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File size: 3,313 Bytes
31c7d49 | 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 | import { OrtWorkerClient } from './runtime/OrtWorkerClient'
import { createTensorPayload } from './runtime/tensors'
const fixture = '/fixtures/generated/context-attention-mha-inv08'
const status = document.querySelector<HTMLPreElement>('#status')!
async function fetchF32(path: string, count: number): Promise<Float32Array> {
const response = await fetch(path)
if (!response.ok) throw new Error(`${response.status} ${response.statusText}: ${path}`)
const buffer = await response.arrayBuffer()
if (buffer.byteLength !== count * 4) {
throw new Error(`${path} has ${buffer.byteLength} bytes; expected ${count * 4}`)
}
return new Float32Array(buffer)
}
function metrics(reference: Float32Array, candidate: Float32Array) {
let maxAbsoluteError = 0
let squaredError = 0
let meanAbsoluteError = 0
for (let index = 0; index < reference.length; index += 1) {
const error = Math.abs(reference[index] - candidate[index])
maxAbsoluteError = Math.max(maxAbsoluteError, error)
meanAbsoluteError += error
squaredError += error * error
}
return {
maxAbsoluteError,
meanAbsoluteError: meanAbsoluteError / reference.length,
rmse: Math.sqrt(squaredError / reference.length),
}
}
async function run() {
const client = new OrtWorkerClient({ onStatus: (event) => { status.textContent = event.message } })
try {
const [q, k, v, official, canonicalOrt] = await Promise.all([
fetchF32(`${fixture}/q.f32`, 4 * 1024),
fetchF32(`${fixture}/k.f32`, 16 * 4101 * 64),
fetchF32(`${fixture}/v.f32`, 16 * 4101 * 64),
fetchF32(`${fixture}/official.f32`, 4 * 1024),
fetchF32(`${fixture}/canonical_ort.f32`, 4 * 1024),
])
const loaded = await client.loadSession({
sessionId: 'context-attention-mha',
manifest: { graphUrl: '/models/triposplat/context_attention_mha_probe.onnx' },
options: { allowWasmFallback: false, graphOptimizationLevel: 'disabled' },
})
const result = await client.runSession({
sessionId: 'context-attention-mha',
inputs: {
Q: createTensorPayload('float32', q, [1, 4, 1024]),
K: createTensorPayload('float32', k, [1, 16, 4101, 64]),
V: createTensorPayload('float32', v, [1, 16, 4101, 64]),
},
outputs: ['Y'],
tag: 'context-attention-mha-inv08',
})
const output = result.outputs.Y
if (!output || output.type !== 'float32' || !(output.data instanceof Float32Array)) {
throw new Error('MultiHeadAttention returned no float32 Y output')
}
const report = {
executionProvider: loaded.executionProvider,
loadMs: loaded.loadMs,
timings: result.timings,
vsOfficial: metrics(official, output.data),
vsCanonicalOrt: metrics(canonicalOrt, output.data),
}
;(window as typeof window & { __CONTEXT_ATTENTION_MHA_RESULT__?: unknown })
.__CONTEXT_ATTENTION_MHA_RESULT__ = report
status.textContent = JSON.stringify(report, null, 2)
} finally {
await client.dispose().catch(() => undefined)
}
}
run().catch((error) => {
const message = error instanceof Error ? `${error.message}\n${error.stack ?? ''}` : String(error)
;(window as typeof window & { __CONTEXT_ATTENTION_MHA_ERROR__?: string })
.__CONTEXT_ATTENTION_MHA_ERROR__ = message
status.textContent = message
})
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