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Fix failing test in travis #1381

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Nov 7, 2018
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Fix failing test in travis #1381

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dsmilkov Nov 7, 2018
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dsmilkov Nov 7, 2018
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dsmilkov Nov 7, 2018
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16 changes: 8 additions & 8 deletions scripts/test-travis.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -25,15 +25,15 @@ if [[ $(node -v) = *v10* ]]; then

# Run the first karma separately so it can download the BrowserStack binary
# without conflicting with others.
yarn run-browserstack --browsers=bs_safari_mac --backend webgl
yarn run-browserstack --browsers=bs_safari_mac --backend webgl --features '{"WEBGL_CPU_FORWARD": false}'

# Run the rest of the karma tests in parallel. These runs will reuse the
# already downloaded binary.
npm-run-all -p -c --aggregate-output \
"run-browserstack --browsers=bs_safari_mac --features '{\"HAS_WEBGL\": false}' --backend cpu" \
"run-browserstack --browsers=bs_ios_11 --backend webgl" \
"run-browserstack --browsers=bs_ios_11 --features '{\"HAS_WEBGL\": false}' --backend cpu" \
"run-browserstack --browsers=bs_firefox_mac" \
"run-browserstack --browsers=bs_chrome_mac" \
"run-browserstack --browsers=bs_chrome_mac --features '{\"WEBGL_CPU_FORWARD\": true}' --backend webgl"
npm-run-all -p -c --aggregate-output \
"run-browserstack --browsers=bs_safari_mac --features '{\"HAS_WEBGL\": false}' --backend cpu" \
"run-browserstack --browsers=bs_ios_11 --backend webgl --features '{\"WEBGL_CPU_FORWARD\": false}'" \
"run-browserstack --browsers=bs_ios_11 --features '{\"HAS_WEBGL\": false}' --backend cpu" \
"run-browserstack --browsers=bs_firefox_mac" \
"run-browserstack --browsers=bs_chrome_mac" \
"run-browserstack --browsers=bs_chrome_mac --features '{\"WEBGL_CPU_FORWARD\": true}' --backend webgl"
fi
27 changes: 18 additions & 9 deletions src/ops/matmul_test.ts
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Expand Up @@ -293,31 +293,40 @@ describeWithFlags('matmul', ALL_ENVS, () => {
it('batched matmul with the matrices being vectors', () => {
const batch = 3;
const sharedDim = 10000;
const a = tf.fill([batch, 1, sharedDim], 0.1);
const b = tf.fill([batch, sharedDim, 1], 0.1);
const values = new Float32Array(batch * sharedDim);
values[10] = 2;

const a = tf.tensor(values, [batch, 1, sharedDim]);
const b = tf.tensor(values, [batch, sharedDim, 1]);
const result = tf.matMul(a, b);
expect(result.shape).toEqual([batch, 1, 1]);
expectArraysClose(result, [100, 100, 100]);
expectArraysClose(result, [4, 0, 0]);
});

it('batched matmul with matrix x vector', () => {
const batch = 3;
const sharedDim = 10000;
const a = tf.fill([batch, 2, sharedDim], 0.1);
const b = tf.fill([batch, sharedDim, 1], 0.1);
const values = new Float32Array(batch * sharedDim);
values[10] = 2;

const a = tf.ones([batch, 2, sharedDim]);
const b = tf.tensor(values, [batch, sharedDim, 1]);
const result = tf.matMul(a, b);
expect(result.shape).toEqual([batch, 2, 1]);
expectArraysClose(result, [100, 100, 100, 100, 100, 100]);
expectArraysClose(result, [2, 2, 0, 0, 0, 0]);
});

it('batched matmul with vector x matrix', () => {
const batch = 3;
const sharedDim = 10000;
const a = tf.fill([batch, 1, sharedDim], 0.1);
const b = tf.fill([batch, sharedDim, 2], 0.1);
const values = new Float32Array(batch * sharedDim);
values[10] = 2;

const a = tf.tensor(values, [batch, 1, sharedDim]);
const b = tf.ones([batch, sharedDim, 2]);
const result = tf.matMul(a, b);
expect(result.shape).toEqual([batch, 1, 2]);
expectArraysClose(result, [100, 100, 100, 100, 100, 100]);
expectArraysClose(result, [2, 2, 0, 0, 0, 0]);
});

it('Matrix * vector propagates NaNs', () => {
Expand Down