Merge pull request #46 from pulp-platform/yt/fma-model

Add accurate FMA model.

Author: yvantor

golden-model/FP16/scripts/addmax.py renamed to golden-model/FP16/addmax.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = max (( X[m_size, n_size] + W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP16/scripts/addmin.py renamed to golden-model/FP16/addmin.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = min (( X[m_size, n_size] + W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP16/scripts/gemm.py renamed to golden-model/FP16/gemm.py

@@ -5,14 +5,21 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
+import redmule_fma as fma
 
 # COMPUTE:
 # Z[m_size, k_size] = ( X[m_size, n_size] max W[n_size, k_size] ) + Y[m_size, k_size]
@@ -38,10 +45,10 @@
 
 # We want to perform a GEMM, of the kind Z = Y + X*W
 # Test Matrices
-X = torch.rand(m_size, n_size).half()
-W = torch.rand(n_size, k_size).half()
-Y = torch.rand(m_size, k_size).half()
-Z = torch.rand(m_size, k_size).half()
+X = torch.rand(m_size, n_size)
+W = torch.rand(n_size, k_size)
+Y = torch.rand(m_size, k_size)
+Z = torch.rand(m_size, k_size)
 
 print("\nInput Data: ")
 print("\nX is: ", X, X.shape, X.dtype)
@@ -54,7 +61,12 @@
 f.write('fp16 Y[MID_CH*OUT_CH] = {'+dump.tensor_to_string(Y)+'};\n')
 
 print("\nComputing matrix multiplication..")
-Z = torch.add(input = Y, other = torch.mm(input = X, mat2 = W))
+
+X_np = X.cpu().numpy()
+W_np = W.cpu().numpy()
+Y_np = Y.cpu().numpy()
+Z = fma.matrix_multiply_with_bittrue_fma(X_np, W_np, Y_np)
+Z = torch.from_numpy(Z).to(dtype=torch.float16)
 
 print("\nZ is: ", Z, Z.shape, Z.dtype)
 f.write('fp16 Z[IN_CH*OUT_CH] = {'+dump.tensor_to_string(Z)+'};\n')

golden-model/FP16/scripts/maxmin.py renamed to golden-model/FP16/maxmin.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = min(max ( X[m_size, n_size], W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP16/scripts/minmax.py renamed to golden-model/FP16/minmax.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = max (min ( X[m_size, n_size], W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP16/scripts/mulmax.py renamed to golden-model/FP16/mulmax.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = max (( X[m_size, n_size] x W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP16/scripts/mulmin.py renamed to golden-model/FP16/mulmin.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = min(( X[m_size, n_size] x W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP8/scripts/addmax.py renamed to golden-model/FP8/addmax.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = max (( X[m_size, n_size] + W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP8/scripts/addmin.py renamed to golden-model/FP8/addmin.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = min (( X[m_size, n_size] + W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP8/scripts/gemm.py renamed to golden-model/FP8/gemm.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = ( X[m_size, n_size] max W[n_size, k_size] ) + Y[m_size, k_size]

golden-model/FP8/scripts/maxmin.py renamed to golden-model/FP8/maxmin.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = min(max ( X[m_size, n_size], W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP8/scripts/minmax.py renamed to golden-model/FP8/minmax.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = max (min ( X[m_size, n_size], W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP8/scripts/mulmax.py renamed to golden-model/FP8/mulmax.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = max (( X[m_size, n_size] x W[n_size, k_size] ), Y[m_size, k_size])

golden-model/FP8/scripts/mulmin.py renamed to golden-model/FP8/mulmin.py

@@ -5,14 +5,20 @@
 # Yvan Tortorella <[email protected]>
 #
 
+import os
+import sys
 import numpy as np
-import torch 
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import argparse
+
+include_path = os.getenv('IncludeDir')
+if include_path:
+    sys.path.insert(0, os.path.abspath(include_path))
+
 import dump_utils as dump
-import os
 
 # COMPUTE:
 # Z[m_size, k_size] = min(( X[m_size, n_size] x W[n_size, k_size] ), Y[m_size, k_size])