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Start to cleanup/unify accelerate and common back-ends (Part 1/N) (#1777)
* start to cleanup/unify accelerate and common back-ends * more progress * simplify * add half type and allow infs in simd exp * unify softmax + quantized, more dispatches to simd quantized mm * add sin/cos, use simd in vector-scalar ops * faster CPU vectorize quant * faster erf/erfinv
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@@ -207,8 +207,8 @@ class TestQuantized(mlx_tests.MLXTestCase):
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with self.subTest(shape=(B, M, N), group_size=group_size, bits=bits):
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x_shape = (1, N) if B == 0 else (B, 1, N)
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w_shape = (N, M) if B == 0 else (B, N, M)
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x = mx.random.normal(shape=x_shape, key=k1)
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w = mx.random.normal(shape=w_shape, key=k2)
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x = 1e-1 * mx.random.normal(shape=x_shape, key=k1)
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w = 1e-1 * mx.random.normal(shape=w_shape, key=k2)
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w_q, scales, biases = mx.quantize(w, group_size, bits)
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w_hat = mx.dequantize(w_q, scales, biases, group_size, bits)
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y_q = mx.quantized_matmul(
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