1) Linear and MLP operators support qlen>1; 2) All operators now share a...

1) Linear and MLP operators support qlen>1; 2) All operators now share a single memory buffer; 3) Refactor CPUInfer submit/sync logic.

ktransformers/ktransformers_ext/CMakeLists.txt

@@ -22,14 +22,13 @@ option(LLAMA_AVX2                            "llama: enable AVX2"
 option(LLAMA_AVX512                          "llama: enable AVX512"                             OFF)
 option(LLAMA_AVX512_VBMI                     "llama: enable AVX512-VBMI"                        OFF)
 option(LLAMA_AVX512_VNNI                     "llama: enable AVX512-VNNI"                        OFF)
+option(LLAMA_AVX512_BF16                     "llama: enable AVX512-BF16"                        OFF)
 option(LLAMA_FMA                             "llama: enable FMA"                                OFF)
 # in MSVC F16C is implied with AVX2/AVX512
 if (NOT MSVC)
     option(LLAMA_F16C                        "llama: enable F16C"                               OFF)
 endif()
 option(LLAMA_AVX512_FANCY_SIMD               "llama: enable AVX512-VL, AVX512-BW, AVX512-DQ, AVX512-VNNI"                        OFF)
-option(LLAMA_AVX512_BF16                     "llama: enable AVX512-BF16"                                                         OFF)
-
 
 # Architecture specific
 # TODO: probably these flags need to be tweaked on some architectures

ktransformers/ktransformers_ext/bench/bench_linear.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-25 10:31:59
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:32:51
+LastEditTime : 2024-08-06 10:35:35
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,15 +15,18 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
+input_size = 16384
+output_size = 5120
+stride = 16
+group_max_len = 1024
+layer_num = 10
+qlen = 1
+CPUInfer = cpuinfer_ext.CPUInfer(64)
+warm_up_iter = 1000
+test_iter = 10000
+
 def bench_linear(quant_mode: str):
     with torch.inference_mode(mode=True):
-        input_size = 16384
-        output_size = 5120
-        stride = 16
-        layer_num = 10
-        CPUInfer = cpuinfer_ext.CPUInfer(64)
-        warm_up_iter = 1000
-        test_iter = 10000
 
         hidden_type = 30 # ggml_type::GGML_TYPE_BF16
         if quant_mode == "fp32":
@@ -66,30 +69,37 @@ def bench_linear(quant_mode: str):
         projs = []
         for _ in range(layer_num):
             proj = torch.randn((output_size, input_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
-            config = cpuinfer_ext.linear.LinearConfig(input_size, output_size, stride, proj.data_ptr(), proj_type, hidden_type)
+            config = cpuinfer_ext.linear.LinearConfig(input_size, output_size, stride, group_max_len, proj.data_ptr(), proj_type, hidden_type)
             linear = cpuinfer_ext.linear.Linear(config)
             projs.append(proj)
             linears.append(linear)
+        input = torch.randn((layer_num, qlen, input_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
+        output = torch.empty((layer_num, qlen, output_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
 
         # warm up
         for i in range(warm_up_iter):
-            linear = linears[i % layer_num]
-            input = torch.randn((1, input_size), dtype=torch.bfloat16).contiguous()
-            output = torch.empty((1, output_size), dtype=torch.bfloat16).contiguous()
-            CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
+            CPUInfer.submit(
+                linears[i % layer_num].forward(
+                    qlen, 
+                    input[i % layer_num].data_ptr(), 
+                    output[i % layer_num].data_ptr()
+                )
+            )
             CPUInfer.sync()
 
         # test
-        total_time = 0
+        start = time.perf_counter()
         for i in range(test_iter):
-            linear = linears[i % layer_num]
-            input = torch.randn((1, input_size), dtype=torch.bfloat16).contiguous()
-            output = torch.empty((1, output_size), dtype=torch.bfloat16).contiguous()
-            start = time.perf_counter()
-            CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
+            CPUInfer.submit(
+                linears[i % layer_num].forward(
+                    qlen, 
+                    input[i % layer_num].data_ptr(), 
+                    output[i % layer_num].data_ptr()
+                )
+            )
             CPUInfer.sync()
-            end = time.perf_counter()
-            total_time += end - start
+        end = time.perf_counter()
+        total_time = end - start
         print('Quant mode: ', quant_mode)
         print('Time(s): ', total_time)
         print('Iteration: ', test_iter) 

ktransformers/ktransformers_ext/bench/bench_linear_torch.py

@@ -14,14 +14,17 @@ import time
 import torch
 import torch.nn.quantized as nnq
 
+scale, zero_point = 0.1, 0  # Adjust scale and zero_point based on your dataset
+
+input_size = 16384
+output_size = 5120
+layer_num = 10
+qlen = 1
+warm_up_iter = 1000
+test_iter = 10000
+
 def bench_linear(quant_mode: str):
     with torch.inference_mode(mode=True):
-        input_size = 16384
-        output_size = 5120
-        layer_num = 10
-        warm_up_iter = 1000
-        test_iter = 10000
-
         if quant_mode == "fp32":
             proj_type = torch.float32
             bytes_per_elem = 4.000000
@@ -41,37 +44,32 @@ def bench_linear(quant_mode: str):
         for _ in range(layer_num):
             proj = torch.randn((output_size, input_size), dtype = torch.float32, device = "cuda").to("cpu").contiguous()
             if quant_mode == "qint8":
-                scale, zero_point = 0.1, 0  # Adjust scale and zero_point based on your dataset
                 proj_q = torch.quantize_per_tensor(proj, scale, zero_point, torch.qint8)
                 quantized_layer = nnq.Linear(input_size, output_size)
                 quantized_layer.set_weight_bias(proj_q, None)
                 projs.append(quantized_layer)
             else:
                 projs.append(proj.to(proj_type))
+        input = torch.randn((layer_num, qlen, input_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
 
         # warm up
         for i in range(warm_up_iter):
-            input = torch.randn((1, input_size), dtype=torch.float32).contiguous()
-            if quant_mode == "qint8":
-                input_q = torch.quantize_per_tensor(input, scale, zero_point, torch.quint8)
-                quantized_layer = projs[i % layer_num]
-                t_output = quantized_layer(input_q)
+            if isinstance(projs[i % layer_num], nnq.Linear):
+                input_q = torch.quantize_per_tensor(input[i % layer_num].to(torch.float32), scale, zero_point, torch.quint8)
+                t_output = projs[i % layer_num](input_q)
             else:
-                t_output = torch.mm(input.to(proj_type), projs[i % layer_num].t())
+                t_output = torch.mm(input[i % layer_num].to(proj_type), projs[i % layer_num].t())
 
         # test
-        total_time = 0
+        start = time.perf_counter()
         for i in range(test_iter):
-            input = torch.randn((1, input_size), dtype=torch.float32).contiguous()
-            start = time.perf_counter()
-            if quant_mode == "qint8":
-                input_q = torch.quantize_per_tensor(input, scale, zero_point, torch.quint8)
-                quantized_layer = projs[i % layer_num]
-                t_output = quantized_layer(input_q)
+            if isinstance(projs[i % layer_num], nnq.Linear):
+                input_q = torch.quantize_per_tensor(input[i % layer_num].to(torch.float32), scale, zero_point, torch.quint8)
+                t_output = projs[i % layer_num](input_q)
             else:
-                t_output = torch.mm(input.to(proj_type), projs[i % layer_num].t())
-            end = time.perf_counter()
-            total_time += end - start
+                t_output = torch.mm(input[i % layer_num].to(proj_type), projs[i % layer_num].t())
+        end = time.perf_counter()
+        total_time = end - start
         print('Quant mode: ', quant_mode)
         print('Time(s): ', total_time)
         print('Iteration: ', test_iter) 

ktransformers/ktransformers_ext/bench/bench_mlp.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-16 10:43:18
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:32:55
+LastEditTime : 2024-08-06 10:36:04
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,15 +15,18 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
+hidden_size = 5120
+intermediate_size = 3072
+stride = 16
+group_max_len = 1024
+layer_num = 10
+qlen = 1
+CPUInfer = cpuinfer_ext.CPUInfer(64)
+warm_up_iter = 1000
+test_iter = 10000
+
 def bench_mlp(quant_mode: str):
     with torch.inference_mode(mode=True):
-        hidden_size = 5120
-        intermediate_size = 3072
-        stride = 16
-        layer_num = 10
-        CPUInfer = cpuinfer_ext.CPUInfer(64)
-        warm_up_iter = 1000
-        test_iter = 10000
 
         hidden_type = 30 # ggml_type::GGML_TYPE_BF16
         if quant_mode == "fp32":
@@ -93,32 +96,39 @@ def bench_mlp(quant_mode: str):
             gate_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
             up_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
             down_proj = torch.randn((hidden_size, intermediate_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
-            config = cpuinfer_ext.mlp.MLPConfig(hidden_size, intermediate_size, stride, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
+            config = cpuinfer_ext.mlp.MLPConfig(hidden_size, intermediate_size, stride, group_max_len, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
             mlp = cpuinfer_ext.mlp.MLP(config)
             gate_projs.append(gate_proj)
             up_projs.append(up_proj)
             down_projs.append(down_proj)
             mlps.append(mlp)
+        input = torch.randn((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
+        output = torch.empty((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
 
         # warm up
         for i in range(warm_up_iter):
-            mlp = mlps[i % layer_num]
-            input = torch.randn((1, hidden_size), dtype=torch.bfloat16).contiguous()
-            output = torch.empty((1, hidden_size), dtype=torch.bfloat16).contiguous()
-            CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
+            CPUInfer.submit(
+                mlps[i % layer_num].forward( 
+                    qlen, 
+                    input[i % layer_num].data_ptr(), 
+                    output[i % layer_num].data_ptr()
+                )
+            )
             CPUInfer.sync()
 
         # test
-        total_time = 0
+        start = time.perf_counter()
         for i in range(test_iter):
-            mlp = mlps[i % layer_num]
-            input = torch.randn((1, hidden_size), dtype=torch.bfloat16).contiguous()
-            output = torch.empty((1, hidden_size), dtype=torch.bfloat16).contiguous()
-            start = time.perf_counter()
-            CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
+            CPUInfer.submit(
+                mlps[i % layer_num].forward( 
+                    qlen, 
+                    input[i % layer_num].data_ptr(), 
+                    output[i % layer_num].data_ptr()
+                )
+            )
             CPUInfer.sync()
-            end = time.perf_counter()
-            total_time += end - start
+        end = time.perf_counter()
+        total_time = end - start
         print('Quant mode: ', quant_mode)
         print('Time(s): ', total_time)
         print('Iteration: ', test_iter) 

ktransformers/ktransformers_ext/bench/bench_mlp_torch.py

@@ -14,17 +14,38 @@ import time
 import torch
 import torch.nn.quantized as nnq
 
+scale, zero_point = 0.1, 0  # Adjust scale and zero_point based on your dataset
+
+hidden_size = 5120
+intermediate_size = 3072
+layer_num = 10
+qlen = 1
+warm_up_iter = 1000
+test_iter = 10000
+
 def act_fn(x):
     return x / (1.0 + torch.exp(-x))
 
