[ExecuTorch][WebGPU] Register-tile the q4gsw quantized-linear kernel (#20500)

Author: pytorchbot

backends/webgpu/runtime/WebGPUUtils.h

@@ -18,6 +18,12 @@
 
 namespace executorch::backends::webgpu::utils {
 
+// Ceiling division for non-negative integers (mirrors Vulkan's utils::div_up).
+template <typename T>
+inline T div_up(T a, T b) {
+  return (a + b - 1) / b;
+}
+
 // Clamp workgroup size to device limit (SwiftShader caps at 128).
 inline uint32_t clamp_workgroup_size(WGPUDevice device, uint32_t desired) {
   WGPULimits limits = {};
@@ -34,7 +40,7 @@ inline uint32_t compute_1d_workgroup_count(
     uint32_t num_threads,
     uint32_t workgroup_size,
     const char* op_name) {
-  uint32_t count = (num_threads + workgroup_size - 1) / workgroup_size;
+  uint32_t count = div_up(num_threads, workgroup_size);
   WGPULimits limits = {};
   uint32_t max_count =
       wgpuDeviceGetLimits(device, &limits) == WGPUStatus_Success &&
@@ -70,4 +76,16 @@ make_uniform(WGPUDevice device, const void* data, size_t size) {
   return buf;
 }
 
+// Clamp a 1D workgroup count to the device limit, for grid-stride kernels that
+// loop over any excess work (vs compute_1d_workgroup_count, which throws).
+inline uint32_t clamp_workgroup_count(WGPUDevice device, uint32_t desired) {
+  WGPULimits limits = {};
+  uint32_t max_count =
+      wgpuDeviceGetLimits(device, &limits) == WGPUStatus_Success &&
+          limits.maxComputeWorkgroupsPerDimension > 0
+      ? limits.maxComputeWorkgroupsPerDimension
+      : 65535u; // WebGPU spec-default floor
+  return std::min(desired, max_count);
+}
+
 } // namespace executorch::backends::webgpu::utils

backends/webgpu/runtime/ops/quantized_linear/QuantizedLinear.cpp

@@ -9,6 +9,7 @@
 #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/quantized_linear/q4gsw_linear_coop4_wgsl.h>
 #include <executorch/backends/webgpu/runtime/ops/quantized_linear/q4gsw_linear_wgsl.h>
 
 #include <webgpu/webgpu.h>
@@ -34,6 +35,10 @@ struct Q4gswParams {
 };
 static_assert(sizeof(Q4gswParams) == 32, "Q4gswParams must be 32 bytes");
 
+// Register-tile dims; MUST match TM/TN in q4gsw_linear.wgsl.
+constexpr int64_t kQ4gswTileM = 4;
+constexpr int64_t kQ4gswTileN = 4;
+
 // et_vk.linear_q4gsw args: [in, weight, scales, group_size, bias, out].
 void q4gsw_linear_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   const int in_id = args.at(0);
@@ -85,10 +90,6 @@ void q4gsw_linear_impl(WebGPUGraph& graph, const std::vector<int>& args) {
         "WebGPU linear_q4gsw: N*K_packed must be a multiple of 4 (u32-packed)");
   }
 
-  // One workgroup per output row (M); validate dispatch before any alloc.
-  const uint32_t workgroup_count =
-      utils::compute_1d_workgroup_count(device, M, 1, "linear_q4gsw");
-
   // fp32-only byte-size guards (no runtime dtype); fp16 scales -> bail.
   const uint64_t scales_numel =
       static_cast<uint64_t>(num_groups) * static_cast<uint64_t>(padded_N);
@@ -116,6 +117,35 @@ void q4gsw_linear_impl(WebGPUGraph& graph, const std::vector<int>& args) {
         "WebGPU linear_q4gsw: scales dims too small for K/N");
   }
 
