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llama: use sliding window for phi3 #8627

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merged 3 commits into from
Jul 25, 2024
Merged

llama: use sliding window for phi3 #8627

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03cb5cd
use sliding window for phi3
FanShupei Jul 22, 2024
b4e3de6
fix typo, "data_swa" -> "data"
FanShupei Jul 22, 2024
19d6ad9
[conver_hf_to_gguf.py] add phi3 sliding window
FanShupei Jul 24, 2024
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1 change: 1 addition & 0 deletions convert_hf_to_gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2078,6 +2078,7 @@ def set_gguf_parameters(self):
self.gguf_writer.add_rope_dimension_count(rope_dims)
self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"]))

# write rope scaling for long context (128k) model
rope_scaling = self.find_hparam(['rope_scaling'], True)
Expand Down
37 changes: 28 additions & 9 deletions src/llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4974,6 +4974,7 @@ static void llm_load_hparams(
} break;
case LLM_ARCH_PHI3:
{
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

switch (hparams.n_layer) {
Expand Down Expand Up @@ -10843,7 +10844,7 @@ struct llm_build_context {
struct ggml_tensor * inp_pos = build_inp_pos();

// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();

for (int il = 0; il < n_layer; ++il) {
auto residual = inpL;
Expand Down Expand Up @@ -10901,7 +10902,7 @@ struct llm_build_context {

cur = llm_build_kv(ctx0, lctx, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il);
}

if (il == n_layer - 1) {
Expand Down Expand Up @@ -14108,18 +14109,23 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
"causal attention is not supported by this model"
);

if (lctx.inp_KQ_mask) {
if (lctx.inp_KQ_mask || lctx.inp_KQ_mask_swa) {
// NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
if (cparams.causal_attn && !lctx.is_encoding) {
const int64_t n_kv = kv_self.n;
const int64_t n_tokens = batch.n_tokens;

GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));

float * data = (float *) lctx.inp_KQ_mask->data;
float * data = nullptr;
float * data_swa = nullptr;

if (lctx.inp_KQ_mask) {
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
data = (float *) lctx.inp_KQ_mask->data;
}

if (lctx.inp_KQ_mask_swa) {
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_swa->buffer));
data_swa = (float *) lctx.inp_KQ_mask_swa->data;
}

Expand All @@ -14142,7 +14148,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
f = 0.0f;
}
}
data[h*(n_kv*n_tokens) + j*n_kv + i] = f;

if (data) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
}

// may need to cut off old tokens for sliding window
if (data_swa) {
Expand All @@ -14154,9 +14163,19 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}

for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (int j = 0; j < n_kv; ++j) {
data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
if (data) {
for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (int j = 0; j < n_kv; ++j) {
data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
}
}
}

if (data_swa) {
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data_swa is also modified by the code above (line 14158). It is used by gemma2.

Overwriting it here may break gemma2

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Sorry I'm not familiar with gemma2, so I haven't test the PR on gemma2. I only test this PR with Phi3 on CPU.

I do not understand when it should padded to GGML_KD_MASK_PAD or not. I pad data_swa to GGML_KD_MASK_PAD because it looks like the original just forgets to do so. Actually, padding data_swa or not does not affect the correctness of Phi3 on CPU.

I'm confused on the original code that data is explicitly padded to GGML_KQ_MASK_PAD but data_swa is not. Is this the intended behavior? If yes, I'm happy to revert the change (padding data_swa to GGML_KQ_MASK_PAD). but I still want someone could explain to me what GGML_KQ_MASK_PAD actually means.

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@ngxson I agree with @FanShupei here, I think data_swa should also be padded. I don't see why not, since the ranges of data written here and above do not overlap.

Not sure why this worked before though. Padding data_swa seems saner than leaving the values uninitialized.

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Both should be padded. The padding is necessary so that GPU kernels (such as the Metal Flash-Attention) not perform extra checks for out-of-bounds access when working on chunks of data

for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (int j = 0; j < n_kv; ++j) {
data_swa[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
}
}
}
}
Expand Down
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