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Add Continuous Decoding support in GQA #21523

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merged 28 commits into from
Sep 13, 2024
Merged

Add Continuous Decoding support in GQA #21523

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ee47ba4
gqa supports interactive
aciddelgado Jul 26, 2024
d938816
lint, clang, clean-up manually
aciddelgado Jul 26, 2024
01cd33b
Merge branch 'main' into aciddelgado/gqa_interactive
aciddelgado Jul 29, 2024
fdc84b4
new idea, seqlense_q
aciddelgado Jul 30, 2024
1cddf5f
cpu update
aciddelgado Jul 31, 2024
8e3483e
cpu almost works but segfaults on non-interactive prompt but we gotta…
aciddelgado Jul 31, 2024
60fe746
single batch implementation unclean
aciddelgado Aug 6, 2024
dd3c4a6
clean up code
aciddelgado Aug 6, 2024
3565dc2
clang lint
aciddelgado Aug 6, 2024
bd83af7
changes
aciddelgado Aug 8, 2024
aaa9866
trigger pipelines and whatnot
aciddelgado Aug 27, 2024
d4e72f8
merge main
aciddelgado Aug 27, 2024
d8f37c0
pipeline
aciddelgado Aug 27, 2024
548ab9b
pipelines
aciddelgado Aug 27, 2024
8358819
merge main
aciddelgado Sep 4, 2024
5ff6050
minor rotary test change
aciddelgado Sep 6, 2024
255fa1c
pls
aciddelgado Sep 6, 2024
11c4a0e
fixes
aciddelgado Sep 9, 2024
4a86c55
docs
aciddelgado Sep 10, 2024
0be4962
address comments
aciddelgado Sep 10, 2024
c0cb4c5
docs
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019b058
comments
aciddelgado Sep 11, 2024
e46ac2d
remove cuda helper
aciddelgado Sep 11, 2024
a9e4c76
rocm
aciddelgado Sep 11, 2024
d92e0f4
lint
aciddelgado Sep 12, 2024
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3 changes: 2 additions & 1 deletion onnxruntime/contrib_ops/cpu/bert/attention_common.h
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Expand Up @@ -114,7 +114,8 @@ struct GroupQueryAttentionParameters {
int local_window_size;
bool kv_share_buffer;
bool is_packed_qkv;
bool is_prompt; // determines if seqlens_k is past or kv sequence length tensor
bool is_interactive; // indicates whether we have past context and seqlen > 1
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bool is_prompt; // indicates whether this is first decoding step
bool do_rotary;
bool rotary_interleaved;
bool use_smooth_softmax;
Expand Down
11 changes: 4 additions & 7 deletions onnxruntime/contrib_ops/cpu/bert/attention_helper.h
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Expand Up @@ -236,19 +236,16 @@ T* ConcatStateChunkGQA(const T* past,
size_t past_buff_chunk_length,
size_t past_chunk_length,
size_t new_chunk_length,
bool is_prompt,
bool past_present_share_buffer,
std::ptrdiff_t i) {
T* start = present + i * present_buff_chunk_length;

T* p = start;
if (!is_prompt) {
if (!past_present_share_buffer) {
const T* src_past = past + i * past_buff_chunk_length;
memcpy(p, src_past, past_chunk_length * sizeof(T));
}
p += past_chunk_length;
if (!past_present_share_buffer && past_chunk_length > 0) {
const T* src_past = past + i * past_buff_chunk_length;
memcpy(p, src_past, past_chunk_length * sizeof(T));
}
p += past_chunk_length;

memcpy(p, chunk, new_chunk_length * sizeof(T));
return start;
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177 changes: 89 additions & 88 deletions onnxruntime/contrib_ops/cpu/bert/gqa_attention_base.h
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31 changes: 23 additions & 8 deletions onnxruntime/contrib_ops/cpu/bert/group_query_attention.cc
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Expand Up @@ -45,7 +45,7 @@
const Tensor* past_key = context->Input<Tensor>(3);
const Tensor* past_value = context->Input<Tensor>(4);
const Tensor* seqlens_k = context->Input<Tensor>(5);
const Tensor* total_seqlen = context->Input<Tensor>(6);
const Tensor* total_seqlen_tensor = context->Input<Tensor>(6);
const Tensor* cos_cache = context->Input<Tensor>(7);
const Tensor* sin_cache = context->Input<Tensor>(8);

