1#![allow(unused_imports)]
19
20use crate::buffer::{
21 Arena, ReadbackLayout, ReadbackStaging, TinyReadbackStaging, decode_mapped_readback_f32,
22 decode_tiny_mapped_f32, encode_readback_copies, plan_f32_uniform, read_f32_many_pooled,
23 schedule_readback_map, use_tiny_readback, wait_readback_map,
24};
25use crate::device::wgpu_device;
26use crate::kernels::{
27 ArgmaxParams, AttentionBwdParams, AttentionParams, BatchElementwiseRegionParams, BinaryParams,
28 Conv1dParams, Conv2dParams, Conv3dParams, CopyParams, CumsumBwdParams, CumsumParams,
29 DequantMatmulParams, ElementwiseRegionParams, ExpandParams, FmaParams, FusedResidualLnParams,
30 FusedResidualLnTeeParams, FusedResidualRmsNormParams, GatherAxisParams, GatherBwdParams,
31 GatherParams, GroupedMatmulParams, GruParams, Kernel, LayerNormBwdParams, LayerNormParams,
32 Mamba2Params, MatmulParams, MatmulQkvParams, NarrowConcatParams, Pool1dParams, Pool2dParams,
33 Pool3dParams, ReduceParams, RmsNormBwdParams, RnnParams, RopeBwdParams, RopeParams,
34 SampleParams, ScatterAddParams, SceParams, SelectiveScanParams, SoftmaxParams, TopKParams,
35 TransposeParams, UmapKnnParams, UnaryParams, WelchPeaksGpuParams, WhereParams, argmax_kernel,
36 attention_bwd_kernel, attention_kernel, batch_elementwise_region_kernel, binary_kernel,
37 cast_f32_to_f16_kernel, compare_kernel, concat_kernel, conv1d_kernel, conv2d_kernel,
38 conv3d_kernel, copy_kernel, cumsum_backward_kernel, cumsum_kernel, dequant_matmul_kernel,
39 elementwise_region_kernel, elementwise_region_spatial_kernel, expand_kernel, fma_kernel,
40 fused_residual_ln_kernel, fused_residual_ln_tee_kernel, fused_residual_rms_norm_kernel,
41 gather_axis_kernel, gather_backward_acc_kernel, gather_backward_zero_kernel, gather_kernel,
42 gather_split_kernel, grouped_matmul_kernel, gru_kernel,
43 layer_norm_backward_gamma_partial_kernel, layer_norm_backward_gamma_reduce_kernel,
44 layer_norm_backward_input_kernel, layernorm_kernel, mamba2_kernel,
45 matmul_coop_f16_vulkan_active_kernel, matmul_coop_f16_vulkan_kernel,
46 matmul_coop_f32_active_kernel, matmul_coop16_kernel, matmul_f16_compute_kernel,
47 matmul_f16w_kernel, matmul_kernel, matmul_qkv_coop_f16_vk_active_kernel,
48 matmul_qkv_coop_f16_vk_kernel, matmul_qkv_coop_f32_kernel, matmul_qkv_kernel,
49 matmul_wide_active_kernel, matmul_wide_kernel, narrow_kernel, pool1d_kernel, pool2d_kernel,
50 pool3d_kernel, reduce_kernel, rms_norm_backward_kernel, rms_norm_backward_param_kernel,
51 rnn_kernel, rope_backward_kernel, rope_kernel, sample_kernel, scatter_add_kernel,
52 selective_scan_kernel, softmax_cross_entropy_kernel, softmax_kernel, topk_kernel,
53 transpose_kernel, umap_knn_kernel, unary_f16_mirror_kernel, unary_kernel,
54 welch_peaks_gpu_kernel, where_kernel,
55};
56use rlx_ir::dynamic::{bind_graph, has_dynamic_dims, infer_bindings_from_f32_inputs, same_binding};
57use rlx_ir::op::{Activation, BinaryOp, CmpOp, MaskKind, ReduceOp};
58use rlx_ir::shape::DimBinding;
59use rlx_ir::{Graph, NodeId, Op};
60use std::collections::{HashMap, HashSet};
61use std::num::NonZeroU64;
62
63use super::*;
64
65impl WgpuExecutable {
66 pub fn set_rng(&mut self, rng: rlx_ir::RngOptions) {
68 *self.rng.write().expect("rng lock") = rng;
69 }
70
71 pub fn set_active_extent(&mut self, extent: Option<(usize, usize)>) {
75 self.active_extent = extent;
76 }
77
78 pub fn set_param(&mut self, name: &str, data: &[f32]) {
79 const STASH_MAX_BYTES: usize = 16 * 1024 * 1024;
80 if data.len() * 4 <= STASH_MAX_BYTES {
81 self.stashed_params.insert(name.to_string(), data.to_vec());
82 }
83 if self.coop_f16_vk {
84 crate::coop_f16_vk::refresh_wide_b_flag(&mut self.coop_f16_vk_wide_b, name, data);
85 }
86 if self.unresolved.is_some() {
87 self.pending_params.insert(name.to_string(), data.to_vec());
88 return;
89 }
90 let dev = wgpu_device().expect("rlx-wgpu: device gone");
91 if let Some(&id) = self.param_offsets.get(name)
92 && self.arena.has(id)
93 {
94 self.arena.write_f32(&dev.queue, id, data);
95 }
96 }
97
98 pub fn set_param_bytes(&mut self, name: &str, data: &[u8]) {
103 if self.unresolved.is_some() {
104 self.pending_param_bytes
105 .insert(name.to_string(), data.to_vec());
106 return;
107 }
108 let dev = wgpu_device().expect("rlx-wgpu: device gone");
109 if let Some(&id) = self.param_offsets.get(name)
110 && self.arena.has(id)
111 {
112 dev.queue
113 .write_buffer(&self.arena.buffer, self.arena.offset(id) as u64, data);
114 }
115 }
116
117 pub fn set_gpu_handle_feed(&mut self, handle_name: &str, output_index: usize) {
118 self.gpu_handle_feeds
119 .insert(handle_name.to_string(), output_index);
120 }
121}