if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
# ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
res = "hunyuan"
+ if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
+ # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
+ res = "falcon-h1"
+ if chkhsh == "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86":
+ # ref: https://huggingface.co/tiiuae/Falcon-H1-1B-Base
+ res = "falcon-h1"
+ if chkhsh == "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896":
+ # ref: https://huggingface.co/tiiuae/Falcon-H1-7B-Base
+ res = "falcon-h1"
+ if chkhsh == "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b":
+ # ref: https://huggingface.co/tiiuae/Falcon-H1-34B-Base
+ res = "falcon-h1"
if res is None:
logger.warning("\n")
def set_gguf_parameters(self):
d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
- d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
+ d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or 2 * d_model
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 128
- head_dim = self.find_hparam(["head_dim"], optional=True) or 64
+ head_dim = self.find_hparam(["mamba_d_head", "head_dim"], optional=True) or 64
n_group = self.find_hparam(["n_groups"], optional=True) or 1
rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
# Fail early for models which don't have a block expansion factor of 2
# TODO: does this really matter?
- assert d_inner == 2 * d_model
- assert d_inner % head_dim == 0
+ # skip the assertion for FalconH1 Model
+ if self.model_arch != gguf.MODEL_ARCH.FALCON_H1:
+ assert d_inner == 2 * d_model
+ assert d_inner % head_dim == 0
self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
self.gguf_writer.add_embedding_length(d_model)
data_torch = data_torch.reshape((*data_torch.shape, 1))
elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_NORM, bid):
d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
- d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
+ d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or 2 * d_model
n_group = self.hparams.get("n_groups", 1)
data_torch = data_torch.reshape((n_group, d_inner // n_group))
self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
+@ModelBase.register("FalconH1ForCausalLM")
+class FalconH1Model(Mamba2Model):
+ model_arch = gguf.MODEL_ARCH.FALCON_H1
+
+ def __init__(self, *args, **kwargs):
+ # Set the hparam prefixes for Falcon Mamba2
+ self.hparam_prefixes = ["mamba"]
+
+ # Initialize the base Mamba2Model
+ super().__init__(*args, **kwargs)
+
+ # Use Llama conversion for attention
+ self._transformer_model_class = LlamaModel
+
+ # n_group and d_inner are used during reshape_tensors for mamaba2
+ self.n_group = self.find_hparam(["n_groups"])
+ self.d_inner = self.find_hparam(["mamba_d_ssm"])
+ self.d_head = self.find_hparam(["d_head"])
+
+ # Initialize any Falcon Mamba2 specific attributes
+ self.has_attention = True # Falcon Mamba2 has attention components
+
+ # Load Falcon-H1 multipliers from hyperparameters
+ self.attention_in_multiplier = self.find_hparam(["attention_in_multiplier"], optional=True)
+ self.attention_out_multiplier = self.find_hparam(["attention_out_multiplier"], optional=True)
+ self.ssm_in_multiplier = self.find_hparam(["ssm_in_multiplier"], optional=True)
+ self.ssm_out_multiplier = self.find_hparam(["ssm_out_multiplier"], optional=True)
+ self.mlp_multipliers = self.find_hparam(["mlp_multipliers"], optional=True)
+ self.ssm_multipliers = self.find_hparam(["ssm_multipliers"], optional=True)
+ self.intermediate_size = self.find_hparam(["intermediate_size"])
+ self.key_multiplier = self.find_hparam(["key_multiplier"], optional=True)
+
+ def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any:
+ prefixed = []
+ for pfx in self.hparam_prefixes:
+ prefixed.extend(
+ "_".join([pfx, k])
+ for k in keys
+ )
+ keys = list(keys) + prefixed
+ return super().find_hparam(keys, *args, **kwargs)
+
+ def set_vocab(self):
+ self._set_vocab_gpt2()
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ tensors = list(super().modify_tensors(data_torch, name, bid))
+ tensor = tensors[0][1]
+
+ if "down_proj" in name:
+ tensor = tensor * self.mlp_multipliers[1]
+ elif "gate_proj" in name:
+ tensor = tensor * self.mlp_multipliers[0]
+ elif "k_proj" in name:
+ tensor = tensor * self.key_multiplier * self.attention_in_multiplier
+ elif "q_proj" in name:
+ tensor = tensor * self.attention_in_multiplier
+ elif "v_proj" in name:
+ tensor = tensor * self.attention_in_multiplier
+ elif "o_proj" in name:
+ tensor = tensor * self.attention_out_multiplier
+ elif "out_proj" in name:
+ tensor = tensor * self.ssm_out_multiplier
+ elif "in_proj" in name:
+ tensor = tensor * self.ssm_in_multiplier
+ zxbcdt_multipliers = self.hparams["ssm_multipliers"]
+ intermediate_size = self.hparams["mamba_d_ssm"]
+ groups_time_state_size = self.hparams["mamba_n_groups"] * self.hparams["mamba_d_state"]
+ tensor[:intermediate_size, :] *= zxbcdt_multipliers[0]
+ tensor[intermediate_size:2 * intermediate_size, :] *= zxbcdt_multipliers[1]
+ tensor[2 * intermediate_size:2 * intermediate_size + groups_time_state_size, :] *= zxbcdt_multipliers[2]
+ tensor[2 * intermediate_size + groups_time_state_size:2 * intermediate_size + 2 * groups_time_state_size, :] *= zxbcdt_multipliers[3]
+ tensor[2 * intermediate_size + 2 * groups_time_state_size:, :] *= zxbcdt_multipliers[4]
+ elif "lm_head" in name:
+ tensor = tensor * self.hparams["lm_head_multiplier"]
+ elif "embed_tokens" in name:
+ tensor = tensor * self.hparams["embedding_multiplier"]
+ elif "mamba.norm" in name:
+ tensor = tensor.reshape(self.n_group, self.d_inner // self.n_group)
+
+ tensors = [(tensors[0][0], tensor)]
+ return tensors
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+
+ ## General Params ##
+ self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+ # Override some Mamba2 defaults
+ self.gguf_writer.add_block_count(self.block_count)
+ self.gguf_writer.add_context_length(self.hparams.get("max_position_embeddings", 0))
+ self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+
+ ## Attention params ##
+ self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) # Override value 0 from Mamba2
+ self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+ self.gguf_writer.add_key_length(self.hparams["head_dim"])
+ self.gguf_writer.add_value_length(self.hparams["head_dim"])
+
+ ## Validation ##
+ assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported"
+ assert self.d_inner % self.d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {self.d_head}"
+
+ # Add any other Falcon Mamba2 specific configuration
+ self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
+
+
@ModelBase.register("HunYuanMoEV1ForCausalLM")
class HunYuanMoEModel(TextModel):
model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE
default: type = LLM_TYPE_UNKNOWN;
}
} break;
+ case LLM_ARCH_FALCON_H1:
+ {
+ // Common parameters
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ // SSM parameters
+ ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
+ ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
+ ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
+ ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+ ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
+
+ std::fill(hparams.recurrent_layer_arr.begin(), hparams.recurrent_layer_arr.end(), true);
+
+ switch (hparams.n_layer) {
+ case 36:
+ type = LLM_TYPE_0_5B; break;
+ case 24:
+ type = LLM_TYPE_1_5B; break;
+ case 66:
+ type = LLM_TYPE_1B; break;
+ case 32:
+ type = LLM_TYPE_3B; break;
+ case 44:
+ type = LLM_TYPE_7B; break;
+ case 72:
+ type = LLM_TYPE_34B; break;
+ default:
+ type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
case LLM_ARCH_HUNYUAN_MOE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
} break;
+ case LLM_ARCH_FALCON_H1:
+ {
+ // Common
+ const int64_t hidden_size = hparams.n_embd; // hidden_size
+
+ // mamba2 Mixer SSM params
+ const int64_t ssm_conv_kernel_size = hparams.ssm_d_conv; // ssm_conv_kernel_size
+ const int64_t ssm_n_groups = hparams.ssm_n_group; // ssm_n_groups
+ const int64_t ssm_state_size = hparams.ssm_d_state; // ssm_state_size
+ const int64_t ssm_intermediate_size = hparams.ssm_d_inner; // TODO expand