Expand All @@ -61,7 +61,7 @@
num_heads_,
kv_num_heads_,
seqlens_k,
total_seqlen,
total_seqlen_tensor,
scale_,
softcap_));

Expand Down Expand Up @@ -103,6 +103,7 @@
}

if (do_rotary_) {
// Initialize rotary parameters
rotary_embedding_helper::RotaryParameters rotary_params = {};
rotary_params.batch_size = batch_size;
rotary_params.sequence_length = sequence_length;
Expand All @@ -114,17 +115,29 @@
rotary_params.seq_stride = head_size;
rotary_params.head_stride = sequence_length * rotary_params.seq_stride;
rotary_params.batch_stride = (packed_qkv ? (num_heads_ + 2 * kv_num_heads_) : num_heads_) * rotary_params.head_stride;
rotary_params.position_ids_format = sequence_length == 1 ? 1 : 0;
rotary_params.position_ids_format = parameters.is_interactive || !parameters.is_prompt ? 1 : 0;
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rotary_params.transposed = true;
auto* tp = context->GetOperatorThreadPool();
std::vector<int64_t> pos_ids(sequence_length == 1 ? batch_size : 1);
if (sequence_length == 1) {
// Generate position ids
const int pos_ids_size = (parameters.is_prompt && !parameters.is_interactive) ? 1 : batch_size * sequence_length;
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std::vector<int64_t> pos_ids(pos_ids_size);
if (parameters.is_prompt) {
pos_ids[0] = static_cast<int64_t>(0);
} else {
// Note: As of now, interactive decoding supports only batch size 1 and token generation supports only sequence length 1.

Check warning on line 127 in onnxruntime/contrib_ops/cpu/bert/group_query_attention.cc

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GitHub Actions / Optional Lint C++ 

[cpplint] reported by reviewdog 🐶
 Lines should be <= 120 characters long  [whitespace/line_length] [2]

Raw Output:
onnxruntime/contrib_ops/cpu/bert/group_query_attention.cc:127:  Lines should be <= 120 characters long  [whitespace/line_length] [2]
for (int b = 0; b < batch_size; b++) {
pos_ids[b] = static_cast<int64_t>(seqlens_k->Data<int32_t>()[b]);
for (int s = 0; s < sequence_length; s++) {
const int total_seqlen = seqlens_k->Data<int32_t>()[b] + 1;
const int past_seqlen = total_seqlen - sequence_length;
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if (past_seqlen + s < total_seqlen) {
pos_ids[b * sequence_length + s] = static_cast<int64_t>(past_seqlen) + s;
} else {
pos_ids[b * sequence_length + s] = static_cast<int64_t>(1);
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}
}
}
} else {
pos_ids[0] = static_cast<int64_t>(0);
}
// Initialize separate buffers for rotary embeddings
const T* q_input;
const T* k_input;
T* q_rotary;
Expand All @@ -149,6 +162,7 @@
Q = RotaryQ;
K = RotaryK;
}
// Run rotary embedding for Q and K
ORT_RETURN_IF_ERROR(RunRotaryEmbedding<T>(tp, rotary_params, q_input,
pos_ids.data(), cos_cache->Data<T>(),
sin_cache->Data<T>(), q_rotary, rotary_interleaved_));
Expand All @@ -161,6 +175,7 @@
ORT_RETURN_IF_ERROR(RunRotaryEmbedding<T>(tp, rotary_params, k_input,
pos_ids.data(), cos_cache->Data<T>(),
sin_cache->Data<T>(), k_rotary, rotary_interleaved_));
// Pack V into rotary QKV buffer
if (packed_qkv) {
const T* v_input = k_input + kv_num_heads_ * sequence_length * head_size;
T* v_rotary = k_rotary + kv_num_heads_ * sequence_length * head_size;
Expand Down
33 changes: 26 additions & 7 deletions onnxruntime/contrib_ops/cpu/bert/group_query_attention_helper.h
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Expand Up @@ -168,14 +168,13 @@ Status CheckInputs(const Tensor* query,
"Input 'past_key' and 'past_value' shall be both present or both absent.");
}

