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未验证 提交 9f2ccf5b 编写于 作者: T tensor-tang 提交者： GitHub
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Merge pull request #13237 from tensor-tang/refine/op/peephole 

refine fusion lstm/peephole and fusion gru
上级 225ecee5 718033e1
显示空白变更内容
内联 并排
Showing with 254 addition and 329 deletion
paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
浏览文件 @ 9f2ccf5b
......@@ -11,6 +11,7 @@
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/fc_lstm_fuse_pass.h"
#include <string>
#include "paddle/fluid/framework/lod_tensor.h"
......
paddle/fluid/operators/fusion_gru_op.cc
浏览文件 @ 9f2ccf5b
......@@ -30,14 +30,7 @@ void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const {
"Input(WeightX) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasInput("WeightH"),
"Input(WeightH) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("XX"), "Output(XX) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
"Output(ReorderedH0) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
"Output(BatchedInput) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedOut"),
"Output(BatchedOut) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(Hidden) of GRU should not be null.");
......@@ -80,15 +73,20 @@ void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const {
}
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchedOut", out_dims);
ctx->ShareLoD("X", "Hidden");
int xx_width;
if (ctx->Attrs().Get<bool>("use_seq")) {
xx_width = wx_dims[1];
} else {
xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
"Output(ReorderedH0) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
"Output(BatchedInput) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedOut"),
"Output(BatchedOut) of GRU should not be null.");
ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchedOut", out_dims);
}
ctx->SetOutputDim("XX", {x_dims[0], xx_width});
ctx->ShareLoD("X", "XX");
......
paddle/fluid/operators/fusion_lstm_op.cc
浏览文件 @ 9f2ccf5b
......@@ -38,16 +38,6 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
"Output(Hidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Cell"),
"Output(Cell) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
"Output(BatchedInput) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedHidden"),
"Output(BatchedHidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedCell"),
"Output(BatchedCell) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
"Output(ReorderedH0) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ReorderedC0"),
"Output(ReorderedC0) of LSTM should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
......@@ -88,9 +78,8 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
PADDLE_ENFORCE_EQ(b_dims[0], 1,
"The first dimension of Input(Bias) should be 1.");
auto use_peepholes = ctx->Attrs().Get<bool>("use_peepholes");
PADDLE_ENFORCE_EQ(b_dims[1], (use_peepholes ? 7 : 4) * frame_size,
PADDLE_ENFORCE_EQ(
b_dims[1], (ctx->Attrs().Get<bool>("use_peepholes") ? 7 : 4) * frame_size,
"The second dimension of Input(Bias) should be "
"7 * %d if enable peepholes connection or"
"4 * %d if disable peepholes",
......@@ -99,17 +88,26 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("Cell", out_dims);
ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchedHidden", out_dims);
ctx->SetOutputDim("BatchedCell", out_dims);
ctx->ShareLoD("X", "Hidden");
ctx->ShareLoD("X", "Cell");
int xx_width;
if (ctx->Attrs().Get<bool>("use_seq")) {
xx_width = wx_dims[1];
} else {
xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
"Output(BatchedInput) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedHidden"),
"Output(BatchedHidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedCell"),
