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max sequence length for memory efficient attention
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@aciddelgado
aciddelgado committed Oct 27, 2023
1 parent db307f3 commit e7a50ee
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Showing 7 changed files with 53 additions and 51 deletions.
1 change: 1 addition & 0 deletions onnxruntime/contrib_ops/cuda/bert/attention_impl.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -372,6 +372,7 @@ Status EfficientAttention(
p.num_heads = parameters.num_heads;
p.sequence_length = parameters.sequence_length;
p.kv_sequence_length = parameters.total_sequence_length;
p.max_sequence_length = parameters.total_sequence_length;
p.qk_head_size = parameters.head_size;
p.v_head_size = parameters.v_head_size;
p.causal = parameters.is_unidirectional;
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13 changes: 7 additions & 6 deletions onnxruntime/contrib_ops/cuda/bert/cutlass_fmha/fmha_launch_template.h
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Expand Up @@ -54,6 +54,7 @@ void LaunchCutlassFmha(const MemoryEfficientAttentionParams& params) {
p.custom_mask_type = Attention::CausalFromBottomRight;
}

// We use max_sequence_length to calculate KV stride
if (params.is_kv_bsnh) {
// Input Q, K, V format is BxSxNxH, output is BxSxNxH
p.q_strideH = params.qk_head_size;
Expand All @@ -68,14 +69,14 @@ void LaunchCutlassFmha(const MemoryEfficientAttentionParams& params) {
p.bias_strideM = nullptr == params.attn_bias ? 0 : p.num_keys;

p.q_strideB = static_cast<int64_t>(p.q_strideM) * params.sequence_length;
p.k_strideB = static_cast<int64_t>(p.k_strideM) * params.kv_sequence_length;
p.v_strideB = static_cast<int64_t>(p.v_strideM) * params.kv_sequence_length;
p.k_strideB = static_cast<int64_t>(p.k_strideM) * params.max_sequence_length;
p.v_strideB = static_cast<int64_t>(p.v_strideM) * params.max_sequence_length;
p.bias_strideB = params.is_attn_bias_batched ? static_cast<int64_t>(p.bias_strideH) * params.num_heads : 0;
} else {
// Input K, V format is BxNxSxH, Input Q is BxSxNxH, output is BxSxNxH
p.q_strideH = params.qk_head_size;
p.k_strideH = params.kv_sequence_length * params.qk_head_size;
p.v_strideH = params.kv_sequence_length * params.v_head_size;
p.k_strideH = params.max_sequence_length * params.qk_head_size;
p.v_strideH = params.max_sequence_length * params.v_head_size;
p.bias_strideH = nullptr == params.attn_bias ? 0 : p.num_queries * p.num_keys;

p.q_strideM = params.num_heads * params.qk_head_size;
Expand All @@ -85,8 +86,8 @@ void LaunchCutlassFmha(const MemoryEfficientAttentionParams& params) {
p.bias_strideM = nullptr == params.attn_bias ? 0 : p.num_keys;

p.q_strideB = params.num_heads * params.qk_head_size * params.sequence_length;
p.k_strideB = params.num_heads * params.qk_head_size * params.kv_sequence_length;
p.v_strideB = params.num_heads * params.v_head_size * params.kv_sequence_length;
p.k_strideB = params.num_heads * params.qk_head_size * params.max_sequence_length;
p.v_strideB = params.num_heads * params.v_head_size * params.max_sequence_length;
p.bias_strideB = params.is_attn_bias_batched ? static_cast<int64_t>(p.bias_strideH) * params.num_heads : 0;
}
}
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5 changes: 2 additions & 3 deletions onnxruntime/contrib_ops/cuda/bert/group_query_attention.cc
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Expand Up @@ -154,9 +154,8 @@ Status GroupQueryAttention<T>::ComputeInternal(OpKernelContext* context) const {
has_memory_efficient_attention(sm, sizeof(T) == 2);
// allocate buffers
size_t kv_buffer_bytes = 0;
// need a buffer if we must ungroup kv or if kv-cache is present
const bool needs_buff = ((parameters.num_heads != parameters.kv_num_heads) ||
(past_key != nullptr && parameters.present_sequence_length != parameters.past_sequence_length + parameters.kv_sequence_length));
// need a buffer if we must ungroup kv
const bool needs_buff = (parameters.num_heads != parameters.kv_num_heads);
if (use_memory_efficient_attention && needs_buff) {
kv_buffer_bytes = (sizeof(T) * parameters.batch_size * parameters.num_heads * (parameters.past_sequence_length + parameters.kv_sequence_length) * parameters.head_size);