+ const int64_t ssm_num_heads = hparams.ssm_dt_rank; // ssm_num_heads
+ const int64_t ssm_conv_dim = ssm_intermediate_size + 2 * ssm_n_groups * ssm_state_size;
+ const int64_t ssm_projection_size = ssm_intermediate_size + ssm_conv_dim + ssm_num_heads;
+
+ // attn params
+ const int64_t attn_num_attention_head = hparams.n_head(0); // rename to: attn_num_attention_head
+ const int64_t attn_num_key_value_head = hparams.n_head_kv(0);
+
+ // ffn params
+ const int64_t ffn_intermediate_size = hparams.n_ff(0);
+
+ // embeddings
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, 0);
+
+ // output
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hidden_size, n_vocab}, TENSOR_NOT_REQUIRED);
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {hidden_size}, 0);
+
+ // if output is NULL, init from the input tok embed
+ if (output == NULL) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, TENSOR_DUPLICATED);
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = layers[i];
+
+ /*SSM LAYERS*/
+ // ssm in
+ layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {hidden_size, ssm_projection_size}, 0);
+ // ssm 1d conv
+ layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {ssm_conv_kernel_size, ssm_conv_dim}, 0);
+ layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {ssm_conv_dim}, TENSOR_NOT_REQUIRED);
+ // ssm_dt
+ layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {ssm_num_heads}, 0);
+ // no "weight" suffix for these
+ layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, ssm_num_heads}, 0);
+ layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, ssm_num_heads}, 0);
+ // ssm_norm
+ layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {ssm_intermediate_size / ssm_n_groups, ssm_n_groups}, TENSOR_NOT_REQUIRED);
+ // out_proj
+ layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {ssm_intermediate_size, hidden_size}, 0);
+
+ /*ATTENTION LAYERS*/
+ // attention layers (with optional bias)
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {hidden_size, n_embd_head_k * attn_num_attention_head}, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {hidden_size, attn_num_key_value_head * n_embd_head_k}, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {hidden_size, attn_num_key_value_head * n_embd_head_v}, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * attn_num_attention_head, hidden_size}, 0);
+ layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
+ layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {attn_num_key_value_head * n_embd_head_k}, TENSOR_NOT_REQUIRED);
+ layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {attn_num_key_value_head * n_embd_head_v}, TENSOR_NOT_REQUIRED);
+ layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {hidden_size}, 0);
+
+
+ // feed forward (w/ optional biases)
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, i), {hidden_size}, 0);
+ layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {hidden_size, ffn_intermediate_size}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { ffn_intermediate_size, hidden_size}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {hidden_size, ffn_intermediate_size}, 0);
+
+ layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
+ layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
+ layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
+ }
+ } break;
case LLM_ARCH_HUNYUAN_MOE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
- // cb(cur, "mamba_out", il);
+ cb(cur, "mamba_out", il);
return cur;
}
}
};
+struct llm_build_falcon_h1 : public llm_graph_context {
+ const llama_model & model;
+
+ llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params), model(model) {
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ // Build the inputs in the recurrent & kv cache
+ auto * inp = build_inp_mem_hybrid();
+
+ const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_tensor * inpSA = inpL;
+
+ cur = build_norm(inpL,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, hparams.rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, hparams.rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ cb(Qcur, "Qcur-post-rope", il);