"Output(BatchedCell) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
"Output(ReorderedH0) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ReorderedC0"),
"Output(ReorderedC0) of LSTM should not be null.");
ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchedHidden", out_dims);
ctx->SetOutputDim("BatchedCell", out_dims);
}
ctx->SetOutputDim("XX", {x_dims[0], xx_width});
ctx->ShareLoD("X", "XX");
......@@ -242,8 +240,8 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
auto* cell_out = ctx.Output<LoDTensor>("Cell"); \
bool use_peepholes = ctx.Attr<bool>("use_peepholes"); \
bool is_reverse = ctx.Attr<bool>("is_reverse");
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
bool use_peepholes = ctx.Attr<bool>("use_peepholes");
#define INIT_BASE_SIZES \
auto x_dims = x->dims(); /* T x M*/ \
......@@ -254,172 +252,183 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
const int D3 = D * 3; \
const int D4 = wh_dims[1];
#define INIT_BASE_INPUT_DATAS \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/ \
const T* wc_data = bias->data<T>() + D4; \
/* for peephole only*/ \
Tensor checked_cell; \
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/ \
checked_cell_data = checked_cell.mutable_data<T>({2, D}, place); \
}
/// Compute LSTM
#define GEMM_WH_ADDON(bs, prev, out) \
blas.GEMM(CblasNoTrans, CblasNoTrans, bs, D4, D, static_cast<T>(1), prev, D, \
wh_data, D4, static_cast<T>(1), out, D4)
// gates: W_ch, W_ih, W_fh, W_oh
#define GET_Ct(ct_1, gates, ct) \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
act_cand(D, gates, gates); \
blas.VMUL(D, gates, gates + D, gates + D); \
blas.VMUL(D, ct_1, gates + D2, gates + D2); \
blas.VADD(D, gates + D, gates + D2, ct)
#define GET_Ht(ct, gates, ht) \
/* H_t = act_cell(C_t) * ogated */ \
act_cell(D, ct, gates + D2); \
blas.VMUL(D, gates + D2, gates + D3, ht)
#define GET_Ct_NOH0C0(gates, ct) \
/* C_t = igated * cgated*/ \
act_gate(D, gates + D, gates + D); \
act_cand(D, gates, gates); \
blas.VMUL(D, gates, gates + D, ct)
#define COMPUTE_CtHt_NOH0C0(gates, ct, ht) \
GET_Ct_NOH0C0(gates, ct); \
act_gate(D, gates + D3, gates + D3); \
GET_Ht(ct, gates, ht)
#define COMPUTE_CtHt_PEEPHOLE_NOH0C0(gates, ct, ht) \
GET_Ct_NOH0C0(gates, ct); \
/* get outgated, put W_oc * C_t on igated */ \
blas.VMUL(D, wc_data + D2, ct, gates + D); \
blas.VADD(D, gates + D, gates + D3, gates + D3); \
act_gate(D, gates + D3, gates + D3); \
GET_Ht(ct, gates, ht)
#define COMPUTE_CtHt(gates, ct_1, ct, ht) \
act_gate(D3, gates + D, gates + D); \
GET_Ct(ct_1, gates, ct); \
GET_Ht(ct, gates, ht)
#define COMPUTE_CtHt_PEEPHOLE(gates, ct_1, ct, ht) \
/* get fgated and igated*/ \
blas.VMUL(D, wc_data, ct_1, checked_cell_data); \
blas.VMUL(D, wc_data + D, ct_1, checked_cell_data + D); \
blas.VADD(D2, checked_cell_data, gates + D, gates + D); \
act_gate(D2, gates + D, gates + D); \
GET_Ct(ct_1, gates, ct); \
/* get ogated*/ \
blas.VMUL(D, wc_data + D2, ct, gates + D); \
blas.VADD(D, gates + D, gates + D3, gates + D3); \
act_gate(D, gates + D3, gates + D3); \
GET_Ht(ct, gates, ht)
void SeqCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_VEC_FUNC
INIT_BASE_INPUT_DATAS
auto x_lod = x->lod();
const int total_T = x_dims[0];
const int N = x_lod[0].size() - 1; // batch size
const T* x_data = x->data<T>();
const int N = x_lod[0].size() - 1;
const T* h0_data = h0 ? h0->data<T>() : nullptr;
const T* c0_data = c0 ? c0->data<T>() : nullptr;
const T* bias_data = bias->data<T>();
const T* wc_data = bias_data + D4; // w_ic, w_fc, w_oc
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* cell_out_data = cell_out->mutable_data<T>(ctx.GetPlace());
// use local variable
framework::DDim check_dims({3, D});
Tensor checked_cell; // w_ic * Ct-1, w_fc * Ct-1, w_oc * Ct