+  // M==1 decode -> coop4 GEMV (needs K%8==0 && gs%8==0); else tiled GEMM.
+  const uint32_t wg_size =
+      utils::clamp_workgroup_size(device, kQ4gswLinearWorkgroupSizeX);
+  const bool use_gemv = (M == 1u && K % 8u == 0u && gs % 8u == 0u);
+  const char* shader_src = use_gemv ? kQ4gswLinearCoop4WGSL : kQ4gswLinearWGSL;
+  uint32_t workgroup_count;
+  if (use_gemv) {
+    // coop4: fixed 64 lanes, 1 workgroup per output, grid-strided over M*N.
+    const uint64_t outputs =
+        static_cast<uint64_t>(M) * static_cast<uint64_t>(N);
+    if (outputs == 0u || outputs > UINT32_MAX) {
+      throw std::runtime_error("WebGPU linear_q4gsw: M*N out of range");
+    }
+    workgroup_count =
+        utils::clamp_workgroup_count(device, static_cast<uint32_t>(outputs));
+    if (workgroup_count == 0u) {
+      throw std::runtime_error("WebGPU linear_q4gsw: zero GEMV dispatch");
+    }
+  } else {
+    const int64_t total_tiles = utils::div_up<int64_t>(M, kQ4gswTileM) *
+        utils::div_up<int64_t>(N, kQ4gswTileN);
+    if (total_tiles > static_cast<int64_t>(UINT32_MAX)) {
+      throw std::runtime_error(
+          "WebGPU linear_q4gsw: tile count exceeds the 1D dispatch limit");
+    }
+    workgroup_count = utils::compute_1d_workgroup_count(
+        device, static_cast<uint32_t>(total_tiles), wg_size, "linear_q4gsw");
+  }
+
   // Optional bias: real buffer if present, else a dummy for the fixed layout.
   uint32_t has_bias = 0;
   WGPUBuffer bias_buffer = nullptr;
@@ -156,7 +186,7 @@ void q4gsw_linear_impl(WebGPUGraph& graph, const std::vector<int>& args) {
 
   WGPUShaderSourceWGSL wgsl_desc = {};
   wgsl_desc.chain.sType = WGPUSType_ShaderSourceWGSL;
-  wgsl_desc.code = {kQ4gswLinearWGSL, WGPU_STRLEN};
+  wgsl_desc.code = {shader_src, WGPU_STRLEN};
   WGPUShaderModuleDescriptor shader_desc = {};
   shader_desc.nextInChain = &wgsl_desc.chain;
   WGPUShaderModule shader = wgpuDeviceCreateShaderModule(device, &shader_desc);
@@ -186,8 +216,6 @@ void q4gsw_linear_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   WGPUPipelineLayout pipeline_layout =
       wgpuDeviceCreatePipelineLayout(device, &pl_desc);
 
-  const uint32_t wg_size =
-      utils::clamp_workgroup_size(device, kQ4gswLinearWorkgroupSizeX);
   WGPUConstantEntry wg_size_constant = {};
   wg_size_constant.key = {"wg_size", WGPU_STRLEN};
   wg_size_constant.value = static_cast<double>(wg_size);
@@ -196,8 +224,9 @@ void q4gsw_linear_impl(WebGPUGraph& graph, const std::vector<int>& args) {
   pipeline_desc.layout = pipeline_layout;
   pipeline_desc.compute.module = shader;
   pipeline_desc.compute.entryPoint = {"main", WGPU_STRLEN};
-  pipeline_desc.compute.constantCount = 1;
-  pipeline_desc.compute.constants = &wg_size_constant;
+  // coop4 GEMV uses fixed @workgroup_size(64); only the GEMM has an override.
+  pipeline_desc.compute.constantCount = use_gemv ? 0u : 1u;
+  pipeline_desc.compute.constants = use_gemv ? nullptr : &wg_size_constant;
   WGPUComputePipeline pipeline =
       wgpuDeviceCreateComputePipeline(device, &pipeline_desc);
 

backends/webgpu/runtime/ops/quantized_linear/q4gsw_linear.wgsl

@@ -18,47 +18,74 @@ struct Params {
 
 override wg_size: u32 = 64u;
 