+def mlp_torch(input, gate_proj, up_proj, down_proj):
+    if isinstance(gate_proj, nnq.Linear):
+        input_q = torch.quantize_per_tensor(input.to(torch.float32), scale, zero_point, torch.quint8)
+        gate_buf = gate_proj(input_q)
+        up_buf = up_proj(input_q)
+        gate_buf = gate_buf.dequantize()
+        up_buf = up_buf.dequantize()
+        intermediate = act_fn(gate_buf) * up_buf
+        intermediate_q = torch.quantize_per_tensor(intermediate, scale, zero_point, torch.quint8)
+        expert_output = down_proj(intermediate_q)
+        ret = expert_output.dequantize()
+    else:
+        gate_buf = torch.mm(input.to(gate_proj.dtype), gate_proj.t())
+        up_buf = torch.mm(input.to(up_proj.dtype), up_proj.t())
+        intermediate = act_fn(gate_buf) * up_buf
+        ret = torch.mm(intermediate.to(down_proj.dtype), down_proj.t())
+    return ret
+
 def bench_mlp(quant_mode: str):
     with torch.inference_mode(mode=True):
-        hidden_size = 5120
-        intermediate_size = 3072
-        layer_num = 10
-        warm_up_iter = 1000
-        test_iter = 10000
-
         if quant_mode == "fp32":
             proj_type = torch.float32
             bytes_per_elem = 4.000000
@@ -48,7 +69,6 @@ def bench_mlp(quant_mode: str):
             up_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
             down_proj = torch.randn((hidden_size, intermediate_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
             if quant_mode == "qint8":
-                scale, zero_point = 0.1, 0  # Adjust scale and zero_point based on your dataset
                 gate_proj_q = torch.quantize_per_tensor(gate_proj, scale, zero_point, torch.qint8)
                 quantized_gate = nnq.Linear(hidden_size, intermediate_size)
                 quantized_gate.set_weight_bias(gate_proj_q, None)
@@ -65,58 +85,18 @@ def bench_mlp(quant_mode: str):
                 gate_projs.append(gate_proj.to(proj_type))
                 up_projs.append(up_proj.to(proj_type))
                 down_projs.append(down_proj.to(proj_type))
+        input = torch.randn((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
 
         # warm up
         for i in range(warm_up_iter):
-            input = torch.randn((1, hidden_size), dtype=torch.float32).contiguous()
-            if quant_mode == "qint8":
-                input_q = torch.quantize_per_tensor(input, scale, zero_point, torch.quint8)
-                quantized_gate = gate_projs[i % layer_num]
-                gate_buf = quantized_gate(input_q)
-                quantized_up = up_projs[i % layer_num]
-                up_buf = quantized_gate(input_q)
-                gate_buf = gate_buf.dequantize()
-                up_buf = up_buf.dequantize()
-                intermediate = act_fn(gate_buf) * up_buf
-                intermediate_q = torch.quantize_per_tensor(intermediate, scale, zero_point, torch.quint8)
-                quantized_down = down_projs[i % layer_num]
-                t_output = quantized_down(intermediate_q)
-            else:
-                gate_proj = gate_projs[i%layer_num]
-                up_proj = up_projs[i%layer_num]
-                down_proj = down_projs[i%layer_num]
-                gate_buf = torch.mm(input.to(proj_type), gate_proj.t())
-                up_buf = torch.mm(input.to(proj_type), up_proj.t())
-                intermediate = act_fn(gate_buf) * up_buf
-                t_output = torch.mm(intermediate.to(proj_type), down_proj.t())
+            mlp_torch(input[i % layer_num], gate_projs[i % layer_num], up_projs[i % layer_num], down_projs[i % layer_num])
 
         # test
-        total_time = 0
+        start = time.perf_counter()
         for i in range(test_iter):
-            input = torch.randn((1, hidden_size), dtype=torch.float32).contiguous()
-            start = time.perf_counter()
-            if quant_mode == "qint8":
-                input_q = torch.quantize_per_tensor(input, scale, zero_point, torch.quint8)
-                quantized_gate = gate_projs[i % layer_num]
-                gate_buf = quantized_gate(input_q)
-                quantized_up = up_projs[i % layer_num]
-                up_buf = quantized_gate(input_q)
-                gate_buf = gate_buf.dequantize()
-                up_buf = up_buf.dequantize()
-                intermediate = act_fn(gate_buf) * up_buf
-                intermediate_q = torch.quantize_per_tensor(intermediate, scale, zero_point, torch.quint8)
-                quantized_down = down_projs[i % layer_num]
-                t_output = quantized_down(intermediate_q)
-            else:
-                gate_proj = gate_projs[i%layer_num]
-                up_proj = up_projs[i%layer_num]
-                down_proj = down_projs[i%layer_num]
-                gate_buf = torch.mm(input.to(proj_type), gate_proj.t())
-                up_buf = torch.mm(input.to(proj_type), up_proj.t())
-                intermediate = act_fn(gate_buf) * up_buf
-                t_output = torch.mm(intermediate.to(proj_type), down_proj.t())
-            end = time.perf_counter()
-            total_time += end - start
+            mlp_torch(input[i % layer_num], gate_projs[i % layer_num], up_projs[i % layer_num], down_projs[i % layer_num])
+        end = time.perf_counter()
+        total_time = end - start
         print('Quant mode: ', quant_mode)
         print('Time(s): ', total_time)
         print('Iteration: ', test_iter) 

ktransformers/ktransformers_ext/bench/bench_moe.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-25 10:32:05
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:33:00
+LastEditTime : 2024-08-06 10:41:28
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,21 +15,21 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
+expert_num = 160
+hidden_size = 5120
+intermediate_size = 1536
+stride = 16
+group_min_len = 10
+group_max_len = 1024
+n_routed_experts = 6
+layer_num = 10
+qlen = 1
+CPUInfer = cpuinfer_ext.CPUInfer(64)
+warm_up_iter = 1000
+test_iter = 10000
+
 def bench_moe(quant_mode: str):
     with torch.inference_mode(mode=True):
-        expert_num = 10
-        hidden_size = 5120
-        intermediate_size = 1536
-        stride = 16
-        group_min_len = 10
-        group_max_len = 1024
-        n_routed_experts = 6
-        layer_num = 10
-        qlen = 1
-        CPUInfer = cpuinfer_ext.CPUInfer(64)
-        warm_up_iter = 1000
-        test_iter = 10000
-
         hidden_type = 30 # ggml_type::GGML_TYPE_BF16
         if quant_mode == "fp32":
             gate_type = 0 # ggml_type::GGML_TYPE_F32
@@ -104,32 +104,38 @@ def bench_moe(quant_mode: str):
             up_projs.append(up_proj)
             down_projs.append(down_proj)
             moes.append(moe)
-        expert_ids = torch.randint(0, expert_num, (layer_num, qlen, n_routed_experts), dtype=torch.int64, device = "cuda").to("cpu").contiguous()
+        expert_ids = torch.stack([torch.stack([torch.randperm(expert_num, dtype=torch.int64, device = "cuda")[:n_routed_experts] for _ in range(qlen)]) for _ in range(layer_num)]).to("cpu").contiguous()
         weights = torch.rand((layer_num, qlen, n_routed_experts), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
         input = torch.randn((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
         output = torch.empty((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
 
         # warm up
         for i in range(warm_up_iter):
-            CPUInfer.submit(moes[i % layer_num].forward, 
-                            qlen, 
-                            n_routed_experts, 
-                            expert_ids[i % layer_num].data_ptr(), 
-                            weights[i % layer_num].data_ptr(),
-                            input[i % layer_num].data_ptr(), 
-                            output[i % layer_num].data_ptr())
+            CPUInfer.submit(
+                moes[i % layer_num].forward( 
+                    qlen, 
+                    n_routed_experts, 
+                    expert_ids[i % layer_num].data_ptr(), 
+                    weights[i % layer_num].data_ptr(),
+                    input[i % layer_num].data_ptr(), 
+                    output[i % layer_num].data_ptr()
+                )
+            )
             CPUInfer.sync()
 
         # test
         start = time.perf_counter()
         for i in range(test_iter):
-            CPUInfer.submit(moes[i % layer_num].forward, 
-                            qlen, 
-                            n_routed_experts, 
-                            expert_ids[i % layer_num].data_ptr(), 
-                            weights[i % layer_num].data_ptr(),
-                            input[i % layer_num].data_ptr(), 
-                            output[i % layer_num].data_ptr())
+            CPUInfer.submit(
+                moes[i % layer_num].forward( 
+                    qlen, 
+                    n_routed_experts, 
+                    expert_ids[i % layer_num].data_ptr(), 
+                    weights[i % layer_num].data_ptr(),
+                    input[i % layer_num].data_ptr(), 
+                    output[i % layer_num].data_ptr()
+                )
+            )
             CPUInfer.sync()
         end = time.perf_counter()
         total_time = end - start

ktransformers/ktransformers_ext/bench/bench_moe_torch.py

@@ -14,19 +14,71 @@ import time
 import torch
 import torch.nn.quantized as nnq
 
+scale, zero_point = 0.1, 0  # Adjust scale and zero_point based on your dataset
+
+expert_num = 160
+hidden_size = 5120
+intermediate_size = 1536
+n_routed_experts = 6
+layer_num = 10
+qlen = 1
+warm_up_iter = 1000
+test_iter = 10000
+
 def act_fn(x):
     return x / (1.0 + torch.exp(-x))
 
+def mlp_torch(input, gate_proj, up_proj, down_proj):
+    if isinstance(gate_proj, nnq.Linear):
+        input_q = torch.quantize_per_tensor(input.to(torch.float32), scale, zero_point, torch.quint8)
+        gate_buf = gate_proj(input_q)
+        up_buf = up_proj(input_q)
+        gate_buf = gate_buf.dequantize()
+        up_buf = up_buf.dequantize()
+        intermediate = act_fn(gate_buf) * up_buf
+        intermediate_q = torch.quantize_per_tensor(intermediate, scale, zero_point, torch.quint8)
+        expert_output = down_proj(intermediate_q)
+        ret = expert_output.dequantize()
+    else:
+        gate_buf = torch.mm(input.to(gate_proj.dtype), gate_proj.t())
+        up_buf = torch.mm(input.to(up_proj.dtype), up_proj.t())
+        intermediate = act_fn(gate_buf) * up_buf
+        ret = torch.mm(intermediate.to(down_proj.dtype), down_proj.t())
+    return ret
+
+def moe_torch(input, expert_ids, weights, gate_proj, up_proj, down_proj):
+    cnts = expert_ids.new_zeros((expert_ids.shape[0], expert_num))
+    cnts.scatter_(1, expert_ids, 1)
+    tokens_per_expert = cnts.sum(dim=0)
+    idxs = expert_ids.view(-1).argsort()
+    sorted_tokens = input[idxs // expert_ids.shape[1]]
+
+    outputs = []
+    start_idx = 0
+    for i, num_tokens in enumerate(tokens_per_expert):
+        end_idx = start_idx + num_tokens
+        if num_tokens == 0:
+            continue
+        tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+        expert_out = mlp_torch(tokens_for_this_expert, gate_proj[i], up_proj[i], down_proj[i])
+        outputs.append(expert_out)
+        start_idx = end_idx
+
+    outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+
+    new_x = torch.empty_like(outs)
+    new_x[idxs] = outs
+    t_output = (
+        new_x.view(*expert_ids.shape, -1)
+        .type(weights.dtype)
+        .mul_(weights.unsqueeze(dim=-1))
+        .sum(dim=1)
+        .type(new_x.dtype)
+    )
+    return t_output
+
 def bench_moe(quant_mode: str):
     with torch.inference_mode(mode=True):
-        expert_num = 10
-        hidden_size = 5120
-        intermediate_size = 1536
-        n_routed_experts = 6
-        layer_num = 10
-        warm_up_iter = 1000
-        test_iter = 10000
-
         if quant_mode == "fp32":
             proj_type = torch.float32
             bytes_per_elem = 4.000000
@@ -50,7 +102,6 @@ def bench_moe(quant_mode: str):
             up_proj = torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
             down_proj = torch.randn((expert_num, hidden_size, intermediate_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
             if quant_mode == "qint8":
-                scale, zero_point = 0.1, 0  # Adjust scale and zero_point based on your dataset
                 quantized_gate_proj = []
                 quantized_up_proj = []
                 quantized_down_proj = []
@@ -74,82 +125,20 @@ def bench_moe(quant_mode: str):
                 gate_projs.append(gate_proj.to(proj_type))
                 up_projs.append(up_proj.to(proj_type))
                 down_projs.append(down_proj.to(proj_type))
+        expert_ids = torch.stack([torch.stack([torch.randperm(expert_num, dtype=torch.int64, device = "cuda")[:n_routed_experts] for _ in range(qlen)]) for _ in range(layer_num)]).to("cpu").contiguous()
+        weights = torch.rand((layer_num, qlen, n_routed_experts), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
+        input = torch.randn((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
 