+ cb(Kcur, "Kcur-post-rope", il);
+ cb(Vcur, "Vcur-post-rope", il);
+
+ ggml_tensor * attn_out = build_attn(inp, gf,
+ model.layers[il].wo, NULL,
+ Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+ cb(attn_out, "attn_out", il);
+
+ cur = build_norm(inpL,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ // Mamba2 layer
+ cb(cur, "ssm_in", il);
+
+ ggml_tensor * ssm_out = build_mamba2_layer(inp, gf, cur, ubatch, il);
+ cb(ssm_out, "ssm_out", il);
+
+ // // Aggregation
+ cur = ggml_add(ctx0, attn_out, ssm_out);
+ inpSA = ggml_add(ctx0, cur, inpSA);
+ cb(cur, "layer_out", il);
+
+ if (il == n_layer - 1 && inp_out_ids) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ ggml_tensor * ffn_inp = inpSA;
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = build_ffn(cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, inpSA);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = build_norm(cur,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ // lm_head
+ cur = build_lora_mm(model.output, cur);
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+ }
+
+ ggml_tensor * build_mamba2_layer(
+ llm_graph_input_mem_hybrid * inp,
+ ggml_cgraph * gf,
+ ggml_tensor * cur,
+ const llama_ubatch & ubatch,
+ int il) const {
+ const auto * kv_state = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
+
+ const auto kv_head = kv_state->get_head();
+
+ const int64_t d_conv = hparams.ssm_d_conv;
+ const int64_t d_inner = hparams.ssm_d_inner;
+ const int64_t d_state = hparams.ssm_d_state;
+ const int64_t n_head = hparams.ssm_dt_rank;
+ const int64_t head_dim = d_inner / n_head;
+ const int64_t n_group = hparams.ssm_n_group;
+ const int64_t n_seqs = ubatch.n_seqs;
+
+ const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+ GGML_ASSERT(n_seqs != 0);
+ GGML_ASSERT(ubatch.equal_seqs);
+ GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+ ggml_tensor * conv_states_all = kv_state->get_r_l(il);
+ ggml_tensor * ssm_states_all = kv_state->get_s_l(il);
+
+ ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs);
+ conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs);
+
+ // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
+ cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
+
+ // d_in_proj = 2 * self.d_inner + 2 * self.ngroups * self.d_state + self.nheads
+
+ // {n_embd, d_in_proj} @ {n_embd, n_seq_tokens, n_seqs} => {d_in_proj, n_seq_tokens, n_seqs}
+ ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur);
+ cb(zxBCdt, "zxBCdt", il);
+
+ // split the above in three
+ ggml_tensor * z = ggml_view_4d(ctx0, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*zxBCdt->nb[0], zxBCdt->nb[1], zxBCdt->nb[2], 0);
+ ggml_tensor * xBC = ggml_view_3d(ctx0, zxBCdt, d_inner + 2*n_group*d_state, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], d_inner*ggml_element_size(zxBCdt));
+ ggml_tensor * dt = ggml_view_3d(ctx0, zxBCdt, n_head, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], (2*d_inner + 2*n_group*d_state)*ggml_element_size(zxBCdt));
+
+ // conv
+ {
+ // => {d_conv - 1 + n_seq_tokens, d_inner + 2*n_group*d_state, n_seqs}
+ ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, xBC), 0);
+
+ // copy last (d_conv - 1) columns back into the state cache
+ ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs, conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));
+
+ ggml_build_forward_expand(gf,
+ ggml_cpy(ctx0, last_conv,
+ ggml_view_1d(ctx0, conv_states_all,
+ (d_conv - 1)*(d_inner + 2*n_group*d_state)*(n_seqs),
+ kv_head*(d_conv - 1)*(d_inner + 2*n_group*d_state)*ggml_element_size(conv_states_all))));
+
+ // 1D convolution
+ // The equivalent is to make a self-overlapping view of conv_x
+ // over d_conv columns at each stride in the 3rd dimension,
+ // then element-wise multiply that with the conv1d weight,
+ // then sum the elements of each row,
+ // (the last two steps are a dot product over rows (also doable with mul_mat))
+ // then permute away the ne[0] dimension,
+ // and then you're left with the resulting x tensor.
+ // For simultaneous sequences, all sequences need to have the same length.
