File size: 6,153 Bytes
49b0523 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | {% if usesF16 %}
enable f16;
{% endif %}
{% macro offset_fn(fn_name, opShape, opRank, op_same, op_numel, outShape, outRank, out_numel) %}
fn {{ fn_name }}({% if out_numel != 0 and op_numel != 1 %}out_index: u32{% endif %}) -> u32 {
{% if out_numel == 0 %}
return 0u;
{% elif op_numel == 1 %}
return 0u;
{% elif op_same %}
return out_index;
{% else %}
var offset = 0u;
{% for axis in range(outRank) %}
{% set op_axis = axis - (outRank - opRank) %}
{% if op_axis >= 0 and opShape[op_axis] != 1 %}
{% set c_stride = namespace(value=1) %}
{% for j in range(axis + 1, outRank) %}
{% set c_stride.value = c_stride.value * outShape[j] %}
{% endfor %}
{% set op_stride = namespace(value=1) %}
{% for j in range(op_axis + 1, opRank) %}
{% set op_stride.value = op_stride.value * opShape[j] %}
{% endfor %}
{% if c_stride.value == 1 %}
let coord{{ axis }} = out_index % {{ outShape[axis] }}u;
{% else %}
let coord{{ axis }} = (out_index / {{ c_stride.value }}u) % {{ outShape[axis] }}u;
{% endif %}
{% if op_stride.value == 1 %}
offset = offset + coord{{ axis }};
{% else %}
offset = offset + coord{{ axis }} * {{ op_stride.value }}u;
{% endif %}
{% endif %}
{% endfor %}
return offset;
{% endif %}
}
{%- endmacro %}{% macro broadcast_offset_call(fn_name, opShape, outShape, out_index) %}
{% set op_numel = namespace(value=1) %}
{% for d in opShape %}{% set op_numel.value = op_numel.value * d %}{% endfor %}
{% set out_numel = namespace(value=1) %}
{% for d in outShape %}{% set out_numel.value = out_numel.value * d %}{% endfor %}
{{ fn_name }}({% if out_numel.value != 0 and op_numel.value != 1 %}{{ out_index }}{% endif %})
{%- endmacro %}
{{ env.wgsl.resourceDeclarations }}
const HIDDEN: u32 = {{ hiddenSize }}u;
const EPSILON: f32 = {{ epsilon }};
const WG: u32 = {{ workgroupSize }}u;
var<workgroup> partial: array<f32, WG>;
var<workgroup> row_mean: f32;
var<workgroup> row_inv: f32;
{% set xNumel = namespace(value=1) %}
{% for dim in source.xShape %}
{% set xNumel.value = xNumel.value * dim %}
{% endfor %}
{% set scaleNumel = namespace(value=1) %}
{% for dim in source.scaleShape %}
{% set scaleNumel.value = scaleNumel.value * dim %}
{% endfor %}
{% if scaleNumel.value != 1 %}
{{ offset_fn("scale_offset", source.scaleShape, source.scaleShape | length, source.scaleShape == source.xShape, scaleNumel.value, source.xShape, source.xShape | length, xNumel.value) }}
{% endif %}
{% if hasBias %}
{% set biasNumel = namespace(value=1) %}
{% for dim in source.biasShape %}
{% set biasNumel.value = biasNumel.value * dim %}
{% endfor %}
{% if biasNumel.value != 1 %}
{{ offset_fn("bias_offset", source.biasShape, source.biasShape | length, source.biasShape == source.xShape, biasNumel.value, source.xShape, source.xShape | length, xNumel.value) }}
{% endif %}
{% endif %}
{% macro wgsl_tree_fold_stmt(a, op, idx, svar) %}
{% if op == "max" %}
{{ a }}[{{ idx }}] = max({{ a }}[{{ idx }}], {{ a }}[{{ idx }} + {{ svar }}]);
{%- else %}