auto checked_cell_data =
checked_cell.mutable_data<T>(check_dims, ctx.GetPlace());
T* xx_data = xx->mutable_data<T>(place);
T* h_out_data = hidden_out->mutable_data<T>(place);
T* c_out_data = cell_out->mutable_data<T>(place);
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D4, M, x_data, wx_data,
xx_data, bias->data<T>());
int xx_offset = D4;
int gate_offset = D;
if (is_reverse) {
const int offset = (total_T - 1) * D;
xx_data = xx_data + offset * 4;
hidden_out_data = hidden_out_data + offset;
cell_out_data = cell_out_data + offset;
h_out_data = h_out_data + offset;
c_out_data = c_out_data + offset;
xx_offset = -D4;
gate_offset = -D;
}
auto move_step = [&]() {
xx_data = xx_data + xx_offset;
hidden_out_data = hidden_out_data + gate_offset;
cell_out_data = cell_out_data + gate_offset;
};
for (int i = 0; i < N; ++i) {
int bid = is_reverse ? N - 1 - i : i;
int seq_len = x_lod[0][bid + 1] - x_lod[0][bid];
const T* prev_c_data = nullptr;
const T* prev_h_data = nullptr;
int tstart = 0;
if (h0_data) {
prev_h_data = h0_data + bid * D;
prev_c_data = c0_data + bid * D;
} else {
// If step == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros. Then W_h * H_t-1 can be skipped
#define MOVE_ONE_STEP \
prev_h_data = h_out_data; \
prev_c_data = c_out_data; \
xx_data = xx_data + xx_offset; \
h_out_data = h_out_data + gate_offset; \
c_out_data = c_out_data + gate_offset
#define PROCESS_H0C0_DEFINES \
int bid = is_reverse ? N - 1 - i : i; \
int seq_len = x_lod[0][bid + 1] - x_lod[0][bid]; \
const T* prev_c_data = nullptr; \
const T* prev_h_data = nullptr; \
int tstart = 0
#define PROCESS_H0C0_PEEPHOLE \
PROCESS_H0C0_DEFINES; \
if (h0_data) { \
prev_h_data = h0_data + bid * D; \
prev_c_data = c0_data + bid * D; \
} else { \
COMPUTE_CtHt_PEEPHOLE_NOH0C0(xx_data, c_out_data, h_out_data); \
MOVE_ONE_STEP; \
tstart = 1; \
}
// ~C_t
act_cand(D, xx_data, xx_data);
if (use_peepholes) {
// I_t, F_t
act_gate(D2, xx_data + D, xx_data + D);
} else {
// I_t, F_t, O_t
act_gate(D3, xx_data + D, xx_data + D);
#define PROCESS_H0C0 \
PROCESS_H0C0_DEFINES; \
if (h0_data) { \
prev_h_data = h0_data + bid * D; \
prev_c_data = c0_data + bid * D; \
} else { \
COMPUTE_CtHt_NOH0C0(xx_data, c_out_data, h_out_data); \
MOVE_ONE_STEP; \
tstart = 1; \
}
// C_t = I_t * ~C_t
blas.VMUL(D, xx_data, xx_data + D, cell_out_data);
if (use_peepholes) {
// + W_oc * C_t for peephole connection
blas.VMUL(D, wc_data + D2, cell_out_data, checked_cell_data + D2);
blas.VADD(D, xx_data + D3, checked_cell_data + D2, xx_data + D3);
// O_t
act_gate(D, xx_data + D3, xx_data + D3);
for (int i = 0; i < N; ++i) {
PROCESS_H0C0_PEEPHOLE
for (int step = tstart; step < seq_len; ++step) {
GEMM_WH_ADDON(1, prev_h_data, xx_data);
COMPUTE_CtHt_PEEPHOLE(xx_data, prev_c_data, c_out_data, h_out_data);
MOVE_ONE_STEP;
}
// hidden out= act_state(cellout) * outgate
act_cell(D, cell_out_data, xx_data + D2);
// H_t = O_t * act_state(C_t)
blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
// prev
prev_h_data = hidden_out_data;
prev_c_data = cell_out_data;
tstart = 1;
move_step();
}
for (int step = tstart; step < seq_len; ++step) {
// + W_h * H_t-1
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D4, D, static_cast<T>(1),
prev_h_data, D, wh_data, D4, static_cast<T>(1), xx_data, D4);
// ~C_t
act_cand(D, xx_data, xx_data);
if (use_peepholes) {
// + W_ic|W_fc * C_t-1 for peephole connection
blas.VMUL(D, wc_data, prev_c_data, checked_cell_data);
blas.VMUL(D, wc_data + D, prev_c_data, checked_cell_data + D);
blas.VADD(D2, xx_data + D, checked_cell_data, xx_data + D);
// I_t, F_t
act_gate(D2, xx_data + D, xx_data + D);
} else {
// I_t, F_t, O_t
act_gate(D3, xx_data + D, xx_data + D);
for (int i = 0; i < N; ++i) {
PROCESS_H0C0
