llama : support Mamba Selective State Space Models (llama/5328)
* mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.gg/alloc-enc-results
compilade
Georgi Gerganov
parent
9ae0d18856
commit
6e9d3aa32d
379
ggml.c
379
ggml.c
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@ -1841,6 +1841,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
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"FLASH_ATTN",
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"FLASH_FF",
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"FLASH_ATTN_BACK",
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"SSM_CONV",
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"SSM_SCAN",
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"WIN_PART",
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"WIN_UNPART",
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"GET_REL_POS",
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@ -1863,7 +1865,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
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"CROSS_ENTROPY_LOSS_BACK",
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};
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static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
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static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
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static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"none",
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@ -1929,6 +1931,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"flash_attn(x)",
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"flash_ff(x)",
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"flash_attn_back(x)",
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"ssm_conv(x)",
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"ssm_scan(x)",
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"win_part(x)",
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"win_unpart(x)",
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"get_rel_pos(x)",
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@ -1951,7 +1955,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"cross_entropy_loss_back(x,y)",
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};
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static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
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static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
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static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
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@ -6154,6 +6158,108 @@ struct ggml_tensor * ggml_flash_attn_back(
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return result;
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}
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// ggml_ssm_conv
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struct ggml_tensor * ggml_ssm_conv(
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struct ggml_context * ctx,
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struct ggml_tensor * s,
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struct ggml_tensor * x,
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struct ggml_tensor * c,
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struct ggml_tensor * sq) {
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GGML_ASSERT(ggml_is_3d(s));
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GGML_ASSERT(ggml_is_matrix(x));
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GGML_ASSERT(ggml_is_matrix(c));
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GGML_ASSERT(ggml_is_matrix(sq));
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GGML_ASSERT(sq->type == GGML_TYPE_I32);
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const int64_t d_conv = c->ne[0];
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const int64_t d_inner = c->ne[1];
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const int64_t n_tokens = x->ne[1];
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const int64_t n_kv = s->ne[2];
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GGML_ASSERT( s->ne[0] == d_conv - 1);
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GGML_ASSERT( s->ne[1] == d_inner);
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GGML_ASSERT( x->ne[0] == d_inner);
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GGML_ASSERT(sq->ne[0] == n_kv);
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GGML_ASSERT(sq->ne[1] == n_tokens);
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bool is_node = false;
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if (s->grad || x->grad || c->grad || sq->grad) {
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GGML_ASSERT(false); // TODO: implement
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is_node = true;
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}
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// 2-in-1 concatenated x and conv_states, {d_inner, n_tokens} with {d_conv, d_inner, n_kv}
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struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (d_inner*n_tokens) + (d_conv*d_inner*n_kv));
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result->op = GGML_OP_SSM_CONV;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src[0] = s;
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result->src[1] = x;
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result->src[2] = c;
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result->src[3] = sq;
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return result;
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}
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// ggml_ssm_scan
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struct ggml_tensor * ggml_ssm_scan(
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struct ggml_context * ctx,
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struct ggml_tensor * s,
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struct ggml_tensor * x,