         # warm up
         for i in range(warm_up_iter):
-            expert_ids = torch.randint(0, expert_num, (n_routed_experts,), dtype=torch.int64).contiguous()
-            weights = torch.rand((n_routed_experts,), dtype=torch.float32).contiguous()
-            input = torch.randn((1, hidden_size), dtype=torch.float32).contiguous()
-            if quant_mode == "qint8":
-                input_q = torch.quantize_per_tensor(input, scale, zero_point, torch.quint8)
-                t_output = torch.zeros((1, hidden_size), dtype=torch.float32).contiguous()
-                gate_proj = gate_projs[i%layer_num]
-                up_proj = up_projs[i%layer_num]
-                down_proj = down_projs[i%layer_num]
-                for i, expert_id in enumerate(expert_ids):
-                    quantized_gate = gate_proj[expert_id]
-                    gate_buf = quantized_gate(input_q)
-                    quantized_up = up_proj[expert_id]
-                    up_buf = quantized_up(input_q)
-                    gate_buf = gate_buf.dequantize()
-                    up_buf = up_buf.dequantize()
-                    intermediate = act_fn(gate_buf) * up_buf
-                    intermediate_q = torch.quantize_per_tensor(intermediate, scale, zero_point, torch.quint8)
-                    quantized_down = down_proj[expert_id]
-                    expert_output = quantized_down(intermediate_q)
-                    expert_output = expert_output.dequantize()
-                    t_output += weights[i] * expert_output
-            else:
-                t_output = torch.zeros((1, hidden_size), dtype=proj_type).contiguous()
-                gate_proj = gate_projs[i%layer_num]
-                up_proj = up_projs[i%layer_num]
-                down_proj = down_projs[i%layer_num]
-                for i, expert_id in enumerate(expert_ids):
-                    gate_buf = torch.mm(input.to(proj_type), gate_proj[expert_id].t())
-                    up_buf = torch.mm(input.to(proj_type), up_proj[expert_id].t())
-                    intermediate = act_fn(gate_buf) * up_buf
-                    expert_output = torch.mm(intermediate.to(proj_type), down_proj[expert_id].t())
-                    t_output += weights[i] * expert_output
+            moe_torch(input[i % layer_num], expert_ids[i % layer_num], weights[i % layer_num], gate_projs[i % layer_num], up_projs[i % layer_num], down_projs[i % layer_num])
 
         # test
-        total_time = 0
+        start = time.perf_counter()
         for i in range(test_iter):
-            expert_ids = torch.randint(0, expert_num, (n_routed_experts,), dtype=torch.int64).contiguous()
-            weights = torch.rand((n_routed_experts,), dtype=torch.float32).contiguous()
-            input = torch.randn((1, hidden_size), dtype=torch.float32).contiguous()
-            start = time.perf_counter()
-            if quant_mode == "qint8":
-                input_q = torch.quantize_per_tensor(input, scale, zero_point, torch.quint8)
-                t_output = torch.zeros((1, hidden_size), dtype=torch.float32).contiguous()
-                gate_proj = gate_projs[i%layer_num]
-                up_proj = up_projs[i%layer_num]
-                down_proj = down_projs[i%layer_num]
-                for i, expert_id in enumerate(expert_ids):
-                    quantized_gate = gate_proj[expert_id]
-                    gate_buf = quantized_gate(input_q)
-                    quantized_up = up_proj[expert_id]
-                    up_buf = quantized_up(input_q)
-                    gate_buf = gate_buf.dequantize()
-                    up_buf = up_buf.dequantize()
-                    intermediate = act_fn(gate_buf) * up_buf
-                    intermediate_q = torch.quantize_per_tensor(intermediate, scale, zero_point, torch.quint8)
-                    quantized_down = down_proj[expert_id]
-                    expert_output = quantized_down(intermediate_q)
-                    expert_output = expert_output.dequantize()
-                    t_output += weights[i] * expert_output
-            else:
-                t_output = torch.zeros((1, hidden_size), dtype=proj_type).contiguous()
-                gate_proj = gate_projs[i%layer_num]
-                up_proj = up_projs[i%layer_num]
-                down_proj = down_projs[i%layer_num]
-                for i, expert_id in enumerate(expert_ids):
-                    gate_buf = torch.mm(input.to(proj_type), gate_proj[expert_id].t())
-                    up_buf = torch.mm(input.to(proj_type), up_proj[expert_id].t())
-                    intermediate = act_fn(gate_buf) * up_buf
-                    expert_output = torch.mm(intermediate.to(proj_type), down_proj[expert_id].t())
-                    t_output += weights[i] * expert_output
-            end = time.perf_counter()
-            total_time += end - start
+            moe_torch(input[i % layer_num], expert_ids[i % layer_num], weights[i % layer_num], gate_projs[i % layer_num], up_projs[i % layer_num], down_projs[i % layer_num])
+        end = time.perf_counter()
+        total_time = end - start
         print('Quant mode: ', quant_mode)
         print('Time(s): ', total_time)
         print('Iteration: ', test_iter) 

ktransformers/ktransformers_ext/cpu_backend/cpuinfer.h

 /**
- * @Description  :  
+ * @Description  :
  * @Author       : chenht2022
  * @Date         : 2024-07-16 10:43:18
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
- * @LastEditTime : 2024-07-25 10:33:42
- * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
-**/
+ * @LastEditors  : chenht2022
+ * @LastEditTime : 2024-08-07 09:47:43
+ * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+ **/
 #ifndef CPUINFER_CPUINFER_H
 #define CPUINFER_CPUINFER_H
 
@@ -17,6 +17,7 @@
 #include <queue>
 #include <thread>
 #include <vector>
+#include "cuda_runtime.h"
 
 #include "backend.h"
 #include "task_queue.h"
@@ -39,16 +40,39 @@ class CPUInfer {
     }
 
     template <typename Func, typename Obj, typename... Args>
-    void submit(Func f, Obj* obj, Args... args) {
+    void enqueue(Func f, Obj* obj, Args... args) {
         task_queue_->enqueue([=]() {
             std::invoke(f, *obj, args..., backend_);
         });
     }
 
+    void submit(std::pair<intptr_t, intptr_t> params) {
+        void (*func)(void*) = (void (*)(void*))params.first;
+        void* args = (void*)params.second;
+        *((CPUInfer**)args) = this;
+        func(args);
+    }
+
     void sync() {
         task_queue_->sync();
     }
 
+    void submit_with_cuda_stream(intptr_t user_cuda_stream, std::pair<intptr_t, intptr_t> params) {
+        void (*func)(void*) = (void (*)(void*))params.first;
+        void* args = (void*)params.second;
+        *((CPUInfer**)args) = this;
+        cudaLaunchHostFunc((cudaStream_t)user_cuda_stream, (cudaHostFn_t)func, args);
+    }
+
+    static void sync_(void* cpu_infer_ptr) {
+        CPUInfer* cpuinfer = (CPUInfer*)cpu_infer_ptr;
+        cpuinfer->sync();
+    }
+
+    void sync_with_cuda_stream(intptr_t user_cuda_stream) {
+        cudaLaunchHostFunc((cudaStream_t)user_cuda_stream, (cudaHostFn_t)&sync_, (void*)this);
+    }
+
    public:
     Backend* backend_;
     TaskQueue* task_queue_;

ktransformers/ktransformers_ext/examples/test_linear.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-25 10:32:05
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:34:00
+LastEditTime : 2024-08-06 10:36:59
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,23 +15,23 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
-with torch.inference_mode(mode=True):
-    input_size = 16384
-    output_size = 5120
-    stride = 32
-    proj_type = 1 # ggml_type::GGML_TYPE_F16
-    hidden_type = 1 # ggml_type::GGML_TYPE_F16
-    layer_num = 10
-    CPUInfer = cpuinfer_ext.CPUInfer(48)
-    validation_iter = 100
-    warm_up_iter = 1000
-    test_iter = 10000
+input_size = 16384
+output_size = 5120
+stride = 32
+group_max_len = 1024
+proj_type = 1 # ggml_type::GGML_TYPE_F16
+hidden_type = 1 # ggml_type::GGML_TYPE_F16
+qlen = 30
+layer_num = 10
+CPUInfer = cpuinfer_ext.CPUInfer(48)
+validation_iter = 100
 
+with torch.inference_mode(mode=True):
     linears = []
     projs = []
     for _ in range(layer_num):
         proj = torch.randn((output_size, input_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
-        config = cpuinfer_ext.linear.LinearConfig(input_size, output_size, stride, proj.data_ptr(), proj_type, hidden_type)
+        config = cpuinfer_ext.linear.LinearConfig(input_size, output_size, stride, group_max_len, proj.data_ptr(), proj_type, hidden_type)
         linear = cpuinfer_ext.linear.Linear(config)
         projs.append(proj)
         linears.append(linear)
@@ -39,11 +39,17 @@ with torch.inference_mode(mode=True):
     # validation
     for i in range(validation_iter):
         linear = linears[i % layer_num]
-        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
+        input = torch.randn((qlen, input_size), dtype=torch.float16).contiguous()
+        output = torch.empty((qlen, output_size), dtype=torch.float16).contiguous()
         input = input / 100
 
-        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
+        CPUInfer.submit(
+            linear.forward(
+                qlen,
+                input.data_ptr(),
+                output.data_ptr()
+            )
+        )
         CPUInfer.sync()
         # print('cpuinfer output', output)
 