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[cpplint] onnxruntime/contrib_ops/cuda/bert/group_query_attention.cc#L160 

 Lines should be <= 120 characters long  [whitespace/line_length] [2]
Raw output 
onnxruntime/contrib_ops/cuda/bert/group_query_attention.cc:160:  Lines should be <= 120 characters long  [whitespace/line_length] [2]
}
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29 changes: 14 additions & 15 deletions onnxruntime/contrib_ops/cuda/bert/group_query_attention_impl.cu
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Expand Up @@ -506,20 +506,17 @@ Status EfficientAttention(
const void* query = reinterpret_cast<const void*>(data.query);
const void* key = reinterpret_cast<const void*>(data.key);
const void* value = reinterpret_cast<const void*>(data.value);
int final_kv_seqlen = kv_sequence_length;
if (data.past_key != nullptr) {
// Past key case
// concatenate new kv to past kv
if (data.past_key == data.present_key) {
ORT_RETURN_IF_ERROR(LaunchConcatKVInPlace(parameters, data, stream, max_threads_per_block));
final_kv_seqlen = past_sequence_length + kv_sequence_length;
} else {
ORT_RETURN_IF_ERROR(LaunchConcatNewToPastKV(parameters, data, stream, max_threads_per_block));
final_kv_seqlen = present_sequence_length;
}
const bool is_bsnh = past_kv_format == AttentionQkvFormat::Q_K_V_BSNH;
if (num_heads == kv_num_heads && present_sequence_length == past_sequence_length + kv_sequence_length) {
// Use present kv directly under specific conditions
if (num_heads == kv_num_heads) {
// Use present kv directly if not grouped
key = reinterpret_cast<const void*>(data.present_key);
value = reinterpret_cast<const void*>(data.present_value);
} else {
Expand All @@ -528,25 +525,26 @@ Status EfficientAttention(
float2* v_buff = reinterpret_cast<float2*>(data.v);
const float2* k_og = reinterpret_cast<const float2*>(data.present_key);
const float2* v_og = reinterpret_cast<const float2*>(data.present_value);
ORT_RETURN_IF_ERROR(LaunchUngroup(parameters, k_buff, v_buff, k_og, v_og, final_kv_seqlen,
ORT_RETURN_IF_ERROR(LaunchUngroup(parameters, k_buff, v_buff, k_og, v_og, past_sequence_length + kv_sequence_length,

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[cpplint] onnxruntime/contrib_ops/cuda/bert/group_query_attention_impl.cu#L528 

 Lines should be <= 120 characters long  [whitespace/line_length] [2]
Raw output 
onnxruntime/contrib_ops/cuda/bert/group_query_attention_impl.cu:528:  Lines should be <= 120 characters long  [whitespace/line_length] [2]
present_sequence_length, is_bsnh, stream, max_threads_per_block));
key = reinterpret_cast<const void*>(data.k);
value = reinterpret_cast<const void*>(data.v);
}
} else if (num_heads == kv_num_heads) {
// no past or present and no need to ungroup... still copy kv into present
// no past or present and no need to ungroup... still copy kv into present buffer
ORT_RETURN_IF_ERROR(LaunchConcatNewToPastKV(parameters, data, stream, max_threads_per_block));
key = reinterpret_cast<const void*>(data.key);
value = reinterpret_cast<const void*>(data.value);
key = reinterpret_cast<const void*>(data.present_key);
value = reinterpret_cast<const void*>(data.present_value);
} else {
// intermediate buffer so q and kv have same num heads... still copy kv into present
// intermediate buffer so q and kv have same num heads... still copy kv into present buffer
ORT_RETURN_IF_ERROR(LaunchConcatNewToPastKV(parameters, data, stream, max_threads_per_block));
float2* k_buff = reinterpret_cast<float2*>(data.k);
float2* v_buff = reinterpret_cast<float2*>(data.v);
const float2* k_og = reinterpret_cast<const float2*>(data.key);
const float2* v_og = reinterpret_cast<const float2*>(data.value);
ORT_RETURN_IF_ERROR(LaunchUngroup(parameters, k_buff, v_buff, k_og, v_og, final_kv_seqlen,
kv_sequence_length, true, stream, max_threads_per_block));
const float2* k_og = reinterpret_cast<const float2*>(data.present_key);
const float2* v_og = reinterpret_cast<const float2*>(data.present_value);
ORT_RETURN_IF_ERROR(LaunchUngroup(parameters, k_buff, v_buff, k_og, v_og, kv_sequence_length,
kv_sequence_length, past_kv_format == AttentionQkvFormat::Q_K_V_BSNH, stream,
max_threads_per_block));
key = reinterpret_cast<const void*>(data.k);
value = reinterpret_cast<const void*>(data.v);
}
Expand All @@ -557,7 +555,8 @@ Status EfficientAttention(
p.batch_size = batch_size;
p.num_heads = num_heads;
p.sequence_length = sequence_length;
p.kv_sequence_length = final_kv_seqlen;
p.kv_sequence_length = past_sequence_length + kv_sequence_length;
p.max_sequence_length = (num_heads == kv_num_heads) ? present_sequence_length : past_sequence_length + kv_sequence_length;

