On Fri, 4 Nov 2022 at 14:00, Prathamesh Kulkarni wrote: > > On Mon, 31 Oct 2022 at 15:27, Richard Sandiford > wrote: > > > > Prathamesh Kulkarni writes: > > > On Wed, 26 Oct 2022 at 21:07, Richard Sandiford > > > wrote: > > >> > > >> Sorry for the slow response. I wanted to find some time to think > > >> about this a bit more. > > >> > > >> Prathamesh Kulkarni writes: > > >> > On Fri, 30 Sept 2022 at 21:38, Richard Sandiford > > >> > wrote: > > >> >> > > >> >> Richard Sandiford via Gcc-patches writes: > > >> >> > Prathamesh Kulkarni writes: > > >> >> >> Sorry to ask a silly question but in which case shall we select 2nd vector ? > > >> >> >> For num_poly_int_coeffs == 2, > > >> >> >> a1 /trunc n1 == (a1 + 0x) / (n1.coeffs[0] + n1.coeffs[1]*x) > > >> >> >> If a1/trunc n1 succeeds, > > >> >> >> 0 / n1.coeffs[1] == a1/n1.coeffs[0] == 0. > > >> >> >> So, a1 has to be < n1.coeffs[0] ? > > >> >> > > > >> >> > Remember that a1 is itself a poly_int. It's not necessarily a constant. > > >> >> > > > >> >> > E.g. the TRN1 .D instruction maps to a VEC_PERM_EXPR with the selector: > > >> >> > > > >> >> > { 0, 2 + 2x, 1, 4 + 2x, 2, 6 + 2x, ... } > > >> >> > > >> >> Sorry, should have been: > > >> >> > > >> >> { 0, 2 + 2x, 2, 4 + 2x, 4, 6 + 2x, ... } > > >> > Hi Richard, > > >> > Thanks for the clarifications, and sorry for late reply. > > >> > I have attached POC patch that tries to implement the above approach. > > >> > Passes bootstrap+test on x86_64-linux-gnu and aarch64-linux-gnu for VLS vectors. > > >> > > > >> > For VLA vectors, I have only done limited testing so far. > > >> > It seems to pass couple of tests written in the patch for > > >> > nelts_per_pattern == 3, > > >> > and folds the following svld1rq test: > > >> > int32x4_t v = {1, 2, 3, 4}; > > >> > return svld1rq_s32 (svptrue_b8 (), &v[0]) > > >> > into: > > >> > return {1, 2, 3, 4, ...}; > > >> > I will try to bootstrap+test it on SVE machine to test further for VLA folding. > > >> > > > >> > I have a couple of questions: > > >> > 1] When mask selects elements from same vector but from different patterns: > > >> > For eg: > > >> > arg0 = {1, 11, 2, 12, 3, 13, ...}, > > >> > arg1 = {21, 31, 22, 32, 23, 33, ...}, > > >> > mask = {0, 0, 0, 1, 0, 2, ... }, > > >> > All have npatterns = 2, nelts_per_pattern = 3. > > >> > > > >> > With above mask, > > >> > Pattern {0, ...} selects arg0[0], ie {1, ...} > > >> > Pattern {0, 1, 2, ...} selects arg0[0], arg0[1], arg0[2], ie {1, 11, 2, ...} > > >> > While arg0[0] and arg0[2] belong to same pattern, arg0[1] belongs to different > > >> > pattern in arg0. > > >> > The result is: > > >> > res = {1, 1, 1, 11, 1, 2, ...} > > >> > In this case, res's 2nd pattern {1, 11, 2, ...} is encoded with: > > >> > with a0 = 1, a1 = 11, S = -9. > > >> > Is that expected tho ? It seems to create a new encoding which > > >> > wasn't present in the input vector. For instance, the next elem in > > >> > sequence would be -7, > > >> > which is not present originally in arg0. > > >> > > >> Yeah, you're right, sorry. Going back to: > > >> > > >> (2) The explicit encoding can be used to produce a sequence of N*Ex*Px > > >> elements for any integer N. This extended sequence can be reencoded > > >> as having N*Px patterns, with Ex staying the same. > > >> > > >> I guess we need to pick an N for the selector such that each new > > >> selector pattern (each one out of the N*Px patterns) selects from > > >> the *same pattern* of the same data input. > > >> > > >> So if a particular pattern in the selector has a step S, and the data > > >> input it selects from has Pi patterns, N*S must be a multiple of Pi. > > >> N must be a multiple of least_common_multiple(S,Pi)/S. > > >> > > >> I think that means that the total number of patterns in the result > > >> (Pr from previous messages) can safely be: > > >> > > >> Ps * least_common_multiple( > > >> least_common_multiple(S[1], P[input(1)]) / S[1], > > >> ... > > >> least_common_multiple(S[Ps], P[input(Ps)]) / S[Ps] > > >> ) > > >> > > >> where: > > >> > > >> Ps = the number of patterns in the selector > > >> S[I] = the step for selector pattern I (I being 1-based) > > >> input(I) = the data input selected by selector pattern I (I being 1-based) > > >> P[I] = the number of patterns in data input I > > >> > > >> That's getting quite complicated :-) If we allow arbitrary P[...] > > >> and S[...] then it could also get large. Perhaps we should finally > > >> give up on the general case and limit this to power-of-2 patterns and > > >> power-of-2 steps, so that least_common_multiple becomes MAX. Maybe that > > >> simplifies other things as well. > > >> > > >> What do you think? > > > Hi Richard, > > > Thanks for the suggestions. Yeah I suppose we can initially add support for > > > power-of-2 patterns and power-of-2 steps and try to generalize it in > > > follow up patches if possible. > > > > > > Sorry if this sounds like a silly ques -- if we are going to have > > > pattern in selector, select *same pattern from same input vector*, > > > instead of re-encoding the selector to have N * Ps patterns, would it > > > make sense for elements in selector to denote pattern number itself > > > instead of element index > > > if input vectors are VLA ? > > > > > > For eg: > > > op0 = {1, 2, 3, 4, 1, 2, 3, 5, 1, 2, 3, 6, ...