@@ -54,30 +60,3 @@ with torch.inference_mode(mode=True):
         diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
         print('diff = ', diff)
         assert(diff < 0.001)
-
-    # warm up
-    for i in range(warm_up_iter):
-        linear = linears[i % layer_num]
-        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-
-    # test
-    total_time = 0
-    for i in range(test_iter):
-        linear = linears[i % layer_num]
-        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        start = time.perf_counter()
-        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-        end = time.perf_counter()
-        total_time += end - start
-    print('Time: ', total_time)
-    print('Iteration: ', test_iter) 
-    print('Time per iteration: ', total_time / test_iter)
-    print('Bandwidth: ', input_size * output_size * 2 * test_iter / total_time / 1000 / 1000 / 1000, 'GB/s')
-    print("All tasks completed.")
\ No newline at end of file

ktransformers/ktransformers_ext/examples/test_mlp.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-25 10:32:05
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:34:03
+LastEditTime : 2024-08-06 10:37:28
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,20 +15,30 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
-with torch.inference_mode(mode=True):
-    hidden_size = 5120
-    intermediate_size = 3072
-    stride = 32
-    gate_type = 1 # ggml_type::GGML_TYPE_F16
-    up_type = 1 # ggml_type::GGML_TYPE_F16
-    down_type = 1 # ggml_type::GGML_TYPE_F16
-    hidden_type = 1 # ggml_type::GGML_TYPE_F16
-    layer_num = 10
-    CPUInfer = cpuinfer_ext.CPUInfer(48)
-    validation_iter = 100
-    warm_up_iter = 1000
-    test_iter = 10000
+hidden_size = 5120
+intermediate_size = 3072
+stride = 32
+group_max_len = 1024
+gate_type = 1 # ggml_type::GGML_TYPE_F16
+up_type = 1 # ggml_type::GGML_TYPE_F16
+down_type = 1 # ggml_type::GGML_TYPE_F16
+hidden_type = 1 # ggml_type::GGML_TYPE_F16
+qlen = 30
+layer_num = 10
+CPUInfer = cpuinfer_ext.CPUInfer(48)
+validation_iter = 100
+
+def act_fn(x):
+    return x / (1.0 + torch.exp(-x))
+
+def mlp_torch(input, gate_proj, up_proj, down_proj):
+    gate_buf = torch.mm(input, gate_proj.t())
+    up_buf = torch.mm(input, up_proj.t())
+    intermediate = act_fn(gate_buf) * up_buf
+    ret = torch.mm(intermediate, down_proj.t())
+    return ret
 
+with torch.inference_mode(mode=True):
     mlps = []
     gate_projs = []
     up_projs = []
@@ -37,7 +47,7 @@ with torch.inference_mode(mode=True):
         gate_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
         up_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
         down_proj = torch.randn((hidden_size, intermediate_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
-        config = cpuinfer_ext.mlp.MLPConfig(hidden_size, intermediate_size, stride, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
+        config = cpuinfer_ext.mlp.MLPConfig(hidden_size, intermediate_size, stride, group_max_len, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
         mlp = cpuinfer_ext.mlp.MLP(config)
         gate_projs.append(gate_proj)
         up_projs.append(up_proj)
@@ -47,52 +57,26 @@ with torch.inference_mode(mode=True):
     # validation
     for i in range(validation_iter):
         mlp = mlps[i % layer_num]
-        input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
+        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
+        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
         input = input / 100
 
-        CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
+        CPUInfer.submit(
+            mlp.forward(
+                qlen,
+                input.data_ptr(), 
+                output.data_ptr()
+            )
+        )
         CPUInfer.sync()
         # print('cpuinfer output', output)
 
-        def act_fn(x):
-            return x / (1.0 + torch.exp(-x))
         gate_proj = gate_projs[i%layer_num]
         up_proj = up_projs[i%layer_num]
         down_proj = down_projs[i%layer_num]
-        gate_buf = torch.mm(input, gate_proj.t())
-        up_buf = torch.mm(input, up_proj.t())
-        intermediate = act_fn(gate_buf) * up_buf
-        t_output = torch.mm(intermediate, down_proj.t())
+        t_output = mlp_torch(input, gate_proj, up_proj, down_proj)
         # print('torch output', t_output)
 
         diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
         print('diff = ', diff)
         assert(diff < 0.001)
-
-    # warm up
-    for i in range(warm_up_iter):
-        mlp = mlps[i % layer_num]
-        input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-
-    # test
-    total_time = 0
-    for i in range(test_iter):
-        mlp = mlps[i % layer_num]
-        input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        start = time.time()
-        CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-        end = time.time()
-        total_time += end - start
-    print('Time: ', total_time)
-    print('Iteration: ', test_iter) 
-    print('Time per iteration: ', total_time / test_iter)
-    print('Bandwidth: ', hidden_size * intermediate_size * 3 * 2 * test_iter / total_time / 1024 / 1024 / 1024, 'GB/s')
-    print("All tasks completed.")
\ No newline at end of file

ktransformers/ktransformers_ext/examples/test_moe.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-25 10:32:05
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:34:06
+LastEditTime : 2024-08-06 10:38:05
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,25 +15,64 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
-with torch.inference_mode(mode=True):
-    expert_num = 10
-    hidden_size = 5120
-    intermediate_size = 1536
-    stride = 32
-    group_min_len = 10
-    group_max_len = 1024
-    gate_type = 1 # ggml_type::GGML_TYPE_F16
-    up_type = 1 # ggml_type::GGML_TYPE_F16
-    down_type = 1 # ggml_type::GGML_TYPE_F16
-    hidden_type = 1 # ggml_type::GGML_TYPE_F16
-    n_routed_experts = 6
-    qlen = 30
-    layer_num = 10
-    CPUInfer = cpuinfer_ext.CPUInfer(48)
-    validation_iter = 100
-    warm_up_iter = 1000
-    test_iter = 10000
+expert_num = 160
+hidden_size = 5120
+intermediate_size = 1536
+stride = 32
+group_min_len = 10
+group_max_len = 1024
+gate_type = 1 # ggml_type::GGML_TYPE_F16
+up_type = 1 # ggml_type::GGML_TYPE_F16
+down_type = 1 # ggml_type::GGML_TYPE_F16
+hidden_type = 1 # ggml_type::GGML_TYPE_F16
+n_routed_experts = 6
+qlen = 30
+layer_num = 10
+CPUInfer = cpuinfer_ext.CPUInfer(48)
+validation_iter = 100
+
+def act_fn(x):
+    return x / (1.0 + torch.exp(-x))
+
+def mlp_torch(input, gate_proj, up_proj, down_proj):
+    gate_buf = torch.mm(input, gate_proj.t())
+    up_buf = torch.mm(input, up_proj.t())
+    intermediate = act_fn(gate_buf) * up_buf
+    ret = torch.mm(intermediate, down_proj.t())
+    return ret
+
+def moe_torch(input, expert_ids, weights, gate_proj, up_proj, down_proj):
+    cnts = expert_ids.new_zeros((expert_ids.shape[0], expert_num))
+    cnts.scatter_(1, expert_ids, 1)
+    tokens_per_expert = cnts.sum(dim=0)
+    idxs = expert_ids.view(-1).argsort()
+    sorted_tokens = input[idxs // expert_ids.shape[1]]
+
+    outputs = []
+    start_idx = 0
+    for i, num_tokens in enumerate(tokens_per_expert):
+        end_idx = start_idx + num_tokens
+        if num_tokens == 0:
+            continue
+        tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+        expert_out = mlp_torch(tokens_for_this_expert, gate_proj[i], up_proj[i], down_proj[i])
+        outputs.append(expert_out)
+        start_idx = end_idx
+
+    outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+
+    new_x = torch.empty_like(outs)
+    new_x[idxs] = outs
+    t_output = (
+        new_x.view(*expert_ids.shape, -1)
+        .type(weights.dtype)
+        .mul_(weights.unsqueeze(dim=-1))
+        .sum(dim=1)
+        .type(new_x.dtype)
+    )
+    return t_output
 
+with torch.inference_mode(mode=True):
     moes = []
     gate_projs = []
     up_projs = []
@@ -51,63 +90,32 @@ with torch.inference_mode(mode=True):
 
     # validation
     for i in range(validation_iter):
-        moe = moes[i % layer_num]
-        expert_ids = torch.randint(0, expert_num, (qlen, n_routed_experts), dtype=torch.int64).contiguous()
+        expert_ids = torch.stack([torch.randperm(expert_num)[:n_routed_experts] for _ in range(qlen)]).contiguous()
         weights = torch.rand((qlen, n_routed_experts), dtype=torch.float32).contiguous()
-        input = torch.randn((qlen, 1, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((qlen, 1, hidden_size), dtype=torch.float16).contiguous()
+        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
+        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
         input = input / 100
         
-        CPUInfer.submit(moe.forward, qlen, n_routed_experts, expert_ids.data_ptr(), weights.data_ptr(), input.data_ptr(), output.data_ptr())
+        moe = moes[i % layer_num]
+        CPUInfer.submit(
+            moe.forward( 
+                qlen,
+                n_routed_experts, 
+                expert_ids.data_ptr(), 
+                weights.data_ptr(), 
+                input.data_ptr(), 
+                output.data_ptr()
+            )
+        )
         CPUInfer.sync()
         # print('cpuinfer output', output)
 
-        def act_fn(x):
-            return x / (1.0 + torch.exp(-x))
-        t_output = torch.zeros((qlen, 1, hidden_size), dtype=torch.float32).contiguous()
         gate_proj = gate_projs[i%layer_num]
         up_proj = up_projs[i%layer_num]
         down_proj = down_projs[i%layer_num]
-        for token_idx in range(qlen):
-            for i, expert_id in enumerate(expert_ids[token_idx]):
-                gate_buf = torch.mm(input[token_idx], gate_proj[expert_id].t())
-                up_buf = torch.mm(input[token_idx], up_proj[expert_id].t())
-                intermediate = act_fn(gate_buf) * up_buf
-                expert_output = torch.mm(intermediate, down_proj[expert_id].t())
-                t_output[token_idx] += weights[token_idx][i] * expert_output
+        t_output = moe_torch(input, expert_ids, weights, gate_proj, up_proj, down_proj)
         # print('torch output', t_output)
 
         diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
         print('diff = ', diff)
         assert(diff < 0.001)
-
-    # warm up
-    for i in range(warm_up_iter):
-        moe = moes[i % layer_num]
-        expert_ids = torch.randint(0, expert_num, (qlen, n_routed_experts), dtype=torch.int64).contiguous()
-        weights = torch.rand((qlen, n_routed_experts), dtype=torch.float32).contiguous()
-        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        CPUInfer.submit(moe.forward, qlen, n_routed_experts, expert_ids.data_ptr(), weights.data_ptr(), input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-
-    # test
-    total_time = 0
-    for i in range(test_iter):
-        moe = moes[i % layer_num]
-        expert_ids = torch.randint(0, expert_num, (qlen, n_routed_experts), dtype=torch.int64).contiguous()
-        weights = torch.rand((qlen, n_routed_experts), dtype=torch.float32).contiguous()
-        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        start = time.perf_counter()
-        CPUInfer.submit(moe.forward, qlen, n_routed_experts, expert_ids.data_ptr(), weights.data_ptr(), input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-        end = time.perf_counter()
-        total_time += end - start
-    print('Time: ', total_time)
-    print('Iteration: ', test_iter) 
-    print('Time per iteration: ', total_time / test_iter)
-    print('Bandwidth: ', hidden_size * intermediate_size * 3 * n_routed_experts * 2 * test_iter / total_time / 1000 / 1000 / 1000, 'GB/s')
-    print("All tasks completed.")
\ No newline at end of file

ktransformers/ktransformers_ext/ext_bindings.cpp

@@ -3,8 +3,8 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-22 02:03:22
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
- * @LastEditTime : 2024-07-25 10:34:23
+ * @LastEditors  : chenht2022
+ * @LastEditTime : 2024-08-07 10:39:37
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
 // Python bindings
@@ -12,7 +12,6 @@
 #include <iostream>
 #include <memory>
 #include "cpu_backend/cpuinfer.h"
-#include "cuda_runtime.h"
 #include "device_launch_parameters.h"
 #include "llamafile/flags.h"
 #include "operators/llamafile/linear.h"
@@ -26,239 +25,155 @@
 namespace py = pybind11;
 using namespace pybind11::literals;
 