-// One workgroup per row m, threads stride N; loop logical K only (in-bounds).
+// Register-tiled GEMM: dequant weight once per (n,k), reused across TM rows.
+const TM: u32 = 4u;
+const TN: u32 = 4u;
+const TILE_ELEMS: u32 = TM * TN; // accumulator size; keeps acc in sync with TM/TN
+
 @compute @workgroup_size(wg_size, 1, 1)
-fn main(
-    @builtin(workgroup_id) wid: vec3<u32>,
-    @builtin(local_invocation_id) lid: vec3<u32>) {
-  let m = wid.x;
-  if (m >= params.M) {
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+  let nrt = (params.M + TM - 1u) / TM;
+  let nct = (params.N + TN - 1u) / TN;
+  let tiles = nrt * nct;
+  // M==0 or N==0 -> tiles==0 -> every thread returns here, so the M-1u/N-1u
+  // clamps below never underflow (the host also rejects M==0/N==0).
+  if (gid.x >= tiles) {
     return;
   }
-  let in_base = m * params.K;
+  let row_tile = gid.x / nct;
+  let col_tile = gid.x % nct;
+  let m0 = row_tile * TM;
+  let n0 = col_tile * TN;
+
+  var acc: array<f32, TILE_ELEMS>;
+  for (var i: u32 = 0u; i < TILE_ELEMS; i = i + 1u) {
+    acc[i] = 0.0;
+  }
 
-  var n: u32 = lid.x;
+  var k: u32 = 0u;
   loop {
-    if (n >= params.N) {
+    if (k >= params.K) {
       break;
     }
-    var acc: f32 = 0.0;
-    var k: u32 = 0u;
-    loop {
-      if (k >= params.K) {
-        break;
-      }
-      // Packed weight byte for (n, k): row stride K_packed bytes, byte k/2.
-      let byte_idx = n * params.K_packed + (k >> 1u);
+    // Load the TM input values for column k once; reused across all TN columns.
+    var in_reg: array<f32, TM>;
+    for (var ml: u32 = 0u; ml < TM; ml = ml + 1u) {
+      let m_eff = min(m0 + ml, params.M - 1u);
+      in_reg[ml] = t_input[m_eff * params.K + k];
+    }
+    for (var nl: u32 = 0u; nl < TN; nl = nl + 1u) {
+      // Clamp to last valid column; overhang result is never stored.
+      let n_eff = min(n0 + nl, params.N - 1u);
+      let byte_idx = n_eff * params.K_packed + (k >> 1u);
       let word = t_weight[byte_idx >> 2u];
       let b = (word >> ((byte_idx & 3u) * 8u)) & 0xFFu;
       var nib: u32;
       if ((k & 1u) == 0u) {
-        nib = b & 0x0Fu;       // even k -> low nibble
+        nib = b & 0x0Fu;         // even k -> low nibble
       } else {
         nib = (b >> 4u) & 0x0Fu; // odd k -> high nibble
       }
       let q = f32(i32(nib) - 8); // +8-shifted on pack; recover signed [-8,7]
-      let scale = t_scales[(k / params.group_size) * params.padded_N + n];
-      acc = acc + t_input[in_base + k] * q * scale;
-      k = k + 1u;
+      let dq = q * t_scales[(k / params.group_size) * params.padded_N + n_eff];
+      for (var ml: u32 = 0u; ml < TM; ml = ml + 1u) {
+        acc[ml * TN + nl] = acc[ml * TN + nl] + in_reg[ml] * dq;
+      }
     }
-    if (params.has_bias != 0u) {
-      acc = acc + t_bias[n];
+    k = k + 1u;
+  }
+
+  for (var ml: u32 = 0u; ml < TM; ml = ml + 1u) {
+    let m = m0 + ml;
+    for (var nl: u32 = 0u; nl < TN; nl = nl + 1u) {
+      let n = n0 + nl;
+      if (m < params.M && n < params.N) {
+        var v = acc[ml * TN + nl];
+        if (params.has_bias != 0u) {
+          v = v + t_bias[n];
+        }
+        t_out[m * params.N + n] = v;
+      }
     }
-    t_out[m * params.N + n] = acc;
-    n = n + wg_size;
   }
 }