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[cpplint] onnxruntime/contrib_ops/cuda/bert/group_query_attention_impl.cu#L559 

 Lines should be <= 120 characters long  [whitespace/line_length] [2]
Raw output 
onnxruntime/contrib_ops/cuda/bert/group_query_attention_impl.cu:559:  Lines should be <= 120 characters long  [whitespace/line_length] [2]
p.qk_head_size = head_size;
p.v_head_size = head_size;
p.causal = parameters.is_unidirectional;
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1 change: 1 addition & 0 deletions onnxruntime/contrib_ops/cuda/bert/packed_attention_impl.cu
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Expand Up @@ -512,6 +512,7 @@ Status FusedScaledDotProductAttentionCutlass(
p.num_heads = parameters.num_heads;
p.sequence_length = parameters.sequence_length;
p.kv_sequence_length = parameters.sequence_length;
p.max_sequence_length = parameters.sequence_length;
p.qk_head_size = parameters.head_size;
p.v_head_size = parameters.v_head_size;
p.causal = false;
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1 change: 1 addition & 0 deletions onnxruntime/contrib_ops/cuda/bert/packed_multihead_attention_impl.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -688,6 +688,7 @@ Status FusedAttentionCutlass(
p.num_heads = parameters.num_heads;
p.sequence_length = parameters.sequence_length;
p.kv_sequence_length = parameters.sequence_length;
p.max_sequence_length = parameters.sequence_length;
p.qk_head_size = parameters.head_size;
p.v_head_size = parameters.v_head_size;
p.causal = false;
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54 changes: 27 additions & 27 deletions onnxruntime/test/python/transformers/test_flash_attn.py
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Expand Up @@ -1231,7 +1231,7 @@ def test_gqa_no_past(self):
]
)
num_h = [(9, 3), (4, 4)] if pipeline_mode else [(6, 6), (6, 3), (9, 9), (9, 3)]
h_sizes = [16, 256] if pipeline_mode else [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]
h_sizes = [16, 256] if pipeline_mode else [32, 40, 64, 80, 96, 128, 160, 192, 224, 256]
if major < 5 or (major == 5 and minor < 3):
return
print("------- MEMORY EFFICIENT ATTENTION ---------")
Expand Down Expand Up @@ -1307,32 +1307,32 @@ def test_gqa_past(self):
rtol=1e-3,
atol=1e-3,
)
if major < 8 or platform.system() != "Linux":
return
print("------- FLASH ATTENTION -------")
os.environ["ORT_DISABLE_FLASH_ATTENTION"] = "0"
for b in batches:
for s, s2 in seqs:
for n, n2 in num_h:
for h in h_sizes:
for causal in [True]:
for past_kv_format in [Formats.BNSH, Formats.BSNH]:
sp = random.randint(1, s2 - s) if s2 - s > 0 else 0
config = Config(b, s, s2, sp, n, n2, h)
parity_check_gqa_past(
config,
causal=causal,
past_format=past_kv_format,
rtol=1e-3,
atol=1e-3,
)
parity_check_gqa_past_no_buff(
config,
causal=causal,
past_format=past_kv_format,
rtol=1e-3,
atol=1e-3,
)
# if major < 8 or platform.system() != "Linux":
# return
# print("------- FLASH ATTENTION -------")
# os.environ["ORT_DISABLE_FLASH_ATTENTION"] = "0"
# for b in batches:
# for s, s2 in seqs:
# for n, n2 in num_h:
# for h in h_sizes:
# for causal in [True]:
# for past_kv_format in [Formats.BNSH, Formats.BSNH]:
# sp = random.randint(1, s2 - s) if s2 - s > 0 else 0
# config = Config(b, s, s2, sp, n, n2, h)
# parity_check_gqa_past(
# config,
# causal=causal,
# past_format=past_kv_format,
# rtol=1e-3,
# atol=1e-3,
# )
# parity_check_gqa_past_no_buff(
# config,
# causal=causal,
# past_format=past_kv_format,
# rtol=1e-3,
# atol=1e-3,
# )


if __name__ == "__main__":
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