// Check seqlens_k tensor (holding past seqlen for token gen)
const auto& seqlens_dim = seqlens_k->Shape().GetDims();
if (seqlens_dim.size() != 1 && seqlens_dim[0] != batch_size) {
const auto& seqlens_k_dim = seqlens_k->Shape().GetDims();
if (seqlens_k_dim[0] != batch_size) {
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return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"seqlens_k must be shape (batch_size).");
}

// Set present sequence length and kv_share_buffer from input total_seqlen tensor
// Set present sequence length from input total_seqlen tensor
if (!onnxruntime::IsScalarOr1ElementVector(total_seqlen)) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"total_sequence_length tensor must be of one element.");
Expand All @@ -195,11 +194,11 @@ Status CheckInputs(const Tensor* query,
}
if (cos_dims[0] < total_sequence_length) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"cos_cache dimension 0 should be not be less than total_sequence_length.");
"cos_cache dimension 0 shall not be less than total_sequence_length.");
}
if (sin_dims[0] < total_sequence_length) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"sin_cache dimension 0 should be not be less than total_sequence_length.");
"sin_cache dimension 0 shall not be less than total_sequence_length.");
}
if (cos_dims[1] > (head_size / 16) * 8 || cos_dims[1] % 8 != 0) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
Expand All @@ -219,7 +218,26 @@ Status CheckInputs(const Tensor* query,
"Input 'cos_cache' and 'sin_cache' shall be both present or both absent.");
}

bool is_prompt = sequence_length != 1;
bool is_interactive = false;
if (sequence_length > 1 && sequence_length != total_sequence_length) {
if (batch_size != 1) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"batch_size must be 1 when sequence_length > 1 and past context is given.");
}
is_interactive = true;
}

bool is_prompt;
if (is_interactive) {
is_prompt = false; // irrelevant for interactive decoding
} else {
// If not interactive, sequence_length is 1 for token gen and arbitrarily large for prompt
is_prompt = (sequence_length == total_sequence_length);
if (!is_prompt && sequence_length != 1) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"sequence_length shall be 1 when it is not prompt.");
}
}

if (parameters != nullptr) {
GroupQueryAttentionParameters* output_parameters = reinterpret_cast<GroupQueryAttentionParameters*>(parameters);
Expand All @@ -235,6 +253,7 @@ Status CheckInputs(const Tensor* query,
output_parameters->rotary_dim = rotary_dim;
output_parameters->is_packed_qkv = is_packed_qkv;
output_parameters->is_unidirectional = true;
output_parameters->is_interactive = is_interactive;
output_parameters->is_prompt = is_prompt;
output_parameters->scale = scale;
output_parameters->softcap = softcap;
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/contrib_ops/cpu/sparse/sparse_attention_base.h
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Expand Up @@ -184,7 +184,7 @@ class SparseAttentionBase {
// Concatenate past_k + k -> present_k
// TODO: avoid copying mutiple times for a group.
k = ConcatStateChunkGQA(past_key, k, present_key, present_buff_chunk_length, past_buff_chunk_length,
past_chunk_length, kv_input_chunk_length, is_prompt, past_present_share_buffer,
is_prompt ? 0 : past_chunk_length, kv_input_chunk_length, past_present_share_buffer,
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i / kv_num_heads_factor);

// Compute Q*K' + AttentionMask
Expand Down Expand Up @@ -365,7 +365,7 @@ class SparseAttentionBase {

// Concatenate past_v + v -> present_v
v = ConcatStateChunkGQA(past_value, v, present_value, present_buff_chunk_length, past_buff_chunk_length,
past_chunk_length, kv_input_chunk_length, is_prompt, past_present_share_buffer,
is_prompt ? 0 : past_chunk_length, kv_input_chunk_length, past_present_share_buffer,
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i / kv_num_heads_factor);

