[ExecuTorch][WebGPU] Dynamic resize hooks for rms_norm, embedding, rope

backends/webgpu/runtime/ops/embedding_q4gsw/EmbeddingQ4gsw.cpp

@@ -16,6 +16,7 @@
 #include <cstdint>
 #include <cstring>
 #include <stdexcept>
+#include <vector>
 
 namespace executorch::backends::webgpu {
 
@@ -36,14 +37,6 @@ static_assert(
     sizeof(EmbeddingParams) == 32,
     "EmbeddingParams must be 32 bytes");
 
-uint64_t numel_of(const std::vector<int64_t>& dims) {
-  uint64_t n = 1;
-  for (int64_t d : dims) {
-    n *= static_cast<uint64_t>(d);
-  }
-  return n;
-}
-
 // arg order mirrors Vulkan EmbeddingQ4gsw.cpp.
 void embedding_q4gsw_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   const int weight_id = args.at(0);
@@ -102,7 +95,7 @@ void embedding_q4gsw_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   }
 
   // Leading index dims flatten row-major (mirrors Vulkan num_indices).
-  const uint64_t out_numel = numel_of(out.dims);
+  const uint64_t out_numel = utils::numel_of(out.dims);
   const uint32_t num_indices = static_cast<uint32_t>(out_numel / embed_dim);
   const uint32_t groups_per_row = static_cast<uint32_t>(scales.dims[1]);
   const uint32_t blocks_per_row = embed_dim / 32u;
@@ -119,9 +112,9 @@ void embedding_q4gsw_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   }
 
   // Per-type byte guards (no runtime dtype): indices i32, weight u8, fp32 rest.
-  const uint64_t indices_numel = numel_of(indices.dims);
-  const uint64_t weight_numel = numel_of(weight.dims);
-  const uint64_t scales_numel = numel_of(scales.dims);
+  const uint64_t indices_numel = utils::numel_of(indices.dims);
+  const uint64_t weight_numel = utils::numel_of(weight.dims);
+  const uint64_t scales_numel = utils::numel_of(scales.dims);
   if (indices_numel != num_indices ||
       indices.nbytes != indices_numel * sizeof(int32_t) ||
       weight.nbytes != weight_numel ||
@@ -230,13 +223,56 @@ void embedding_q4gsw_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   bg_desc.entries = bg_entries;
   WGPUBindGroup bind_group = wgpuDeviceCreateBindGroup(device, &bg_desc);
 
-  graph.add_dispatch(
+  const size_t dispatch_idx = graph.add_dispatch(
       {pipeline, bind_group, workgroup_count, "embedding_q4gsw"});
 
+  // Dynamic shapes: recompute counts/dispatch; out = indices + [embed_dim].
+  const uint32_t gs_u = static_cast<uint32_t>(group_size);
+  WGPUBuffer params_buf = uniform_buffer;
+  graph.add_tensor_resize_hook(
+      indices_id,
+      [indices_id,
+       out_id,
+       embed_dim,
+       blocks_per_row,
+       gs_u,
+       groups_per_row,
+       bytes_per_row,
+       wg_size,
+       dispatch_idx,
+       params_buf](WebGPUGraph& g) {
+        const auto& id = g.cur_dims(indices_id);
+        const uint64_t ni = utils::numel_of(id);
+        const uint64_t total_blocks = ni * blocks_per_row;
+        if (total_blocks > UINT32_MAX) {
+          throw std::runtime_error(
+              "WebGPU embedding_q4gsw: total_blocks exceeds uint32");
+        }
+        std::vector<int64_t> od = id;
+        od.push_back(static_cast<int64_t>(embed_dim));
+        g.set_cur_dims(out_id, od);
+        EmbeddingParams p = {};
+        p.embed_dim = embed_dim;
+        p.blocks_per_row = blocks_per_row;
+        p.num_indices = static_cast<uint32_t>(ni);
+        p.group_size = gs_u;
+        p.groups_per_row = groups_per_row;
+        p.bytes_per_row = bytes_per_row;
+        p.total_blocks = static_cast<uint32_t>(total_blocks);
+        wgpuQueueWriteBuffer(g.queue(), params_buf, 0, &p, sizeof(p));
+        g.dispatch_at(dispatch_idx).workgroup_count_x =
+            utils::compute_1d_workgroup_count(
+                g.device(),
+                static_cast<uint32_t>(total_blocks),
+                wg_size,
+                "embedding_q4gsw(resize)");
+      });
+
   wgpuShaderModuleRelease(shader);
   wgpuBindGroupLayoutRelease(bgl);
   wgpuPipelineLayoutRelease(pipeline_layout);
-  wgpuBufferRelease(uniform_buffer);
+  // Graph owns it so the resize hook can rewrite it; freed in the dtor.
+  graph.own_uniform_buffer(uniform_buffer);
 }
 