for (int step = tstart; step < seq_len; ++step) {
GEMM_WH_ADDON(1, prev_h_data, xx_data);
COMPUTE_CtHt(xx_data, prev_c_data, c_out_data, h_out_data);
MOVE_ONE_STEP;
}
// F_t * C_t-1
blas.VMUL(D, xx_data + D2, prev_c_data, xx_data + D2);
// I_t * ~C_t
blas.VMUL(D, xx_data, xx_data + D, xx_data + D);
// C_t = F_t * C_t-1 + I_t * ~C_t
blas.VADD(D, xx_data + D, xx_data + D2, cell_out_data);
if (use_peepholes) {
// + W_oc * C_t for peephole connection
blas.VMUL(D, wc_data + D2, cell_out_data, checked_cell_data + D2);
blas.VADD(D, xx_data + D3, checked_cell_data + D2, xx_data + D3);
// O_t
act_gate(D, xx_data + D3, xx_data + D3);
}
// hidden out= act_state(cellout) * outgate
act_cell(D, cell_out_data, xx_data + D2);
// H_t = O_t * act_state(C_t)
blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
// prev
prev_h_data = hidden_out_data;
prev_c_data = cell_out_data;
move_step();
} // for each step in batch
} // for each batch
}
#undef PROCESS_H0C0_DEFINES
#undef PROCESS_H0C0_PEEPHOLE
#undef PROCESS_H0C0
#undef MOVE_ONE_STEP
}
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = platform::CPUDeviceContext;
INIT_BASE_INPUT_OUTPUT
if (x->lod()[0].size() == 2) { // batch size == 1
if (x->lod()[0].size() == 2) {
SeqCompute(ctx);
return;
}
INIT_BASE_SIZES
INIT_VEC_FUNC
INIT_BASE_INPUT_DATAS
auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
auto* reordered_c0 = ctx.Output<Tensor>("ReorderedC0");
auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
auto* batched_c_out = ctx.Output<LoDTensor>("BatchedCell");
auto* batched_h_out = ctx.Output<LoDTensor>("BatchedHidden");
const T* x_data = x->data<T>();
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
const T* bias_data = bias->data<T>();
const T* wc_data = bias_data + D4; // w_ic, w_fc, w_oc
auto place = ctx.GetPlace();
T* xx_data = xx->mutable_data<T>(place);
T* batched_input_data = batched_input->mutable_data<T>(place);
T* batched_c_out_data = batched_c_out->mutable_data<T>(place);
......@@ -427,12 +436,6 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
hidden_out->mutable_data<T>(place);
cell_out->mutable_data<T>(place);
// use local variable
framework::DDim check_dims({3, D});
Tensor checked_cell; // w_ic * Ct-1, w_fc * Ct-1, w_oc * Ct
auto checked_cell_data =
checked_cell.mutable_data<T>(check_dims, ctx.GetPlace());
math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
......@@ -454,27 +457,17 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
reordered_h0->Resize({max_bs, D});
reordered_c0->Resize({max_bs, D});
T* prev_batch_h_data = nullptr;
T* prev_batch_c_data = nullptr;
T* cur_batch_in_data = batched_input_data;
T* cur_batch_h_out_data = batched_h_out_data;
T* cur_batch_c_out_data = batched_c_out_data;
auto move_step = [&](int bs) {
cur_batch_in_data += bs * D4;
cur_batch_c_out_data += bs * D;
cur_batch_h_out_data += bs * D;
};
int tstart = 0;
T* prev_h_data = nullptr;
T* prev_c_data = nullptr;
if (h0) {
// reorder h0, c0
T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
T* reordered_c0_data = reordered_c0->mutable_data<T>(place);
const T* h0_data = h0->data<T>();
const T* c0_data = c0->data<T>();
prev_batch_h_data = reordered_h0_data;
prev_batch_c_data = reordered_c0_data;
prev_h_data = reordered_h0_data;
prev_c_data = reordered_c0_data;
size_t sz = sizeof(T) * D;
for (int i = 0; i < max_bs; ++i) {
std::memcpy(reordered_h0_data, h0_data + seq_order[i] * D, sz);
......@@ -483,123 +476,80 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
reordered_c0_data += D;
}
} else {
// Compute with no H0/C0
T* cur_in_data = cur_batch_in_data;
T* cur_c_out_data = cur_batch_c_out_data;
T* cur_h_out_data = cur_batch_h_out_data;
// If step == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros. Then W_h * H_t-1 can be skiped