+ xBC = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
+
+ // bias
+ xBC = ggml_add(ctx0, xBC, model.layers[il].ssm_conv1d_b);
+
+ xBC = ggml_silu(ctx0, xBC);
+ }
+
+ // ssm
+ {
+ // These correspond to V K Q in SSM/attention duality
+ ggml_tensor * x = ggml_view_4d(ctx0, xBC, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*xBC->nb[0], xBC->nb[1], xBC->nb[2], 0);
+
+ ggml_tensor * B = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], d_inner*ggml_element_size(xBC));
+
+ ggml_tensor * C = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], (d_inner + n_group*d_state)*ggml_element_size(xBC));
+
+ // {n_head, n_seq_tokens, n_seqs}
+ dt = ggml_add(ctx0, ggml_cont(ctx0, dt), model.layers[il].ssm_dt_b);
+
+ ggml_tensor * A = model.layers[il].ssm_a;
+
+ // use the states and the indices provided by build_rs
+ // (this is necessary in order to properly use the states before they are overwritten,
+ // while avoiding to make unnecessary copies of the states)
+ auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
+ ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, kv_state->get_size());
+
+ // TODO: use semistructured matrices to implement state-space duality
+ // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
+ return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
+ };
+
+ ggml_tensor * y_ssm = build_rs(inp, gf, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
+
+ // store last states
+ ggml_build_forward_expand(gf,
+ ggml_cpy(ctx0,
+ ggml_view_1d(ctx0, y_ssm, d_state*d_inner*n_seqs, ggml_nelements(x)*x->nb[0]),
+ ggml_view_1d(ctx0, ssm_states_all, d_state*d_inner*n_seqs, kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));
+
+ ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_head, n_seq_tokens, n_seqs, x->nb[1], n_head*x->nb[1], n_seq_tokens*n_head*x->nb[1], 0);
+
+ // TODO: skip computing output earlier for unused tokens
+
+ y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
+ y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
+
+ // grouped RMS norm
+ if (model.layers[il].ssm_norm) {
+ y = ggml_reshape_4d(ctx0, y, d_inner / n_group, n_group, n_seq_tokens, n_seqs);
+ y = build_norm(y, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, il);
+ }
+
+ y = ggml_reshape_3d(ctx0, y, d_inner, n_seq_tokens, n_seqs);
+
+ // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
+ cur = build_lora_mm(model.layers[il].ssm_out, y);
+ }
+
+ // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
+ cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
+ cb(cur, "mamba_out", il);
+ return cur;
+ }
+};
+
struct llm_build_arcee : public llm_graph_context {
llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
/* recurrent_type_v */ GGML_TYPE_F32,
/* recurrent_kv_size */ std::max((uint32_t) 1, cparams.n_seq_max),
/* n_seq_max */ cparams.n_seq_max,
- /* offload */ cparams.offload_kqv);
+ /* offload */ cparams.offload_kqv,
+ /* filter_attn */ (arch == LLM_ARCH_FALCON_H1) ? [&](int32_t) { return true; } : (llama_memory_hybrid::layer_filter_cb)nullptr,
+ /* filter_recr */ (arch == LLM_ARCH_FALCON_H1) ? [&](int32_t) { return true; } : (llama_memory_hybrid::layer_filter_cb)nullptr);
} else {
const auto padding = llama_kv_cache_unified::get_padding(cparams);
{
llm = std::make_unique<llm_build_smollm3>(*this, params, gf);
} break;
+ case LLM_ARCH_FALCON_H1:
+ {
+ llm = std::make_unique<llm_build_falcon_h1>(*this, params, gf);
+ } break;
default:
GGML_ABORT("fatal error");
}
// the pairs of head values are offset by n_rot/2
case LLM_ARCH_FALCON:
+ case LLM_ARCH_FALCON_H1:
case LLM_ARCH_GROK:
case LLM_ARCH_DBRX:
case LLM_ARCH_BERT:
overview / pkg / ggml / sources / llama.cpp / commitdiff