multiples of the step. Fix: remove this incompatible parameterization and quantize directly to a defined codebook. 72: tl.math.round is absent from the current Triton language API and current Triton source, so this kernel is incompatible with current Triton unless an older compatible version is used. Fix: pin a verified historical Triton version or use a currently supported conversion/rounding implementation. 69, 113-116: Every block scale is stored into scales, but the wrapper never reads or returns that tensor. This is unconditional dead allocation and memory traffic. Fix: return the scales as part of a real quantized representation or remove scale_ptr and the store for an explicitly documented fake-quantization operation. 87, 92: A scalar CUDA input reaches orig_shape[-1] and raises IndexError. Fix: require x.ndim >= 1. 92: An input with a zero-sized final dimension reaches reshape(-1, 0), for which the inferred dimension is ambiguous, and raises a reshape error. Fix: reject x.shape[-1] == 0 or implement explicit empty-dimension behavior. 87-90: The fallback and Triton paths accept different weight-shape semantics. The fallback uses broadcasting, while the kernel linearly loads one weight per final-dimension column. Fix: validate one common public input contract before dispatch. 94-98: In eager PyTorch execution, the RMSNorm Triton result is written into an empty_like tensor that is disconnected from both x and weight; the raw launch supplies no autograd formula. The fallback is differentiable, so behavior changes with dispatch. Fix: register the operation and its backward, use a custom autograd implementation, or reject/fallback whenever any input requires gradients. PyTorch recommends registering an autograd formula for a Triton operator. 40, 95, 97: rstd is allocated and written for every row but is never read, returned, or used for backward. Fix: remove rstd_ptr and the store, or use the saved reciprocal standard deviation in a real backward implementation. 97: For an input such as shape (0, 128), rows == 0 and the wrapper constructs a zero-sized launch grid. The wrapper defines no backend-independent empty-input behavior. Fix: return an appropriately shaped empty tensor before launching. 101, 108: block is not validated as a positive integer. block == 0 raises ZeroDivisionError at (-n) % block; negative or non-integer values lead to invalid padding, division, or compile-time parameters. Fix: require isinstance(block, int) and not isinstance(block, bool) and block > 0. 57, 60, 101: block is passed directly as the extent of tl.arange(0, BLOCK). Current Triton requires a supported power-of-two extent no greater than TRITON_MAX_TENSOR_NUMEL. Unsupported values fail during JIT compilation. Fix: validate these constraints before launch. 101, 114-115: NVFP4 has a fixed one-dimensional block size of 16, but this API accepts any block value. Even values that compile implement a different quantization format. Fix: require block == 16, or rename the function as a configurable custom block quantizer. 101, 105: The CPU/non-Triton fallback ignores the public levels_max argument entirely because it calls nvfp4_quantize_1d(x.float(), block) without forwarding it. Consequently, changing levels_max only affects the Triton path. Fix: remove the parameter or pass equivalent semantics to the fallback. 101: levels_max is not validated. Zero, negative, infinite, or NaN values make the scale and clipping equations invalid or degenerate. Fix: require a finite positive value; for standard E2M1, eliminate the free parameter. 106-110: Multidimensional tensors are flattened before block partitioning. If the innermost dimension is not divisible by block, one quantization block contains values from two different rows. That is incompatible with row/innermost-dimension NVFP4 block layout. Fix: partition and pad each innermost-dimension row independently. 111-119: The function does not return FP4 data. It stores dequantized quant * scale values into a float32 tensor and then casts them back to x.dtype. It is therefore a fake-quantize/dequantize operation, despite being named triton_blockwise_nvfp4. Fix: rename it explicitly as fake quantization, or return packed FP4 values together with their scales and layout metadata. 111-119: In eager execution, the Triton quantization output is disconnected from x and has no straight-through estimator or backward. Fix: implement and register the intended gradient rule or restrict the operation to inference. 114: An empty input gives num_blocks == 0 and constructs a zero-sized launch grid. Fix: return an empty output before allocating scales or launching. 67, 73-75, 115: The supplied max_scale=10 is reduced to 8 by floor(log2(...)). With the default levels_max=3.75, the largest possible dequantized magnitude is therefore 3.75 × 8 = 30; larger values silently saturate. Fix: remove the undocumented fixed scale ceiling or expose and validate a deliberately specified saturation policy. 122-130: The GELU fallback and Triton paths have different float64 semantics. The fallback computes F.gelu directly in float64, while the Triton path converts the entire input to float32 and later casts the result back to float64. Fix: reject float64, preserve float64 computation, or make both paths follow the same documented precision policy. 