 } // namespace

backends/webgpu/runtime/ops/rms_norm/RmsNorm.cpp

@@ -7,6 +7,7 @@
  */
 
 #include <executorch/backends/webgpu/runtime/WebGPUGraph.h>
+#include <executorch/backends/webgpu/runtime/WebGPUUtils.h>
 #include <executorch/backends/webgpu/runtime/ops/OperatorRegistry.h>
 #include <executorch/backends/webgpu/runtime/ops/rms_norm/rms_norm_vec4_wgsl.h>
 #include <executorch/backends/webgpu/runtime/ops/rms_norm/rms_norm_wgsl.h>
@@ -187,14 +188,38 @@ void rms_norm_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   static_assert(
       kRmsNormVec4WorkgroupSizeX == 64,
       "must match @workgroup_size and WG_SIZE in rms_norm_vec4.wgsl");
-  graph.add_dispatch({pipeline, bind_group, num_rows});
+  const size_t dispatch_idx =
+      graph.add_dispatch({pipeline, bind_group, num_rows});
+
+  // Dynamic shapes: recompute num_rows + rewrite the UBO for the live input.
+  WGPUBuffer params_buf = uniform_buffer;
+  graph.add_tensor_resize_hook(
+      in_id,
+      [in_id, out_id, row_width, epsilon, dispatch_idx, params_buf](
+          WebGPUGraph& g) {
+        const auto& d = g.cur_dims(in_id);
+        const uint64_t numel = utils::numel_of(d);
+        const uint32_t rows =
+            static_cast<uint32_t>(numel / static_cast<uint64_t>(row_width));
+        if (rows == 0 || rows > 65535u) {
+          throw std::runtime_error(
+              "WebGPU rms_norm: num_rows exceeds the 1D dispatch limit (65535)");
+        }
+        RmsNormParams p = {};
+        p.num_rows = rows;
+        p.row_width = row_width;
+        p.epsilon = epsilon;
+        wgpuQueueWriteBuffer(g.queue(), params_buf, 0, &p, sizeof(p));
+        g.dispatch_at(dispatch_idx).workgroup_count_x = rows;
+        g.set_cur_dims(out_id, d);
+      });
 
   // Release intermediate objects (pipeline and bind_group are kept by dispatch)
   wgpuShaderModuleRelease(shader);
   wgpuBindGroupLayoutRelease(bgl);
   wgpuPipelineLayoutRelease(pipeline_layout);
-  // Drop our ref; the bind group keeps the uniform buffer alive until release.
-  wgpuBufferRelease(uniform_buffer);
+  // Graph owns it so the resize hook can rewrite it; freed in the dtor.
+  graph.own_uniform_buffer(uniform_buffer);
 }
 
 } // namespace

backends/webgpu/runtime/ops/rope/RotaryEmbedding.cpp

@@ -34,16 +34,9 @@ struct RotaryParams {
 };
 static_assert(sizeof(RotaryParams) == 32, "RotaryParams must be 32 bytes");
 
-uint64_t numel_of(const std::vector<int64_t>& dims) {
-  uint64_t n = 1;
-  for (int64_t d : dims) {
-    n *= static_cast<uint64_t>(d);
-  }
-  return n;
-}
-
 // Rotate one (x->out) with the shared shader; freqs shared between xq and xk.
-void add_rope_dispatch(
+// Returns the param-uniform handle so a resize hook can rewrite seq/num_pairs.
+WGPUBuffer add_rope_dispatch(
     WebGPUGraph& graph,
     WGPUDevice device,
     WGPUComputePipeline pipeline,
@@ -58,7 +51,8 @@ void add_rope_dispatch(
     uint32_t workgroup_count) {
   const uint32_t half_dim = head_dim / 2u;
   // out.dims == in.dims (asserted in impl), so this matches the caller's wgc.
-  const uint32_t num_pairs = static_cast<uint32_t>(numel_of(out.dims) / 2u);
+  const uint32_t num_pairs =
+      static_cast<uint32_t>(utils::numel_of(out.dims) / 2u);
 
   RotaryParams params = {};
   params.n_heads = n_heads;
@@ -104,7 +98,9 @@ void add_rope_dispatch(
   graph.add_dispatch(
       {pipeline, bind_group, workgroup_count, "apply_rotary_emb"});
 