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struct ggml_tensor * dt,
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struct ggml_tensor * A,
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struct ggml_tensor * B,
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struct ggml_tensor * C,
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struct ggml_tensor * sq) {
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GGML_ASSERT(ggml_is_contiguous(s));
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GGML_ASSERT(ggml_is_contiguous(x));
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GGML_ASSERT(ggml_is_contiguous(dt));
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GGML_ASSERT(ggml_is_contiguous(A));
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GGML_ASSERT(sq->type == GGML_TYPE_I32);
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GGML_ASSERT(B->nb[0] == ggml_type_size(B->type));
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GGML_ASSERT(C->nb[0] == ggml_type_size(C->type));
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GGML_ASSERT(ggml_are_same_shape(x, dt));
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{
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const int64_t d_state = s->ne[0];
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const int64_t d_inner = s->ne[1];
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const int64_t n_tokens = x->ne[1];
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GGML_ASSERT(x->ne[0] == d_inner);
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GGML_ASSERT(A->ne[0] == d_state);
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GGML_ASSERT(A->ne[1] == d_inner);
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GGML_ASSERT(B->ne[0] == d_state);
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GGML_ASSERT(B->ne[1] == n_tokens);
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GGML_ASSERT(C->ne[0] == d_state);
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GGML_ASSERT(C->ne[1] == n_tokens);
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}
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bool is_node = false;
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if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) {
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GGML_ASSERT(false); // TODO: implement
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is_node = true;
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}
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// 2-in-1 concatenated y and ssm_states, {d_inner, n_tokens} with {d_state, d_inner, n_kv}
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struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s));
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result->op = GGML_OP_SSM_SCAN;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src[0] = s;
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result->src[1] = x;
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result->src[2] = dt;
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result->src[3] = A;
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result->src[4] = B;
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result->src[5] = C;
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result->src[6] = sq;
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return result;
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}
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// ggml_win_part
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struct ggml_tensor * ggml_win_part(
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@ -14771,6 +14877,257 @@ static void ggml_compute_forward_flash_attn_back(
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}
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}
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// ggml_compute_forward_ssm_conv
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static void ggml_compute_forward_ssm_conv_f32(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
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return;
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}
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const struct ggml_tensor * src0 = dst->src[0]; // conv_state
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const struct ggml_tensor * src1 = dst->src[1]; // x
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const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight
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const struct ggml_tensor * src3 = dst->src[3]; // state_seq
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const int ith = params->ith;
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const int nth = params->nth;
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const int nc = src2->ne[0]; // d_conv
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const int nr = src0->ne[1]; // d_inner
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const int n_t = src1->ne[1]; // n_tokens
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const int n_kv = src0->ne[2]; // max number of sequences in the batch
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GGML_ASSERT((nr*n_t) + (nc*nr*n_kv) == ggml_nelements(dst));
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GGML_ASSERT(src0->nb[0] == sizeof(float));
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GGML_ASSERT(src1->nb[0] == sizeof(float));
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GGML_ASSERT(src2->nb[0] == sizeof(float));
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GGML_ASSERT(src3->nb[0] == sizeof(int32_t));
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GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float));
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// for use with the destination state offset between sequences
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GGML_ASSERT(src2->nb[2] == src2->ne[1]*src2->ne[0]*sizeof(float));
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// rows per thread