-// Binding functions for the Linear class
 class LinearBindings {
    public:
-    static void bind_forward(CPUInfer& cpuinfer, Linear* linear, py::args args, py::kwargs kwargs) {
-        auto input = args[0].cast<intptr_t>();
-        auto output = args[1].cast<intptr_t>();
-        cpuinfer.submit(&Linear::forward, linear,
-                        (const void*)input, (void*)output);
-    }
-
-    static void bind_warm_up(CPUInfer& cpuinfer, Linear* linear, py::args args, py::kwargs kwargs) {
-        cpuinfer.submit(&Linear::warm_up, linear);
-    }
-
-    static void bind_functions(CPUInfer& cpuinfer, py::object func, py::args args, py::kwargs kwargs) {
-        auto linear = func.attr("__self__").cast<Linear*>();
-        std::string func_name = py::str(func.attr("__func__").attr("__name__"));
-
-        if (func_name == "forward") {
-            bind_forward(cpuinfer, linear, args, kwargs);
-        } else if (func_name == "warm_up") {
-            bind_warm_up(cpuinfer, linear, args, kwargs);
-        } else {
-            throw py::value_error("Unsupported function: " +
-                                  std::string(func_name));
+    class WarmUpBindinds {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            Linear* linear;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&Linear::warm_up, args_->linear);
+        }
+        static std::pair<intptr_t, intptr_t> interface(Linear& linear) {
+            Args* args = new Args{nullptr, &linear};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
+    class ForwardBindings {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            Linear* linear;
+            int qlen;
+            const void* input;
+            void* output;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&Linear::forward, args_->linear, args_->qlen, args_->input, args_->output);
         }
-    }
+        static std::pair<intptr_t, intptr_t> interface(Linear& linear, int qlen, intptr_t input, intptr_t output) {
+            Args* args = new Args{nullptr, &linear, qlen, (const void*)input, (void*)output};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
 };
 
-// Binding functions for the MLP class
 class MLPBindings {
    public:
-    static void bind_forward(CPUInfer& cpuinfer, MLP* mlp, py::args args, py::kwargs kwargs) {
-        auto input = args[0].cast<intptr_t>();
-        auto output = args[1].cast<intptr_t>();
-        cpuinfer.submit(&MLP::forward, mlp,
-                        (const void*)input, (void*)output);
-    }
-
-    static void bind_warm_up(CPUInfer& cpuinfer, MLP* mlp, py::args args, py::kwargs kwargs) {
-        cpuinfer.submit(&MLP::warm_up, mlp);
-    }
-
-    static void bind_functions(CPUInfer& cpuinfer, py::object func, py::args args, py::kwargs kwargs) {
-        auto mlp = func.attr("__self__").cast<MLP*>();
-        std::string func_name = py::str(func.attr("__func__").attr("__name__"));
-
-        if (func_name == "forward") {
-            bind_forward(cpuinfer, mlp, args, kwargs);
-        } else if (func_name == "warm_up") {
-            bind_warm_up(cpuinfer, mlp, args, kwargs);
-        } else {
-            throw py::value_error("Unsupported function: " +
-                                  std::string(func_name));
+    class WarmUpBindinds {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            MLP* mlp;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&MLP::warm_up, args_->mlp);
         }
-    }
+        static std::pair<intptr_t, intptr_t> interface(MLP& mlp) {
+            Args* args = new Args{nullptr, &mlp};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
+    class ForwardBindings {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            MLP* mlp;
+            int qlen;
+            const void* input;
+            void* output;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&MLP::forward, args_->mlp, args_->qlen, args_->input, args_->output);
+        }
+        static std::pair<intptr_t, intptr_t> interface(MLP& mlp, int qlen, intptr_t input, intptr_t output) {
+            Args* args = new Args{nullptr, &mlp, qlen, (const void*)input, (void*)output};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
 };
 
-// Binding functions for the MOE class
 class MOEBindings {
    public:
-    static void bind_forward(CPUInfer& cpuinfer, MOE* moe, py::args args, py::kwargs kwargs) {
-        int qlen = args[0].cast<int>();
-        int k = args[1].cast<int>();
-        auto expert_ids = args[2].cast<intptr_t>();
-        auto weights = args[3].cast<intptr_t>();
-        auto input = args[4].cast<intptr_t>();
-        auto output = args[5].cast<intptr_t>();
-        cpuinfer.submit(&MOE::forward, moe,
-                        qlen, k, (const uint64_t*)expert_ids, (const float*)weights, (const void*)input, (void*)output);
-    }
-
-    static void bind_warm_up(CPUInfer& cpuinfer, MOE* moe, py::args args, py::kwargs kwargs) {
-        cpuinfer.submit(&MOE::warm_up, moe);
-    }
-
-    static void bind_functions(CPUInfer& cpuinfer, py::object func, py::args args, py::kwargs kwargs) {
-        auto moe = func.attr("__self__").cast<MOE*>();
-        std::string func_name = py::str(func.attr("__func__").attr("__name__"));
-
-        if (func_name == "forward") {
-            bind_forward(cpuinfer, moe, args, kwargs);
-        } else if (func_name == "warm_up") {
-            bind_warm_up(cpuinfer, moe, args, kwargs);
-        } else {
-            throw py::value_error("Unsupported function: " +
-                                  std::string(func_name));
+    class WarmUpBindinds {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            MOE* moe;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&MOE::warm_up, args_->moe);
         }
-    }
-};
-
-struct MOEForwardArgs {
-    CPUInfer* cpuinfer;
-    MOE* moe;
-    int qlen;
-    int k;
-    uint64_t* expert_ids;
-    float* weights;
-    void* input;
-    void* output;
+        static std::pair<intptr_t, intptr_t> interface(MOE& moe) {
+            Args* args = new Args{nullptr, &moe};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
+    class ForwardBindings {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            MOE* moe;
+            int qlen;
+            int k;
+            const uint64_t* expert_ids;
+            const float* weights;
+            const void* input;
+            void* output;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&MOE::forward, args_->moe, args_->qlen, args_->k, args_->expert_ids, args_->weights, args_->input, args_->output);
+        }
+        static std::pair<intptr_t, intptr_t> interface(MOE& moe, int qlen, int k, intptr_t expert_ids, intptr_t weights, intptr_t input, intptr_t output) {
+            Args* args = new Args{nullptr, &moe, qlen, k, (const uint64_t*)expert_ids, (const float*)weights, (const void*)input, (void*)output};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
 };
 
-void submit_moe_forward_with_host_args_ptr(void* host_args_ptr) {
-    MOEForwardArgs* host_args = (MOEForwardArgs*)host_args_ptr;
-    host_args->cpuinfer->submit(&MOE::forward, host_args->moe,
-                                host_args->qlen, host_args->k, host_args->expert_ids, host_args->weights, host_args->input, host_args->output);
-}
-
-void cpuinfer_sync(void* host_args_ptr) {
-    CPUInfer* cpuinfer = (CPUInfer*)host_args_ptr;
-    cpuinfer->sync();
-}
-
 PYBIND11_MODULE(cpuinfer_ext, m) {
-    auto linear_module = m.def_submodule("linear");
+    py::class_<CPUInfer>(m, "CPUInfer")
+        .def(py::init<int>())
+        .def("submit", &CPUInfer::submit)
+        .def("submit_with_cuda_stream", &CPUInfer::submit_with_cuda_stream)
+        .def("sync", &CPUInfer::sync)
+        .def("sync_with_cuda_stream", &CPUInfer::sync_with_cuda_stream);
 
+    auto linear_module = m.def_submodule("linear");
     py::class_<LinearConfig>(linear_module, "LinearConfig")
-        .def(py::init([](int hidden_size, int intermediate_size, int stride, intptr_t proj, int proj_type, int hidden_type) {
-            return LinearConfig(hidden_size, intermediate_size, stride, (void*)proj, (ggml_type)proj_type, (ggml_type)hidden_type);
+        .def(py::init([](int hidden_size, int intermediate_size, int stride, int group_max_len, intptr_t proj, int proj_type, int hidden_type) {
+            return LinearConfig(hidden_size, intermediate_size, stride, group_max_len, (void*)proj, (ggml_type)proj_type, (ggml_type)hidden_type);
         }));
-
     py::class_<Linear>(linear_module, "Linear")
         .def(py::init<LinearConfig>())
-        .def("warm_up", [](Linear& linear) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        })
-        .def("forward", [](Linear& linear, intptr_t input, intptr_t output) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        });
+        .def("warm_up", &LinearBindings::WarmUpBindinds::interface)
+        .def("forward", &LinearBindings::ForwardBindings::interface);
 
     auto mlp_module = m.def_submodule("mlp");
-
     py::class_<MLPConfig>(mlp_module, "MLPConfig")
-        .def(py::init([](int hidden_size, int intermediate_size, int stride, intptr_t gate_proj, intptr_t up_proj, intptr_t down_proj, int gate_type, int up_type, int down_type, int hidden_type) {
-            return MLPConfig(hidden_size, intermediate_size, stride, (void*)gate_proj, (void*)up_proj, (void*)down_proj, (ggml_type)gate_type, (ggml_type)up_type, (ggml_type)down_type, (ggml_type)hidden_type);
+        .def(py::init([](int hidden_size, int intermediate_size, int stride, int group_max_len, intptr_t gate_proj, intptr_t up_proj, intptr_t down_proj, int gate_type, int up_type, int down_type, int hidden_type) {
+            return MLPConfig(hidden_size, intermediate_size, stride, group_max_len, (void*)gate_proj, (void*)up_proj, (void*)down_proj, (ggml_type)gate_type, (ggml_type)up_type, (ggml_type)down_type, (ggml_type)hidden_type);
         }));
-
     py::class_<MLP>(mlp_module, "MLP")
         .def(py::init<MLPConfig>())
-        .def("warm_up", [](MLP& mlp) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        })
-        .def("forward", [](MLP& mlp, intptr_t input, intptr_t output) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        });
+        .def("warm_up", &MLPBindings::WarmUpBindinds::interface)
+        .def("forward", &MLPBindings::ForwardBindings::interface);
 