DUMP_CPU_TENSOR("present_value", v, total_seq_len, head_size);
Expand Down
1 change: 0 additions & 1 deletion onnxruntime/contrib_ops/cuda/bert/cutlass_fmha/fmha_launch_template.h
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Expand Up @@ -42,7 +42,6 @@ struct RightPaddingBatchHook {

auto lse_dim = ceil_div((int32_t)(p.num_queries), kAlignLSE) * kAlignLSE;

// Advance to current batch - in case of different sequence lengths
if (p.seqlen_k_ptr) {
p.num_keys = p.seqlen_k_ptr[batch_id];
}
Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/contrib_ops/cuda/bert/group_query_attention.cc
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Expand Up @@ -253,7 +253,7 @@ Status GroupQueryAttention<T>::ComputeInternal(OpKernelContext* context) const {
data.out_accum = reinterpret_cast<CudaT*>(out_accum_buffer.get());
}
if (seqlens_k_buffer != nullptr) {
data.seqlens_k_total = reinterpret_cast<int*>(seqlens_k_buffer.get());
data.seqlens_k_buff = reinterpret_cast<int*>(seqlens_k_buffer.get());
}
// Memory Efficient Buffers
if (k_buffer != nullptr) {
Expand Down
35 changes: 25 additions & 10 deletions onnxruntime/contrib_ops/cuda/bert/group_query_attention_helper.h
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Expand Up @@ -189,14 +189,13 @@ Status CheckInputs(const Tensor* query,
"Input 'past_key' and 'past_value' shall be both present or both absent.");
}

// Check seqlens_k tensor (holding past seqlen for token gen)
const auto& seqlens_dim = seqlens_k->Shape().GetDims();
if (seqlens_dim.size() != 1 && seqlens_dim[0] != batch_size) {
const auto& seqlens_k_dim = seqlens_k->Shape().GetDims();
if (seqlens_k_dim[0] != batch_size) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"seqlens_k must be shape (batch_size).");
}

// Set present sequence length and kv_share_buffer from input total_seqlen tensor
// Set present sequence length from input total_seqlen tensor
if (!onnxruntime::IsScalarOr1ElementVector(total_seqlen)) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"total_sequence_length tensor must be of one element.");
Expand All @@ -216,11 +215,11 @@ Status CheckInputs(const Tensor* query,
}
if (cos_dims[0] < total_sequence_length) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"cos_cache dimension 0 should be not be less than total_sequence_length.");
"cos_cache dimension 0 shall not be less than total_sequence_length.");
}
if (sin_dims[0] < total_sequence_length) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"sin_cache dimension 0 should be not be less than total_sequence_length.");
"sin_cache dimension 0 shall not be less than total_sequence_length.");
}
if (cos_dims[1] > (head_size / 16) * 8 || cos_dims[1] % 8 != 0) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
Expand All @@ -240,10 +239,25 @@ Status CheckInputs(const Tensor* query,
"Input 'cos_cache' and 'sin_cache' shall be both present or both absent.");
}

bool is_prompt = (sequence_length == total_sequence_length);
if (!is_prompt && sequence_length != 1) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"sequence_length shall be 1 when it is not prompt.");
bool is_interactive = false;
if (sequence_length > 1 && sequence_length != total_sequence_length) {
if (batch_size != 1) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"batch_size must be 1 when sequence_length > 1 and past context is given.");
}
is_interactive = true;
}

bool is_prompt;
if (is_interactive) {
is_prompt = false; // irrelevant for interactive decoding
} else {
// If not interactive, sequence_length is 1 for token gen and arbitrarily large for prompt
is_prompt = (sequence_length == total_sequence_length);
if (!is_prompt && sequence_length != 1) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"sequence_length shall be 1 when it is not prompt.");
}
}

if (parameters != nullptr) {
Expand All @@ -260,6 +274,7 @@ Status CheckInputs(const Tensor* query,
output_parameters->kv_num_heads = kv_num_heads;
output_parameters->rotary_dim = rotary_dim;
output_parameters->is_packed_qkv = is_packed_qkv;
output_parameters->is_interactive = is_interactive;
output_parameters->is_prompt = is_prompt;
output_parameters->scale = scale;
output_parameters->softcap = softcap;
Expand Down
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