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[js/webgpu] Optimize matmulnbits (#22360)
### Description <!-- Describe your changes. --> This PR further optimizes matmulnbits specially for iGPUs. The phi3 demo becomes ~12 tokens/second from ~8 tokens on iGPUs. Some todos: 1. Make the optimization more general, Remove the blockSize = 32 limitation. 2. Tune the parameter, such as workgroupSize, components size (currently only support components = 1), to see the performance change.
This commit is contained in:
Jiajia Qin
GitHub
parent
2bc3754494
commit
8159723ba7
3 changed files with 179 additions and 3 deletions
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@ -868,6 +868,7 @@ class ShaderHelperImpl implements ShaderHelper {
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const paramList = is1DimensionDispatch
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? `@builtin(global_invocation_id) global_id : vec3<u32>,
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@builtin(workgroup_id) workgroup_id : vec3<u32>,
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@builtin(local_invocation_index) local_idx : u32,
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@builtin(local_invocation_id) local_id : vec3<u32>`
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: `@builtin(global_invocation_id) global_id : vec3<u32>,
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@builtin(local_invocation_id) local_id : vec3<u32>,
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@ -876,7 +877,6 @@ class ShaderHelperImpl implements ShaderHelper {
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@builtin(num_workgroups) num_workgroups : vec3<u32>`;
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const globalIdxDefinition = is1DimensionDispatch
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? `let global_idx = global_id.x;
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let local_idx = local_id.x;
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let workgroup_index = workgroup_id.x;`
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: `let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
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workgroup_id.y * num_workgroups[0] + workgroup_id.x;
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@ -266,9 +266,185 @@ export const createMatMulNBitsProgramInfo = (
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};
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};
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// Currently, only support blockSize = 32.
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export const createMatMulNBitsBlockSize32ProgramInfo = (
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inputs: readonly TensorView[],
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attributes: MatMulNBitsAttributes,
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): ProgramInfo => {
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const inputShape = inputs[0].dims;
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const aRank = inputShape.length;
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const dimAOuter = inputShape[aRank - 2];
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const dimInner = attributes.k;
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const dimBOuter = attributes.n;
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const batchDims = inputShape.slice(0, aRank - 2);
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const batchSize = ShapeUtil.size(batchDims);
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const blobSize = inputs[1].dims[2];
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const blobSizeInWords = blobSize / 4;
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const dataType = inputs[0].dataType;
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const aComponents = getMaxComponents(attributes.k);
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const bComponents = getMaxComponents(blobSizeInWords);
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const outputShape = batchDims.concat([dimAOuter, dimBOuter]);
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const workgroupSize = 128;
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const workgroupY = dimBOuter % 8 === 0 ? 8 : dimBOuter % 4 === 0 ? 4 : 1;
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const workgroupX = workgroupSize / workgroupY;
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const tileSize = workgroupX * bComponents * 8; // each uint32 has 8 data.
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const aLengthPerTile = tileSize / aComponents;
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const blocksPerTile = tileSize / attributes.blockSize;
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const dispatchSize = ShapeUtil.size(outputShape) / workgroupY;
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const programUniforms: ProgramUniform[] = [];
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const inputShapeTemp = [batchSize, dimAOuter, dimInner / aComponents];
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const bShape = ShapeUtil.convertShape(inputs[1].dims).slice();
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bShape.splice(-1, 1, blobSizeInWords / bComponents);
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programUniforms.push(...createTensorShapeVariables(inputShapeTemp));
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programUniforms.push(...createTensorShapeVariables(bShape));
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programUniforms.push(...createTensorShapeVariables(inputs[2].dims));
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if (inputs.length === 4) {
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programUniforms.push(...createTensorShapeVariables(ShapeUtil.convertShape(inputs[3].dims)));
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}
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const outputShapeTemp = [batchSize, dimAOuter, dimBOuter];
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programUniforms.push(...createTensorShapeVariables(outputShapeTemp));
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const getShaderSource = (shaderHelper: ShaderHelper) => {
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const inputRank = inputShapeTemp.length;
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const a = inputVariable('a', inputs[0].dataType, inputRank, aComponents);
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const b = inputVariable('b', DataType.uint32, bShape.length, bComponents);
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const scales = inputVariable('scales', inputs[2].dataType, inputs[2].dims.length);
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const inputVariables = [a, b, scales];
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const zeroPoints =
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inputs.length === 4 ? inputVariable('zero_points', DataType.uint32, inputs[3].dims.length) : undefined;
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if (zeroPoints) {
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inputVariables.push(zeroPoints);
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}
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const outputRank = outputShapeTemp.length;
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const output = outputVariable('output', inputs[0].dataType, outputRank);
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const dataType = tensorTypeToWsglStorageType(inputs[0].dataType);
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const readA = () => {
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switch (aComponents) {
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case 1:
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return `
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let a_data0 = vec4<${dataType}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
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let a_data1 = vec4<${dataType}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;
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case 2:
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return `
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let a_data0 = vec4<${dataType}>(sub_a[word_offset], sub_a[word_offset + 1]);
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let a_data1 = vec4<${dataType}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;
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case 4:
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return `
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let a_data0 = sub_a[word_offset];
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let a_data1 = sub_a[word_offset + 1];`;
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default:
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throw new Error(`${aComponents}-component is not supported.`);
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}
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};
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return `
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var<workgroup> sub_a: array<${a.type.value}, ${aLengthPerTile}>;
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var<workgroup> inter_results: array<array<${output.type.value}, ${workgroupX}>, ${workgroupY}>;
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${shaderHelper.declareVariables(...inputVariables, output)}
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${shaderHelper.mainStart([workgroupX, workgroupY, 1])}
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let output_indices = ${output.offsetToIndices(`workgroup_index * ${workgroupY}`)};
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let col = output_indices[2];
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let row = output_indices[1];
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let batch = output_indices[0];
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let n_blocks_per_col = uniforms.b_shape[1];
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let num_tiles = (n_blocks_per_col - 1) / ${blocksPerTile} + 1;
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// Loop over shared dimension.