for (int i = 0; i < max_bs; ++i) { // iterate each data in 1st batch
// ~C_t
act_cand(D, cur_in_data, cur_in_data);
// compute without h0, c0
T* cur_in_data = batched_input_data;
T* cur_h_out_data = batched_h_out_data;
T* cur_c_out_data = batched_c_out_data;
for (int i = 0; i < max_bs; ++i) {
GET_Ct_NOH0C0(cur_in_data, cur_c_out_data);
if (use_peepholes) {
// I_t, F_t
act_gate(D2, cur_in_data + D, cur_in_data + D);
} else {
// I_t, F_t, O_t
act_gate(D3, cur_in_data + D, cur_in_data + D);
blas.VMUL(D, wc_data + D2, cur_c_out_data, cur_in_data + D);
blas.VADD(D, cur_in_data + D, cur_in_data + D3, cur_in_data + D3);
}
// C_t = I_t * ~C_t
blas.VMUL(D, cur_in_data, cur_in_data + D, cur_c_out_data);
if (use_peepholes) {
// + W_oc * C_t for peephole connection
blas.VMUL(D, wc_data + D2, cur_c_out_data, checked_cell_data + D2);
blas.VADD(D, cur_in_data + D3, checked_cell_data + D2,
cur_in_data + D3);
// O_t
act_gate(D, cur_in_data + D3, cur_in_data + D3);
}
// hidden out= act_state(cellout) * outgate
act_cell(D, cur_c_out_data, cur_in_data + D2);
// H_t = O_t * act_state(C_t)
blas.VMUL(D, cur_in_data + D2, cur_in_data + D3, cur_h_out_data);
// move to next data in the same batch
GET_Ht(cur_c_out_data, cur_in_data, cur_h_out_data);
cur_in_data += D4;
cur_c_out_data += D;
cur_h_out_data += D;
}
// move to data for next timestep
prev_batch_h_data = cur_batch_h_out_data;
prev_batch_c_data = cur_batch_c_out_data;
move_step(max_bs);
tstart = 1;
prev_h_data = batched_h_out_data;
prev_c_data = batched_c_out_data;
}
const auto& batch_starts = batched_lod[0];
const int max_seq_len = batch_starts.size() - 1;
for (int step = tstart; step < max_seq_len; ++step) {
const int cur_bs = batch_starts[step + 1] - batch_starts[step];
// + W_h * H_t-1
blas.GEMM(CblasNoTrans, CblasNoTrans, cur_bs, D4, D, static_cast<T>(1),
prev_batch_h_data, D, wh_data, D4, static_cast<T>(1),
cur_batch_in_data, D4);
T* cur_in_data = cur_batch_in_data;
T* cur_c_out_data = cur_batch_c_out_data;
T* cur_h_out_data = cur_batch_h_out_data;
T* prev_c_data = prev_batch_c_data; // NULL if no C0 in step0
T* prev_h_data = prev_batch_h_data; // NULL if no H0 in step0
auto next_data_in_batch = [&]() {
cur_in_data += D4;
cur_c_out_data += D;
cur_h_out_data += D;
prev_c_data = prev_c_data ? prev_c_data + D : nullptr;
prev_h_data = prev_h_data ? prev_h_data + D : nullptr;
};
for (int i = 0; i < cur_bs; ++i) { // iterate each data in same batch
// ~C_t
act_cand(D, cur_in_data, cur_in_data);
const int offset = tstart * max_bs * D;
batched_input_data = batched_input_data + offset * 4;
batched_h_out_data = batched_h_out_data + offset;
batched_c_out_data = batched_c_out_data + offset;
#define DEFINE_CUR \
T* cur_in_data = batched_input_data; \
T* cur_prev_c_data = prev_c_data; \
T* cur_c_out_data = batched_c_out_data; \
T* cur_h_out_data = batched_h_out_data
#define MOVE_ONE_BATCH \
cur_in_data += D4; \
cur_prev_c_data += D; \
cur_c_out_data += D; \
cur_h_out_data += D
#define MOVE_ONE_STEP \
prev_c_data = batched_c_out_data; \
prev_h_data = batched_h_out_data; \
batched_c_out_data = cur_c_out_data; \
batched_h_out_data = cur_h_out_data; \
batched_input_data = cur_in_data
if (use_peepholes) {
// + W_ic|W_fc * C_t-1 for peephole connection
blas.VMUL(D, wc_data, prev_c_data, checked_cell_data);
blas.VMUL(D, wc_data + D, prev_c_data, checked_cell_data + D);
blas.VADD(D2, cur_in_data + D, checked_cell_data, cur_in_data + D);
// I_t, F_t
act_gate(D2, cur_in_data + D, cur_in_data + D);
} else {
// I_t, F_t, O_t
act_gate(D3, cur_in_data + D, cur_in_data + D);
for (int step = tstart; step < max_seq_len; ++step) {
const int cur_bs = batch_starts[step + 1] - batch_starts[step];