     auto moe_module = m.def_submodule("moe");
-
     py::class_<MOEConfig>(moe_module, "MOEConfig")
         .def(py::init([](int expert_num, int routed_expert_num, int hidden_size, int intermediate_size, int stride, int group_min_len, int group_max_len, intptr_t gate_proj, intptr_t up_proj, intptr_t down_proj, int gate_type, int up_type, int down_type, int hidden_type) {
             return MOEConfig(expert_num, routed_expert_num, hidden_size, intermediate_size, stride, group_min_len, group_max_len, (void*)gate_proj, (void*)up_proj, (void*)down_proj, (ggml_type)gate_type, (ggml_type)up_type, (ggml_type)down_type, (ggml_type)hidden_type);
         }));
-
     py::class_<MOE>(moe_module, "MOE")
         .def(py::init<MOEConfig>())
-        .def("warm_up", [](MOE& moe) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        })
-        .def("forward", [](MOE& moe, int k, uint64_t expert_ids, intptr_t weights, intptr_t input, intptr_t output) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        });
-
-    py::class_<CPUInfer>(m, "CPUInfer")
-        .def(py::init<int>())
-        .def("submit",
-             [linear_module, mlp_module, moe_module](CPUInfer& cpuinfer, py::object func, py::args args, py::kwargs kwargs) {
-                 if (py::hasattr(func, "__self__") &&
-                     py::hasattr(func, "__func__")) {
-                     std::string class_name = py::str(func.attr("__self__")
-                                                          .attr("__class__")
-                                                          .attr("__name__"));
-                     if (class_name == "Linear") {
-                         LinearBindings::bind_functions(cpuinfer, func,
-                                                        args, kwargs);
-                     } else if (class_name == "MLP") {
-                         MLPBindings::bind_functions(cpuinfer, func,
-                                                     args, kwargs);
-                     } else if (class_name == "MOE") {
-                         MOEBindings::bind_functions(cpuinfer, func,
-                                                     args, kwargs);
-                     } else {
-                         // handle other classes
-                         throw py::type_error("Unsupported class type: " +
-                                              class_name);
-                     }
-                 } else {
-                     // handle cases where func does not have __self__ or
-                     // __func__
-                     throw py::type_error(
-                         "Invalid function object: missing "
-                         "__self__ or __func__ attribute.");
-                 }
-             })
-        .def("submit_with_cuda_stream",
-             [linear_module, mlp_module, moe_module](CPUInfer& cpuinfer, intptr_t user_cuda_stream, py::object func, py::args args, py::kwargs kwargs) {
-                 if (py::hasattr(func, "__self__") &&
-                     py::hasattr(func, "__func__")) {
-                     std::string class_name = py::str(func.attr("__self__")
-                                                          .attr("__class__")
-                                                          .attr("__name__"));
-                     if (class_name == "MOE") {
-                         std::string func_name = py::str(func.attr("__func__").attr("__name__"));
-                         if (func_name == "forward") {
-                             auto moe = func.attr("__self__").cast<MOE*>();
-                             int qlen = args[0].cast<int>();
-                             int k = args[1].cast<int>();
-                             auto expert_ids = args[2].cast<intptr_t>();
-                             auto weights = args[3].cast<intptr_t>();
-                             auto input = args[4].cast<intptr_t>();
-                             auto output = args[5].cast<intptr_t>();
-                             MOEForwardArgs* moe_forward_args = new MOEForwardArgs{&cpuinfer, moe, qlen, k, (uint64_t*)expert_ids, (float*)weights, (void*)input, (void*)output};
-                             // submit_moe_forward_with_host_args_ptr(moe_forward_args);
-                             cudaLaunchHostFunc((cudaStream_t)user_cuda_stream, (cudaHostFn_t)submit_moe_forward_with_host_args_ptr, moe_forward_args);
-                         } else {
-                             throw py::value_error("Unsupported function: " +
-                                                   std::string(func_name));
-                         }
-                     } else {
-                         // handle other classes
-                         throw py::type_error("Unsupported class type: " +
-                                              class_name);
-                     }
-                 } else {
-                     // handle cases where func does not have __self__ or
-                     // __func__
-                     throw py::type_error(
-                         "Invalid function object: missing "
-                         "__self__ or __func__ attribute.");
-                 }
-             })
-        .def("sync_with_cuda_stream", [](CPUInfer& cpuinfer, intptr_t user_cuda_stream) {
-            // cpuinfer_sync((void*)(&cpuinfer));
-            cudaLaunchHostFunc((cudaStream_t)user_cuda_stream, (cudaHostFn_t)cpuinfer_sync, (void*)(&cpuinfer));
-        })
-        .def("sync", &CPUInfer::sync);
+        .def("warm_up", &MOEBindings::WarmUpBindinds::interface)
+        .def("forward", &MOEBindings::ForwardBindings::interface);
 }

ktransformers/ktransformers_ext/operators/llamafile/linear.cpp

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-12 10:07:58
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:34:58
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -13,9 +13,15 @@ Linear::Linear(LinearConfig config) {
     config_ = config;
     proj_ = config_.proj;
 
-    input_fp32_.resize(config_.input_size);
-    proj_input_.resize(config_.input_size * 4);
-    proj_output_.resize(config_.output_size);
+    std::vector<std::pair<void**, uint64_t>> mem_requests;
+    mem_requests.push_back({(void**)&input_fp32_, sizeof(float) * config_.group_max_len * config_.input_size});
+    mem_requests.push_back({(void**)&proj_input_, config_.group_max_len * config_.input_size * ggml_type_size(ggml_internal_get_type_traits(config_.proj_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.proj_type).vec_dot_type)});
+    mem_requests.push_back({(void**)&proj_output_, sizeof(float) * config_.group_max_len * config_.output_size});
+    shared_mem_buffer.alloc(this, mem_requests);
+}
+
+Linear::~Linear() {
+    shared_mem_buffer.dealloc(this);
 }
 
 void Linear::warm_up(Backend* backend) {
@@ -26,22 +32,42 @@ void Linear::warm_up(Backend* backend) {
         input_fp32[i] = 0;
     }
     from_float(input_fp32.data(), input.data(), config_.input_size, config_.hidden_type);
-    forward(input.data(), output.data(), backend);
+    forward_many(1, input.data(), output.data(), backend);
 }
 
-void Linear::forward(const void* input, void* output, Backend* backend) {
+void Linear::forward_many(int qlen, const void* input, void* output, Backend* backend) {
     const void* proj_input_ptr;
     if (config_.hidden_type == ggml_internal_get_type_traits(config_.proj_type).vec_dot_type) {
         proj_input_ptr = input;
     } else {
-        to_float(input, input_fp32_.data(), config_.input_size, config_.hidden_type);
-        from_float(input_fp32_.data(), proj_input_.data(), config_.input_size, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type);
-        proj_input_ptr = proj_input_.data();
+        to_float(input, input_fp32_, qlen * config_.input_size, config_.hidden_type);
+        from_float(input_fp32_, proj_input_, qlen * config_.input_size, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type);
+        proj_input_ptr = proj_input_;
     }
     int nth = config_.output_size / config_.stride;
     backend->do_work_stealing_job(nth, [&](int task_id) {
-        int ith = task_id % nth;
-        llamafile_sgemm(config_.output_size, 1, config_.input_size / ggml_blck_size(config_.proj_type), proj_, config_.input_size / ggml_blck_size(config_.proj_type), proj_input_ptr, config_.input_size / ggml_blck_size(config_.proj_type), proj_output_.data(), config_.output_size, ith, nth, GGML_TASK_TYPE_COMPUTE, config_.proj_type, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        int ith = task_id;
+        void* proj_ptr = proj_ + ith * config_.stride * config_.input_size * ggml_type_size(config_.proj_type) / ggml_blck_size(config_.proj_type);
+        float* proj_output_ptr = proj_output_ + ith * config_.stride;
+        llamafile_sgemm(config_.stride, qlen, config_.input_size / ggml_blck_size(config_.proj_type), proj_ptr, config_.input_size / ggml_blck_size(config_.proj_type), proj_input_ptr, config_.input_size / ggml_blck_size(config_.proj_type), proj_output_ptr, config_.output_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.proj_type, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        if (config_.stride % ggml_blck_size(config_.hidden_type) == 0) {
+            for (int i = 0; i < qlen; i++) {
+                float* output_fp32_ptr = proj_output_ + i * config_.output_size + ith * config_.stride;
+                void* output_ptr = output + i * config_.output_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type) + ith * config_.stride * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type);
+                from_float(output_fp32_ptr, output_ptr, config_.stride, config_.hidden_type);
+            }
+        }
     });
-    from_float(proj_output_.data(), output, config_.output_size, config_.hidden_type);
+    if (config_.stride % ggml_blck_size(config_.hidden_type) != 0) {
+        from_float(proj_output_, output, qlen * config_.output_size, config_.hidden_type);
+    }
+}
+
+void Linear::forward(int qlen, const void* input, void* output, Backend* backend) {
+    if (qlen <= 0) {
+        return;
+    }
+    int forward_len = std::min(qlen, config_.group_max_len);
+    forward_many(forward_len, input, output, backend);
+    forward(qlen - forward_len, input + forward_len * config_.input_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), output + forward_len * config_.output_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), backend);
 }
\ No newline at end of file

ktransformers/ktransformers_ext/operators/llamafile/linear.h

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-12 10:07:58
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:00
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -22,34 +22,38 @@
 #include "llama.cpp/ggml-quants.h"
 #include "llama.cpp/ggml.h"
 #include "llamafile/sgemm.h"
+#include "shared_mem_buffer.h"
 
 struct LinearConfig {
     int input_size;
     int output_size;
     int stride;
+    int group_max_len;
     void* proj;
     ggml_type proj_type;
     ggml_type hidden_type;
 
     LinearConfig() {}
 
-    LinearConfig(int input_size, int output_size, int stride, void* proj, ggml_type proj_type, ggml_type hidden_type)
-        : input_size(input_size), output_size(output_size), stride(stride), proj(proj), proj_type(proj_type), hidden_type(hidden_type) {}
+    LinearConfig(int input_size, int output_size, int stride, int group_max_len, void* proj, ggml_type proj_type, ggml_type hidden_type)
+        : input_size(input_size), output_size(output_size), stride(stride), group_max_len(group_max_len), proj(proj), proj_type(proj_type), hidden_type(hidden_type) {}
 };
 
 class Linear {
    public:
     Linear(LinearConfig);
+    ~Linear();
     void warm_up(Backend* backend);
-    void forward(const void* input, void* output, Backend* backend);
+    void forward_many(int qlen, const void* input, void* output, Backend* backend);
+    void forward(int qlen, const void* input, void* output, Backend* backend);
 
    private:
     LinearConfig config_;
     void* proj_;  // [output_size * input_size ( /32 if quantized)]
 
-    std::vector<float> input_fp32_;    // [input_size]
-    std::vector<uint8_t> proj_input_;  // [input_size * 4]
-    std::vector<float> proj_output_;   // [output_size]
+    float* input_fp32_;    // [group_max_len * input_size]
+    uint8_t* proj_input_;  // [group_max_len * input_size * ggml_type_size(ggml_internal_get_type_traits(proj_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(proj_type).vec_dot_type)]
+    float* proj_output_;   // [group_max_len * output_size]
 };
 
 #endif
\ No newline at end of file

ktransformers/ktransformers_ext/operators/llamafile/mlp.cpp

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-16 10:43:18
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:04
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -15,14 +15,20 @@ MLP::MLP(MLPConfig config) {
     up_proj_ = config_.up_proj;
     down_proj_ = config_.down_proj;
 