backends/webgpu/runtime/ops/quantized_linear/q4gsw_linear_coop4.wgsl

@@ -0,0 +1,87 @@
+@group(0) @binding(0) var<storage, read_write> t_out: array<f32>;
+@group(0) @binding(1) var<storage, read> t_input: array<f32>;
+@group(0) @binding(2) var<storage, read> t_weight: array<u32>;
+@group(0) @binding(3) var<storage, read> t_scales: array<f32>;
+@group(0) @binding(4) var<storage, read> t_bias: array<f32>;
+
+struct Params {
+  M: u32,
+  N: u32,
+  K: u32,
+  K_packed: u32,
+  group_size: u32,
+  padded_N: u32,
+  has_bias: u32,
+  _pad: u32,
+}
+@group(0) @binding(5) var<uniform> params: Params;
+
+// Cooperative-over-K GEMV with u32-batched coalesced weight loads (64 lanes).
+const WG: u32 = 64u;
+var<workgroup> partial: array<f32, WG>;
+
+@compute @workgroup_size(WG, 1, 1)
+fn main(
+    @builtin(workgroup_id) wid: vec3<u32>,
+    @builtin(num_workgroups) ngrp: vec3<u32>,
+    @builtin(local_invocation_id) lid: vec3<u32>) {
+  let total = params.M * params.N;
+  let stride = ngrp.x;
+  let num_words = params.K >> 3u;          // K / 8 words per row
+  let row_words = params.K_packed >> 2u;   // u32s per weight row (= K/8)
+  var idx = wid.x;
+  loop {
+    if (idx >= total) {
+      break;
+    }
+    let m = idx / params.N;
+    let n = idx % params.N;
+    let in_base = m * params.K;
+    let wbase = n * row_words;
+
+    var acc: f32 = 0.0;
+    var w: u32 = lid.x;
+    loop {
+      if (w >= num_words) {
+        break;
+      }
+      let word = t_weight[wbase + w];
+      let k0 = w << 3u; // first K of this word
+      let scale = t_scales[(k0 / params.group_size) * params.padded_N + n];
+      let ib = in_base + k0;
+      // 4 bytes, low+high nibble each -> 8 consecutive K.
+      for (var bi: u32 = 0u; bi < 4u; bi = bi + 1u) {
+        let byte = (word >> (bi * 8u)) & 0xFFu;
+        let lo = f32(i32(byte & 0x0Fu) - 8);
+        let hi = f32(i32((byte >> 4u) & 0x0Fu) - 8);
+        let kk = bi << 1u;
+        acc = acc + t_input[ib + kk] * lo * scale;
+        acc = acc + t_input[ib + kk + 1u] * hi * scale;
+      }
+      w = w + WG;
+    }
+
+    partial[lid.x] = acc;
+    workgroupBarrier();
+    var s: u32 = WG >> 1u;
+    loop {
+      if (s == 0u) {
+        break;
+      }
+      if (lid.x < s) {
+        partial[lid.x] = partial[lid.x] + partial[lid.x + s];
+      }
+      workgroupBarrier();
+      s = s >> 1u;
+    }
+    if (lid.x == 0u) {
+      var o = partial[0];
+      if (params.has_bias != 0u) {
+        o = o + t_bias[n];
+      }
+      t_out[idx] = o;
+    }
+    workgroupBarrier();
+    idx = idx + stride;
+  }
+}