-  wgpuBufferRelease(uniform_buffer);
+  // Graph owns it so a resize hook can rewrite it; freed in the dtor.
+  graph.own_uniform_buffer(uniform_buffer);
+  return uniform_buffer;
 }
 
 // args: [xq, xk, freqs_cos, freqs_sin, out_list(ValueList[xq_out, xk_out])].
@@ -164,9 +160,9 @@ void apply_rotary_emb_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   }
 
   // All tensors are fp32; output shapes equal their inputs.
-  const uint64_t xq_numel = numel_of(xq.dims);
-  const uint64_t xk_numel = numel_of(xk.dims);
-  const uint64_t freqs_numel = numel_of(freqs_cos.dims);
+  const uint64_t xq_numel = utils::numel_of(xq.dims);
+  const uint64_t xk_numel = utils::numel_of(xk.dims);
+  const uint64_t freqs_numel = utils::numel_of(freqs_cos.dims);
   if (freqs_numel != static_cast<uint64_t>(seq) * half_dim ||
       xq.nbytes != xq_numel * sizeof(float) ||
       xk.nbytes != xk_numel * sizeof(float) ||
@@ -246,7 +242,7 @@ void apply_rotary_emb_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   WGPUComputePipeline pipeline_k =
       wgpuDeviceCreateComputePipeline(device, &pipeline_desc);
 
-  add_rope_dispatch(
+  WGPUBuffer q_ubuf = add_rope_dispatch(
       graph,
       device,
       pipeline_q,
@@ -259,7 +255,8 @@ void apply_rotary_emb_impl(WebGPUGraph& graph, const std::vector<int>& args) {
       seq,
       head_dim,
       xq_wgc);
-  add_rope_dispatch(
+  const size_t q_idx = graph.num_dispatches() - 1;
+  WGPUBuffer k_ubuf = add_rope_dispatch(
       graph,
       device,
       pipeline_k,
@@ -272,6 +269,57 @@ void apply_rotary_emb_impl(WebGPUGraph& graph, const std::vector<int>& args) {
       seq,
       head_dim,
       xk_wgc);
+  const size_t k_idx = graph.num_dispatches() - 1;
+
+  // Dynamic shapes: recompute S/num_pairs + both dispatches; out follows xq/xk.
+  const int xq_out_id = out_list[0];
+  const int xk_out_id = out_list[1];
+  graph.add_tensor_resize_hook(
+      xq_id,
+      [xq_id,
+       xk_id,
+       xq_out_id,
+       xk_out_id,
+       n_heads_q,
+       n_heads_k,
+       head_dim,
+       half_dim,
+       wg_size,
+       q_idx,
+       k_idx,
+       q_ubuf,
+       k_ubuf](WebGPUGraph& g) {
+        const auto& qd = g.cur_dims(xq_id);
+        const auto& kd = g.cur_dims(xk_id);
+        const uint32_t s = static_cast<uint32_t>(qd[qd.size() - 3]);
+        const uint64_t qn = utils::numel_of(qd);
+        const uint64_t kn = utils::numel_of(kd);
+        RotaryParams pq = {};
+        pq.n_heads = n_heads_q;
+        pq.seq = s;
+        pq.head_dim = head_dim;
+        pq.half_dim = half_dim;
+        pq.num_pairs = static_cast<uint32_t>(qn / 2u);
+        RotaryParams pk = pq;
+        pk.n_heads = n_heads_k;
+        pk.num_pairs = static_cast<uint32_t>(kn / 2u);
+        wgpuQueueWriteBuffer(g.queue(), q_ubuf, 0, &pq, sizeof(pq));
+        wgpuQueueWriteBuffer(g.queue(), k_ubuf, 0, &pk, sizeof(pk));
+        g.dispatch_at(q_idx).workgroup_count_x =
+            utils::compute_1d_workgroup_count(
+                g.device(),
+                static_cast<uint32_t>(qn / 2u),
+                wg_size,
+                "apply_rotary_emb(resize)");
+        g.dispatch_at(k_idx).workgroup_count_x =
+            utils::compute_1d_workgroup_count(
+                g.device(),
+                static_cast<uint32_t>(kn / 2u),
+                wg_size,
+                "apply_rotary_emb(resize)");
+        g.set_cur_dims(xq_out_id, qd);
+        g.set_cur_dims(xk_out_id, kd);
+      });
 
   wgpuShaderModuleRelease(shader);
   wgpuBindGroupLayoutRelease(bgl);