GEMM_WH_ADDON(cur_bs, prev_h_data, batched_input_data);
DEFINE_CUR;
for (int i = 0; i < cur_bs; ++i) {
COMPUTE_CtHt_PEEPHOLE(cur_in_data, cur_prev_c_data, cur_c_out_data,
cur_h_out_data);
MOVE_ONE_BATCH;
}
// F_t * C_t-1
blas.VMUL(D, cur_in_data + D2, prev_c_data, cur_in_data + D2);
// I_t * ~C_t
blas.VMUL(D, cur_in_data, cur_in_data + D, cur_in_data + D);
// C_t = F_t * C_t-1 + I_t * ~C_t
blas.VADD(D, cur_in_data + D, cur_in_data + D2, cur_c_out_data);
if (use_peepholes) {
// + W_oc * C_t for peephole connection
blas.VMUL(D, wc_data + D2, cur_c_out_data, checked_cell_data + D2);
blas.VADD(D, cur_in_data + D3, checked_cell_data + D2,
cur_in_data + D3);
// O_t
act_gate(D, cur_in_data + D3, cur_in_data + D3);
MOVE_ONE_STEP;
}
// hidden out= act_state(cellout) * outgate
act_cell(D, cur_c_out_data, cur_in_data + D2);
// H_t = O_t * act_state(C_t)
blas.VMUL(D, cur_in_data + D2, cur_in_data + D3, cur_h_out_data);
// move to next data in same batch
next_data_in_batch();
} else {
for (int step = tstart; step < max_seq_len; ++step) {
const int cur_bs = batch_starts[step + 1] - batch_starts[step];
GEMM_WH_ADDON(cur_bs, prev_h_data, batched_input_data);
DEFINE_CUR;
for (int i = 0; i < cur_bs; ++i) {
COMPUTE_CtHt(cur_in_data, cur_prev_c_data, cur_c_out_data,
cur_h_out_data);
MOVE_ONE_BATCH;
}
MOVE_ONE_STEP;
}
// move to data for next timestep
prev_batch_h_data = cur_batch_h_out_data;
prev_batch_c_data = cur_batch_c_out_data;
move_step(cur_bs);
}
#undef MOVE_ONE_STEP
#undef MOVE_ONE_BATCH
#undef DEFINE_CUR
math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
batched_h_out->set_lod(batched_lod);
......@@ -615,6 +565,16 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
BatchCompute(ctx);
}
}
#undef COMPUTE_CtHt_PEEPHOLE
#undef COMPUTE_CtHt
#undef GET_Ct_NOH0C0
#undef COMPUTE_CtHt_NOH0C0
#undef COMPUTE_CtHt_PEEPHOLE_NOH0C0
#undef GET_Ht
#undef GET_Ct
#undef GEMM_WH_ADDON
#undef INIT_BASE_INPUT_DATAS
#undef INIT_BASE_SIZES
#undef INIT_BASE_INPUT_OUTPUT
#undef INIT_VEC_FUNC
......
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
浏览文件 @ 9f2ccf5b
......@@ -53,12 +53,11 @@ class TestFusionLSTMOp(OpTest):
self.M = 8
self.D = 16
self.has_initial_state = False
self.use_peepholes = False
self.is_reverse = False
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.use_peepholes = False
self.use_seq = False
self.set_conf()
T = sum(self.lod[0])
......@@ -108,7 +107,6 @@ class TestFusionLSTMOp(OpTest):
}
self.attrs = {
'use_peepholes': self.use_peepholes,
'use_seq': self.use_seq,
'is_reverse': self.is_reverse,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
......@@ -178,50 +176,18 @@ class TestFusionLSTMOpPeepholesReverse(TestFusionLSTMOp):
self.is_reverse = True
class TestFusionLSTMOpPoopholesBS1(TestFusionLSTMOp):
class TestFusionLSTMOpPeepholesInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_peepholes = True
self.lod = [[3]]
self.D = 16
class TestFusionLSTMOpSeqInit(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.has_initial_state = True
class TestFusionLSTMOpSeqReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.is_reverse = True
class TestFusionLSTMOpSeqInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.has_initial_state = True
self.is_reverse = True
class TestFusionLSTMOpSeqPeepholes(TestFusionLSTMOp):
class TestFusionLSTMOpPeepholesBS1(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
class TestFusionLSTMOpSeqPeepholesInit(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
self.has_initial_state = True
class TestFusionLSTMOpSeqPeepholesReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
self.is_reverse = True
self.lod = [[2]]
self.D = 8
if __name__ == '__main__':
......
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