-    input_fp32_.resize(config_.hidden_size);
-    gate_input_.resize(config_.hidden_size * 4);
-    up_input_.resize(config_.hidden_size * 4);
-    gate_output_.resize(config_.intermediate_size);
-    up_output_.resize(config_.intermediate_size);
-    intermediate_fp32_.resize(config_.intermediate_size);
-    down_input_.resize(config_.intermediate_size * 4);
-    down_output_.resize(config_.hidden_size);
+    std::vector<std::pair<void**, uint64_t>> mem_requests;
+    mem_requests.push_back({(void**)&input_fp32_, sizeof(float) * config_.group_max_len * config_.hidden_size});
+    mem_requests.push_back({(void**)&gate_input_, config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type)});
+    mem_requests.push_back({(void**)&up_input_, config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type)});
+    mem_requests.push_back({(void**)&gate_output_, sizeof(float) * config_.group_max_len * config_.intermediate_size});
+    mem_requests.push_back({(void**)&up_output_, sizeof(float) * config_.group_max_len * config_.intermediate_size});
+    mem_requests.push_back({(void**)&intermediate_fp32_, sizeof(float) * config_.group_max_len * config_.intermediate_size});
+    mem_requests.push_back({(void**)&down_input_, config_.group_max_len * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type)});
+    mem_requests.push_back({(void**)&down_output_, sizeof(float) * config_.group_max_len * config_.hidden_size});
+    shared_mem_buffer.alloc(this, mem_requests);
+}
+
+MLP::~MLP() {
+    shared_mem_buffer.dealloc(this);
 }
 
 void MLP::warm_up(Backend* backend) {
@@ -33,33 +39,33 @@ void MLP::warm_up(Backend* backend) {
         input_fp32[i] = 0;
     }
     from_float(input_fp32.data(), input.data(), config_.hidden_size, config_.hidden_type);
-    forward(input.data(), output.data(), backend);
+    forward_many(1, input.data(), output.data(), backend);
 }
 
 static float act_fn(float x) {
     return x / (1.0f + expf(-x));
 }
 
-void MLP::forward(const void* input, void* output, Backend* backend) {
+void MLP::forward_many(int qlen, const void* input, void* output, Backend* backend) {
     const void* gate_input_ptr;
     const void* up_input_ptr;
     if (config_.hidden_type == ggml_internal_get_type_traits(config_.gate_type).vec_dot_type && config_.hidden_type == ggml_internal_get_type_traits(config_.up_type).vec_dot_type) {
         gate_input_ptr = up_input_ptr = input;
     } else {
-        to_float(input, input_fp32_.data(), config_.hidden_size, config_.hidden_type);
+        to_float(input, input_fp32_, qlen * config_.hidden_size, config_.hidden_type);
         if (ggml_internal_get_type_traits(config_.gate_type).vec_dot_type == ggml_internal_get_type_traits(config_.up_type).vec_dot_type) {
-            from_float(input_fp32_.data(), gate_input_.data(), config_.hidden_size, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-            gate_input_ptr = up_input_ptr = gate_input_.data();
+            from_float(input_fp32_, gate_input_, qlen * config_.hidden_size, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
+            gate_input_ptr = up_input_ptr = gate_input_;
         } else {
             if (config_.hidden_type != ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) {
-                from_float(input_fp32_.data(), gate_input_.data(), config_.hidden_size, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-                gate_input_ptr = gate_input_.data();
+                from_float(input_fp32_, gate_input_, qlen * config_.hidden_size, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
+                gate_input_ptr = gate_input_;
             } else {
                 gate_input_ptr = input;
             }
             if (config_.hidden_type != ggml_internal_get_type_traits(config_.up_type).vec_dot_type) {
-                from_float(input_fp32_.data(), up_input_.data(), config_.hidden_size, ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
-                up_input_ptr = up_input_.data();
+                from_float(input_fp32_, up_input_, qlen * config_.hidden_size, ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
+                up_input_ptr = up_input_;
             } else {
                 up_input_ptr = input;
             }
@@ -69,35 +75,49 @@ void MLP::forward(const void* input, void* output, Backend* backend) {
     backend->do_work_stealing_job(nth, [&](int task_id) {
         int ith = task_id;
         void* gate_proj_ptr = gate_proj_ + ith * config_.stride * config_.hidden_size * ggml_type_size(config_.gate_type) / ggml_blck_size(config_.gate_type);
-        float* gate_output_ptr = gate_output_.data() + ith * config_.stride;
-        llamafile_sgemm(config_.stride, 1, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_proj_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_input_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.gate_type, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        float* gate_output_ptr = gate_output_ + ith * config_.stride;
+        llamafile_sgemm(config_.stride, qlen, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_proj_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_input_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_output_ptr, config_.intermediate_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.gate_type, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
         void* up_proj_ptr = up_proj_ + ith * config_.stride * config_.hidden_size * ggml_type_size(config_.up_type) / ggml_blck_size(config_.up_type);
-        float* up_output_ptr = up_output_.data() + ith * config_.stride;
-        llamafile_sgemm(config_.stride, 1, config_.hidden_size / ggml_blck_size(config_.up_type), up_proj_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_input_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.up_type, ggml_internal_get_type_traits(config_.up_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
-        for (int i = ith * config_.stride; i < (ith + 1) * config_.stride; i++) {
-            intermediate_fp32_[i] = act_fn(gate_output_[i]) * up_output_[i];
-        }
-        if (config_.stride % ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) == 0) {
-            float* intermediate_fp32_ptr = intermediate_fp32_.data() + ith * config_.stride;
-            void* down_input_ptr = down_input_.data() + ith * config_.stride * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
-            from_float(intermediate_fp32_ptr, down_input_ptr, config_.stride, ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
+        float* up_output_ptr = up_output_ + ith * config_.stride;
+        llamafile_sgemm(config_.stride, qlen, config_.hidden_size / ggml_blck_size(config_.up_type), up_proj_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_input_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_output_ptr, config_.intermediate_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.up_type, ggml_internal_get_type_traits(config_.up_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        for (int i = 0; i < qlen; i++) {
+            for (int j = ith * config_.stride; j < (ith + 1) * config_.stride; j++) {
+                intermediate_fp32_[i * config_.intermediate_size + j] = act_fn(gate_output_[i * config_.intermediate_size + j]) * up_output_[i * config_.intermediate_size + j];
+            }
+            if (config_.stride % ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) == 0) {
+                float* intermediate_fp32_ptr = intermediate_fp32_ + i * config_.intermediate_size + ith * config_.stride;
+                void* down_input_ptr = down_input_ + i * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) + ith * config_.stride * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
+                from_float(intermediate_fp32_ptr, down_input_ptr, config_.stride, ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
+            }
         }
     });
     if (config_.stride % ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) != 0) {
-        from_float(intermediate_fp32_.data(), down_input_.data(), config_.intermediate_size, ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
+        from_float(intermediate_fp32_, down_input_, qlen * config_.intermediate_size, ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
     }
     nth = config_.hidden_size / config_.stride;
     backend->do_work_stealing_job(nth, [&](int task_id) {
         int ith = task_id;
         void* down_proj_ptr = down_proj_ + ith * config_.stride * config_.intermediate_size * ggml_type_size(config_.down_type) / ggml_blck_size(config_.down_type);
-        float* down_output_ptr = down_output_.data() + ith * config_.stride;
-        llamafile_sgemm(config_.stride, 1, config_.intermediate_size / ggml_blck_size(config_.down_type), down_proj_ptr, config_.intermediate_size / ggml_blck_size(config_.down_type), down_input_.data(), config_.intermediate_size / ggml_blck_size(config_.down_type), down_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.down_type, ggml_internal_get_type_traits(config_.down_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        float* down_output_ptr = down_output_ + ith * config_.stride;
+        llamafile_sgemm(config_.stride, qlen, config_.intermediate_size / ggml_blck_size(config_.down_type), down_proj_ptr, config_.intermediate_size / ggml_blck_size(config_.down_type), down_input_, config_.intermediate_size / ggml_blck_size(config_.down_type), down_output_ptr, config_.hidden_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.down_type, ggml_internal_get_type_traits(config_.down_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
         if (config_.stride % ggml_blck_size(config_.hidden_type) == 0) {
-            void* output_ptr = output + ith * config_.stride * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type);
-            from_float(down_output_ptr, output_ptr, config_.stride, config_.hidden_type);
+            for (int i = 0; i < qlen; i++) {
+                float* output_fp32_ptr = down_output_ + i * config_.hidden_size + ith * config_.stride;
+                void* output_ptr = output + i * config_.hidden_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type) + ith * config_.stride * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type);
+                from_float(output_fp32_ptr, output_ptr, config_.stride, config_.hidden_type);
+            }
         }
     });
     if (config_.stride % ggml_blck_size(config_.hidden_type) != 0) {
-        from_float(down_output_.data(), output, config_.hidden_size, config_.hidden_type);
+        from_float(down_output_, output, qlen * config_.hidden_size, config_.hidden_type);
     }
 }
+
+void MLP::forward(int qlen, const void* input, void* output, Backend* backend) {
+    if (qlen <= 0) {
+        return;
+    }
+    int forward_len = std::min(qlen, config_.group_max_len);
+    forward_many(forward_len, input, output, backend);
+    forward(qlen - forward_len, input + forward_len * config_.hidden_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), output + forward_len * config_.hidden_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), backend);
+}
\ No newline at end of file

ktransformers/ktransformers_ext/operators/llamafile/mlp.h

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-12 10:07:58
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:06
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -22,11 +22,13 @@
 #include "llama.cpp/ggml-quants.h"
 #include "llama.cpp/ggml.h"
 #include "llamafile/sgemm.h"
+#include "shared_mem_buffer.h"
 
 struct MLPConfig {
     int hidden_size;
     int intermediate_size;
     int stride;
+    int group_max_len;
     void* gate_proj;
     void* up_proj;
     void* down_proj;
@@ -37,15 +39,17 @@ struct MLPConfig {
 
     MLPConfig() {}
 
-    MLPConfig(int hidden_size, int intermediate_size, int stride, void* gate_proj, void* up_proj, void* down_proj, ggml_type gate_type, ggml_type up_type, ggml_type down_type, ggml_type hidden_type)
-        : hidden_size(hidden_size), intermediate_size(intermediate_size), stride(stride), gate_proj(gate_proj), up_proj(up_proj), down_proj(down_proj), gate_type(gate_type), up_type(up_type), down_type(down_type), hidden_type(hidden_type) {}
+    MLPConfig(int hidden_size, int intermediate_size, int stride, int group_max_len, void* gate_proj, void* up_proj, void* down_proj, ggml_type gate_type, ggml_type up_type, ggml_type down_type, ggml_type hidden_type)
+        : hidden_size(hidden_size), intermediate_size(intermediate_size), stride(stride), group_max_len(group_max_len), gate_proj(gate_proj), up_proj(up_proj), down_proj(down_proj), gate_type(gate_type), up_type(up_type), down_type(down_type), hidden_type(hidden_type) {}
 };
 
 class MLP {
    public:
     MLP(MLPConfig);
+    ~MLP();
     void warm_up(Backend* backend);
-    void forward(const void* input, void* output, Backend* backend);
+    void forward_many(int qlen, const void* input, void* output, Backend* backend);
+    void forward(int qlen, const void* input, void* output, Backend* backend);
 
    private:
     MLPConfig config_;
@@ -53,14 +57,14 @@ class MLP {
     void* up_proj_;    // [intermediate_size * hidden_size ( /32 if quantized)]
     void* down_proj_;  // [hidden_size * intermediate_size ( /32 if quantized)]
 