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const int dr = (nr + nth - 1)/nth;
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// row range for this thread
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const int ir0 = dr*ith;
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const int ir1 = MIN(ir0 + dr, nr);
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const int ir = ir1 - ir0;
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if (n_kv > 1) {
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// multiple sequences means it's hard to know when it's the first time a state is read,
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// so copy them all over to the destination, just to be sure.
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for (int i3 = 0; i3 < n_kv; ++i3) {
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float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]));
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float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + i3*(src2->nb[2]) + nr*n_t*sizeof(float));
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// can't use memcpy because of d_conv vs d_conv - 1
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for (int i1 = 0; i1 < ir; ++i1) {
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for (int i0 = 0; i0 < nc - 1; ++i0) {
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// copy s0 to last (d_conv - 1) columns of s
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s[1 + i0 + i1*nc] = s0[i0 + i1*(nc - 1)];
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}
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}
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}
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}
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for (int i2 = 0; i2 < n_t; ++i2) {
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int32_t * sq = (int32_t *) ((char *) src3->data + i2*(src3->nb[1])); // {n_kv, n_tokens}
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float * x = (float *) ((char *) dst->data + ir0*sizeof(float) + i2*(nr*sizeof(float))); // {d_inner, n_tokens}
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float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + sq[0]*(src2->nb[2]) + nr*n_t*sizeof(float)); // {d_conv, d_inner, n_kv}
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float * s0; // {d_conv - 1, d_inner, n_kv}
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float * x0 = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
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float * c = (float *) ((char *) src2->data + ir0*(src2->nb[1])); // {d_conv, d_inner}
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int ne0s0;
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GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv);
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// avoid needing to copy the state for the first token
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if (i2 == 0) {
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s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_conv - 1, d_inner, n_kv}
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ne0s0 = src0->ne[0];
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} else {
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// the source is the last (d_conv - 1) columns of the destination
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s0 = s + 1;
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ne0s0 = nc;
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}
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// d_inner
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for (int i1 = 0; i1 < ir; ++i1) {
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// shift state left
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for (int i0 = 0; i0 < nc - 1; ++i0) {
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s[i0 + i1*nc] = s0[i0 + i1*ne0s0];
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}
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// insert x on the last column
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s[(nc - 1) + i1*nc] = x0[i1];
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}
|
||||
|
||||
// handle copies when there are multiple output states
|
||||
for (int i3 = 1; i3 < n_kv; ++i3) {
|
||||
int32_t seq = sq[i3];
|
||||
if (0 <= seq && seq < n_kv) {
|
||||
float * s1 = s + (seq - sq[0])*nc*nr;
|
||||
memcpy(s1, s, nc*ir*sizeof(float));
|
||||
} else {
|
||||
// stop at negative or too big seq_ids
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// it seems a little faster when this is separate from the state shift
|
||||
for (int i1 = 0; i1 < ir; ++i1) {
|
||||
// rowwise dot product
|
||||
float sumf = 0.0f;
|
||||
for (int i0 = 0; i0 < nc; ++i0) {
|
||||
int i = i0 + i1*nc;
|
||||
sumf += s[i] * c[i];
|
||||
}
|
||||
x[i1] = sumf;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_ssm_conv(
|
||||
const struct ggml_compute_params * params,
|
||||
struct ggml_tensor * dst) {
|
||||
switch (dst->src[0]->type) {
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_ssm_conv_f32(params, dst);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_ssm_scan
|
||||
|
||||
static void ggml_compute_forward_ssm_scan_f32(
|
||||
const struct ggml_compute_params * params,
|
||||
struct ggml_tensor * dst) {
|
||||
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
const struct ggml_tensor * src0 = dst->src[0]; // s
|
||||
const struct ggml_tensor * src1 = dst->src[1]; // x
|
||||
const struct ggml_tensor * src2 = dst->src[2]; // dt
|
||||
const struct ggml_tensor * src3 = dst->src[3]; // A
|
||||
const struct ggml_tensor * src4 = dst->src[4]; // B
|
||||
const struct ggml_tensor * src5 = dst->src[5]; // C
|
||||
const struct ggml_tensor * src6 = dst->src[6]; // sq
|
||||
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
const int64_t nc = src0->ne[0]; // d_state
|
||||
const int64_t nr = src0->ne[1]; // d_inner
|
||||
const int64_t n_t = src1->ne[1]; // number of tokens in the batch
|
||||
const int64_t n_kv = src0->ne[2]; // max number of sequences in the batch
