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diff --git a/chapters/appendix_a.adoc b/chapters/appendix_a.adoc index 33a4f11..f601d5d 100644 --- a/chapters/appendix_a.adoc +++ b/chapters/appendix_a.adoc @@ -30,37 +30,257 @@ float set_data(uint32_t set, uint32_t index) } ---- -=== Dot product floating-point test data sets +=== Main Inference test data generator -Each test set is indexed by a pair (S, N) where: +This section describes the function tosa_mi_data(S, KS, p, k, i) that generates test data for main inference compliance. +This function takes the following arguments: -* S is the test set number -* N is the number of elements in a single test vector +* S is the test set number which identifies which generator is used +* KS is the kernel size +* p is the parameter number of 0 for the first input (usually data) and 1 for the second input (usually weights) +* k is the index within the kernel in the range 0 \<= k < KS +* i is the index within the tensor to write -Each test set (S, N) contains multiple tests that statistics are calculated over. -The parameter T is the number of tests in a given set. -In the table below, t is the test number within a set in the range 0 to T-1. +Some test data values are scaled by the bound parameter B which is defined in the table below. +B is set to be the largest value that is both representable by the input type and such that B*B does not overflow the accumulator precision. -[cols="1,1,1,5,5"] |=== -| Set S | N range | T | x[k] formula for k < N | w[k] formula for k < N - -| 0 -| 2-25,50,100,1000 -| 10 -| x[k]=set_data(S, 2*t*N+2*k) < 0 ? 0.0 : set_data(S, 2*t*N+2*k+1) -| w[k]=set_data(S, 2*t*N+2*k) < 0 ? set_data(S, 2*t*N+2*k+1) : 0.0 - -| 1 -| 2-25,50,100,1000 -| 1000 -| x[k]=2.0*set_data(S, 2*t*N + k) -| w[k]=2.0*set_data(S, (2*t+1)*N + k) - -| 2 -| 2-25,50,100,1000 -| 1000 -| x[0]=1.0, x[k]=set_data(S, 2*t*N + k)/sqrt(N) for k>0 -| w[0]=1.0, w[k]=set_data(S, (2*t+1)*N + k)/sqrt(N) for k>0 +| inputs type | accumulator type | B value +| fp16 | fp16 | (1<<8) - (1/8) = 255.875 +| fp16 | fp32 | (1<<16) - (1<<5) = 65504 +| bf16 | fp32 | (1<<64) - (1<<56) +| fp32 | fp32 | (1<<64) - (1<<40) +|=== + +==== Test set S=0 generator + +The aim of this generator is to check that sum of products with zero gives zero result. + +[cols="1,9"] +|=== +| p | tosa_mi_data(S, KS, p, k, i) = +| 0 | set_data(2*S, i) < 0 ? 0.0 : set_data(2*S+1, i) +| 1 | set_data(2*S, i) < 0 ? set_data(2*S+1, i) : 0.0 +|=== + +==== Test set S=1 + +The aim of this test set is to check values with large exponents. + +[cols="1,9"] +|=== +| p | tosa_mi_data(S, KS, p, k, i) = +| 0 | (B/sqrt(N))*(0.75 + 0.25*set_data(2*S+0, i)) +| 1 | (B/sqrt(N))*(0.75 + 0.25*set_data(2*S+1, i)) +|=== + +==== Test set S=2 + +The aim of this test set is to check rounding error when accumulating small values onto a large value. +In this case the small values are of similar magnitude. +If the implementation changes the order of the sum, then the test data must also be reordered so that the largest values occur first in the sum. + +[cols="1,9"] +|=== +| p | tosa_mi_data(S, KS, p, k, i) = +| 0 | (k==0) ? 1.0 : set_data(2*S+0, i)/sqrt(KS) +| 1 | (k==0) ? 1.0 : set_data(2*S+1, i)/sqrt(KS) +|=== + +==== Test set S=3 + +The aim of this test set is to check rounding error when accumulating small values onto a large value. +In this case the small values are of varying magnitude. +If the implementation changes the order of the sum, then the test data must also be reordered so that the largest values occur first in the sum. + +[cols="1,9"] +|=== +| p | tosa_mi_data(S, KS, p, k, i) = +| 0 | (k==0) ? 16.0 : exp(2*set_data(2*S+0, 2*i+0)) * set_data(2*S+0, 2*i+1) +| 1 | (k==0) ? 16.0 : exp(2*set_data(2*S+1, 2*i+0)) * set_data(2*S+1, 2*i+1) +|=== + +==== Test set S=4 + +The aim of this test set is to check a mixture of zero and non-zero products. + +[cols="1,9"] +|=== +| p | tosa_mi_data(S, KS, p, k, i) = +| 0 | (k==KS/2) ? +0.5 : (set_data(2*S, i) < 0 ? 