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for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
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let a_col_start = tile * ${aLengthPerTile};
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// load one tile A data into shared memory.
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for (var a_offset = local_idx; a_offset < ${aLengthPerTile}; a_offset += ${workgroupSize})
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{
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let a_col = a_col_start + a_offset;
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if (a_col < uniforms.a_shape[2])
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{
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sub_a[a_offset] = ${a.getByIndices(`${a.type.indices}(batch, row, a_col)`)};
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} else {
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sub_a[a_offset] = ${a.type.value}(0);
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}
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}
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workgroupBarrier();
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// each thread process one block
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let b_row = col + local_id.y;
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let block = tile * ${blocksPerTile} + local_id.x;
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${
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zeroPoints
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? `
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let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2;
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let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u);
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let zero_point_word_index = zero_point_byte_count >> 0x2u;
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let zero_point_byte_offset = zero_point_byte_count & 0x3u;
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let zero_point_nibble_offset: u32 = block & 0x1u;
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let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
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let zero_point_word = ${zeroPoints.getByOffset('zero_point_word_index')} >> zero_point_bits_offset;
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let zero_point = ${dataType}((zero_point_word) & 0xFu);`
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: `
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// The default zero point is 8 for unsigned 4-bit quantization.
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let zero_point = ${dataType}(${8.0});`
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}
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let scale = ${scales.getByOffset(`b_row * n_blocks_per_col + block`)};
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let b_data = ${b.getByIndices(`${b.type.indices}(b_row, block, 0)`)};
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var word_offset = local_id.x * ${attributes.blockSize / aComponents};
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for (var i: u32 = 0; i < ${bComponents}; i++) {
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${readA()}
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let b_value = ${bComponents === 1 ? `b_data` : `b_data[i]`};
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let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
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let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
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let b_quantized_values = mat2x4<${dataType}>(${Array.from(
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{ length: 4 },
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(_, i) => `${dataType}(b_value_lower[${i}]), ${dataType}(b_value_upper[${i}])`,
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).join(', ')});
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let b_dequantized_values = (b_quantized_values - mat2x4<${dataType}>(${Array(8).fill('zero_point').join(',')})) * scale;
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inter_results[local_id.y][local_id.x] += ${Array.from(
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{ length: 2 },
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(_, i) => `${`dot(a_data${i}, b_dequantized_values[${i}])`}`,
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).join(' + ')};
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word_offset += ${8 / aComponents};
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}
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workgroupBarrier();
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}
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if (local_idx < ${workgroupY}) {
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var output_value: ${output.type.value} = ${output.type.value}(0);
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for (var b = 0u; b < ${workgroupX}; b++) {
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output_value += inter_results[local_idx][b];
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}
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if (col + local_idx < uniforms.output_shape[2])
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{
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${output.setByIndices(`${output.type.indices}(batch, row, col + local_idx)`, 'output_value')}
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}
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}
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}`;
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};
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return {
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name: 'BlockwiseMatMulNBits32',
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shaderCache: {
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hint: `${attributes.blockSize};${aComponents};${bComponents};${workgroupX};${workgroupY}`,
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inputDependencies: Array(inputs.length).fill('rank'),
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},
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getRunData: () => ({
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outputs: [{ dims: outputShape, dataType }],
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dispatchGroup: { x: dispatchSize },
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programUniforms,
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}),
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getShaderSource,
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};
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};
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export const matMulNBits = (context: ComputeContext, attributes: MatMulNBitsAttributes): void => {
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validateInputs(context.inputs, attributes);
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context.compute(createMatMulNBitsProgramInfo(context.inputs, attributes));
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if (
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attributes.blockSize === 32 &&
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context.adapterInfo.isVendor('intel') &&
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context.adapterInfo.isArchitecture('gen-12lp')
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) {
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context.compute(createMatMulNBitsBlockSize32ProgramInfo(context.inputs, attributes));
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} else {
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context.compute(createMatMulNBitsProgramInfo(context.inputs, attributes));
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}
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};
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export const parseMatMulNBitsAttributes = (attributes: Record<string, unknown>): MatMulNBitsAttributes =>
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@ -15,7 +15,7 @@ export enum GpuDataType {
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}
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export type GpuDataId = number;
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export type GpuArchitecture = 'ampere';
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export type GpuArchitecture = 'ampere' | 'gen-12lp';
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export type GpuVendor = 'amd' | 'intel' | 'nvidia';
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export interface AdapterInfo {
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isArchitecture: (architecture: GpuArchitecture) => boolean;
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