|
||||
|
||||
GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst));
|
||||
GGML_ASSERT(src0->nb[0] == sizeof(float));
|
||||
GGML_ASSERT(src1->nb[0] == sizeof(float));
|
||||
GGML_ASSERT(src2->nb[0] == sizeof(float));
|
||||
GGML_ASSERT(src3->nb[0] == sizeof(float));
|
||||
GGML_ASSERT(src4->nb[0] == sizeof(float));
|
||||
GGML_ASSERT(src5->nb[0] == sizeof(float));
|
||||
// required for the dot product between s and C, and when copying the states
|
||||
GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float));
|
||||
// required for per-sequence offsets for states
|
||||
GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float));
|
||||
// required to get correct offset for state destination (i.e. src1->nb[2])
|
||||
GGML_ASSERT(src1->nb[2] == src1->ne[0]*src1->ne[1]*sizeof(float));
|
||||
|
||||
// rows per thread
|
||||
const int dr = (nr + nth - 1)/nth;
|
||||
|
||||
// row range for this thread
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
const int ir = ir1 - ir0;
|
||||
|
||||
if (n_kv > 1) {
|
||||
// it's hard to know if the source states have already been copied
|
||||
// when there are multiple, so copy them already.
|
||||
for (int i3 = 0; i3 < n_kv; ++i3) {
|
||||
float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]));
|
||||
float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[2]);
|
||||
memcpy(s, s0, nc*ir*sizeof(float));
|
||||
}
|
||||
}
|
||||
|
||||
for (int i2 = 0; i2 < n_t; ++i2) {
|
||||
int32_t * sq = (int32_t *) ((char *) src6->data + i2*(src6->nb[1])); // {n_kv, n_tokens}
|
||||
float * y = (float *) ((char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
|
||||
float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2]) + src1->nb[2]); // {d_state, d_inner, n_kv}
|
||||
float * s0;
|
||||
float * x = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
|
||||
float * dt = (float *) ((char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1])); // {d_inner, n_tokens}
|
||||
float * A = (float *) ((char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
|
||||
float * B = (float *) ((char *) src4->data + i2*(src4->nb[1])); // {d_state, n_tokens}
|
||||
float * C = (float *) ((char *) src5->data + i2*(src5->nb[1])); // {d_state, n_tokens}
|
||||
|
||||
GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv);
|
||||
|
||||
// avoid needing to copy the state for the first token
|
||||
if (i2 == 0) {
|
||||
s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_state, d_inner, n_kv}
|
||||
} else {
|
||||
// otherwise the source is the same as the destination
|
||||
s0 = s;
|
||||
}
|
||||
|
||||
// d_inner
|
||||
for (int i1 = 0; i1 < ir; ++i1) {
|
||||
// ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
|
||||
float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
|
||||
float x_dt = x[i1] * dt_soft_plus;
|
||||
float sumf = 0.0f;
|
||||
// d_state
|
||||
for (int i0 = 0; i0 < nc; ++i0) {
|
||||
int i = i0 + i1*nc;
|
||||
// state = prev_state * dA + dB * x
|
||||
float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
|
||||
// y = rowwise_dotprod(state, C)
|
||||
sumf += state * C[i0];
|
||||
s[i] = state;
|
||||
}
|
||||
y[i1] = sumf;
|
||||
}
|
||||
|
||||
// handle copies when there are multiple output states
|
||||
for (int i3 = 1; i3 < n_kv; ++i3) {
|
||||
int32_t seq = sq[i3];
|
||||
if (0 <= seq && seq < n_kv) {
|
||||
float * s1 = s + (seq - sq[0])*nc*nr;
|
||||
memcpy(s1, s, nc*ir*sizeof(float));
|
||||
} else {
|
||||
// stop at negative or too big seq_ids
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_ssm_scan(
|
||||
const struct ggml_compute_params * params,
|
||||
struct ggml_tensor * dst) {
|
||||
switch (dst->src[0]->type) {
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_ssm_scan_f32(params, dst);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_win_part
|
||||
|
||||
static void ggml_compute_forward_win_part_f32(
|
||||
|
|
@ -15830,6 +16187,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
|
|||
bool masked = t != 0;
|
||||
ggml_compute_forward_flash_attn_back(params, masked, tensor);
|
||||
} break;
|
||||
case GGML_OP_SSM_CONV:
|
||||
{
|
||||
ggml_compute_forward_ssm_conv(params, tensor);
|
||||
} break;
|
||||
case GGML_OP_SSM_SCAN:
|
||||
{
|
||||
ggml_compute_forward_ssm_scan(params, tensor);
|
||||
} break;
|
||||
case GGML_OP_WIN_PART:
|
||||
{
|
||||
ggml_compute_forward_win_part(params, tensor);
|
||||
|
|
@ -16884,6 +17249,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
{
|
||||
GGML_ASSERT(false); // not supported
|
||||
} break;
|
||||
case GGML_OP_SSM_CONV:
|
||||
case GGML_OP_SSM_SCAN:
|
||||
{
|
||||
GGML_ASSERT(false); // TODO: not implemented
|
||||
} break;
|
||||
case GGML_OP_WIN_PART:
|
||||
case GGML_OP_WIN_UNPART:
|
||||
case GGML_OP_UNARY:
|
||||
|
|
@ -17590,6 +17960,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
|||
{
|
||||
n_tasks = n_threads;
|
||||
} break;
|
||||
case GGML_OP_SSM_CONV:
|
||||
case GGML_OP_SSM_SCAN:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
} break;
|
||||
case GGML_OP_WIN_PART:
|
||||
case GGML_OP_WIN_UNPART:
|
||||
case GGML_OP_GET_REL_POS:
|
||||
|
|
|
|||
19
ggml.h
19
ggml.h
|
|
@ -472,6 +472,8 @@ extern "C" {
|
|||
GGML_OP_FLASH_ATTN,
|
||||
GGML_OP_FLASH_FF,
|
||||
GGML_OP_FLASH_ATTN_BACK,
|
||||
GGML_OP_SSM_CONV,
|
||||
GGML_OP_SSM_SCAN,
|
||||
GGML_OP_WIN_PART,
|
||||
GGML_OP_WIN_UNPART,
|
||||
GGML_OP_GET_REL_POS,
|
||||
|
|
@ -1728,6 +1730,23 @@ extern "C" {
|
|||
struct ggml_tensor * c0,
|
||||
struct ggml_tensor * c1);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_ssm_conv(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * s,
|
||||
struct ggml_tensor * x,
|
||||
struct ggml_tensor * c,
|
||||
struct ggml_tensor * sq);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_ssm_scan(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * s,
|
||||
struct ggml_tensor * x,
|
||||
struct ggml_tensor * dt,
|
||||
struct ggml_tensor * A,
|
||||
struct ggml_tensor * B,
|
||||
struct ggml_tensor * C,
|
||||
struct ggml_tensor * sq);
|
||||
|
||||
// partition into non-overlapping windows with padding if needed
|
||||
// example:
|
||||
// a: 768 64 64 1
|
||||
|
|
|
|||
Loading…
Reference in New Issue