} > > > op1 = {...} > > > with npatterns == 4, nelts_per_pattern == 3, > > > sel = {0, 3} should pick pattern 0 and pattern 3 from op0, > > > so, res = {1, 4, 1, 5, 1, 6, ...} > > > Not sure if this is correct tho. > > > > This wouldn't allow us to represent things like a "duplicate one > > element", or "copy the leading N elements from the first input and > > the other elements from elements N+ of the second input", which we > > can with the current scheme. > > > > The restriction about each (unwound) selector pattern selecting from the > > same input pattern only applies to case where the selector pattern is > > stepped (and only applies to the stepped part of the pattern, not the > > leading element). The restriction is also local to this code; it > > doesn't make other VEC_PERM_EXPRs invalid. > Hi Richard, > Thanks for the clarifications. > Just to clarify your approach with an eg: > Let selected input vector be: > arg0: {a0, b0, c0, d0, > a0 + S, b0 + S, c0 + S, d0 + S, > a0 + 2S, b0 + 2S, c0 + 2S, dd + 2S, ...} > where arg0 has npatterns = 4, and nelts_per_pattern = 3. > > Let sel = {0, 0, 1, 2, 2, 4, ...} > where sel_npatterns = 2 and sel_nelts_per_pattern = 3 > > So, the first pattern in sel: > p1: {0, 1, 2, ...} which will select {a0, b0, c0, ...} > which would be incorrect, since they belong to different patterns in arg0. > So to select elements from same pattern in arg0, we need to divide p1 > into at least N1 = P_arg0 / S0 = 4 distinct patterns. > > Similarly for second pattern in sel: > p2: {0, 2, 4, ...}, we need to divide it into > at least N2 = P_arg0 / S1 = 2 distinct patterns. > > Select N = max(N1, N2) = 4 > So, the selector will be extended to N * Ps * Es = 4 * 2 * 3 == 24 elements, > and will be re-encoded with N*Ps = 8 patterns: > > re-encoded sel: > {a0, b0, c0, d0, a0 + S, b0 + S, c0 + S, d0 + S, > a0 + 2S, b0 + 2S, c0 + 2S, d0 + 2S, a0 + 3S, b0 + 3S, c0 + 3S, d0 + 3S, > a0 + 4S, b0 + 4S, c0 + 4s, d0 + 4S, a0 + 5S, b0 + 5S, c0 + 5S, d0 + 5S, > ...} > > with 8 patterns, > p1: {a0, a0 + 2S, a0 + 4S, ...} > p2: {b0, b0 + 2S, b0 + 4S, ...} > ... > which select elements from same pattern from same input vector. > Does this look correct ? > > For feasibility, we can check initially that sel_npatterns, arg0_npatterns, > arg1_npatterns are powers of 2 and for each stepped pattern, > it's stepped size S is a power of 2. I suppose this will be sufficient > to ensure that sel can be re-encoded with N*Ps npatterns > such that each new pattern selects elements from same pattern > of the input vector ? > > Then compute N: > N = 1; > for (every pattern p in sel) > { > op = corresponding input vector for pattern; > S = step_size (p); > N_pattern = max (S, npatterns (op)) / S; > N = max(N, N_pattern) > } > > and re-encode selector with N*Ps patterns. > I guess rest of the patch will mostly stay the same. Hi, I have attached a POC patch based on the above approach. For the above eg: arg0 = {1, 11, 2, 12, 3, 13, ...} // npatterns = 2, nelts_per_pattern = 3, and sel = {0, 0, 0, 1, 0, 2, ...} with sel_npatterns == 2 and sel_nelts_per_pattern == 3. For pattern, {0, 1, 2, ...} it will select elements from different patterns from arg0, which is incorrect. So we choose N = P1/S = 2/1 = 2, where P1 is number of elements in arg0. So re-encoded sel = { 0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, ...} with following patterns: p1 = { 0, ... } p2 = { 0, 2, 4, ... } p3 = { 0, ... } p4 = { 1, 3, 5, ... } which should be correct since each element from the respective patterns in sel chooses elements from same pattern from arg0. So, res = { 1, 1, 1, 11, 1, 2, 1, 12, 1, 3, 1, 13, ... } Does this look correct ? Thanks, Prathamesh > > Thanks, > Prathamesh > > > > > Thanks, > > Richard > > > > > > > > Thanks, > > > Prathamesh > > >> > > >> > I suppose it's fine since if the user defines mask to have pattern {0, > > >> > 1, 2, ...} > > >> > they intended result to have pattern with above encoding. > > >> > Just wanted to confirm if this is correct ? > > >> > > > >> > 2] Could you please suggest a test-case for S < 0 ? > > >> > I am not able to come up with one :/ > > >> > > >> svrev is one way of creating negative steps. > > >> > > >> Thanks, > > >> Richard > > >> > > >> > > > >> > Thanks, > > >> > Prathamesh > > >> >> > > >> >> > which is an interleaving of the two patterns: > > >> >> > > > >> >> > { 0, 2, 4, ... } a0 = 0, a1 = 2, S = 2 > > >> >> > { 2 + 2x, 4 + 2x, 6 + 2x } a0 = 2 + 2x, a1 = 4 + 2x, S = 2