-    std::vector<float> input_fp32_;         // [hidden_size]
-    std::vector<uint8_t> gate_input_;       // [hidden_size * 4]
-    std::vector<uint8_t> up_input_;         // [hidden_size * 4]
-    std::vector<float> gate_output_;        // [intermediate_size]
-    std::vector<float> up_output_;          // [intermediate_size]
-    std::vector<float> intermediate_fp32_;  // [intermediate_size]
-    std::vector<uint8_t> down_input_;       // [intermediate_size * 4]
-    std::vector<float> down_output_;        // [hidden_size]
+    float* input_fp32_;         // [group_max_len * hidden_size]
+    uint8_t* gate_input_;       // [group_max_len * hidden_size * ggml_type_size(ggml_internal_get_type_traits(gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(gate_type).vec_dot_type)]
+    uint8_t* up_input_;         // [group_max_len * hidden_size * ggml_type_size(ggml_internal_get_type_traits(up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(up_type).vec_dot_type)]
+    float* gate_output_;        // [group_max_len * intermediate_size]
+    float* up_output_;          // [group_max_len * intermediate_size]
+    float* intermediate_fp32_;  // [group_max_len * intermediate_size]
+    uint8_t* down_input_;       // [group_max_len * intermediate_size * ggml_type_size(ggml_internal_get_type_traits(down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(down_type).vec_dot_type)]
+    float* down_output_;        // [group_max_len * hidden_size]
 };
 
 #endif
\ No newline at end of file

ktransformers/ktransformers_ext/operators/llamafile/moe.cpp

 /**
- * @Description  :  
+ * @Description  :
  * @Author       : chenht2022
  * @Date         : 2024-07-22 02:03:22
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:07
- * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
-**/
+ * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+ **/
 #include "moe.h"
 #include <iostream>
 #include "unistd.h"
 
-void* MOE::buffer_ = nullptr;
-
 MOE::MOE(MOEConfig config) {
     config_ = config;
     gate_proj_ = config_.gate_proj;
     up_proj_ = config_.up_proj;
     down_proj_ = config_.down_proj;
 
-    if (MOE::buffer_ == nullptr) {
-        uint64_t buffer_size = 0;
-        buffer_size += sizeof(float) * config_.group_max_len * config_.hidden_size;
-        buffer_size += config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-        buffer_size += config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
-        buffer_size += config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-        buffer_size += config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
-        buffer_size += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-        buffer_size += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-        buffer_size += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-        buffer_size += config_.routed_expert_num * config_.group_max_len * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
-        buffer_size += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.hidden_size;
-        buffer_size += sizeof(float) * config_.group_max_len * config_.hidden_size;
-        buffer_ = malloc(buffer_size);
-    }
-
-    uint64_t offset = 0;
-    s_input_fp32_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.hidden_size;
-    s_gate_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-    s_up_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
+    std::vector<std::pair<void**, uint64_t>> s_mem_requests;
+    s_mem_requests.push_back({(void**)&s_input_fp32_, sizeof(float) * config_.hidden_size});
+    s_mem_requests.push_back({(void**)&s_gate_input_, config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type)});
+    s_mem_requests.push_back({(void**)&s_up_input_, config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type)});
     s_gate_output_.resize(config_.routed_expert_num);
     s_up_output_.resize(config_.routed_expert_num);
     s_intermediate_fp32_.resize(config_.routed_expert_num);
     s_down_input_.resize(config_.routed_expert_num);
     s_down_output_.resize(config_.routed_expert_num);
     for (int i = 0; i < config_.routed_expert_num; i++) {
-        s_gate_output_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.intermediate_size;
-        s_up_output_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.intermediate_size;
-        s_intermediate_fp32_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.intermediate_size;
-        s_down_input_[i] = (uint8_t*)(buffer_ + offset);
-        offset += config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
-        s_down_output_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.hidden_size;
+        s_mem_requests.push_back({(void**)&s_gate_output_[i], sizeof(float) * config_.intermediate_size});
+        s_mem_requests.push_back({(void**)&s_up_output_[i], sizeof(float) * config_.intermediate_size});
+        s_mem_requests.push_back({(void**)&s_intermediate_fp32_[i], sizeof(float) * config_.intermediate_size});
+        s_mem_requests.push_back({(void**)&s_down_input_[i], config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type)});
+        s_mem_requests.push_back({(void**)&s_down_output_[i], sizeof(float) * config_.hidden_size});
     }
-    s_output_fp32_ = (float*)(buffer_ + offset);
+    s_mem_requests.push_back({(void**)&s_output_fp32_, sizeof(float) * config_.hidden_size});
+    shared_mem_buffer.alloc(this, s_mem_requests);
 
-    offset = 0;
+    std::vector<std::pair<void**, uint64_t>> m_mem_requests;
     m_input_fp32_.resize(config_.group_max_len);
     m_gate_input_.resize(config_.group_max_len);
     m_up_input_.resize(config_.group_max_len);
     for (int i = 0; i < config_.group_max_len; i++) {
-        m_input_fp32_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.hidden_size;
-        m_gate_input_[i] = (uint8_t*)(buffer_ + offset);
-        offset += config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-        m_up_input_[i] = (uint8_t*)(buffer_ + offset);
-        offset += config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
+        m_mem_requests.push_back({(void**)&m_input_fp32_[i], sizeof(float) * config_.hidden_size});
+        m_mem_requests.push_back({(void**)&m_gate_input_[i], config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type)});
+        m_mem_requests.push_back({(void**)&m_up_input_[i], config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type)});
     }
-    m_local_gate_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-    m_local_up_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
-    m_local_gate_output_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-    m_local_up_output_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-    m_local_intermediate_fp32_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-    m_local_down_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.routed_expert_num * config_.group_max_len * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
-    m_local_down_output_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.hidden_size;
+    m_mem_requests.push_back({(void**)&m_local_gate_input_, config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type)});
+    m_mem_requests.push_back({(void**)&m_local_up_input_, config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type)});
+    m_mem_requests.push_back({(void**)&m_local_gate_output_, sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size});
+    m_mem_requests.push_back({(void**)&m_local_up_output_, sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size});
+    m_mem_requests.push_back({(void**)&m_local_intermediate_fp32_, sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size});
+    m_mem_requests.push_back({(void**)&m_local_down_input_, config_.routed_expert_num * config_.group_max_len * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type)});
+    m_mem_requests.push_back({(void**)&m_local_down_output_, sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.hidden_size});
     m_output_fp32_.resize(config_.group_max_len);
     for (int i = 0; i < config_.group_max_len; i++) {
-        m_output_fp32_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.hidden_size;
+        m_mem_requests.push_back({(void**)&m_output_fp32_[i], sizeof(float) * config_.hidden_size});
     }
+    shared_mem_buffer.alloc(this, m_mem_requests);
 
     m_local_pos_.resize(config_.group_max_len);
     for (int i = 0; i < config_.group_max_len; i++) {
@@ -107,6 +72,10 @@ MOE::MOE(MOEConfig config) {
     m_local_down_output_ptr_.resize(config_.expert_num);
 }
 
+MOE::~MOE() {
+    shared_mem_buffer.dealloc(this);
+}
+
 void MOE::warm_up(Backend* backend) {
     std::vector<float> input_fp32(config_.hidden_size);
     std::vector<uint8_t> input(config_.hidden_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type));

ktransformers/ktransformers_ext/operators/llamafile/moe.h

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-22 02:03:22
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:10
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -22,6 +22,7 @@
 #include "llama.cpp/ggml-quants.h"
 #include "llama.cpp/ggml.h"
 #include "llamafile/sgemm.h"
+#include "shared_mem_buffer.h"
 
 struct MOEConfig {
     int expert_num;
@@ -48,13 +49,13 @@ struct MOEConfig {
 class MOE {
    public:
     MOE(MOEConfig);
+    ~MOE();
     void warm_up(Backend* backend);
     void forward_one(int k, const uint64_t* expert_ids, const float* weights, const void* input, void* output, Backend* backend);
     void forward_many(int qlen, int k, const uint64_t* expert_ids, const float* weights, const void* input, void* output, Backend* backend);
     void forward(int qlen, int k, const uint64_t* expert_ids, const float* weights, const void* input, void* output, Backend* backend);
 
    private:
-    static void* buffer_;
     MOEConfig config_;
     void* gate_proj_;  // [expert_num * intermediate_size * hidden_size ( /32 if quantized)]
     void* up_proj_;    // [expert_num * intermediate_size * hidden_size ( /32 if quantized)]

ktransformers/ktransformers_ext/operators/llamafile/shared_mem_buffer.cpp

+/**
+ * @Description  :
+ * @Author       : chenht2022
+ * @Date         : 2024-08-05 04:49:08
+ * @Version      : 1.0.0
+ * @LastEditors  : chenht2022 
+ * @LastEditTime : 2024-08-05 09:21:29
+ * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+ **/
+#include "shared_mem_buffer.h"
+#include <cstdio>
+
+SharedMemBuffer::SharedMemBuffer() {
+    buffer_ = nullptr;
+    size_ = 0;
+}
+
+SharedMemBuffer::~SharedMemBuffer() {
+    if (buffer_) {
+        free(buffer_);
+    }
+}
+
+void SharedMemBuffer::alloc(void* object, std::vector<std::pair<void**, uint64_t>> requests) {
+    uint64_t size = 0;
+    for (auto& request : requests) {
+        size += request.second;
+    }
+    if (size > size_) {
+        if (buffer_) {
+            free(buffer_);
+        }
+        buffer_ = malloc(size);
+        size_ = size;
+        for (auto& obj_requests : hist_requests_) {
+            for (auto& requests : obj_requests.second) {
+                arrange(requests);
+            }
+        }
+    }
+    arrange(requests);
+    hist_requests_[object].push_back(requests);
+}
+
+void SharedMemBuffer::dealloc(void* object) {
+    hist_requests_.erase(object);
+}
+
+void SharedMemBuffer::arrange(std::vector<std::pair<void**, uint64_t>> requests) {
+    uint64_t offset = 0;
+    for (auto& request : requests) {
+        *(request.first) = buffer_ + offset;
+        offset += request.second;
+    }
+}

ktransformers/ktransformers_ext/operators/llamafile/shared_mem_buffer.h

+/**
+ * @Description  :
+ * @Author       : chenht2022
+ * @Date         : 2024-08-05 04:49:08
+ * @Version      : 1.0.0
+ * @LastEditors  : chenht2022 
+ * @LastEditTime : 2024-08-05 06:36:41
+ * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+ **/
+
+#ifndef CPUINFER_SHAREDMEMBUFFER_H
+#define CPUINFER_SHAREDMEMBUFFER_H
+
+#include <cstdint>
+#include <cstdlib>
+#include <map>
+#include <vector>
+
+class SharedMemBuffer {
+   public:
+    SharedMemBuffer();
+    ~SharedMemBuffer();
+
+    void alloc(void* object, std::vector<std::pair<void**, uint64_t>> requests);
+    void dealloc(void* object);
+
+   private:
+    void* buffer_;
+    uint64_t size_;
+    std::map<void*, std::vector<std::vector<std::pair<void**, uint64_t>>>> hist_requests_;
+
+    void arrange(std::vector<std::pair<void**, uint64_t>> requests);
+};
+
+static SharedMemBuffer shared_mem_buffer;
+
+#endif
\ No newline at end of file