{{ a }}[{{ idx }}] = {{ a }}[{{ idx }}] + {{ a }}[{{ idx }} + {{ svar }}];
{%- endif %}
{% endmacro %}
{% macro wgsl_tree_fold(arrays, op="add", idx="lid", wg="WORKGROUP_SIZE", svar="stride", typed=false, form="tail", breakInline=false, bodyInline=false, barrierFirst=false) %}
var {{ svar }}{{ ": u32 " if typed else " " }}= {{ wg }} / 2u;
loop {
{% if form == "head" %}
{% if breakInline %}
if ({{ svar }} == 0u) { break; }
{% else %}
if ({{ svar }} == 0u) {
break;
}
{% endif %}
{% endif %}
{% if bodyInline %}
if ({{ idx }} < {{ svar }}) { {{ wgsl_tree_fold_stmt(arrays[0], op, idx, svar) }} }
{% else %}
if ({{ idx }} < {{ svar }}) {
{% for a in arrays %}
{{ wgsl_tree_fold_stmt(a, op, idx, svar) }}
{% endfor %}
}
{% endif %}
{% if form == "head" %}
{% if barrierFirst %}
workgroupBarrier();
{{ svar }} = {{ svar }} / 2u;
{% else %}
{{ svar }} = {{ svar }} / 2u;
workgroupBarrier();
{% endif %}
{% else %}
workgroupBarrier();
if ({{ svar }} == 1u) {
break;
}
{{ svar }} = {{ svar }} / 2u;
{% endif %}
}
{%- endmacro %}
// Reusing partial after this reduction requires a barrier between the read of
// partial[0] and the next write, or the next round can race the prior readers.
{% set trailingBarrier = trailingBarrier is defined and trailingBarrier %}
fn reduce_sum(value: f32, tid: u32) -> f32 {
partial[tid] = value;
workgroupBarrier();
{{ wgsl_tree_fold(["partial"], idx="tid", wg="WG", form="head") }}
{% if trailingBarrier %}
let total = partial[0];
workgroupBarrier();
return total;
{% else %}
return partial[0];
{% endif %}
}
@compute @workgroup_size(WG, 1, 1)
fn main(@builtin(workgroup_id) wg: vec3<u32>, @builtin(local_invocation_id) lid: vec3<u32>) {
let row = wg.x + wg.y * params.rowStride;
if (row >= params.rows) {
return;
}
let tid = lid.x;
let base = row * HIDDEN;
var local_sum = 0.0;
for (var d = tid; d < HIDDEN; d = d + WG) {
let value = f32(x[base + d]);
local_sum = local_sum + value;
}
let sum = reduce_sum(local_sum, tid);
if (tid == 0u) {
row_mean = sum / f32(HIDDEN);
}
workgroupBarrier();
var local_var_sum = 0.0;
for (var d = tid; d < HIDDEN; d = d + WG) {
let diff = f32(x[base + d]) - row_mean;
local_var_sum = local_var_sum + diff * diff;
}
let var_sum = reduce_sum(local_var_sum, tid);
if (tid == 0u) {
let variance = var_sum / f32(HIDDEN);
row_inv = inverseSqrt(variance + EPSILON);
{% if writeMean %}
mean_out[row] = row_mean;
{% endif %}
{% if writeInvStdDev %}
inv_std_out[row] = row_inv;
{% endif %}
}
workgroupBarrier();
for (var d = tid; d < HIDDEN; d = d + WG) {
let index = base + d;
let normalized = (f32(x[index]) - row_mean) * row_inv;
var value = normalized * f32(scale[{% if scaleNumel.value == 1 %}0u{% else %}{{ broadcast_offset_call("scale_offset", source.scaleShape, source.xShape, "index") }}{% endif %}]);
{% if hasBias %}
value = value + f32(bias[{% if biasNumel.value == 1 %}0u{% else %}{{ broadcast_offset_call("bias_offset", source.biasShape, source.xShape, "index") }}{% endif %}]);
{% endif %}
y[index] = {{ scalar }}(value);
}
}
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