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Fix ffn_down quantization mix for MoE models (#4927)

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* Fix ffn_down quantization mix for MoE models

In #4872 I did not consider the part where every third
tensor is quantized with more bits. Fir MoE this leads to tensors
of the same layer being quantized with different number of bits,
which is not considered as a possibility in the inference implementation
(it is assumed all experts use the same quantization).

* Fix the fix

* Review suggestion

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow 2024-01-14 10:53:39 +02:00 committed by GitHub
parent 5f5fe1bd60
commit a128c38de8
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1 changed files with 26 additions and 8 deletions
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llama.cpp
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@ -8480,13 +8480,31 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
new_type = GGML_TYPE_Q8_0; new_type = GGML_TYPE_Q8_0;
} }
} else if (name.find("ffn_down") != std::string::npos) { } else if (name.find("ffn_down") != std::string::npos) {
const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
int i_layer, n_layer;
if (n_expert == 1) {
i_layer = qs.i_feed_forward_w2;
n_layer = qs.n_feed_forward_w2;
} else {
// Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
// sprinkled in the model. Hence, simply dividing i_feed_forward_w2 by n_expert does not work
// for getting the current layer as I initially thought, and we need to resort to parsing the
// tensor name.
n_layer = qs.n_feed_forward_w2 / n_expert;
if (sscanf(name.c_str(), "blk.%d.ffn_down", &i_layer) != 1) {
throw std::runtime_error(format("Failed to determine layer for tensor %s", name.c_str()));
}
if (i_layer < 0 || i_layer >= n_layer) {
throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name.c_str(), n_layer));
}
}
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) { else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
if (qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) new_type = GGML_TYPE_Q4_K; if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
} }
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q5_K new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
: arch != LLM_ARCH_FALCON || use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q4_K : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
: GGML_TYPE_Q3_K; : GGML_TYPE_Q3_K;
} }
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
@ -8494,14 +8512,14 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
} }
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
if (arch == LLM_ARCH_FALCON) { if (arch == LLM_ARCH_FALCON) {
new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q6_K : new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
} else { } else {
if (use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
} }
} }
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) { else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
new_type = GGML_TYPE_Q5_K; new_type = GGML_TYPE_Q5_K;
} }
++qs.i_feed_forward_w2; ++qs.i_feed_forward_w2;