125-130: In eager execution, the GELU Triton output is disconnected from x, whereas torch.nn.functional.gelu supplies autograd. Fix: register the GELU backward formula, use the PyTorch fallback when gradients are required, or document and enforce inference-only use. 128-129: An empty CUDA input computes grid = (0,) and attempts a zero-sized launch without a wrapper-level empty-tensor contract. Fix: return torch.empty_like(x) before launch when x.numel() == 0. Send back the complete code with all the fixes. Fix each of the listed errors one by one, making sure to actually correct them so that there are 0 errors remaining. Keep the original imports, since the files exist. Write out every single character; do not abbreviate anything. Fix every error. There must be exactly one file. Do not write anything else; just output the complete code, and it must not contain any comments. Never, under any circumstances, use simplified, substitute, dummy, simulated, or fake code. Write the entire file as complete, unabridged, production-ready code in a single code block. It must be 100% error-free, a complete, error-free file, and must be submitted as a downloadable file. These requirements are mandatory and must be strictly adhered to. If no list of errors is provided, you must find all the errors and fix them. If there were comments in the original code, delete them. And most importantly: YOU MUST NEVER SIMPLIFY! from typing import Optional import torch TRITON_AVAILABLE = False try: import triton import triton.language as tl TRITON_AVAILABLE = True except Exception: triton = None tl = None TRITON_AVAILABLE = False if TRITON_AVAILABLE: @triton.jit def _rmsnorm_fwd_kernel( x_ptr, w_ptr, out_ptr, rstd_ptr, n_cols, eps, BLOCK: tl.constexpr, ): row = tl.program_id(0) x_row = x_ptr + row * n_cols out_row = out_ptr + row * n_cols acc = tl.zeros([BLOCK], dtype=tl.float32) for offset in range(0, n_cols, BLOCK): cols = offset + tl.arange(0, BLOCK) mask = cols < n_cols vals = tl.load(x_row + cols, mask=mask, other=0.0).to(tl.float32) acc += vals * vals mean = tl.sum(acc, axis=0) / n_cols rstd = 1.0 / tl.sqrt(mean + eps) tl.store(rstd_ptr + row, rstd) for offset in range(0, n_cols, BLOCK): cols = offset + tl.arange(0, BLOCK) mask = cols < n_cols vals = tl.load(x_row + cols, mask=mask, other=0.0).to(tl.float32) weight = tl.load(w_ptr + cols, mask=mask, other=0.0).to(tl.float32) tl.store(out_row + cols, vals * rstd * weight, mask=mask) @triton.jit def _blockwise_absmax_quant_kernel( x_ptr, out_ptr, scale_ptr, n_elements, levels_max, min_scale, max_scale, BLOCK: tl.constexpr, ): pid = tl.program_id(0) offsets = pid * BLOCK + tl.arange(0, BLOCK) mask = offsets < n_elements vals = tl.load(x_ptr + offsets, mask=mask, other=0.0).to(tl.float32) amax = tl.max(tl.abs(vals), axis=0) scale = amax / levels_max scale = tl.maximum(scale, min_scale) scale = tl.minimum(scale, max_scale) exponent = tl.floor(tl.log2(scale)) scale = tl.exp2(exponent) tl.store(scale_ptr + pid, scale) normalized = vals / scale step = 0.125 quant = tl.math.round(normalized / step) * step quant = tl.minimum(quant, levels_max) quant = tl.maximum(quant, -levels_max) tl.store(out_ptr + offsets, quant * scale, mask=mask) @triton.jit def _gelu_kernel(x_ptr, out_ptr, n_elements, BLOCK: tl.constexpr): pid = tl.program_id(0) offsets = pid * BLOCK + tl.arange(0, BLOCK) mask = offsets < n_elements x = tl.load(x_ptr + offsets, mask=mask, other=0.0).to(tl.float32) cdf = 0.5 * (1.0 + tl.math.erf(x * 0.7071067811865476)) tl.store(out_ptr + offsets, x * cdf, mask=mask) def triton_rmsnorm(x: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6) -> torch.Tensor: if not TRITON_AVAILABLE or not x.is_cuda: variance = x.float().pow(2).mean(dim=-1, keepdim=True) return (x.float() * torch.rsqrt(variance + eps) * weight.float()).to(x.dtype) orig_shape = x.shape flat = x.reshape(-1, orig_shape[-1]).contiguous().float() rows, cols = flat.shape out = torch.empty_like(flat) rstd = torch.empty(rows, device=x.device, dtype=torch.float32) block = 1024 if cols >= 1024 else 256 _rmsnorm_fwd_kernel[(rows,)](flat, weight.contiguous().float(), out, rstd, cols, eps, BLOCK=block) return out.reshape(orig_shape).to(x.dtype) def triton_blockwise_nvfp4(x: torch.Tensor, block: int = 16, levels_max: float = 3.75) -> torch.Tensor: if not TRITON_AVAILABLE or not x.is_cuda: from hybrid_moe.quant import nvfp4_quantize_1d return nvfp4_quantize_1d(x.float(), block).to(x.dtype) flat = x.reshape(-1).contiguous().float() n = flat.numel() pad = (-n) % block if pad: flat = torch.cat([flat, flat.new_zeros(pad)]) out = torch.empty_like(flat) num_blocks = flat.numel() // block scales = torch.empty(num_blocks, device=x.device, dtype=torch.float32) _blockwise_absmax_quant_kernel[(num_blocks,)]( flat, out, scales, flat.numel(), levels_max, 1e-6, 1e1, BLOCK=block ) if pad: out = out[:n] return out.reshape(x.shape).to(x.dtype) def triton_gelu(x: torch.Tensor) -> torch.Tensor: if not TRITON_AVAILABLE or not x.is_cuda: return torch.nn.functional.gelu(x) flat = x.reshape(-1).contiguous().float() out = torch.empty_like(flat) block = 1024 grid = ((flat.numel() + block - 1) // block,) _gelu_kernel[grid](flat, out, flat.numel(), BLOCK=block) return out.reshape(x.shape).to(x.dtype)