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llama : add phixtral support (wip)

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Georgi Gerganov 2024-01-13 14:19:13 +02:00
parent 15ebe59210
commit 9998ecd191
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4 changed files with 107 additions and 11 deletions
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13
convert-hf-to-gguf.py
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@ -1080,10 +1080,15 @@ class Phi2Model(Model):
def set_gguf_parameters(self):
block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"])
rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"])
n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"])
n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"])
if "partial_rotary_factor" in self.hparams:
rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"])
n_rot = int(rot_pct * n_embd) // n_head
else:
n_rot = get_key_opts(self.hparams, ["rotary_dim", "n_rot"])
self.gguf_writer.add_name("Phi2")
self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["n_positions", "max_position_embeddings"]))
@ -1093,10 +1098,14 @@ class Phi2Model(Model):
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
self.gguf_writer.add_layer_norm_eps(get_key_opts(self.hparams, ["layer_norm_epsilon", "layer_norm_eps"]))
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
self.gguf_writer.add_rope_dimension_count(n_rot)
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_add_bos_token(False)
# phixtral
self.gguf_writer.add_expert_count(self.hparams.get("num_local_experts", 0))
self.gguf_writer.add_expert_used_count(self.hparams.get("num_experts_per_tok", 0))
class PlamoModel(Model):
def set_vocab(self):

3
gguf-py/gguf/constants.py
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@ -393,9 +393,12 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
]
# TODO
}

3
gguf-py/gguf/tensor_mapping.py
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@ -173,6 +173,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_GATE_INP: (
"layers.{bid}.feed_forward.gate", # mixtral
"model.layers.{bid}.block_sparse_moe.gate", # mixtral
"transformer.h.{bid}.moe.gate", # phixtral
),
# Feed-forward up
@ -198,6 +199,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_UP_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w3", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral
"transformer.h.{bid}.moe.mlp.{xid}.fc1", # phixtral
),
# AWQ-activation gate
@ -240,6 +242,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_DOWN_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w2", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", # mixtral
"transformer.h.{bid}.moe.mlp.{xid}.fc2", # phixtral
),
MODEL_TENSOR.ATTN_Q_NORM: (

99
llama.cpp
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@ -578,8 +578,11 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
},
},
{
@ -1425,16 +1428,20 @@ struct llama_layer {
struct ggml_tensor * ffn_down; // w2
struct ggml_tensor * ffn_up; // w3
// ff bias
struct ggml_tensor * ffn_down_b; // b2
struct ggml_tensor * ffn_up_b; // b3
struct ggml_tensor * ffn_act;
// ff MoE
struct ggml_tensor * ffn_gate_inp;
struct ggml_tensor * ffn_gate_exp[LLAMA_MAX_EXPERTS];
struct ggml_tensor * ffn_down_exp[LLAMA_MAX_EXPERTS];
struct ggml_tensor * ffn_up_exp [LLAMA_MAX_EXPERTS];
// ff bias
struct ggml_tensor * ffn_down_b; // b2
struct ggml_tensor * ffn_up_b; // b3
struct ggml_tensor * ffn_act;
// ff MoE bias
struct ggml_tensor * ffn_down_b_exp[LLAMA_MAX_EXPERTS];
struct ggml_tensor * ffn_up_b_exp [LLAMA_MAX_EXPERTS];
};
struct llama_kv_cell {
@ -3696,11 +3703,29 @@ static bool llm_load_tensors(
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, false);
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
if (layer.ffn_gate_inp == nullptr) {
GGML_ASSERT(hparams.n_expert == 0);
GGML_ASSERT(hparams.n_expert_used == 0);
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
} else {
GGML_ASSERT(hparams.n_expert > 0);
GGML_ASSERT(hparams.n_expert_used > 0);
for (uint32_t x = 0; x < hparams.n_expert; ++x) {
layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), {n_ff, n_embd});
layer.ffn_down_b_exp[x] = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN_EXP, "bias", i, x), {n_embd});
layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff});
layer.ffn_up_b_exp[x] = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP_EXP, "bias", i, x), {n_ff});
}
}
}
} break;
case LLM_ARCH_PLAMO:
@ -5704,7 +5729,7 @@ struct llm_build_context {
}
// FF
{
if (model.layers[il].ffn_gate_inp == nullptr) {
ffn_output = llm_build_ffn(ctx0, attn_norm_output,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
@ -5712,6 +5737,62 @@ struct llm_build_context {
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(ffn_output, "ffn_out", il);
} else {
// MoE branch
ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
cb(logits, "ffn_moe_logits", il);
ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
cb(probs, "ffn_moe_probs", il);
// select experts
ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
cb(selected_experts->src[0], "ffn_moe_argsort", il);
ggml_tensor * weights = ggml_get_rows(ctx0,
ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
cb(weights, "ffn_moe_weights", il);
weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
cb(weights_sum, "ffn_moe_weights_sum", il);
weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
cb(weights, "ffn_moe_weights_norm", il);
// compute expert outputs
ggml_tensor * moe_out = nullptr;
for (int i = 0; i < n_expert_used; ++i) {
ggml_tensor * cur_expert;
ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exp, n_expert, selected_experts, i, cur);
#pragma message "TODO: implement ggml_add_id"
//cur_up = ggml_add_id(ctx0, cur_up, model.layers[il].ffn_up_b_exp, n_expert, selected_experts, i);
cb(cur_up, "ffn_moe_up", il);
cur_up = ggml_gelu(ctx0, cur_up);
cb(cur_up, "ffn_moe_gelu", il);
cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exp, n_expert, selected_experts, i, cur_up); // [n_tokens, n_embd]
#pragma message "TODO: implement ggml_add_id"
//cur_expert = ggml_add_id(ctx0, cur_expert, model.layers[il].ffn_down_b_exp, n_expert, selected_experts, i);
cb(cur_expert, "ffn_moe_down", il);
cur_expert = ggml_mul(ctx0, cur_expert,
ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
cb(cur_expert, "ffn_moe_weighted", il);
if (i == 0) {
moe_out = cur_expert;
} else {
moe_out = ggml_add(ctx0, moe_out, cur_expert);
cb(moe_out, "ffn_moe_out", il);
}
}
ffn_output = moe_out;
}
cur = ggml_add(ctx0, cur, ffn_output);