0.0 : B*set_data(2*S+1, i)) +| 1 | (k==KS/2) ? -0.5 : (set_data(2*S, i) < 0 ? B*set_data(2*S+1, i) : 0.0) +|=== +==== Test set S=5 + +The aim of this test set is to check signed inputs of large range. + +[cols="1,9"] |=== +| p | tosa_mi_data(S, KS, p, k, i) = +| 0 | (B/sqrt(KS))*set_data(2*S+0, i) +| 1 | (B/sqrt(KS))*set_data(2*S+1, i) +|=== + +=== Main Inference operator test data + +For each operator, this section defines how to generate test data for test set S. +For the results to be statistically significant the operation must calculate at least MIN_DOT_PRODUCTS dot products. +For most operations this means that the output tensor must have at least MIN_DOT_PRODUCTS output values. +For most operations batch size can be increased if necessary so that this holds. +For this version of the specification, MIN_DOT_PRODUCTS is set to 1000. + +==== CONV2D + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`N*OH*OW*OC >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = KW*KH*IC; +for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) { + input [ n, iy, ix, ic] = tosa_mi_data(S, KS, 0, ((iy % KH)*KW+(ix % KW))*IC+ic, ((n*IH+iy)*IW+ix)*IC+ic); +} +for (0 <= oc < OC, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) { + weight[oc, ky, kx, ic] = tosa_mi_data(S, KS, 1, (ky*KW+kx)*IC+ic, ((oc*KH+ky)*KW+kx)*IC+ic); +} +---- + +==== CONV3D + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`N*OD*OH*OW*OC >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = KD*KW*KH*IC; +for (0 <= n < N, 0 <= id < UD, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) { + input [ n, id, iy, ix, ic] = tosa_mi_data(S, KS, 0, (((id % KD)*KH+(iy % KH))*KW+(ix % KW))*IC+ic, (((n*ID+id)*IH+iy)*IW+ix)*IC+ic); +} +for (0 <= oc < OC, 0 <= kd < KD, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) { + weight[oc, kd, ky, kx, ic] = tosa_mi_data(S, KS, 1, ((kd*KH+ky)*KW+kx)*IC+ic, (((oc*KD+kd)*KH+ky)*KW+kx)*IC+ic); +} +---- + +==== DEPTHWISE_CONV2D + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`N*OH*OW*C*M >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = KW*KH*C; +for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= c < C) { + input [ n, iy, ix, c] = tosa_mi_data(S, KS, 0, ((iy % KH)*KW+(ix % KW))*C+c, ((n*IH+iy)*IW+ix)*C+c); +} +for (0 <= ky < KH, 0 <= kx < KW, 0 <= c < C, 0 <= m < M) { + weight[ky, kx, c, m] = tosa_mi_data(S, KS, 1, (ky*KW+kx)*C+c, ((ky*KW+kx)*C+c)*M+m); +} +---- + +==== FULLY_CONNECTED + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`N*OC >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = IC; +for (0 <= n < N, 0 <= ic < IC) { + input [ n, ic] = tosa_mi_data(S, KS, 0, ic, n*IC+ic); +} +for (0 <= oc < OC, 0 <= ic < IC) { + weight[oc, ic] = tosa_mi_data(S, KS, 1, ic, oc*IC+ic); +} +---- + +==== MATMUL + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`N*H*W >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = C; +for (0 <= n < N, 0 <= y < H, 0 <= c < C) { + A[n, y, c] = tosa_mi_data(S, KS, 0, c, (n*H+y)*C+c); +} +for (0 <= n < N, 0 <= c < C, 0 <= x < W) { + B[n, c, x] = tosa_mi_data(S, KS, 1, c, (n*C+c)*W+x); +} +---- + +==== TRANSPOSE_CONV2D + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`N*OH*OW*OC >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = KW*KH*IC; +for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) { + input [ n, iy, ix, ic] = tosa_mi_data(S, KS, 0, ((iy % KH)*KW+(ix % KW))*IC+ic, ((n*IH+iy)*IW+ix)*IC+ic); +} +for (0 <= oc < OC, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) { + weight[oc, ky, kx, ic] = tosa_mi_data(S, KS, 1, (ky*KW+kx)*IC+ic, ((oc*KH+ky)*KW+kx)*IC+ic); +} +---- + +==== FFT2D + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`N*H*W >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = 2*H*W; +for (0 <= n < N, 0 <= y < H, 0 <= x < W) { + input_real[n, y, x] = tosa_mi_data(S, KS, 0, y*W+x, ((0*N+n)*H+y)*IW+x); + input_imag[n, y, x] = tosa_mi_data(S, KS, 0, y*W+x, ((1*N+n)*H+y)*IW+x); +} +for (0 <= y < H, 0 <= x < W, 0 <= m < H, 0 <= n < W) { + weight_real[y, x, m, n] = real(exp(2*pi*i*((m*h/H) + (n*w/W)))); + weight_imag[y, x, m, n] = imag(exp(2*pi*i*((m*h/H) + (n*w/W)))); +} +---- + +==== REDUCE_SUM + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`tensor_size(shape) >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = shape1[axis]; +for (index in shape1) { + input[index] = tosa_mi_data(S, KS, 0, index[axis], tensor_index_to_offset(index)); +} +for (0 <= c < KS) { + weight[c] = 1; +} +---- + +==== AVG_POOL2D + +The following generates input test data for test set S. +For compliant implementation, the test must pass whenever the attributes satisfy: +`N*OH*OW*C >= MIN_DOT_PRODUCTS` + +[source,c++] +---- +KS = KY*KX; +for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= c < C) { + input [ n, iy, ix, c] = tosa_mi_data(S, KS, 0, ((iy % KH)*KW+(ix % KW))*C+c, ((n*IH+iy)*IW+ix)*C+c); +} +for (0 <= ky < KH, 0 <= kx < KW, 0 <= c < C, 0 <= m < M) { + weight[ky, kx] = 1/KS; +} +---- |